Ky Yeu Ht Cntt2013

i

HI THO TON QUC V CNG NGH THNG TIN 2013 n v t chc: Trng i hc Cn Th Khoa Cng ngh Thng tin v Truyn thng

BAN CH O Trng ban - PGS.TS. H Thanh Ton Hiu trng Trng i hc Cn Th, Tng bin tp Tp ch Khoa hc

Trng i hc Cn Th. Ph ban - PGS.TS. Vn X Ph Hiu trng Trng i hc Cn Th. Thnh vin - PGS.TS. Nguyn Thanh Phng, Ph Hiu trng, Trng i hc Cn Th - PGS.TS. Trn Cao - Trng Khoa CNTT&TT, Trng i hc Cn Th

BAN T CHC Trng ban - PGS.TS. Trn Cao Trng Khoa CNTT&TT, HCT Ph ban - TS. L Vn Khoa Trng phng Qun l Khoa hc, HCT - PGS.TS. Hunh Xun Hip Ph Khoa CNTT&TT, HCT (thng trc) Thnh vin - PGS.TS. inh c Anh V Ph Hiu trng Trng H CNTT TP.HCM - PGS.TS. Nguyn Xun Huy Vin KHCN Vit Nam - PGS.TS. Bi Th Bu Hu Trng khoa Khoa hc T nhin, HCT - TS. Trnh Quc Lp Trng khoa S phm, HCT - TS. Nguyn Ch Ngn Trng khoa Cng ngh, HCT - Ths. on Ha Minh Ph Khoa CNTT&TT, HCT - TS. L Nguyn oan Khi Ph Trng phng Qun l Khoa hc, HCT - TS. Trn Ngc Nguyn Gim c S Khoa hc & Cng ngh TP. Cn Th

BAN CHNG TRNH Trng ban - TS. Ng B Hng Ph Khoa CNTT&TT, HCT Ph ban - TS. Thanh Ngh Khoa CNTT&TT, HCT - TS. Phm Nguyn Khang Khoa CNTT&TT, HCT (thng trc) Thnh vin - Prof. Dr. Alexis Drogoul UMMISCO-IRD, Vietnam - Prof. Dr. Benot Gaudou IRIT-Toulouse University, France - Dr. Julie Dugdale LIG-Grenoble University, France - Dr. Nicolas Marilleau UMMISCO-IRD, France - Dr. Muriel Visani University of La Rochelle, France - Dr. Hiromitsu HATTORI Kyoto University, Japan - Dr. Patrick Taillandier IDEES-Rouen University, France - GS.TS. Phan Th Ti HBK TP.HCM - PGS.TSKH. Nguyn Xun Huy Vin CNTT H Ni

ii

- PGS.TS. Lng Chi Mai Vin CNTT H Ni - PGS.TS. L Hoi Bc H KHTN TP.HCM - PGS.TS. inh c Anh V H CNTT TP.HCM - PGS.TS. Trn an Th H KHTN P.HCM - PGS.TS. Nguyn nh Thc H KHTN TP.HCM - PGS.TS. Trn Cao Khoa CNTT&TT, HCT - PGS.TS. Hunh Xun Hip Khoa CNTT&TT, HCT - PGS.TS. Nguyn Hiu Trung K. MT&TNTN, HCT - PGS.TS. V Quang Minh K. MT&TNTN, HCT - TS. Vn Phm ng Tr K. MT&TNTN, HCT - TS. Nguyn Hu Khnh Khoa KHTN, HCT - TS. V Vn Ti Khoa KHTN, HCT - TS. Nguyn Hi Thanh V Khoa hc & Cng ngh - B GD&T - TS. H Bo Quc H KHTN TP.HCM - TS. Nguyn Hu Trng H Nha Trang - TS. H Tng Vinh IFI H Ni - TS. Nguyn Hng Quang IFI H Ni - TS. Nguyn Th Minh Huyn H KHTN H Ni - TS. Trng Ch Tn H Lt - TS. Trng Minh Nht Quang H KTCN Cn Th - TS. L Thanh Vn HBK TP.HCM - TS. Nguyn Vn Ha H An Giang - TS. Trng Quc Bo Khoa Cng Ngh, HCT - TS. Lng Vinh Quc Danh Khoa Cng Ngh, HCT - TS. L Quyt Thng Khoa CNTT&TT, HCT - TS. Phm Th Xun Lc Khoa CNTT&TT, HCT - TS. Trng Quc nh Khoa CNTT&TT, HCT - TS. Nguyn Thi Nghe Khoa CNTT&TT, HCT - TS. Phm Th Ngc Dim Khoa CNTT&TT, HCT - TS. Trn Nguyn Minh Th Khoa CNTT&TT, HCT - TS. L Vn Lm Khoa CNTT&TT, HCT - TS. Phm Th Phi Khoa CNTT&TT, HCT

BAN TH K Trng ban - TS. Nguyn Thi Nghe Khoa CNTT&TT, HCT Thnh vin - TS. Trn Nguyn Minh Th Khoa CNTT&TT, HCT - Ths. Phan Phng Lan Khoa CNTT&TT, HCT - Ths. Thi Minh Tun Khoa CNTT&TT, HCT - Ths. Trng Th Thanh Tuyn Khoa CNTT&TT, HCT

iii

BAN K THUT Trng ban: - Ths. Nguyn Cng Huy Khoa CNTT&TT, HCT Thnh vin - Ths. Phm Th Trc Phng Khoa CNTT&TT, HCT - Ks. Trn Cao Tr Khoa CNTT&TT, HCT - Ks. Nguyn Thnh Tun Khoa CNTT&TT, HCT

BAN XUT BN Trng ban - PGS.TS. Nguyn Thanh Phng Ph Hiu trng, Trng i hc Cn Th Thnh vin - Ths. Trn Thanh in Trung tm TTT&QTM, HCT - TS. Nguyn Thi Nghe Khoa CNTT&TT, HCT - KS. Nguyn Tn Phng Qun l Khoa hc, HCT

BAN L TN Trng ban: - Ths. Nguyn Th Thy Chung Khoa CNTT&TT, HCT Ph ban: - Ths. inh Lm Mai Chi Khoa CNTT&TT, HCT Thnh vin - Ths. Trn Minh Tn Khoa CNTT&TT, HCT - Ths. Phm Xun Hin Khoa CNTT&TT, HCT - CN. Nguyn Thanh Tm Khoa CNTT&TT, HCT - CN. H Quang Thi Khoa CNTT&TT, HCT - CN. Cao Hong Giang Khoa CNTT&TT, HCT

v

LI NI U

c s ch o ca Ban Gim Hiu Trng i Hc Cn Th, Khoa Cng Ngh Thng Tin v Truyn Thng (CNTT&TT) t chc Hi tho Ton quc v Cng ngh Thng tin nm 2013 (ICT2013). Mc tiu ca Hi tho nhm thc y nghin cu khoa hc v lnh vc CNTT&TT khu vc ng bng sng Cu Long (BSCL) ni ring v c nc ni chung. Ti Hi tho, cc nh khoa hc, cc nh qun l trong v ngoi nc s gp g, chia s nhng kinh nghim, nhng kt qu nghin cu mi nht ca mnh v CNTT&TT. Bn cnh , Hi tho cn l mi trng nhng ngi lm cng tc nghin cu khoa hc, nghin cu sinh, hc vin cao hc c iu kin trao i, tm kim s ti tr hp tc. Hi tho nhn c 75 bi vit di dng tm tt, trong 62 bi tham gia chnh thc, do cc nh khoa hc, ging vin, nghin cu sinh, hc vin t hu ht cc vin nghin cu, trng ca 13 tnh thnh khu vc BSCL, TP. H Ch Minh, Bnh Dng, Lt, H Ni v Hi Phng. Cc bi vit c trnh by bng mt trong hai ngn ng Vit v Anh, bao ph cc lnh vc nh: cng ngh tri thc, cng ngh a phng tin, tnh ton hiu nng cao, khai ph d liu v my hc, m hnh ha, m phng, x l ngn ng, nhn dng v x l nh, an ninh mng, in ton m my, cng ngh phn mm, cc h thng thng minh v h thng h tr quyt nh. Ngoi ra, y cng l dp nh gi v tnh hnh v kt qu t chc trin khai, ng dng cng ngh thng tin, cng ngh tri thc trn cc lnh vc kinh t, x hi, quc phng, an ninh, mi trng, bin i kh hu; v l c hi nh hnh cc hng nghin cu ng dng cng ngh thng tin, cng ngh tri thc khu vc BSCL. c s ng ca Ban Ch o v Ban T Chc hi tho ICT2013, Ban Chng Trnh vi cc thnh vin l nhng chuyn gia v CNTT&TT trong v ngoi nc, tin hnh quy trnh phn bin tuyn chn cc bi bo c cht lng cng b ti hi tho. Bi vit c phn bin qua 2 vng: vng 1 do Ban Chng Trnh ca hi tho thc hin (mi bi vit do 2 phn bin nh gi) chn lc li 50 bi t tiu chun ng trong K yu Hi tho; vng 2 tuyn chn li 23 bi xut sc nht ng trong Tp ch Khoa hc Trng i hc Cn Th. Ban Chng Trnh v Ban Bin Tp chn thnh cm n cc nh khoa hc, ging vin, nghin cu sinh, hc vin, nhit tnh gi bi bo co, nh gi phn bin cc bo co m bo tnh khoa hc ca cc bi bo c chn lc v cng b ti hi tho.

Cn Th, ngy 01 thng 11 nm 2013

vii

Bi trnh by 1: Xu hng cng ngh v D liu ln (Tech Trends & Big Data) Nguyn Khim - Cng ty IBM Vit Nam Tip ni nghin cu c tin hnh t nm 2004, nm 2012, cng ty IBM lm tip iu tra v cc xu hng cng ngh c nh hng trong vng 3 n 5 nm ti (bo co c tn "The Global Technology Fast Track"). iu tra ny tham kho kin ca 1200 ngi gi vai tr ph trch cng ngh trong t chc (22% l nh qun l, 53% lm CNTT, v 25% ngi lm kinh doanh) trong 16 ngnh cng nghip (c ngnh gio dc), 13 nc. Trong nghin cu ny ch ra 4 xu hng cng ngh trong thi gian ti l in ton m my, in ton di ng, mng x hi v phn tch d liu. Phn tip theo gii thiu IBM c nhng gii php/sn phm g ng vi xu hng v gii thiu i nt v khi nim d liu ln (Big Data).

Bi trnh by 2: Current Challenges of Biomedical Informatics for the Next Generation Medicine Tien Tuan Dao, University of Technology of Compigne, France

Next generation medicine relates to the application of new theories, methods and techniques of the biomedical informatics field for the precise, accurate and objective diagnosis, treatment and monitoring of human diseases.

Currently, in silico medicine dealing with computer aided modeling and simulation is one of the most challenging research topics to achieve such clinical objectives. In this talk, an overview of biomedical informatics field and in silico medicine will be addressed. Then, a next generation clinical decision support system for the musculoskeletal disorders will be presented and discussed.

Finally, an education program with scholarships related partially to the biomedical informatics field at the Master level will be introduced.

Bi trnh by 3: ng dng CNTT&TT xy dng h thng mng cm bin phc v gim st mi trng v cnh bo thin tai Nguyn Trung Nhn, G S TTTT TPCT Trong khun kh chng trnh hp tc v ng dng v pht trin cng ngh thng tin - truyn thng (CNTT-TT) Vit Nam - Nht Bn, ngy 8/9/2011, Vin Cng ngh phn mm v ni dung s Vit Nam thuc B Thng tin v Truyn thng phi hp vi Trung tm Pht trin cng ngh li thuc Tp on Panasonic - Nht Bn t chc hi tho hp tc Vit - Nht ng dng CNTT cnh bo mi trng, phng trnh v gim nh thin tai. Sau hi tho hp tc Vit Nht, UBND thnh ph Cn Th mnh dn giao cho S Thng tin v Truyn thng tip nhn v trin khai D n th im xy dng h thng mng cm bin v gim st phc v cnh bo mi trng v gim nh thin tai ca thnh ph.

viii

H thng gim st mi trng v cnh bo sm thin tai c chc nng thu thp thng tin, o c thng xuyn, t ng bng cm bin, lin kt vi Trung tm d liu mi trng ca thnh ph Cn Th bng nhiu phng thc truyn dn khc nhau (cp quang, Wifi, 3G) nm bt thng tin lin tc v bin i kh hu, mi trng (cht lng ngun nc) v cung cp d liu phc v phng chng thin tai.

Trm quan trc c lp t v s dng pin nng lng mt tri, Camera gim st t ng c th nhn thy trc tip ti khu vc quan trc v c th iu chnh Zoom, xoay 360 t ngi xem, d liu kh tng o c nhit khng kh, m, kh p, bc x mt tri, lng ma, hng gi v tc gi v d liu cht lng nc: mc nc, nhit nc, nng PH, tng cht rn ho tan TDS, ch s DO, dn in, c v mn ca nc.

ix

MC LC

Nhn dng mt ngi vi gii thut Haar Like Feature Cascade Of Boosted Classifiers v c trng Sift Chu Ngn Khnh, Thanh Ngh, V Tri Thc v Phm Nguyn Khang ................................................ 1 ng dng m hnh thy lc hai chiu m phng c tnh thy lc v tnh ton bi xi vng ca sng nh An Nguyn Phng Tn, Vn Phm ng Tr v V Quc Thnh ............................................................ 11 A compact autonomous display in space using water drops Van-La Le, Dinh-Duy Phan and Thanh-Xuyen Vo ... 21

ng dng m hnh Stella d on s sodic ha trong t vng ven bin tnh Sc Trng Nguyn Hu Kit, L Quang Tr, V Th Gng v Nguyn Tun Anh ................................................... 27 Gii php p ng nhu cu nc cho h thng canh tc la huyn Ng Nm (Sc Trng) trong thi gian xm nhp mn Hng Minh Hong, Vn Phm ng Tr v Nguyn Hiu Trung ............................................................. 37 ng thi dng chy trn h thng sng chnh vng h lu sng Tin di tc ng cng trnh cng p Ba Lai Trn Th L Hng, Vn Phm ng Tr, Nguyn Thnh Tu v Phan Th Ngc Dip ........................ 48 Phn vng sinh thi nng nghip ng bng sng Cu Long: iu kin hin ti v nhng thay i di tc ng ca bin i kh hu Nguyn Hiu Trung, Vn Phm ng Tr v V Th Phng Linh ........................................................... 58 Dch v gim st ti ng dng cho cc nn tng in ton m my Bi Minh Qun v Ng B Hng ........................... 67

H thng qun l t ng ghi nhn tnh trng s dng thit b in qua mng cc b Lng Vinh Quc Danh, Nguyn Vn Khanh, V Duy Tn v V Minh Tr ......................................... 74

M phng din bin ngp di tc ng ca lng ma ti ng bng sng Cu Long Ng Tng Dn, Hunh Xun Hip v Vn Phm ng Tr ................................................................ 82 A Vietnamese mobile semantic path finder using cloud based database and conceptual graphs Le Cong Nga, Nguyen Ngoc Tan, Pham Cong Thien and Quan Thanh Tho ............................................. 93

Phn mm m phng tng tc theo m hnh Client/Server dng trong ging dy trc tuyn t xa m Quang Hng Hi, Phan Quc Tn v T Nguyn Nht Quang ............................................ 101 Bc u xy dng h thng h tr khuyn nng qua mng thng tin di ng Lng Th Anh, Nguyn Thi Nghe v Nguyn Ch Ngn .................................................................... 108

Thit k ng dng nhn dng ch vit tay trn h thng nhng Trn Song Ton ................................................... 118 Truy vn nh mu da trn vng c trng v ch k nh phn Vn Th Thnh v Nguyn Phng Hc ............. 126 Cc thut ton lp lun trong h thng bin lun tru tng C Vnh Lc v Phan Minh Dng ........................ 132 Kho st v p dng lp trnh hng kha cnh thc hin kim chng ngn ng rng buc i tng Trn Lm Qun, V Vn Hiu, Phan ng Hng v inh Anh Tun ..................................................... 140

x

Trin khai kho d liu phn tn cho h thng thng tin phng chng bin i kh hu s dng Mysql Replication Nguyn Th Trc Ly v Phm Th Xun Lc ........ 147 To lin kt gia mt h thng kt xut vi mt c s d liu quan h Phan Th Phng Nam v Phm Th Xun Lc ... 158 nh gi hiu qu ca phng php o v bn a chnh s dng cng ngh GPS ng so vi my ton c in t L Thanh Hip, Trn Cao , V Quang Minh v Ron Ngc Chin ................................................. 167 Cng ngh WebGis ng dng trong qun l tin xung ging v tnh hnh dch hi tnh An Giang Trng Ch Quang, V Quang Minh, L Vn Thnh v Nguyn Phc Thnh ...................................... 175

Pht hin i tng chuyn ng trong nh theo thi gian thc Trn Nguyn Ngc v V Xun Thu .................... 184 H thng d on kt qu hc tp ca sinh vin s dng th vin h thng gi m ngun m MyMediaLite Hunh L Thanh Nhn v Nguyn Thi Nghe ..... 192 Truy vn trn c s d liu phn tn s dng tc t di ng Trn nh Ton ................................................... 202 Nghin cu iu khin tc nghn trong dch v truyn ti a ng Khu Vn Nht v Nguyn Hng Sn ................. 207

Hi tho ton quc v CNTT nm 2013 Trng i hc Cn Th

1

NHN DNG MT NGI VI GII THUT HAAR LIKE FEATURE CASCADE OF BOOSTED CLASSIFIERS V C TRNG SIFT Chu Ngn Khnh1 Thanh Ngh2 V Tri Thc, Phm Nguyn Khang3 1B mn Tin hc, Khoa KT-CN-MT, Trng i hc An Giang 2 B mn Mng my tnh, Khoa CNTT&TT, Trng i hc Cn Th 3B mn Khoa hc My tnh, Khoa CNTT&TT, Trng i hc Cn Th Thng tin chung: Ngy nhn: Ngy chp nhn: Title: Face recognition using Haar Like Feature Cascade of Boosted Classifiers Algorithm and SIFT Feature

T kha: Biu din c trng khng i SIFT, nhn dng khun mt, Bayes th ngy vi lng ging, c trng Haar-like, k lng ging, m hnh phn tng cascade Keywords: Scale-invariant feature transform - SIFT, face recognition, naive Bayes nearest neighbor, Haar-like features, k nearest neighbor kNN, cascade of boosted classifiers CBC

ABSTRACT In this paper, we present a new method, combining Haar Like Feature -Cascade of Boosted Classifiers and Scale-Invariant Feature Transform (SIFT) for online face recognition. Haar Like Features combine with AdaBoost algorithm and Cascade stratified model which allow detecting and extracting facial image very quickly and accurately. The representation of the images are based on Scale-Invariant Feature Transform method whose features are invariant to image scale, translational movement, rotation, a part range of affine distortion, and change in illumination, addition of noise and obscuration. For object recognition, we propose using k nearest neighbors (kNN), the reversibility of kNN and Naive Bayes Nearest Neighbor (NBNN). Test results on real datasets (including facial images of 20 people, each person has 20 different images) show that the kNN method, the reversibility method and NBNN achieve the accuracy of 82.40%, 85.11% and 92.63% respectively.

TM TT Trong bi ny, chng ti trnh by phng php kt hp Haar Like Feature -Cascade of Boosted Classifiers(CBC) v cc c trng cc b khng i (Scale-Invariant Feature Transform - SIFT), cho nhn dng mt ngi trc tuyn. Cc c trng Haar Like kt hp thut ton AdaBoost v m hnh phn tng Cascade cho php pht hin v rt trch nh khun mt nhanh v chnh xc. nh khun mt c biu din bng cc c trng cc b khng i (SIFT), khng b thay i trc nhng bin i t l nh, tnh tin, php quay, khng b thay i mt phn i vi php bin i hnh hc affine (thay i gc nhn) v mnh vi nhng thay i v sng, s nhiu v che khut. nhn dng i tng, chng ti xut s dng cc thut ton k lng ging (k Nearest Neighbor kNN), kNN o ngc v Bayes th ngy vi lng ging (Naive Bayes Nearest Neighbor NBNN). Kt qu th nghim trn tp d liu thc t (gm nh khun mt ca 20 ngi mi ngi gm 20 nh) cho thy cc phng php kNN, kNN o ngc v NBNN t c chnh xc ln lt l 82.40%, 85.11% v 92.63%.


2

1 GII THIU Cng vi s bng n thng tin, s pht trin

cng ngh cao, s giao tip gia con ngi v my tnh ang thay i rt nhanh, gi y giao tip ny khng cn n thun dng nhng thit b nh chut, bn phm... m c th thng qua cc biu hin ca khun mt. Cc h thng giao tip ngi my ang c pht trin rt nhiu. Trong s , c th ni n h thng nhn dng mt ngi bng hnh nh. Nhn dng mt ngi l xc nh danh tnh t ng cho tng nh i tng ngi da vo ni dung ca nh. Nhn dng mt ngi c ng dng nhiu trong thc t nh xc minh ti phm, camera chng trm, h thng chm cng, lu tr thng tin khun mt cc my ATM, cc bi gi xe siu th, v.v.

H thng nhn dng mt ngi bao gm hai bc: pht hin khun mt v nh danh t ng i tng. Cng vic chnh da vo cc k thut rt trch c trng t nh i tng v thc hin i snh nh danh t ng. Hiu qu ca h thng nhn dng ph thuc vo cc phng php s dng.

Cc nghin cu trc y (W.Bledsoe et al., 1960s), (Goldstein et al., 1970s), s dng tip cn da trn cc c trng nh mt, tai, mu tc, dy mi t ng nhn dng. (Kirby & Sirovich, 1988), (Turk & Pentland, 1991) p dng phng php phn tch thnh phn chnh v thut ton eigenfaces nhn dng khun mt. (Trn Phc Long &Nguyn Vn Lng, 2003), dng mng nron d tm khun mt

trong nh, kt hp vi phng php phn tch thnh phn chnh v bin i cosine ri rc rt ra cc c trng l u vo cho b nhn dng my hc SVM v m hnh Markov n HMM. (Lu Boun Vinh & Hong Phng Anh, 2004) s dng thut ton AdaBoost d tm khun mt kt hp vi thut ton FSVM tin hnh nhn dng mt ngi.

Gn y, hng tip cn da trn cc c trng cc b khng i SIFT ca David G. Lowe c quan tm nhiu. c trng cc b SIFT khng b thay i trc nhng bin i t l nh, tnh tin, php quay, khng b thay i mt phn i vi php bin i hnh hc affine (thay i gc nhn) v mnh vi nhng thay i v sng, s nhiu v che khut. (M. Aly, 2006) s dng cc c trng SIFT nhn dng mt ngi. (Kumar & Padmavati, 2012) xut cc cch tnh khong cch khc nhau khi so khp cc c trng SIFT nhn dng mt ngi, gp phn ci thin tc so khp cc c trng SIFT.

Trong bi bo ny, chng ti xut s dng cc c trng Haar Like vi thut ton AdaBoost v m hnh phn tng Cascade nh v khun mt trc tuyn kt hp vi phng php biu din nh bng cc c trng bt bin SIFT v phng php i snh SIFT da trn k lng ging (kNN), kNN o ngc (Jegou et al., 2011) v thut ton NBNN (O.Boiman, 2008) nhn dng mt ngi trc tuyn. M hnh h thng nh sau (Hnh 1):


3

H thng hot ng nh sau: Thu nh t th gii thc thng qua webcam (camera). S dng gii thut Haar Like CBC pht hin khun mt ngi. Rt trch nh khun mt ngi va pht hin c ta s thu c nh i tng. Tnh c trng SIFT ca nh i tng. Tin hnh nhn dng bng cch so khp SIFT da vo kNN, kNN o ngc hoc s dng thut ton NBNN.

Phn tip theo ca bi vit c trnh by nh sau: phn 2 trnh by ngn gn v thut ton pht hin khun mt Haar Like Features - Cascade of Boosted Classifiers; phn 3 trnh by ngn gn v biu din nh bng cc c trng cc b khng i SIFT, phn 4 trnh by phng php nh danh i tng da vo cc c trng SIFT, phn 5 trnh by cc kt qu

thc nghim trc khi phn kt lun v hng pht trin. 2 NH V KHUN MT 2.1 c trng Haar Like

c trng Haar Like c to thnh bng vic kt hp cc hnh ch nht en, trng vi nhau theo mt trt t, mt kch thc no . Hnh di y m t 4 c trng Haar Like c bn nh sau:

pht hin khun mt cc c trng Haar Like c bn trn c m rng thnh cc c trng cnh, c trng ng v c trng tm (Hnh 3).

.

Webcam nh thu c Haar Like Features -CBC

Pht hin khun mt

Nhn dng khun mt C s d liu SIFT

So khp NBNN

Xc minh danh tnh

Tnh c trng SIFT

Hnh 2: Cc c trng Haar Like c bn

Hnh 1: H thng nhn dng mt ngi trc tuyn


4

Gi tr ca c trng Haar Like l s chnh

lch gia tng cc im nh ca cc vng en v cc vng trng. c th tnh nhanh cc c trng ny Viola v Jones gii thiu khi nim nh tch phn (Integral Image). Integral Image l mt mng hai chiu vi kch thc bng kch thc ca nh cn tnh gi tr c trng Haar Like. Di y l m t cch tnh Integral Image:

Gi tr ca Integral Image ti im P c ta (x, y) c tnh nh sau: yyxx yxiyxii ',' )','(),( (1)

Sau khi tnh c Integral Image vic tnh tng cc gi tr mc xm ca mt vng nh bt k no trn nh thc hin theo cch sau, v d tnh gi tr ca vng D trong hnh 5 nh sau: D=A+B+C+D-(A+B)-(A+C)+A

Tip theo, s dng phng php my hc AdaBoost xy dng b phn loi mnh vi chnh xc cao. 2.2 Thut ton Adaboost

AdaBoost (Freund & Schapire, 1995) l mt b phn loi mnh phi tuyn phc, hot ng

trn nguyn tc kt hp tuyn tnh cc b phn loi yu to nn mt b phn loi mnh. AdaBoost s dng trng s nh du cc mu kh nhn dng. Trong qu trnh hun luyn c mi b phn loi yu c xy dng th thut ton s tin hnh cp nht li trng s chun b cho vic xy dng b phn loi tip theo. Cp nht bng cch tng trng s ca cc mu nhn dng sai v gim trng s ca cc mu c nhn dng ng bi b phn loi yu va xy dng. Bng cch ny th b phn loi sau c th tp trung vo cc mu m b phn loi trc n lm cha tt. Cui cng cc b phn loi yu s c kt hp li ty theo mc tt ca chng to nn mt b phn loi mnh.

B phn loi yu hk c biu din nh sau: (2)

Vi x l ca s con cn xt, k l ngng, fk l gi tr c trng Haar Like v pk l h s quyt nh chiu ca phng trnh. 2.3 M hnh phn tng Cascade

Cascade of Boosted Classifiers l m hnh phn tng vi mitng l mt m hnh AdaBoost s dng b phn lp yu l cy quyt nh vi cc c trng Haar-Like.

Trong qu trnh hun luyn, b phn lp phi duyt qua tt c cc c trng ca mu trong tp hun luyn. Vic ny tn rt nhiu thi gian. Tuy nhin, trong cc mu a vo, khng phi mu no cng thuc loi kh nhn dng, c nhng mu background rt d nhn ra (gi y nhng mu background n gin). i vi nhng mu ny, ch cn xt mt hay mt vi c trng n gin l c th nhn dng c

A

C

B

D

P1 P2

P3 P4

(x, y)

c trng cnh c trng ng c trng tm

Hnh 3: Cc c trng Haar Like m rng

Hnh 4: Cch tnh Integral Image

Hnh 5: Tnh nhanh gi tr ca vng nh D


5

ch khng cn xt tt c cc c trng. Nhng i vi cc b phn loi thng thng th cho d mu cn nhn dng l d hay kh n vn phi xt tt c cc c trng m n rt ra c trong qu trnh hc. Do , chng tn thi gian x l mt cch khng cn thit.

M hnh Cascade of Classifiers c xy dng nhm rt ngn thi gian x l, gim thiu nhn dng lm (false alarm) cho b phn loi. Cascade trees gm nhiu tng (stage hay cn gi l layer), mi tng l mt m hnh AdaBoost vi b phn lp yu l cc cy quyt nh. Mt mu c phn loi l i tng th n cn phi i qua ht tt c cctng. Cc tng sau c hun luyn bng nhng mu m negative (khng phi mt ngi) m tng trc n nhn dng sai, tc l n s tp trung hc t cc mu background kh hn, do s kt hp cc tng AdaBoost ny li s gip b phn loi gim thiu nhn dng lm. Vi cu trc ny, nhng mu background d nhn dng s b loi ngay t nhng tng u tin, gip p ng tt nht thi gian x l v vn duy tr c hiu qu pht hin khun mt. 3 BIU DIN C TRNG KHNG I

Rt trch cc c trng nh l mt bc quan trng trong nhn dng nh. Bc ny gip biu din nh bng cc c trng quan trng m gii thut c th thc hin nhn dng nh t cc c trng ny. Hai tip cn chnh cho biu din nh hin nay l: s dng nt c trng ton cc (global features) nh vc-t bitmap, t chc mu (color histogram) v s dng nt c trng

cc b (local features) nh im c trng, vng c trng biu din nh. Tip cn th nht n gin nhng li khng tht s hiu qu v cch biu din ny khng thch hp vi nhng bin i v gc nhn, bin i t l, php quay, sng, s che khut, s bin dng, s xo trn ca hnh nn v s bin i trong ni b lp. Ngc li, tip cn th hai, c trng cc b SIFT (Lowe, 2004) li rt mnh vi nhng thch thc ny v t c hiu qu cao trong nhn dng v tm kim nh. Chnh v l do , chng ti xut s dng cc nt c trng cc b SIFT biu din nh phc v cho qu trnh nhn dng.

Cc bc thc hin rt trch c trng SIFT c m t tm tt nh sau. nh c a v dng mc xm. Cc im c trng c tnh trn nh ny bng cch s dng cc gii thut pht hin im c trng cc b (local feature detector) nh l Harris-Affine, Hessian-Affine. Nhng im c trng ny c th l cc tr cc b ca php ton DoG (Difference of Gaussian) hoc l cc i ca php ton LoG (Laplace of Gaussian). Sau , vng xung quanh cc im c trng c xc nh v m t bng cc vc-t m t cc b. Vc-t m t SIFT c nh gi rt cao bi gii chuyn mn trong vic biu din cc vng xung quanh im c trng bi v n khng i i vi nhng bin i t l, tnh tin, php quay, v khng i mt phn i vi nhng thay i v gc nhn, ng thi n cng rt mnh vi nhng thay i v sng, s che khut, nhiu.

Hnh 6: c trng cc b SIFT c tnh ton t vng xung quanh im c bit (vng trn): gradient

ca nh (tri), vc-t m t (phi)


6

Hnh 6 minh ha mt v d ca vc-t m t SIFT c xy dng t vng cc b xung quanh mt im c trng. Mi vc-t m t l mt ma trn 4x4 cc t chc . Mi t chc c 8 khong tng ng vi 8 hng. Do , mi vc-t m t SIFT l mt vc-t 4x4x8=128 chiu. Lc ny, mi nh c biu din bi mt tp cc vc-t m t SIFT. 4 PHNG PHP NH DANH

Trong bi bo ny, chng ti xut s dng thut ton so khp cc vc-t m t SIFT ca nh khun mt da trn kNN, kNN o ngc v thut ton NBNN. 4.1 So khp SIFT da trn kNN

i snh nh truy vn vi cc nh khc trong c s d liu, trc tin cn trch xut tp c trng t nh truy vn tng ng, sau tin hnh so khp cc c trng SIFT ca nh truy vn vi tt c cc c trng SIFT ca cc nh trong c s d liu. Bc chnh trong k thut i snh s thc hin tm tp con c trng so khp nhau hai nh, thc hin vic ny s tm cc cp c trng trng nhau ln lt hai nh. Tp con cc c trng so khp chnh l vng nh tng ng.

Qu trnh so khp thc hin nh sau: vi vc-t SIFT A, ta s dng gii thut kNN tm B v C l hai vc-t SIFT gn A nht v nh. Nu t l khong cch ca (A, B) v khong cch ca (A, C) nh hn hoc bng 0.8 th SIFT B c gi l khp vi SIFT A (Lowe, 2004).

Thut ton nhn dng s tm nh ca i tng c s lng SIFT khp vi cc SIFT ca nh truy vn nhiu nht.

Hnh 7: Minh ha so khp SIFT

4.2 So khp SIFT da trn kNN o ngc tng kNN o ngc xut bi (Jegou

et al., 2011) c th c tm tt nh sau. B c gi l lng ging ca A khi A cng phi l lng ging ca B. Xt v d minh ha trong hnh 8. Cho vc-t 5, 3 lng ging ca n l vc-t 7, 3, 2. Trong khi , xt vc-t 3, 3 lng ging ca n l 2, 4, 1. Hay ni cch khc, vc-t 3 thuc 3 lng ging ca vc-t 5, trong khi vc-t 5 khng thuc 3 lng ging ca vc-t 3. iu ny chng t vc-t 3 khng tht s l lng ging ca vc-t 5.

y chng ti xut thc hin tm lng ging o ngc khi thc hin so khp SIFT nh danh. Thut ton so khp vi kNN o ngc t nh truy vn Q n nh I nh sau:

1

23

4

5

6

7

8

Hnh 8: V d lng ging khng o ngc


7

- Bc 1: Tnh ton tt c cc m t d1, d2, dn ca nh truy vn Q. - Bc 2: Idi , , tm lng ging gn nht v nh ca di trong I: )(

1 iIdNN , )(

2 iIdNN

- Bc 3: QdNN iI j ),( , tm lng ging gn nht v nh ca )( iI dNN j trong Q: ))(()),(( *2

*1 iIiI dNNddNNd jj

- Bc 4: )( iI dNN j , nu iiI ddNNd j ))((*1 hoc iiI ddNNd j ))((*2 , thc hin tip bc 5.

- Bc 5: nu

8.0

||)(||||)(||

2

1

iIi

iIi

dNNddNNd

, th )(1 iI

dNN c xem l khp vi di.

Kt qu ca thut ton nhn dng l nh ca i tng c s lng SIFT khp vi cc SIFT ca nh truy vn nhiu nht. 4.3 Phng php NBNN (Naive Bayes

Nearest Neighbor)

NBNN xut bi (O. Boiman, 2008) l phng php phn lp nh bng tnh ton trc

tip khong cch t nh truy vn n lp (t nh truy vn Q n lp C). Trc ht cn tnh cc m t d1, d2, , dn ca nh truy vn Q. Tip n, cn xc nh lng ging gn nht ca di trong ton b cc m t ca lp C l NNC(di). nh Q c gn cho lp C c khong cch t di n NNC(di) l nh nht. Thut ton NBNN c tm tt nh sau:

- Bc 1: Tnh ton tt c cc m t d1, d2, , dn ca nh truy vn Q. - Bc 2: Cdi , tm lng ging gn nht ca di trong C: NNC(di). - Bc 3:

2

1)(minarg

n

iiCi

CdNNd

5 KT QU THC NGHIM Chng ti tin hnh tin hnh nh gi hiu

qu ca h thng nhn dng mt ngi nh xut (s dng thut ton Haar Like Feature Cascade of Boosted Classifiers v cc c trng SIFT). Chng ti ci t h thng nhn dng ny bng ngn ng lp trnh C/C++ s dng th vin m ngun m OpenCV ca Intel (G. Bradski & A. Kaehler, 2012), trn mt my tnh c nhn chy h iu hnh Linux vi bn phn phi Ubuntu.

Bc pht hin mt ngi thu c t camera (webcam) s c thc hin thng qua hun luyn m hnh m hnh phn tng vi mi tng l mt m hnh AdaBoost s dng b phn lp yu l cy quyt nh vi cc c trng Haar-Like (h tr bi opencv_createsamples v opencv_haartraining ca OpenCV) trn tp nh (mt ngi v khng phi mt ngi).

Chng ti tin hnh to tp d liu nh ca 20 ngi, mi ngi c 20 nh khun mt vi nhng hng/biu hin khc nhau (Hnh 9). Tp d liu ny dng trong cc thc nghim nh gi hiu qu ca h thng nhn dng. Tip n, chng trnh s dng m hnh phn tng hun luyn pht hin mt ngi, rt trch ra khun mt (dng frontal v profile). Chng ti, s dng lp SiftFeatureDectector v SiftDescriptorExtractor t th vin OpenCV rt trch cc c trng SIFT ca tt c cc khun mt (khng phi nh gc) v lu vo c s d liu SIFT. Sau , thc thi gii thut kNN tm hai lng ging gn nht v lng ging o ngc hoc mt lng ging gn nht tng ng vi thut ton nhn da trn so khp SIFT v NBNN.


8

Hnh 9: C s d liu nh

Nghi thc kim tra trong thc nghim ca chng ti l ly ngu nhin 2/3 tp d liu lm tp hc (hay c s d liu i tng), 1/3 tp d liu cn li lm tp kim tra. Chng ti thc hin kim th 5 ln v so snh cc kt qu thu c t vic s dng gii thut kNN, kNN o ngc, NBNN. Vic rt trch c khun mt ngi t nh gc lm gim s lng SIFT ca i tng, nh vy h thng c tng tc trong qu trnh nhn dng vi chnh xc cao hn. Vi nh c kch thc 500x500 pixels th c khong 2000 SIFT. Nhng khi trch xut c khun mt ngi trong nh th s lng SIFT trung bnh cn khong 200 SIFT. S lng SIFT gim i khng nhng khng lm gim chnh xc ca chng trnh m cn lm

cho chnh xc c tng ln, v cc c trng khng cn thit hoc lm nh hng xu n kt qu nhn dng i tng c b i. Ta thy s lng SIFT khng cn thit l ln hn rt nhiu so vi SIFT c ngha trong nhn dng.

Chng ti cng mun so snh kt qu ca ba phng php xut vi m hnh ti t trc quan (2000 t trc quan) v m hnh my hc SVM (hm nhn RBF vi gamma = 0.1 v hng s C = 1000) nh xut bi (G. Csurka et al., 2004).

Kt qu nhn dng trn 5 ln kim th nh trnh by trong bng 1, hnh 10.


9

Ln kNN kNN (o ngc) NBNN SVM 1 81.95% 83.46% 93.23% 91.73%

2 84.21% 86.47% 94.74% 92.48%

3 84.21% 84.21% 90.98% 88.72%

4 81.20% 86.47% 93.23% 92.48%

5 80.45% 84.96% 90.98% 91.72%

Trung bnh 82.40% 85.11% 92.63% 91.43%

Bng 1: chnh xc ca cc thut ton nhn dng

Hnh 10: chnh xc ca cc thut ton nhn dng

T bng kt qu cho thy chnh xc ca h thng nhn dng da vo thut ton so khp SIFT s dng kNN, kNN o ngc cho kt qu chnh xc ln lt l 82.40%, 85.11%. Phng php so khp SIFT vi kNN o ngc ci thin phn no chnh xc ca phng php so khp SIFT vi kNN (tng t 82.40% ln 85.11%). Tuy nhin thut ton so khp SIFT s dng NBNN cho chnh xc khi nhn dng cao nht l 92.63%, cao hn c m hnh ti t v my hc SVM. Trong khi my hc SVM yu cu iu chnh cc siu tham s. Ngc li m hnh NBNN rt n gin v khng s dng bt k tham s v cng khng cn qu trnh hun luyn (tng t kNN, kNN o ngc). iu ny gip cho h thng nhn

dng tr nn n gin hn, d ci t nhng li cho kt qu vi chnh xc cao. 6 KT LUN V HNG PHT TRIN

Chng ti va trnh by s kt hp phng php pht hin mt ngi vi Haar Like Feature - Cascade of Boosted Classifiers v s so khp cc c trng cc b khng i SIFT, cho nhn dng mt ngi trc tuyn. Kt qu th nghim trn tp d liu nh ca 20 ngi cho thy h thng do chng ti xut cho php nhn dng mt ngi mt cch hiu qu v chnh xc trn 92%. Hn na, tng ca vic rt trch khun mt trc khi biu din nh bng cc c trng khng i SIFT l rt cn thit v nu ch x l nh th th chng trnh s hon ton khng thy c cc im c bit ca khun

0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%

100.00%

Ln 1 Ln 2 Ln 3 Ln 4 Ln 5

chnh xc ca thut ton khi phn lp

kNN kNN (o ngc) NBNN SVM


10

mt v v th kh nng nhn dng sai l rt cao. Chnh v vy, bc pht hin mt ngi gp phn ci thin tc cng nh chnh xc ca thut ton nhn dng.

Trong tng lai, chng ti thc hin cc thc nghim vi d liu nh nhiu hn, cho php nh gi cc phng php nhn dng khc nhau da trn c trng SIFT. Ngoi ra, cn phi s dng cc chin lc to ch mc to ch mc cho cc tp c trng SIFT, ci thin tc tm kim lng ging khi so khp trong c s d liu SIFT.

TI LIU THAM KHO 1. Trn Phc Long, Nguyn Vn Lng. Nhn

dng ngi da vo thng tin khun mt xut hin trn nh. Tp. H Ch Minh, VN, 2003.

2. Thanh Ngh, Phm Nguyn Khang. Nguyn l my hc. NXB i hc Cn Th, 2012.

3. Thanh Ngh. Khai m d liu. NXB i hc Cn Th, 2011.

4. Lu Boun Vinh, Hong Phng Anh. Nghin cu v xy dng h thng nhn dng mt ngi da trn FSVM v AdaBoost. Tp. H Ch Minh, VN, 2004.

5. M. Ali. Face Recognition Using SIFT Features. Technical Report, Caltech, 2006.

6. G. Bradski and A. Kaehler. Learning OpenCV. O'Reilly Media, 2012.

7. O. Boiman, In Defense of Nearest-Neighbor Based Image Classification. In CVPR, 2008.

8. G. Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cdric Bray. Visual Categorization with Bags of Keypoints. France, 2004.

9. C. Fernandez, M.A. Vicente. Face recognition using multiple interest point detectors and SIFT descriptors. Miguel Hernandez University, Spain.

10. Y. Freund, Robert E.Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In Computational Learning Theory: Eurocolt 95. Springer-Verlag, 1995.

11. Seok-Wun Ha, Yong-Ho Moon. Multiple Object Tracking Using SIFT Features and Location Matching. International Journal of Smart Home, Vol. 5, No. 4, October 2011.

12. H. Jegou, C. Schmid, H. Harzallah, J. Verbeek. Accurate image search using the contextual dissimilarity measure. Inria Grenoble, St. Ismier, France, 2011.

13. H. Kumar, Padmavati. Face Recognition using SIFT by varying Distance Calculation Matching Method. International Journal of Computer Application, Vol. 47, No. 3, June 2012.

14. R. Laganire. OpenCV 2 Computer Vision Application Programming Cookbook. Packt Publishing Ltd, 2011.

15. R. Lienhart, A. Kuranov, V. Pisarevsky. Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. Microprocessor Research Lab, Intel Labs, Intel Corporation, Santa Clara, CA 95052, USA.

16. R. Lienhart, J. Maydt. An Extended Set of Haar-like Features for Rapid Object Detection. Intel Labs, Intel Corporation, Santa Clara, CA 95052, USA.

17. Lindeberg. Detecting salient blob-like image structures and their scales with a scale-space primal sketch: a method for focus-of-attention. Journal of Applied Statistics, 1993.

18. Lindeberg. Scale-space theory: A basic tool for analysing structures at different scales. Journal of Applied Statistics, 1994.

19. David G.Lowe. Object Recognition from Local Scale-Invariant Features. International Conference on Computer Vision, Corfu, Greece, 1999.

20. David G.Lowe. Distinctive image features from Scale-Invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.

21. J. Meynet. Fast Face Detection Using AdaBoost. 16th July, 2003.

22. P. Viola and M. Jones. Robust Real-time Object Detection. International Journal of Computer Vision, 2001.

23. P. Viola and M. Jones. Robust Real-time Face Detection. International Journal of Computer Vision, Kluwer Academic Publishers, Netherlands, 2004.


11

NG DNG M HNH THY LC HAI CHIU M PHNG C TNH THY LC V TNH TON BI XI VNG CA SNG NH AN Nguyn Phng Tn(*)(1), Vn Phm ng Tr(1), V Quc Thnh(1) (1) Khoa Mi trng v Ti nguyn Thin nhin, Trng i hc Cn Th (*) Email: [email protected]

Thng tin chung: Ngy nhn: Ngy chp nhn: Title: Application a tow-dimensional hydrodynamic model for simulations of hydraulic characteristics and calculation deposition and erosion in the Dinh An estuary.

T kha: Bi lng, xi l, ca sng, m hnh thy lc hai chiu (CCHE2D), ng Bng Sng Cu Long Keywords: Deposition, erosion, river mouth, two-dimensional hydraulic model (CCHE2D), and Vietnamese Mekong Delta.

ABSTRACT In the recent years, estuary deposition and erosion due to hydrodynamics changes are among the major problems of the Dinh An estuary, one of the two river mouths of the Hau River in the Vietnamese Mekong Delta. Such natural events are projected to be greater in terms of magnitude and unpredictable in terms of time due to (i) the operation of hydropower dams in the upstream section of the Mekong, leading to changes of flows regime and sediment loaded patterns along the river; (ii) on-going and planned projects of hydraulic construction to meet different water requirements of agricultural land uses in the delta; and, (iii) sea level rise. In this study, a two-dimensional hydraulic model (CCHE2D) is used to calculate to hydrodynamic, deposition and erosion patterns in the Dinh An estuary. The hydraulic component of the model is calibrated and validated based on the flows measured in August 2012. The sediment transport component is calculated according to the calculated data of hydraulic properties and referenced sediment data (including: suspended sediment concentration, transport sediment rate). The results obtained from this study will set the stage for subsequent studies to calculate and predict deposition and erosion rates for river systems in the Vietnamese Mekong Delta.

TM TT Trong nhng nm gn y, bi lng v xi l l mt trong nhng vn chnh ca sng nh An, mt trong hai ca sng ca sng Hu ng bng sng Cu Long. Qu trnh t nhin ny c d bo s nghim trng hn v khng th d on c trong tng lai do: (i) xy dng v vn hnh cc p thy in thng ngun sng Mekong, dn n thay i ca ch dng chy v vn chuyn trm tch dc b sng; (ii) cc cng trnh thy li ang c xy dng v quy hoch phc v sn xut nng nghip ng bng; (iii) mc nc bin dng. Trong nghin cu ny, m hnh thy lc hai chiu (CCHE2D) c s dng xc nh ng thi dng chy, bi lng v xi l ca sng nh An. Cc thnh phn thy lc ca m hnh c hiu chnh v kim nh da trn s liu o c vo thng 8 nm 2012. Cc thnh phn vn chuyn trm tch c tnh ton da vo cc c tnh thy lc v s liu trm tch tham kho t cc bi bo (bao gm: nng trm tch l lng v vn chuyn trm tch y). Kt qu thu c t nghin cu ny s to tin cho cc nghin cu tip theo tnh ton v d bo bi lng, xi l cho cc h thng ng bng sng Cu Long.


12

1 GII THIU Sng Hu l mt trong hai phn lu ca

sng Mekong, trc khi ra bin ng, sng Hu chia thnh hai nhnh: nh An v Trn (Hnh 1). Ca sng nh An l mt trong hai ca sng gi vai tr ln trong pht trin kinh t vng bn o C Mau (Hoa Mnh Hng et al., 2008). Tuy nhin, trong nhng nm gn y vng ca sng nh An thng xuyn xy ra qu trnh bi lng, xi l, v xm nhp mn (Bi Hng Long & Tng Phc Hong Sn, 2003; Hoa Mnh Hng, et al., 2008; L Anh Tun, 2010). Theo kt qu nghin cu t nh v tinh t nm 1989 2001, ca nh An xi l khong 4.5m/nm v bi lng khong 9.5m/nm (Nguyn Vit Thanh et al., 2011). Trong tng lai gn, s gia tng cc hot ng pht trin cc nc thng ngun (v d: vic gia tng ly nc phc v cho nng nghip vng ng bc Thi Lan, d kin xy dng cc p trn dng chnh sng Mekong thuc Lo v Campuchia) c d bo s lm thay i ch dng chy trn cc dng sng BSCL. Bn cnh , do nh hng ca bin i kh hu ton cu nn nc bin c th dng ln thm khong 30cm vo nm 2050 (Bng 1) (B Ti Nguyn v Mi Trng, 2009). Chnh v th, vic tm hiu ng thi

dng chy v xc nh xu hng bi lng, xi l l cn c quan trng nh gi tnh n nh ca h thng sng cng nh gp phn gii thch ng thi ngun ti nguyn nc mt trn h thng dng sng (di tc ng ca s thay i ca dng chy thng ngun v ng thi thy triu) nhm m bo s pht trin bn vng ca vng, c bit trong cng tc qun l ngun ti nguyn nc mt.

Ngy nay, vi s tin b ca khoa hc my tnh cng vi s pht trin ca k thut tnh ton hin i, cc m hnh ton thy lc mt chiu (1D) c xy dng v ng dng kh nhiu trong vic tm hiu ng thi dng chy BSCL (Trn Quc t et al., 2012; Vn Phm ng Tr et al., 2012). Tuy nhin, cc m hnh thy lc mt chiu (1D) hin nay ch dng li mc tnh ton gi tr thy lc trung bnh tng mt ct c a vo tnh ton trong m hnh v khng xt n dng chy ngang nh trong m hnh thy lc hai chiu (2D) v th cha phn nh ht ng thi dng chy. Do , vic ng dng m hnh thy lc 2D nhm xc nh ng thi dng chy, xu hng bi lng v xi l vng ca sng l cn thit c bit l trong bi cnh c s thay i ng k ca iu kin thy vn t nhin, tnh hnh pht trin trn ton lu vc sng Mekong v nc bin dng.

Hnh 1: Vng nghin cu - on ca sng nh An (cch ca bin khong 7km)


13

Bng 1: Mc nc bin dng (cm) cc kch bn (B Ti Nguyn v Mi Trng, 2009)Kch bn Cc mc thi gian ca th k 21

2020 2030 2040 2050 2060 2070 2080 2090 2100 Thp (B1) 11 17 23 28 35 42 50 57 65 Trung bnh (B2) 12 17 23 30 37 46 54 64 75 Cao (A1FI) 12 17 24 33 44 57 71 86 100

2 PHNG PHP NGHIN CU 2.1 Kho st thc a

Thit b o k thut s ADCP (Acoustic Doppler Current Profiler), thit b cm ng (sensor) v h thng nh v ton cu (Global Position System - GPS) c s dng trong chuyn i thc a t ngy 23/08/2012 n ngy 31/08/2012. Thit b ADCP dng o c ct lp dng chy bng sng hi m theo hiu ng Doppler. Thit b ny c gn pha bn ngoi thn tu (Hnh 2) v c kt ni vi my tnh ghi nhn li gi tr lu lng v su ti cc mt ct, vn tc dng chy thng qua phn mm Winriver II. Thit b cm ng (sensor) dng o gi tr mc nc u vo v gi tr mc nc dng kim nh m hnh. GPS c dng xc nh v tr ti im o c v im xi l (Hnh 3).

Hnh 2: Tu kho st v cc thit b o c

Hnh 3: o c thc a xc nh xi l/bi lng

gn ca sng D liu o c t ADCP s c x l t

phn mm Winriver II, sau mi ln xut kt qu phn mm Winriver II s cho ra kt qu lu lng, su, v vn tc dng chy cho tng mt ct (Teledyne RD Instrument, 2007). 2.2 M hnh thu lc CCHE2D

Xc nh ng thi dng chy v s thay i hnh thi sng l qu trnh phc tp trong sng/knh t nhin v cn c s h tr ca cc m hnh ton thch hp (Wu, 2007). M hnh mt chiu thng s dng m phng c h thng sng, trong khi m hnh 2 v 3 chiu thng s dng m phng chi tit hn v s thay i ng thi dng chy cng nh s thay i hnh thi ca mt on sng (Wu, 2007). Bn cnh , do hn ch ca cc s liu c thu thp theo chiu thng ng (t mt nc xung y sng), m hnh thy lc 2 chiu thng c chp nhn trong nghin cu (Jia & Wang, 1999). Hin nay, c mt s m hnh thy ng lc c kh nng tnh ton m phng

Sensor ADCP


14

vn chuyn bn ct v d bo s thay i hnh thi sng nh: CCHE2D, Delft2D- River, MIKE21C, TABS-MD v TELEMAC; trong , m hnh CCHE2D va c th m phng c bn cht thy ng lc hc hai chiu (Langendoen, 2001) va l phn mm min ph nn m hnh CCHE2D c la chn cho nghin cu.

M hnh CCHE2D ca trng i hc cng ngh Mississippi l m hnh m phng qu trnh truyn thu lc, cht lng nc, chuyn ng trm tch l lng, din bin lng dn, gm 3 hp phn chnh sau (Hnh 4):

- M hnh to li (CCHE2D Mesh Generator): to li gi tr trn h to cong.

- M hnh s (CCHE2D Numerical model): tnh ton qu trnh thy lc.

- M hnh giao din ha (CCHE2D- GUI Graphical Users Intreface): nhp cc thng s m hnh v s liu o c.

Hnh 4: Thnh phn ca m hnh CHE2D

Cao mc nc t do c tnh ton bi phng trnh lin tc (PT1):

+

() +

() = 0

(PT1)

Phng trnh ng lng theo phng x (PT2):

+

+

=

+

() +

+

(PT2)

Phng trnh ng lng theo phng y (PT3):

+

+

=

+

+

+

(PT3)

Trong : cao mc nc (m); t: thi gian (s); h: su ct nc (m); u,v : vn tc trung bnh theo hai phng x v y (m/s); fr: h s ma st y; fCor: thng s Corilolis; , , , : ng sut Reynolds (N/m2); , : ng sut tip tuyn y

M hnh CCHE2D chp nhn s duy chuyn trm tch trong hai lp: lp trm tch pha trn v lp trm tch y pha di, trong dng trm tch trn chim hu nh ton b su dng chy h v dng trm tch nm lp y c b dy kh nh .

Sau khi phn tch theo chiu su, phng trnh chuyn ti bn ct l lng c dng sau (PT4):

( ) +

(

) + ( ) = ( )

+

( )

+

+

+

(PT4)

Phng trnh lin tc vt cht y (PT5): 1 +

() +

+

= (PT5)


15

Trong : : nng trm tch (kg/m3); u,v : vn tc trung bnh theo hai phng x v y (m/s); : h s khuch tn ri ca trm tch; : h s phn tn bn ct; : b dy lp bn ct y; , : hm s ngun m t qu trnh xi l v bi lng; : rng; : nng trung bnh ca trm tch ti y (kg/m3); ,: thnh phn chuyn ti trm tch y. 2.3 Phng php gii cc phng trnh trong m hnh CCHE2D:

Cc phng trnh trn c gii bng phng php phn t hiu qu (Efficient Element Method). Phng php ny c gi l phng php phn t hu hn c bit, v h phng trnh c gii trong h ta cong (, ), ng vi li cong tnh ton ca min thc.

Trong li cong min tnh, cc thnh phn vn tc c gii t phng trnh ng lng bng phng php sai phn hu hn s n 9 nt theo khng gian v mc nc c gii t phng trnh lin tc trn phn t lin kt 4 im (Hnh 5).Trong li cong tnh ton, thnh phn vn tc c b tr ti nt li, mc nc c b tr ti tm li nh Hnh 6.

Hnh 5: Li cong xen k trong tnh ton u, v, z

Hnh 6: V tr tnh ton thnh phn vn tc v

mc nc trong li tnh 2.4 Thnh lp m hnh

Li tnh: li c to thnh gm 9000 nt phn b dc min li tnh c xc nh bi 30 dc theo hng dng chy v 300 vung gc vi hng dng chy.

Cao y: thut ton ni suy cao y ca m hnh gm 2 thut ton: ni suy ngu nhin (Random Database) v ni suy tam gic (Triangulation interpolation). Phng php ni suy tam gic c s dng v cho gi tr gn ng vi gi tr thc o.

iu kin bin tnh ton thy lc: bin trn l chui gi tr lu lng (Q) thc o t 9 gi ngy 30/08/2013 n 3 gi ngy 31/08/2013; bin di l chui gi tr mc nc (Z) thc o t 9 gi ngy 30/08/2013 n 3 gi ngy 31/08/2013 (Hnh 7).

iu kin bin tnh ton bi lng v xi l: ti bin u vo, thit lp nng trm tch l 0.25kg/m3 v thnh phn cc ht trm tch l lng nh Bng 2 (Eric Wolanski 1996); trm tch vn chuyn y l 0.034kg/m/s v phn cc ht trm tch y nh Bng 3 (Albers & Lieberman, 2011; Eric Wolanski 1996; Walling, 2009); thnh phn mu trm tch v rng nh Bng 4 (Rijn, 1993; Zou Xue et al., 2010).

Bng 2: Thnh phn trm tch l lng Kch Thc

(mm) Thnh phn phn trm

(%) 0.00100 25 0.00425 75 0.12500 0


16

Bng 3: Thnh phn trm tch y Kch Thc

(mm) Thnh phn phn trm

(%) 0.00100 0 0.00425 0 0.12500 100

Bng 4: Thnh phn mu trm tch Kch thc

(mm) Thnh phn

phn trm (%) rng

0.00100 24.03 0.8 0.00425 48.26

0.12500 27.71

Bc thi gian: Bc thi gian c chn trong tnh ton l 60 giy, thit lp thi gian cho im quan st l 60 giy c ngha l sau 1 gi th m hnh s lu chp kt qu li mt ln ti im quan st, dng so snh vi kt qu thc o ti im quan st.

Hnh 7: Li, bin, cao y sng, mt ct

2.5 Hiu chnh v kim nh m hnh Tnh ton thy lc: m hnh c hiu

chnh da vo thng s thy lc thc o t 9 gi ngy 30/08/2013 n 3 gi ngy 31/08/2013 bng cch thay i h s nhm Mannings n da vo su ct nc trong m hnh. H s nhm l mt gi tr quan trng trong vic tnh ton trong knh h, c th thay i theo lu lng, su, c im t nhin v cu trc ca knh; h s nhm ca sng/knh trn nn ph sa ca ng bng nm

trong khong 0.02-0.05 (Chow, 1959). Da vo gi tr h s nhm ca ng bng thc hin phng php th sai (Phm Th Bo, 2009) cho n khi tm ra h s nhm thch hp cho qu trnh m phng (gi tr m phng gn bng gi tr thc o). H s nhm c tnh theo cch sau:

- Ti nt li c su ct nc nh nht (hmin) s c h s nhm ln nht (); ti nt li c su ct nc ln nht (h) s c h s nhm nh nht (n); cc v tr khc s thay i tuyn tnh tng bc (Wendt, 2008) theo ct nc trong gii hn [nti n] theo cng thc:

n = n + (n n) hh

h h

Trong : : h s nhm ti nt i;n,n, h s nhm ln nht v nh nht;h,h: su ct nc ln nht v nh nht; h: su ct nc ti nt th i.

Sau khi hiu chnh m hnh c kim nh bng thng s thy lc o t 9 gi 30 pht ngy 30/08/2013 n 3 gi 30 pht ngy 31/08/2013.

Tnh ton xi l: trong m hnh bi xi ca sng nh An c ba h s cn quan tm l: h s ph hp ca trm tch l lng , rng ca mu trm tch p v chiu di li tnh ph hp ca bn ct y Ls. Trong chiu di li tnh c tnh theo cng thc sau (Qamar & Baig, 2012):

= 3.. = 3 0.125 3110.804. 0.671. = 32.644()

Trong :

= () = 0.125

().(.) = 3110.804

= =.

.() =....(). = 0.671


17

Trong : Ls: chiu di li tnh ph hp; d50: kch thc ht trm tch ti y; : t trng ca trm tch; :t trng ca nc; : trng lng ring ca trm tch; : trng lng ring ca nc.

Wu v Li (1992) ngh rng h s ph hp ca trm tch l lng: =1 cho trng hp xi l mnh, =0.25 cho trng hp lng ng mnh v =0.5 cho trng hp bi lng v xi l yu. Do khu vc h lu sng Hu thng xy ra qu trnh bi lng (Nguyn Vn Lp et al., 2000; Zuo Xue et al., 2012) nn h s =0.25 c la chn tnh ton qu trnh bi lng v xi l.

Theo nghin cu ca Xue et al. (2010) trm tch l lng ca sng nh An c rng cao v thnh phn trm tch ch yu l st, bn v ct. S bi lng ca cc thnh phn trm tch trn c rng khong 0.8 (Rijn, 1993),

do trong m hnh ny s s dng gi tr rng 0.8 cho m phng. 3 KT QU V THO LUN 3.1 Kt qu tnh ton thy lc

M hnh c hiu chnh thy lc thng qua vic thay i h s nhm thy lc Mannings n, vi h s nhm thy lc nm trong khong 0.018-0.03 cho kt qu mc nc ti im quan st ph hp vi kt qu thc o (Hnh 8 A); kt qu hiu chnh h s nhm ny ph hp vi cc kt qu nghin cu trc y ca Nguyn Thnh Tu et al. (2013), Trng Th Yn Nhi (2013) v Lm M Phng et al. (2013). Kt qu kim nh m hnh cho kt qu kh tt vi h s Nash Sutcliffe NS=0.978 gn bng 1cho thy chnh xc kt qu m phng (Hnh 8 B). Nh vy, m hnh c thit lp vi b thng s kim nh l ng tin cy.

Hnh 8: Mc nc thc o v m phng ti im quan st

Vn tc dng chy (Hnh 9) ti mt ct 2 (Hnh 7) cho kt qu ti thi im 18 gi c xu hng tng dn theo su iu ny ph hp nghin cu trc y ca Lane et al. (1999).

Da vo kt qu kim nh v s thay i vn tc trn tng phn on ca dng chy cho thy m hnh c th p cho tnh ton bi lng v xi l.

-1.5

-1

-0.5

0

0.5

1

1.5

9:00 15:00 21:00 3:00

Thc o M phng

Thi gian

m A

-1.5

-1

-0.5

0

0.5

1

1.5

9:30 15:30 21:30 3:30

Thc o M phng

Bm

Thi gian


18

Hnh 9: Vn tc dng chy ti mt ct 2

3.2 Kt qu tnh ton bi lng v xi l Kt qu tnh ton bi lng v xi l ti mt

ct 1 (Hnh 7) cho thy sau thi gian m phng c s xi l b tri c th l 1.8 x 10-6 m (Hnh 10) ; ti mt ct s 3 (Hnh 7) l 2.1 x 10-5m, bi lng lng dn sng l 8.5 x 10-6m (Hnh 11). S xi l b tri ti hai v tr mt ct 1 v

3 ph hp vi im xc nh xi l b trong chuyn kho st thc a (Hnh 3). Xu hng bi lng gn ca sng ti mt ct s 3 ph hp vi kt qu nghin cu trc y ca Xue et al. (2012).

Hnh 10: Thay i y sng sau thi gian m phng

0

0.2

0.4

0.6

-20

-15

-10

-5

0

5

0 500 1000 1500

Cao y sng ban u Vn tc dng chy

Cao

y s

ng(m

)

Vn

Tc

dng

chy

(m/s

)Mt ct ngang

-2.E-06

-2.E-06

-1.E-06

-5.E-07

0.E+00

5.E-07

-20

-15

-10

-5

0

5

0 500 1000 1500

Cao y sng ban u Thay i cao y sng sau thi gian m phng

B tri

Mt ct ngang

Cao

y

sng

(m)

Thay

i

y s

ng(m

)


19

Hnh 11: Thay i y sng sau thi gian m phng 4 KT LUN

Bi lng v xi l l tnh trng chung ca cc ca sng ven bin BSCL vi quy m khng gian v thi gian khc nhau. Kt qu tnh ton cho thy vng ca nh An c xu hng bi lng gn ca sng v xi l dc b tri.

Qua nghin cu cho thy m hnh thy lc hai chiu (CCHE2D) cho kt qu m phng thy lc c tin cy cao v c trin vng ng dng cho nhiu vng khc nhau phc v nghin cu khoa hc. Tuy nhin, do hn ch v thi gian v s liu o c, nghin cu ny ch m phng trong thi gian ngn nn cha phn

nh ht c tnh thy lc cng nh thay i hnh thi lng sng nhng phn no cho thy s bin ng hnh thi vng ca sng.

Hin nay, phn ln cc ng dng m hnh thy lc BSCL cn b hn ch v mt s liu (s liu o c c hoc khng c s liu thc o). Trong bi cnh c s thay i ln v lu lng v hnh thi lng sng, vic p dng cc thit b o c hin i (nh thit b ADCP, thit b o mc nc sensor) thu thp cc s liu thc t nhm mc tiu phc v cho vic nghin cu ng thi a mo v thy lc l iu cn c quan tm.

TI LIU THAM KHO 1. Albers, T., & Lieberman, N.

v.,2011.Curent and Erosion Modelling Servey.,Deutsche Gesellschaft fr Internationale Zusammenarbeit (GiZ) GmbH. 73.

2. B Ti Nguyn v Mi Trng,2009.Kch Bn Bin i Kh Hu v Nc Bin Dng Cho Vit Nam.H Ni.36

3. Bi Hng Long, & Tng Phc Hong Sn,2003.c im a Hnh V Bin ng Lung Lch Vng Ca Sng nh An.K.-. Tuyn tp bo co hi tho khoa hc cc ti KC.09-02, KC.09-06,TTKHCNQG.107-127

4. Chow, V. T., 1959. Open Channel Hydraulics.Blackburn Press.700

5. Eric Wolanski , N. N. H., Le Trong Dao, Nguyen Huu Nhan. Nguyen Ngoc Thuy.,

1996. Fine- sediment Dynamic in the Mekong River Estuary, Vietnam. Estuarine, Coastal and Shelt Science ,43 565-582.

6. Hoa Mnh Hng, Nguyn Quang Thnh, & Phan Th Thanh Hng, 2008. ng Lc Pht Trin Vng Ca Sng Hu (Ca nh An- Tranh ). Cc Khoa Hc V Tri t, 30: 130-135.

7. Jia, Y., & Wang, S. S. Y., 1999. Numerical Model for Channel Flow and Morphological Change Studies. Journal of Hydraulic Engineering, 125(9): 924-933.

8. Lm M Phng, Vn Phm ng Tr, & Trn Quc t, 2013. ng Dng M Hnh Ton Thy Lc Mt Chiu nh Gi v D Bo Tnh Hnh Xm NHp Mn Trn H Thng Sng Chnh Trn a Bn Tnh

-2.00E-05

-1.00E-05

0.00E+00

1.00E-05

2.00E-05

3.00E-05

-20

-15

-10

-5

0

5

0 500 1000 1500 2000 2500

Cao y sng ban u Thay i cao y sng sau thi gian m phng

Cao

y

sng

(m)

Thay

i

y s

ng(m

)

B Tri

Mt ct ngang(m)


20

Tr Vinh. Tp Ch Khoa Hc- i Hc Cn Th, 25 (2013): 68-75.

9. Lane, S. N., Bradbrook, K. F., Richards, K. S., Biron, P. A., & Roy, A. G., 1999. The application of computational fluid dynamics to natural river channels: three-dimensional versus two-dimensional approaches. Geomorphology, 29: 1-20.

10. Langendoen, E. J.,2001.Evaluation of the effectiveness of selected computer models of depth-averaged free surface flow and sediment transport to predict the effects of hydraulic structures on river morphology.Project Report, USDA-ARS National Sedimentation Laboratory, Oxford M.S.

11. L Anh Tun,2010.Tc ng Ca Bin i Kh Hu v Nc Bin Dng Ln Tnh a Dng Sinh Hc v Xu Th Di Dn Ca Vng Bn o C Mau, ng Bng Sng Cu Long. Hi tho khoa hc "Bo tn cc gi tr d tr sinh quyn v h tr c dn vng ven bin tnh C Mau trc bin i kh hu", Thnh ph C Mau, 25/04/2010.9.

12. Nguyn Thnh Tu, Vn Phm ng Tr, & Nguyn Hiu Trung, 2013. ng Thi Dng Chy Vng T Gic Long Xuyn Di Tc ng Ca Bao Ngn L. Tp Ch Khoa Hc- i Hc Cn Th, 25 (2013): 85-93.

13. Nguyn Vn Lp, T Th Kim Oanh, & Tateishi, M., 2000. Late Holocene depositional environments and coastal evolution of the Mekong River Delta, Southern Vietnam. Journal of Asian Asian Earth Scienes, 18(2000) 427-439.

14. Nguyn Vit Thanh, Hai, Z. J., & Hau, L. P., 2011. Morphological evolution of navigation channel in Dinh An estuary, Vietnam. River, Coast and Estuarne Morphodynamics: RCEM2011 Tsinghua University Press, Beijing 469-482.

15. Phm Th Bo,2009.Cc phng php gii quyt bi ton trn my tnh. Khoa Ton Tin,Trng i hc Khoa hc T nhin.

16. Qamar, M. U., & Baig, F., 2012. Calibration of CCHE2D for sediment simulation of Tarbela Reservoir. Proceedings of the World Congress on Engineering I 978-988.

17. Rijn, L. C. V., 1993. Principles of Sediment Transport in Rivers, Estuaries

and Coastal Seas.University of Utrecht Department of Physical Geography.The Netherlands.700

18. Teledyne RD Instrument. (2007). Winriver II User's Guide.

19. Trn Quc t, Nguyn Hiu Trung, & Likitdecharote, K., 2012. M Phng Xm Nhp Mn ng Bng Sng Cu Long Di Tc ng Mc Nc Bin Dng V S Suy Gim Lu Lng T Thng Ngun. Tp Ch Khoa Hc, Trng i Hc Cn Th, s 21b: 141-150

20. Trng Th Yn Nhi, V. P. . T., Nguyn Thy Kiu Dim, Nguyn Hiu Trung, 2013. ng Dng M Hnh Ton M Phng c Tnh Thy Lc V Din Bin Cht Lng Nc Trn Tuyn Knh Xng, Thnh Ph Sc Trng. Tp ch Khoa hc Trng i Hc Cn Th, s 9 (2013): 76-84.

21. Vn Phm ng Tr, Popescu, I., Griensven, A. v., Solomatine, D., Trung, N. H., & Green, A., 2012. A study of the climate change impacts on fluvial flood propagation in the Vietnamese Mekong Delta. Hydrol. Earth Syst, 9: 7227-7270.

22. Walling, D. E. (2009).Chapter 6 The Sediment Load of the Mekong River.Department of Geography, University of Exeter, The Queens Drive, Exeter, Devon EX4 4QJ, UK.113-142

23. Wendt, J. F., 2008. Computational Fluid Dynamics.Belgium.72 Chausse de Waterloo B-1640 Rhode- Saint- Gense.299 pp

24. Wu, W., 2007. Computational River DynamicsTaylor & Francis, London 2007.

25. Wu, W., & Li, Y., 1992. One and two- dimensional nesting model for river flow and sedimentation. Proc. 5th Int.Symp. on River Sedimnet.

26. Xue, Z., He, R., Liu, J. P., & Warner, J. C., 2012. Modeling transport and deposition of the Mekong River sediment. Elsevier, 37(2012): 66:78.

27. Xue, Z., Liu, J. P., DeMaster, D., Lap, N. V., & Oanh, T. T. K., 2010. Late Holocene Evolution of the Mekong Subaqueous Delta, Southern Vietnam. Marine Geology, 18: 427-439.


21

A COMPACT AUTONOMOUS DISPLAY IN SPACE USING WATER DROPS

Van-La Le1, Dinh-Duy Phan1, Thanh-Xuyen Vo1 1 Computer Engineering Faculty, University of Information Technology VietNam National University HCM city

Thng tin chung:

Title: A Compact Autonomous Display in Space Using Water Drops

Keywords: Water curtain, water display, water Drop, water bits, water control, control,solenoid valve, bitmap image processing,

ABSTRACT We present in this paper the design and implementation of an autonomous in-space display system that is able to display digital images stored in computer using water drops. This concept is realized by exploring the ability of controllable solenoid valve. The system consists of two main components. The first is an array of nozzles equipped with controllable solenoid valve hanged up high producing water drops and creating a display surface. The second is embedded software that processes the digital image and issuing control signal to turn the solenoids open/close with respect to the binary values of pixels.

TM TT Chng ti trnh by trong bi bo ny thit k v thc hin mt hthng hin th trong khng gian c lp c kh nng hin th nh sc lu tr trong my tnh s dng git nc. tng ny c thc hin bng cch s dng kh nng iu khin ca van in t. Hthng bao gm hai thnh phn chnh. u tin l mt mng cc thit b vi phun vi van in t iu khin c treo ln cao to ra git nc v to ra mt b mt hin th. Th hai l phn mm nhng x lnh s v pht tn hiu iu khin m / ng cc van solenoid ngvi cc gi tr nh phn ca im nh.

I. INTRODUCTION It is desirable to transfer digital visual

information, i.e. images and videos, into physical-touchable display for not only virtual reality but also decoration purposes [1] [2]. Some previous works, such as [2] [3] [6] to name a few, have been conducted to realize this goal using water drops discharged from nozzles in replacement of pixels. The resulted display is not limited to two-dimension by arranging the nozzles into an array [2] [5], three-dimensional visual information is also possible using a matrix of nozzles [1] [3] [6]. However, these systems are quite cumbersome and typically rely on an external device, i.e. projector, to project visual information into continuously water drops to

create visual effects. These disadvantages make them unsuitable to exhibit in a public space. In this work, we propose a new concept and develop a prototype system which is to display digital visual information into space by filling space with small water drops. This concept can be distinguished from the previous work by that the water drops will display the information themselves and an external projector is not necessary anymore. To do this, we additionally use controllable solenoid valves [4] associated with the nozzles and an embedded software to control the array of solenoid valves to regulate the water drops falling from a tank to display visual information on the display plane. For simplicity, let us consider a binary image whose pixel values are 0s or 1s. In order to transform this image into space, we scan the image line-by-


22

line bottom up and switch the controllable solenoid valves on/off respective to the value of pixels. As a result, the image will be displayed by the discrete water drops on the display surface. A simulated result of this system can be seen in Fig. 1. In general case, there exist standard image processing algorithms to convert color images with different color depth and formats into binary images. This autonomous system can be significantly smaller in size and can be exhibited in a public space. The audience can feel as if the virtual space and real space exist together completely.

Fig. 1. A simulated result of in-space display using

water drops

II. HARDWARE DESIGN The overall design of the system is illustrated in the Fig. 1. The key components of the system are described as below: Nozzles and solenoid valves. The nozzles, each of which is attached with a controllable solenoid valve, are arranged in an array as in Fig. 5. The solenoid valve is an electromechanical device used for controlling liquid or gas flow [4]. It is controlled by electrical current, which is run through a coil. When the coil is energized, a magnetic field is created, causing a plunger inside the coil to move. Depending on the design of the valve, the plunger will either open solenoid valve or close the valve. When electrical current is removed from the coil, the valve will return to its de-energized state. Fig. 3 exhibits a typical solenoid valve and its design principle. The distance between two consecutive nozzles is 3 centimeters. As expected, this distance is smaller, the resolution of the display is better. However, this distance is limited by the size the solenoid valve.

Fig. 2. Overall design of the autonomous in-space display system


23

Fig. 3. (a) Principle of controllable solenoid valve. (b) Appearance of a solenoid valve

Processing and controlling system. This system is the brain of the system. It consists of a microcontroller and control line connected to the solenoid valves. The microcontroller is in charge of digital image processing (i.e. scaling, color-to-binary conversion, image scanning) and issue control signals transmitted by the control line to each solenoid valve. We use the well-known Atmel ATmega16 [7] in this design since it has been proven powerful and robust.

Control circuits. Since the control signals on the control line are at low voltage while the solenoid valves require higher voltage to be triggered. Therefore, to drive the control signals to control the states of solenoid valves, the signals need to go through an amplifier. We use a power amplifier circuit using power MOSFET IRF540 [8] for this purpose. Additionally, a bit-shifting 595 circuit is also needed to shift the bits encoding pixel values to the amplifier. Design of these two circuits are shown in Fig. 6 and Fig. 7.

Display plane. The display plane is created by the water drops falling down from the nozzles associated by a solenoid valve. In order to create a water drop, a solenoid valve needs to change its state according to the chain close-open-close in an appropriate time period which is depended on the control signal issued by the microcontroller.

III. SOFTWARE IMPLEMENTATION The basic processing flow of the embedded

software on the Atmel ATmega16 is shown as in Fig. 4.

The input is an arbitrary digital image which is converted into binary image where the value of each pixel is 0 or 1. In order to accomplish

this, the color image will be firstly converted into grey-scale image following (1):

BGRI 11.059.03.0

Fig. 4. Processing flow of the embedded software

where R, G, B are the values of red, green, and blue channels of each pixel respectively whereas I is the intensity of the resulted pixel. Consequently, the grey image is converted into binary image using thresholding technique. That is, if the intensity value of a pixel is greater than given threshold, it will be set to black (1); otherwise, it will be set white (0).

Afterward, the image is scaled into a compatible size so that its width in pixel equals to the number of solenoid valves. This step is essential since each solenoid valve will be responsible for only one pixel. It means the state of each solenoid valve will be set to close/open respective to the value 0/1 of the corresponding pixel.

Finally, the program will scan the resulted binary image row-by-row bottom-up. At each scan, based on the value of the current row of the image, the control signals will be issued and sent over the control line to the array of solenoid valves. The pixels of 1s will open the solenoid valve to let the water flow through. On contrary, the pixels of 0s will close the corresponding solenoid valves.


24

Fig. 5. (a) Plastic water pipes connects metal water pipe to solenoid valves, (b) Binding of nozzles and

solenoid valves.

Fig. 6. Design of bit-shifting 595 circuit.

Fig. 7. Design of amplifier circuit using power MOSFET IRF540


25

IV. RESULTS

We designed and implemented a system of 192 solenoid valves. Some results of this system can be seen as in Fig. 8 to Fig. 10. Beside digital images, drawings with text with different fonts can also be displayed because they can be converted into binary images easily.

A key parameter that needs to be determined so that the image can be properly displayed is the delay between two consecutive rows of the image. If the delay is so small, the water image will be shrinked vertically. Conversely, too long delay will cause the water image stretched out. Our experiments show that a delay of 50 ms is a good choice.

As expected, the system works well with the simple images, such as those one in Fig. 8 and Fig. 9, while complicated images with much details sometimes yield unexpected results. Fig. 10 shows one of such images. However, this is long-standing shortcoming of image processing algorithm, not the system itself.

Fig. 8. Mitsubishi logo

Fig. 9: B letter is displayed on water curtain

clearly

Fig. 10. Complicated images with much details

sometimes yield unexpected results

V. CONCLUSION AND FUTURE WORK In this paper, we introduced the concept of an

autonomous in-space display using water drops and developed a prototype of its. By controlling solenoid valves the induced water drops can display visual information itself freeing the system from an external device such as projector and making the system suitable for exhibition in public space. In the future, we will find a solution to shorten the distance between two consecutive nozzles which will lead to better resolution. Appropriate placement of light source to improve the contrast of the display is also worth investigation. Last but not least, in order to enhance the usability of the system, an user


26

software on mobile devices that allows users choosing and manipulating images, text and drawings to display would be absolutely useful.

ACKNOWLEDGMENT The work was carried out in part of the project

Building Water Screen Control Software by Computer sponsored by the University of Information Technology (UIT), Vietnam National University Ho Chi Minh city. The authors would like to thank UIT for financial sponsorship.

REFERENCES

[1] S. Eitoku, T. Tanikawa, Y. Suzuki, Display Composed of Water Drops for Filling Space with Materialized Virtual Three-dimensional Objects. In: IEEE Virtual Reality Conference 2006.

[2] S. Eitoku, K. Hashimoto, T. Tanikawa, Controllable water particle display. In: Proceedings of the 2006 ACM SIGCHI

International Conference on Advances in Computer Entertainment Technology

[3] P.C. Barnum, S.G. Narasimhan, T. Kanade, A multi-layered display with water drops, ACM SIGGRAPH 2010.

[4] Solenoid valve information. http://www.solenoid-valve-info.com

[5] M. HajiHeydari, S. Mohammadi, Positioning and Control of Nozzles and Water Particles in Decorative water curtain and water screens. Journal of Automation, Mobile Robotics & Intelligent Systems, vol. 6, no. 4, 2012.

[6] P. Barnum, S. Narasimhan, T. Kanade, A Projector-Camera System for Creating a Display with Water Drops. Workshop on Projector-Camera Systems (PROCAMS), in conjunction with CVPR, June 2009.

[7] ATmega16, http://www.atmel.com/Images/doc2466.pdf

[8] Power MOSFET IRF540, http://www.irf.com/product-info/datasheets/data/irf540n.pdf

Hi tho quc gia v CNTT nm 2013 Trng i hc Cn Th

27

NG DNG M HNH STELLA D ON S SODIC HA TRONG T VNG VEN BIN TNH SC TRNG Nguyn Hu Kit1, L Quang Tr2, V Th Gng3, Nguyn Tun Anh4 1B mn Ti nguyn t ai, Khoa Mi trng v ti nguyn thin nhin, Trng i hc Cn Th 2Vin nghin cu bin i kh hu, Trng i hc Cn Th 3B mn Khoa hc t, Khoa Nng nghip v sinh hc ng dung, Trng i hc Cn Th 4Phng Ti nguyn v mi trng huyn Vnh Chu, tnh Sc Trng

Thng tin chung: Ngy nhn: Ngy chp nhn: Title: Application of STELLA model to predict the soil sodification in the coastal areas of Soc Trang province

T kha: S sodic ha, h thng tm- la, h thng tm, STELLA, Sc Trng Keywords: Sodification, shrimp-rice systems, shrimp systems, STELLA, Soc Trang

ABSTRACT Three ecological zones such as fresh, brackish and saline water were distinguished in the coastal area of Soc Trang province. The objective of this study was to evaluate the soil salinity and some selected soil properties to provide the basic data for STELLA model to predict the soil sodification.Based on the experiments that were carried out on three ecological zones of Soc Trang province, the study was done by soil sampling in 5 times from the dry season 2006 to dry season 2008 on three rice fields and nine shrimp ponds of three shrimp systems: shrimp-rice; extensive shrimp and intensive shrimp in one and two shrimp cycles. Running model showed that the sodification process will be continued with high ESP values up to 85% in extensive and intensive shrimp systems while lower in shrimp-rice systems (17%) in the dry season 2015. STELLA model was an effective simulation to predict sodification process and support make decisions for better management of rice and shrimp systems.

TM TT Vng ven bin tnh Sc Trng c chia thnh ba vng sinh thi ngt, l v mn r rt. Mc tiu nghin cu ca ti nhm nh gi s mn ha, c tnh mi trng t d on kh nng sodic ho trong t cc m hnh canh tc qua s dng m hnh STELLA. Trn c s cc th nghim trn 3 vng sinh thi ca tnh Sc Trng c thc hin, nghin cu ny tin hnh qua 5 t thu mu t t ma kh nm 2006 n ma kh nm 2008 trn ba rung la canh tc hai v v ba v, chn ao nui tm trn ba h thng Tm la, Tm qung canh ci tin (QCCT), Tm bn thm canh/thm canh 1 v 2 v (BTC/TC). Kt qu d bo tin trnh sodic ho trong t cho thy t cc m hnh nui tm b sodic ha, t s tip tc b sodic vi gi tr ESP (Exchange Sodium Percentage) tng cao n khong 85% vo ma kh nm 2015. Ring m hnh tm- la, tin trnh sodic ho c pht trin chm hn, tng n khong 17%. M hnh STELLA l cng c hu hiu m phng qu trnh sodic ha trong t theo thi gian h tr cho cc quyt trong vic qun l v canh tc la, tm.


28

1 GII THIU M hnh ha v m phng c s dng rt

rng ri trong khoa hc k thut v khoa hc qun l, cng ngh thng tin c bc tin nhy vt nn k thut m phng c pht trin ln mt mc cao hn, phong ph hn (Nguyn Cng Hin v Nguyn Phm Thc Anh, 2006). M hnh gip chng ta hiu r ng thi ca cc tin trnh trong th gii thc qua m phng vi vi tnh v c n gin ha, c gi nh to nn tnh cch hot ng ca mt h thng Cc phn mm s dng trong m hnh ha c s dng rng ri gii quyt nhng thng s cho tin on tng lai. Phn mm STELLA 8.0 (Systems thinking in an experrimental learning lad with animation) l mt chng trnh m phng vi phng php lp trnh bng s kt ni cc biu tng c ng dng rt thnh cng trong nhiu ng dng thc tin c on trong thi gian di vi iu kin m hnh c thm nh tnh chnh xc vi cc thng s a vo (Ng Ngc Hng, 2008). Tnh cht ha hc ca t mn, t sodic ha gy nh hng bt li cho cy la pht trin l hm lng Na ho tan cao trong dung dch t v Na cao trn phc h hp thu (ESP >15%) l mt trong nhng tr ngi chnh ca t canh tc Vit Nam, xy ra cho cc vng ven bin ca ng bng (V Th Gng, 2003). Tin trnh sodic ha t ven bin nh hng nhiu n c cu sn xut cy trng v nui thy sn ca vng ven bin tnh Sc Trng vi 72 km b bin chy dc theo Bin ng lm nh hng n nhiu mt kinh t x hi ca tnh (Nguyn Hu Kit, 2007). V th, c im mi trng t, s sodic ho trong t v mi quan h ca cc yu t ny trong h thng canh tc tm-la, tm qung canh ci tin, tm bn thm canh, thm canh cn c nghin cu theo di v d on gip lnh o a phng nm tng quan v hin ti v tng lai tin trnh nhim mn c cc bin php quy hoch v qun l cho ph hp trong s dng t ai. Theo L Quang Tr v V Th Gng (2006) c im vng nghin cu theo Hnh 1 nh sau: Vng I: L vng c h thng bao c nc

ngt quanh nm phc v sn xut. Tuy nhin vng ny c mt phn din tch b nhim mn nh nn c chia thnh hai vng: Vng ngt Ia v nhim mn nh Ib; Vng II: Do nh hng ca thi gian mn, hin trng s dng v iu kin t khc nhau nn vng ny c chia thnh 02 vng ph l: Vng IIa gian mn t 01/1- 30/4 v vng IIb mn sm hn khong thng 12 n thng 5 nm sau; Vng III: y l vng b nhim mn quanh nm. 2 PHNG PHP NGHIN CU 2.1 Phng php thu mu v phn tch t Mi m hnh canh tc c b tr th nghim lp li 3 ln theo mi vng sinh thi chnh (Hnh 1) v tin hnh thu mu t su 0-20cm. Cc t thu mu c thc hin theo thi gian nh sau: t 1:Gia v tm 1; s la v 1; Tm-La gia v tm (T4,/2006), cui ma kh. t 2 Cui v tm 1; s la v 2 (T9/ 2006), gia ma ma. t 3: u v tm 1 (T,3/2007), gia ma kh t 4: Cui v tm 1 nm 2007 (T 9/2007), cui v la H Thu, gia ma ma t 5: u v tm 1 (T 4/2008), gia ma kh, chun b s la - EC: Cn 10g t pha vi 25 ml nc ct theo t l t, nc 1:2,5 o bng my WTW. - Na+ trao i: c o bng dung dch trch BaCl2 0,1M trong mu t bng my hp thu nguyn t. S dng dung dch CsCl gim s tng tc ion. - Kh nng trao i cation ca t (CEC: Cation exchange capacity): Xc nh CEC bng dung dch trch BaCl2 0,01M khng m (Houba et al.,1995). Mu t c bo ha vi dung dch BaCl2, ngha l trong phc h hp thu ch c cation Ba2+. Sau cho MgSO4 bit nng vo. Tt c Ba2+ hin din trong phc h hp thu c trao i vi Mg2+ v kt ta thnh dng kh ha tan BaSO4. Chun Mg2+ cn tha trong dung dch s tnh ton c lng Mg2+ hp th v tnh c tr s CEC.


29

- Theo V Th Gng (2001), phn trm natri trao i ESP (Exchange Sodium Percentage) c tnh ton da trn c s kh nng hp th cation ca t (CEC) v Na trao i theo cng thc.

ESP (%) =CECNa

x100, trong :

Na+ (meq/100g); CEC (meq/100g)

Hnh 1: Bn phn vng sinh thi v v tr 12 im nghin cu

2.2 Phng php d on s sodic ha bng phn mm STELLA Theo Ng Ngc Hng (2008), vic xy dng cc lc vng lp nhn qu l cn thit trong xy dng m hnh. Vic chuyn t lc sang m hnh my tnh bao gm: - Kho st c tnh v vai tr ca cc bin trong m hnh.

Chuyn t lc vng lp nhn qu sang lu , c 5 kiu bin h thng (system variables) in hnh: Bin tnh trng (state variable), bin tc (rate variable), bin c nh (constant variable), bin tr (auxiliary variable), bin ngoi sinh (exogenous variable).

D liu t u vo ca m hnh c thu thp vo 5 thi im t ma kh nm 2006 n ma kh nm 2008 bao gm cc ch tiu nh gi mc mn ho ca t theo thi gian nh: mn trong t, lng ma (chia theo ma ma v ma kh), t l natri tch lu, t l natri ra tri, s v nui tm trong mt nm, khong cch t vng khng nhim mn n vng nhim mn tng t 0-20 cm. Nhng yu t ny tc ng ln nhau to nn mt chui mc xch cht ch, hnh thnh cc mi quan h trong m hnh m phng s sodic ha ca t qua tr s cui l ESP theo chu k ma ma (thng 5 n thng10) v ma kh (thng 11 n thng 4 nm sau) t ma kh nm 2008 n ma ma nm 2015.


30

S Sodic ho ca t ph thuc vo rt nhiu yu t v nhiu mi quan h phc tp. m hnh ny vic m phng ch yu da vo cc yu t nh hng n natri trao i tch lu trong t, v ch tiu CEC trong t t quan st ESP ca t trong m hnh. C th cc mi quan h nh sau: - Vic d bo s Sodic ho da trn ch s ca ESP trong m hnh. ESP=(Natri trao i tch lu/CEC)*100, da vo cng thc tnh ESP cho thy natri trao i v CEC l hai yu t nh hng n ESP, mt trong hai yu t ny thay

i s dn n ESP thay i. CEC l bin c nh (constant variable), l s liu thc t c a vo m hnh. Natri trao i tch lu s l bin tnh trng (state variable). - Natri trao i tch lu ph thuc vo natri trao i v s ra tri natri trao i. Natri trao i tch lu tng khi lng natri trao i nhiu hn natri ra tri v ngc li. Hai bin natri trao i v natri trao i ra tri l hai bin tc (rate variable). S lin quan cc ch tiu nh gi s sodic ho trong t cc m hnh c trnh by trong Hnh 2.

Hnh 2: Lu mi quan h cc bin ca h thng m phng s sodic ha trong t

3 KT QU NGHIN CU 3.1 mn v tng s mui tan trong t mn trong t th hin qua tr s EC ca t c trnh by Hnh 3 cho thy t canh tc la c mn thp di ngng gy hi cho cy trng. So vi t nui tm, mn trong

t nui tm cao nht m hnh tm mt v v 2 v, 5-6 mS/cm. Din bin mn theo thi gian, m hnh tm la c mn cao trong gia ma ma 2006 v 2007. Kt qu ny cho thy cn c lng ma ra mn cho canh tc la. t nui tm mt v hai v thuc vng sinh thi III c mn cao nht (4-6 mS/cm).

Hnh 3: mn t la v t nui tm tng mt 0-20cm

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

2 La, 3 La Tm - La Tm QCCT Tm BTC/TC1 v

Tm BTC/TC2 vM hnh

EC (m

S/cm

)

4.06

9.06

3.07

9.07

4.08


31

Tng s mui tan trong t la bin ng trong khong 0,8-2, trong khi tng mui tan trong t nui tm rt cao 8-11 (Hnh 4). m hnh tm la, theo thi gian, tng mui ho tan trong t cng cao. Cn c lng nc ngt ra mn cho la pht trin tt. Kt qu ny

cho thy t b mn ho sau thi gian c chuyn sang nui tm. mn trong t nui tm mt v v hai v cao ph hp cho s pht trin ca tm, chn t mn cng gip duy tr mn thch hp cho sinh trng ca tm nht l vo ma ma.

Hnh 4: Tng mui ho tan trong tng mt (0-20cm) t la v t nui tm

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

2 La, 3La

Tm - La TmQCCT

TmBTC/TC 1

v

TmBTC/TC 2

vM hnh

Tng

mu

i ha

tan

(%)

4.06

9.063.07

9.07

4.08

3.2 S sodic ha trong t Trn c s xc nh lng Na trao i v phn trm Na trao i trn phc h hp thu (ESP) trong t, nhm t canh tc la vng sinh thi ngt c ESP thp, di ngng chuyn thnh t mn sodic trong nm 2006 v 2007. Tuy nhin qua phn tch t trong ma kh 2008, ESP vt trn ngng t b sodic. C th c s xm nhim mn trong ma kh khu vc ny. t nui tm mt v v hai v thuc vng sinh thi II v vng sinh thi III u c tr s ESP vt trn ngng 15 (Hnh 5). t nui tm vng 3 c ESP rt cao, 75%. So snh

theo thi gian qua ba ma kh, mc sodic ho cng tng cao, cao nht vo ma kh nm 2008. Vi mc sodic ho trong t cao nh th, nu cn thit chuyn i tr li canh tc la s rt kh thc hin v cn thi gian rt di v c bin php ci to t. Nhn chung t rt kh ra mn c th chuyn sang canh tc nng nghip. Ring i vi t canh tc la- tm trong vng sinh thi II, tuy t b nhim mn trong ma kh, vo thi gian nui tm (4/2006) t tng mt vn cha b sodic ho (ESP khong 12,2%) c l nh t c ra mn v trng la trong ma ma.

Hnh 5: S sodic ho trong t tng mt nui tm (0-20cm)

Vng

0

15

30

45

60

75

90

La I Tm II Tm III

ESP

(%)

4-06

9-06

3-07

9-07

4-08


32

3.3 Kt qu m phng s sodic ha t trong cc m hnh canh tc theo thi gian 3.3.1 M hnh hai v ba v la Bin tc natri trao i ph thuc vo t l natri trao i tch lu, t l natri trao i tch

ly ca t th ph thuc vo mc nhim mn ca t. Qua Hnh 6 cho thy s lin quan cc ch tiu nh gi mc mn ho trong t m hnh canh tc hai v ba v la.

Hnh 6. Lu mi quan h cc bin ca h thng m phng s Sodic ha trong t canh tc hai v ba v la

Kt qu m phng gi tr ESP c trnh by qua Hnh 7 biu din din bin thay i ca ESP ti vng trng la trong 16 ma k t nm 2008. Vo ma kh nm 2008 gi tr ESP = 14% v c xu hng vt qua ngng sodic ha vo ma kh nm 2014 (ESP = 16%). Qua th ng biu din ca m hnh m phng cho thy ESP c s tng gim theo chu k ma, ma ma ESP c xu hng gim so vi ma kh do Na+ trn phc h hp thu c trao

i v ra tri, chu k c lp i lp li tng vo ma kh v gim vo ma ma. S nhim mn ph thuc vo khong cch n vng nhim mn. iu ny chng t c s thm lu mn t vng nui tm n vng trng la, nu vic ngn mn trit th ESP s gim dn qua ra mn trong nhng ma ma. Ngc li nu khng ch ng c vic ngn mn nhng vng canh tc la gn vng nhim mn s c xu hng t b Sodic ho.

Hnh 7: Kt qu m phng s Sodic ha trong t canh tc la hai v ba v


33

3.3.2 M hnh Tm- La S lin quan cc ch tiu nh gi mc mn ho trong t m hnh Tm-la trnh by trong Hnh 8.

Hnh 8: Lu mi quan h cc bin ca h thng m phng s Sodic ha trong t canh tc Tm-la

m hnh ny cc mi quan h ban u cng ging nh m hnh canh tc la, CEC v natri trao i tch ly l hai yu t nh hng chnh n ESP. Natri trao i tch ly l bin chu nh

hng bi s tch ly natri v s ra tri natri trao i. Hai bin tc natri trao i v natri trao i ra tri cng thnh lp tng t nh m hnh canh tc la.

Hnh 9: Kt qu m phng s Sodic ha trong t canh tc Tm-la

Kt qu m phng qua Hnh 9 din bin trn th cho thy vo thng 4/2008 (ma kh) tng ng vi u v tm c ESP=14% di ngng Sodic, n ma ma 2008 ESP gim hn so vi u ma, ESP= 9% do gi nc ngt canh tc la, tng quan ca th th mc ESP tng v gim theo tng ma v ph hp vi thc t s liu phn tch trc . ESP ca m hnh tm la vo ma kh nm 2010 vt ngng Sodic, ESP ln n 16%. T ma kh nm 2011 n nm 2015 ESP c xu hng gia tng v vt ngng sodic ha.

3.3.3 M hnh tm qung canh ci tin Qua Hnh 10 cho thy cu trc ca m hnh tm qung canh c t trn c s ca m hnh tm la, s lin quan cc ch tiu nh gi mc mn ha ca m hnh tm qun canh tng t nh m hnh tm la. Kt qu m phng qua Hnh 11 cho thy thy vo thng 4/2008 (ma kh) tng ng vi u v tm t vt ngng Sodic, gi tr ban u ca m hnh ESP= 48%. Trong nhng ma tip theo ESP tip tc tng nh v t gi tr khong 60% n ma kh nm 2015.

Ma kh 2008 Ma 2009 Kh 2011 Ma 2012 Kh 2014 Ma 2015


34

Hnh 10: Lu mi quan h cc bin ca h thng m phng s Sodic ha trong t canh tc Tm qung canh ci tin

Hnh 11: Kt qu m phng s Sodic ha trong t canh tc tm qung canh ci tin

3.3.4 M hnh tm thm canh mt v v hai v c m phng tng t nh m hnh tm qung canh, nhng m hnh ny c thm hai

bin mi l s v nui tm v h s nhim mn (Hnh 12).

Hnh 12: Lu mi quan h cc bin ca h thng m phng s Sodic ha trong t canh tc Tm thm canh bn thm canh mt v hai v

Ma kh 2008 Ma 2009 Kh 2011 Ma 2012 Kh 2014 Ma 2015


35

Gi thuyt v hai bin ny c gii thch nh sau: thc t s liu nm 2008 cho thy nu s v nui tm l mt th CEC trong t s thp hn so vi CEC trong t ca m hnh nui tm hai v. T cho thy t l natri tch ly trong t ca m hnh canh tc tm mt v thp hn m hnh tm hai v. Do ph hp cho thc t th m hnh ny thm mt bin mi l bin h s nhim mn.

m hnh tm thm canh mt v, kt qu m phng cho thy din bin ton b trong sut qu trnh m phng th t ca m hnh ny trng thi Sodic, ESP nh nht ca m hnh l 35% vo cui nm 2008, trong nhng nm ti t c xu hng b nhim mn tng dn gi tr ESP c th t khong 85% vo ma ma nm 2015 (Hnh 13).

Hnh 13: Kt qu m phng s Sodic ha trong t canh tc tm thm canh mt v

m hnh tm thm canh hai v cho thy t b sodic ha cao hn so vi t ca m hnh tm thm canh mt v, gi tr ESP thp nht ca m hnh ny t 36% vo cui nm 2008, theo thi

gian t c xu hng tng ESP cao do nui v tm th hai nn phi gi nc mn lin tc (Hnh 14).

Hnh 14: Kt qu m phng s Sodic ha trong t canh tc tm thm canh hai v

Ma kh 2008 Ma 2009 Kh 2011 Ma 2012 Kh 2014 Ma 2015

Ma kh 2008 Ma 2009 Kh 2011 Ma 2012 Kh 2014 Ma 2015


36

Tm li, kt qu d bo tin trnh sodic ho trong t cho thy t m hnh canh tc la hai v ba v t b sodic ha vo ma kh nm 2014 v 2015. t m hnh tm- la t b sodic ha vo ma kh cc nm 2010- 2015. t cc m hnh nui tm QCCT, tm BTC/TC hai v ba v t sodic ha quanh nm t nm 2008 2015.

4 KT LUN M phng da theo phn mm STELLA, mc mn ha t trong nm nm ti s tng nhanh nu lin tc gi nc mn canh tc hai v nui tm trong nm. mn ca t gim vo ma ma nhng t b sodic ho. H thng Tm-la c mc mn ha trong t chm v vn canh tc la c vo ma ma. t m hnh canh tc la b nhim mn t vng nui tm ln cn, theo thi gian mn cng tng, nhng chm hn so vi h thng Tm-la. ng dng m hnh STELLA 8.0 trong d on s sodic ha trong t cho thy l cng c mnh m v tin ch vi nhiu chc nng trong vic xut cc gii php cho quy hoch s dng t vng ven bin. Cn c quy trnh thm nh lin quan vic so snh s vn hnh ca m hnh qua kt qu ca u ra so vi d liu thc t thu mu phn tch tip theo v hiu chnh mt s thng s thch hp nng cao tin cy ca kt qu m phng. TI LIU THAM KHO 1. L Quang Tr v V Th Gng, 2006. Bo co

kt qu nghin cu giai on I: Theo di s thay i v nh gi cht lng t vng nui trng thy sn tnh Sc Trng. Chng trnh hp tc nghin cu gia S Ti nguyn mi trng tnh Sc Trng v Trng i hc Cn Th.

2. Ng Ngc Hng, 2008. Nguyn l v ng dng m hnh ton trong nghin cu sinh hc, nng nghip v mi trng. Nh xut bn nng nghip TP. H Ch Minh.

3. Nguyn Cng Hin v Nguyn Phm Thc Anh, 2006. M hnh ha h thng v m phng. Nh xut bn khoa hc k thut.

4. Nguyn Hu Kit, 2008. Lin kt phn mm PRIMER v RESTORE nh gi tnh bn vng cc m hnh canh tc vng chuyn i nui trng thy sn mn l tnh C Mau v Sc Trng. ti nghin cu khoa hc cp Trng i hc Cn Th nm 2007.

5. V Th Gng (2001). Cc tr ngi ca t trong sn xut nng nghip, B mn Khoa Hc t v QL, Khoa Nng nghip v SHD, i hc Cn Th.

6. V Th Gng (2003). nh gi cht lng t, nc v xut bin php s dng t thch hp cho m hnh canh tc la tm ti huyn M Xuyn, tnh Sc Trng, Chng trnh hp tc nghin cu gia B mn Khoa Hc t- Qun L t ai v S Khoa hc cng ngh tnh Sc Trng.

7. V.J.G. Houba, J.J. Van Der Lee, I. Novozamsky (1995). Soil and plant analysis, part 5B soil analysis procedures, sixth edition 1995, Department of Soil Science and Plant Nutrition Wageningen Agricultural University, 217p.


37

GII PHP P NG NHU CU NC CHO H THNG CANH TC LA HUYN NG NM (SC TRNG) TRONG THI GIAN XM NHP MN Hng Minh Hong(1), Vn Phm ng Tr(1) v Nguyn Hiu Trung(1) Thng tin chung: Ngy nhn: Ngy chp nhn: Title: Possible solutions for water supply in agriculture in Nga Nam (Soc Trang) during the peaks of saline intrusion

T kha: Lu tr nc, xm nhp mn, cn bng nc v xm nhp mn v tip cn t duy h thng. Keywords: Water storage, salinity intrusion, water balance, and system thinking approach.

ABSTRACT Sea-water intrusion and its negative impacts on rice farming systems in coastal plains of the Vietnamese Mekong Delta (VMD) are increasing rapidly (in terms of space and time). The natural and human-driven factors of such events in the VMD are highly complex. Such events are projected to be even more severe in the future given great compound impacts from sea level rise and upstream discharge degradation in the dry season. The Nga Nam District of Soc Trang Province is the area for rice production and highly affected to the system of agricultural production by sea-water intrusion. Therefore, main focus of this study is to investigate water storage capacity in the canals which can be used to irrigate rice-fields during the water-shortage time (caused by salinity intrusion). The physical features of the study area (including: local weather, canals system, existing farming system) and bio-characteristics of crops (including: growing period and water demand at each growing stage) are collected in order to build a mutual relationship between the demand and supply (of water) during the crop season. The above factors are synthesized todeveloped into a dynamic system model which it describes the variation of the actual water in rice farming systems over time. Results showed that increasing the canals area in the field can supply sufficient water to irrigate the rice systems during peaks period of sea-water intrusion. The enlarged area of river surface in canals of 40.000m2 with the depth of 1.7m will provide sufficient water for rice during 15 days in the cerent and capility 20 days of sea-water intrusion in the future. This study could provide a good insight to local farmers and state agencies in the coastal areas to adapt to new climate patterns, especially during the shortage of freshwater resources.

TM TT Xm nhp mn tc ng tiu cc n h thng canh tc la vng ven bin ng Bng Sng Cu Long (BSCL). S bin ng ca t nhin v hot ng con ngi tc ng qua li vi nhau rt phc tp BSCL. Nhng tc ng ny c d bo s cn nghim trng hn trong tng lai do mc nc bin dng v suy thoi lu lng nc t thng ngun vo ma kh. Huyn Ng Nm ca tnh Sc Trng l vng chuyn sn xut la v ang chu nh hng ln n h thng sn xut nng nghip do xm nhp mn. Mc tiu chnh ca nghin cu ny l tng kh nng lu tr nc trong cc knh ni ng nhm cung cp nc cho cy la trong

Documents

Ky Yeu Ht Cntt2013