Sesors & Trasducers, Vol. 8, Issue, November 04, pp. 57-6 Sesors & Trasducers 04 by IFSA Publshg, S. L. http://www.sesorsportal.com Study o a Fre Detecto System Based o Support Vector Mache Ye Xaotg, Wu Shasha, 3 Xu Jgg, Faculty of Electroc ad Electrcal Egeerg, Huay Isttute of Techology, Huaa Jagsu 3003, Cha 3 Departmet of Electrcal Egeerg, Jagsu Huaa Techca College, Huaa Jagsu 300, Cha Tel.: 3555985 E-mal: xaotgye@63.com Receved: 6 September 04 /Accepted: 30 October 04 /Publshed: 30 November 04 Abstract: It s very mportat to research the predcto of fre, whch s sgfcat to the people ad ato. The tradtoal fre detecto system based o eural etwork has the dsadvatages of over learg, trapped local mmum, etc. Ths paper proposes a ew fre detecto system based o support vector mache (SVM. Gas sesors, smoke sesor ad temperature sesor are composed to be a sesor array. The fre detecto model s establshed, cludg sample selecto, predcto model trag predcto, output modules, etc. The SVM trasform the complcated olear problem to the lear problem the hgh dmesoal plae. The expermetal results show that fre detecto system based o support vector mache had hgh recogto rate ad relablty, t overcomes the dsadvatages of tradtoal methods. Copyrght 04 IFSA Publshg, S. L. Keywords: Support vector mache, Fre detecto, Sesor array, Patter recogto.. Itroducto Fre extremely harm to the lfe safety, property, evromet ad ecologcal balace. The accurate predcto the early s very mportat for the cotrol of fre. Fre alarm s the key techology fre-fghtg doma of varous coutres. The tradtoal fre alarm methods such as threshold method ad process method, mostly accordg to sgle parameter of the fre, the recogto techque of whch had dsadvatages of smplfcato, poor adaptablty ad hgh false alarm rate []. Recetly, the tellget mult-parameter fre alarm methods s bee rse. The artfcal eural etwork as the ma techology s researched wdely. The artfcal eural etwork ca be affected by the complexty of samples ad etwork structure whch lead to problems of over learg, poor geeralzato ablty ad low fre recogto rate []. Support vector mache (SVM s a ew tellget learg method, whch s based o structural rsk mmzato of statstcal learg theory [3-5]. Sample vectors are mapped to hgh dmesoal feature space. Nolear problem of prmary spaces s trasformed to a lear problem of hgh dmesoal feature space. Ier product operato of hgh dmesoal feature space s trasformed to kerel fucto calculato, whch avods the complex olear calculato of hgh dmesoal feature space ad the dmeso dsaster. SVM whch ca avod over learg ad local mmum s more geeralzed performace ad http://www.sesorsportal.com/html/digest/p_500.htm 57
Sesors & Trasducers, Vol. 8, Issue, November 04, pp. 57-6 covergece speed as compared to artfcal eural etwork whch s based o the emprcal rsk mmzato prcple. Ths paper proposes a ew fre alarm model based o support vector mache ad mult-sesor formato fuso. The expermetal results show that mult-sesor data ca be dsposed tellgetly ad realze the fre predcto. The predcto accuracy of SVM fre alarm model s more tha eural etworks. The relablty ad accuracy of fre alarm system are creased. Accordg to dualty theory, Formula ( s trasformed to quadratc programmg problem, whch s solved by Lagrage optmzato method. m + ε =, = ( a ( a a ( a = y ( a a a The costrat codto s K( x (4. Support Vector Regresso (SVR.. SVR Algorthm ( a a = 0, =,,, = a C a = l, 0,,,, (5 The tal research of SVM s classfcato. Recetly the applcato of SVM s exteded to regresso ad tme seres predcto. Support vector regresso (SVR s used to exted the cocluso of patter recogto classfcato to the real fucto. I the case of less statstcal samples, SVR algorthm based o structural rsk mmzato prcple ca crease the learg geeralzato ad mmze the emprcal rsk ad cofdece terval, whch has good statstcal law. The gve trag sample set s S = {( x, y =,,, }, the put ad output are x R, y R respectvely. Φ makes the put data be mapped to hgh dmesoal feature space through olear mappg. It realzes the lear regresso ths ew space. The regresso fucto s f ( x = w Φ ( x + b ( = Based o estmato rsk mmzato, costruct the mmzato obectve fucto: m w + C( ξ + ξ, ( = = where C s the pealty parameter, ξ ad ξ are the slack varables. The costrat codto s y f ( x ε + ξ, =,,, y + f ( x ε + ξ, =,,,, (3 ξ 0, ξ 0, =,,, where ε s the error parameter. The Selecto of C ad ε has sgfcat fluece to geeralzato of SVR ad the computatoal complexty. C s used to balace the structural rsk value ad model complexty. ε s used to determe mmum fttg error of SVR. T The Optmal Soluto s a ˆ = ( aˆ, aˆ, aˆ, aˆ. I Formula (4 ad (5, a ad a are Lagrage multpler. K ( x s kerel fucto. Select a 0 < aˆ < C / compoets of â. Calculate b = y a = ( aˆ aˆ K( x + ε ; Or Select 0 < aˆ < C / compoets of â. Calculate b = y = ( aˆ aˆ K( x ε. Costruct support vector lear regresso fucto: = f ( x = ( a a K( x + b (6 Complex olear samples are mapped to hgh dmesoal feature space. It s o eed to calculate olear fucto for regresso fucto. It s kow from formula (6 that the calculato of kerel fucto ca avod complcated operatos of hgh dmeso. Accordg to Hlbert-Schmdt theorem, K ( x satsfes the Mercer codto s er product kerel. K( x = Φ( x Φ( x (7 There are some commo kerel fuctos. I. Lear kerel fucto K( x = ( x x (8 II. M order polyomal kerel fucto K ( x + m = ( x x (9 III. Neural etwork kerel fucto K( x = tah[ c( x x + c ] (0 58
Sesors & Trasducers, Vol. 8, Issue, November 04, pp. 57-6 IV. RBF kerel fucto K ( x exp x x = σ.. SVR Mache Learg Based o structural rsk mmzato prcple, SVR mache learg make the learg model wth gve lmted trag samples, whch ca obta the relatoshp betwee put ad output, predct ad udge the ukow sample data. The algorthm steps of SVR mache learg s show as Fg.. detectg pot cludg temperature, smoke cocetrato ad cocetrato of CO ad O were motored by expermet. Some related data were outputted by the sesor array made up of temperature sesor, smoke sesor ad gas sesor. Output data of sesor array were set to computer to stadardzato ad ormalzato process through sgal processg crcut ad mult-fucto data acqusto card. Trag samples ad test samples were selected from the processed output data. Trag samples were traed by SVR learg mache. Durg the course of trag, parameters were desged ad compared to choose the optmal parameters. Test samples were used to test the traed SVR learg mache ad verfy trag results. ( x, y â Fg.. Idetfcato prcple of fre alarm based o SVR. K x ( Fg.. The steps of SVR mache learg. 3. SVR Recogto of Fre Alarm There are smoke, toxc ad harmful gas, the chage of Oxyge Cotet ad the rse temperature fre. The tradtoal recogto method of fre was to motor oe parameter of fre, or oly some certa parameters, the relatoshp betwee whch was depedet wthout fuso processg. So false alarm always happeed. I ths paper temperature sesor, smoke sesor ad gas sesor of CO ad O were adopted to compose the sesor array fre alarm system. SVR was adopted to realze the data fuso processg of fre parameters ad fre alarm. The detfcato prcple of fre alarm based o SVR was show as Fg.. Fre parameters of the 4. Expermetal Results The expermetal data were temperature, smoke cocetrato, CO cocetrato ad O cocetrato. A temperature sesor, a smoke sesor ad a CO sesor ad a O sesor were adopted to compose the sesor array for the measuremet of fre parameters. For the comparso betwee two measuremet results of SVR ad artfcal eural etwork, BP algorthm s a commo algorthm of artfcal eural etwork, whch was selected to establsh eural etwork model ths paper. Ths Neural Network was a 3 layers BP etwork of 4-0-3 Structure. Iput layer had 5 odes that were temperature ( o C, smoke cocetrato (mg/m 3, CO cocetrato (mg/m 3 ad O cocetrato (mg/m 3 from put sesor array. Output layer had 3 odes that were of probablty values of smolderg stage, developg stage ad ope fre stage. The expermet measured 30 data sets, 0 data sets of whch were selected as trag samples to tra the etwork; the rest 0 data sets were selected as test samples to verfy the learg effect of the etwork. Aalyss result of BP eural etwork wth 5 radom data sets was show as Table. 59
Sesors & Trasducers, Vol. 8, Issue, November 04, pp. 57-6 Table. Aalyss result wth BP eural etwork. Expected output Predcted output Groups Temperature Smoke CO O SmoldergDevelopg Ope fre SmoldergDevelopgOpe fre stage stage stage stage stage stage 5 0.0 5.0 00 0.7 0. 0. 0.645 0.06 0.09 30 0.5 8.0 80 0.9 0.3 0. 0.93 0.88 0.07 3 40. 7.5 400 0. 0.9 0. 0.05 0.935 0.0 4 50. 35.0 750 0. 0. 0.9 0.09 0.07 0.884 5 60 3. 40.0 500 0. 0. 0.5 0.097 0.094 0.53 Type of kerel fuctos, parameters, pealty parameter C ad error requremet parameter ε are eeded the SVR model. I commo kerel fuctos, the model of polyomal kerel fucto s relatvely complcated, ad operatoal speed s slow. Whle eural etwork kerel fuctos sometmes has the problem of o covergece results. RBF kerel fucto has the balace betwee the computg tme ad predcto accuracy. Complex olear samples are mapped to hgh dmesoal feature space by the RBF kerel fucto, whch s faled for the lear kerel fucto. So RBF kerel fucto was pcked for the SVR kerel fucto ths paper. It s mportat for the establshg of SVR model that the dfferet kerel wdth was chose for the cotrol of the costat σ, pealty parameter C ad error requremet parameter ε. Based o the theoretcal aalyss ad the comparso of expermetal results, the optmal parameter combato was determed by the mmum error, whe C=350, σ =., ε =0.00, predcto accuracy was hgher. Aalyss result of SVR compared wth the aalyss result of BP eural etwork wth 5 radom data sets was show as Table. Table. Aalyss result wth SVR. Expected output Predcted output Groups Temperature Smoke CO O SmoldergDevelopg Ope fre SmoldergDevelopg Ope fre stage stage stage stage stage stage 5 0.0 5.0 00 0.7 0. 0. 0.673 0.03 0.997 30 0.5 8.0 80 0.9 0.3 0. 0.889 0.9 0.04 3 40. 7.5 400 0. 0.9 0. 0.0 0.879 0.0 4 50. 35.0 750 0. 0. 0.9 0.097 0.0 0.97 5 60 3. 40.0 500 0. 0. 0.5 0.998 0.04 0.495 From the Table ad Table, the predcto average error smolderg stage of BP eural etwork s 5.8 %, the predcto average error developg stage s 5.38 %, ad the predcto average error ope fre stage s 6.8 %. The predcto average error smolderg stage of SVR s.4 %, the predcto average error developg stage s.7 %, ad the predcto average error ope fre stage s.4 %. Expermets showed that recogto accuracy of BP eural etwork that tellget fre alarm system s always adopted curretly was sgfcatly lower tha the SVR method. Error covergece curve of SVR trag s show as Fg. 3. After trag, the predcto fttg precso s 9.99995 0-5, the mea square error acheved the request of trag precso, the whole trag process s coverget ad the etwork learg precso s hgh. 5. Coclusos Ths paper presets SVR fre alarm system that has good recogto effect from the fre alarm expermet. Fg. 3. Curve of SVR trag. Compared wth mature BP eural etwork recogto algorthm of ths feld recetly, SVR algorthm avods local mmum value ad has the advatages of global optmzato, strog geeralzato ablty ad hgh recogto accuracy. I addto, SVR s good at olear regresso ad quattatve detfcato of parameters. It plays a 60
Sesors & Trasducers, Vol. 8, Issue, November 04, pp. 57-6 better role the feld of quattatve detfcato ad lays a sold foudato for the further research. Ackowledgemets Ths proect was supported by Scece Foudato of Huay scece ad techology bureau (HAG03039. The authors wsh to thak Huay scece ad techology bureau for the support of the research. The authors also wshe to thak all volved parters for ther cotrbuto to the research. Refereces []. Xgpeg Lu, Jqua Zhag, Weyg Ca, Zhu Tog, Iformato dffuso-based spato-temporal rsk aalyss of grasslad fre dsaster orther Cha, Kowledge Based Systems, 3, 00, pp. 53-60. []. Sloper J. E, Hes E., Detectg errors the ATLAS TDAQ system: A eural etworks ad support vector maches approach, Proceedgs of the Coferece o Computatoal Itellgece for Measuremet Systems ad Applcatos, -3 May 009, pp. 5-57. [3]. Huag H. P., Lu Y. H., Fuzzy Support Vector Maches for Patter Recogto ad Data Mg, Iteratoal Joural of Fuzzy Systems, Vol. 4, No. 3, 00, pp. 86-835. [4]. Chrstopher J. C. Burges, A Tutoral o Support Vector Maches for Patter Recogto, Data Mg ad Kowledge Dscovery, 998, pp. -67. [5]. Ayd I., Karakose M., Ak E., Artfcal mmue based support vector mache algorthm for fault dagoss of ducto motors, Proceedgs of the Coferece o Electrcal Maches ad Power Electrocs, 0- Sept. 007, pp. 7-. 04 Copyrght, Iteratoal Frequecy Sesor Assocato (IFSA Publshg, S. L. All rghts reserved. (http://www.sesorsportal.com 6