TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK

Inernaional Journal of Innovaive Managemen, Informaion & Producion ISME Inernaionalc2011 ISSN 2185-5439 Volume 2, Number 1, June 2011 PP. 57-67 TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK SANFA CAI, LIN ZHAO AND ZHIZHI ZHANG School of Economics & Managemen Tongi Universiy Shanghai, 200092, China csf@ongi.edu.cn ABSTRACT. Tool ousourcing faces many riss. If he ousourcing enerprise canno carry ou he analysis, appraisal and conrol of ool ousourcing sufficienly, hen he ousourcing enerprise obains no benefis and suffers losses. This paper analyzes he ris facors of ool ousourcing and hen esablishes an index sysem ha provides forewarning of problems in ool ousourcing. Finally, we provide a heoreical basis for he ris managemen of ool ousourcing. Keywords: BP Neural Newor; Fishbone Diagram; Tool Ousourcing; Ris Esimaion 1. Inroducion. The essence of ool ousourcing managemen is he principal-agen relaionship beween ousourcing enerprises and ool service suppliers (Aron e al., 2005). Informaion asymmery, informaion disorion and he uncerainy of mare environmen beween clien and agen will cause many inds of ris during he period of implemening ool ousourcing. Afer implemening ool ousourcing, he service qualiy and qualiy supervision sysem are no perfec. How o effecively encourage ool service suppliers and gain win-win resul beween ousourcing enerprises and service suppliers is an imporan problem. Consequenly, because here are sill many ris facors during he period of ool ousourcing, he ousourcing enerprise should ae considerae analysis and avoidance. Lonsdale (1999), Whimore (2006), Peibone (2009) respecively sudied ousourcing ris problems in aspecs of ousourcing ris source, ousourcing ris esimaion and ousourcing ris avoidance policy. Zhang (2005), Da (2005), Kainz (2001) sudied ool managemen from he following aspecs as managemen model and managemen process. Nowadays, domesic and inernaional research abou ousourcing ris is mainly limied o ris idenificaion and conrol sraegy in he period of ousourcing. A compleely reasonable mehod of ousourcing ris evaluaion which can gain a global admission and accepance does no exis. 2. BP Neural Newor Model. 2.1. The Inroducion of BP Neural Newor. BP neural newor is a muli-level feedforward neural newor based on BP algorihm. As a paralleled and dispersed reamen model, BP neural newor has he characerisics of nonlinear mapping, self-adaping

58 SANFA CAI, LIN ZHAO AND ZHIZHI ZHANG learning and faul-olerance propery and could simulae in he complicaed and capricious invesmen and operaion environmen. This paper maes full use of BP neural newor o esimae ool ousourcing ris, which can provide alarm for ousourcing enerprises when abnormal siuaion appears and arac more aenion from ousourcing enerprises o solve his problem and hen guaranee company s ool ousourcing safey. Tool ousourcing ris evaluaion maing use of BP neural newor is no only more obecive and accurae, bu also saes he close relaionship beween he facors of ool ousourcing ris index sysem and evaluaion oucomes. So he ool ousourcing ris evaluaion model based on BP neural newor has a grea prioriy. 2.2. BP Neural Newor Srucure and Algorihm. The learning process of BP neural newor consiss of four pars (Wang e al., 2000): (1) Inpu model clocwise propagaion (Inpu model is from inpu layer o oupu layer via middle layer); (2) Oupu error aniclocwise propagaion (The oupu error is from oupu layer o inpu layer via middle layer); (3) Circular memory raining (The calculaion process is operaing in he roaion and circulaion beween model clocwise propagaion and error aniclocwise propagaion); (4) Judge of learning resuls (I is o udge he global error wheher prone o minimum value or no). The Procedure of whole learning process of BP neural newor: (1) Iniializaion, assign connecion weighs W, i V and hreshold θ, r, i= 1,2,..., n, = 1,2,..., p, = 1,2,..., q, = 1,2,..., m, a random value beween -1 o +1. (2) Randomly selec a couple models A = [ a1, a2,... an ], YK = [ y1, y2... yq ] and hen provide i o BP neural newor, and hen calculae middle layer s differen neurons inpu s (acivaed value) using inpu model A = [ a1, a2,... an ] wih connecion weighs W i and hresholdθ, and hen calculae s hrough acivaion funcion. 1 f ( x) = (1) 1 x + e (3) Calculae differen unies oupu of middle layerb : b = f( s ) (2) s n = Wi. ai θ i= 1 (4) Calculae differen unies inpu l (acivaed value)of oupu layer wih oupu b of middle layer, connecion weighsv and hreshold r and hen calculae he response value c of differen unies by acivaion funcion wihl, c = f( l ) (4) l p = 1 = V. b (3) γ, = 1,2,..., q (5)

TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK 59 (5) Calculae calibraion error d of differen unis wih expeced oupu model Y = [ y, y y ] and BP neural newor s pracical oupu c : 1 2 q d (6) Calculae correcion error e ( = y c ). c (1 c ), = 1,2,..., q (6) e of middle layer wih V, d, b : q = [ d. V = 1 ] b (1 b ), = 1, 2,..., p (7) (7) Calculae new connecion weighs beween middle layer and oupu layer wih d V, and r :, b, V ( N + 1) = V ( N) + α. d. b (8) γ ( N + 1) = γ ( N) + α. d (9) N : learning imes (8) Calculae new connecion weighs beween inpu layer and middle layer wih e, a, W andθ : i i i i i W ( N + 1) = W ( N) + β. e. a (10) θ ( N + 1) = θ ( N) + βe (11) (9) Randomly selec nex couple of learning model and hen provide i o BP neural newor, reurn o sep 3, unil all m couples models could be rained. (10) Randomly re-selec a couple models from m learning models and hen reurn o sep 3, unil he newor global error funcion E is less han he preliminarily-seing limi value(newor can converge) or learning circui number is greaer han preliminarily-seing value(newor can converge). (11) The end: In he above learning procedures, sep 3 6 is he clocwise propagaion process of inpu learning model; sep 7 8 is he aniclocwise propagaion process of newor error; he raining and convergence process is fulfilled respecively by sep 9and sep 10. 3. Tool Ousourcing Ris Idenificaion and Design. 3.1. Tool Ousourcing Ris Source. Tool ousourcing ris mainly arises from ousourcing decision and ousourcing execuion wo periods, as following able 1. TABLE 1. Tool ousourcing ris source Tool Ousourcing Decision Sage Sraegy ris Transacion ris ousourcing Managemen ris ris source Ousourcing Relaionship ris Execuion Sage Ou-of-conrol ris

60 SANFA CAI, LIN ZHAO AND ZHIZHI ZHANG 3.2. The Facors of Tool Ousourcing Ris. Tool ousourcing ris conains sraegy ris, ransacion ris, managemen ris, relaionship ris and ou-of-conrol ris. The fishbone diagram as figure 1 (Cai e al., 2009): Relaionship Ris Managemen Ris Ou-of-conrol Ris ⑴ ⑵ ⑶ ⑷ ⑹ ⑺ ⑸ ⑻ ⑼ ⑿ Tool Ousourcing Managemen Ris ⑾ ⑽ ⒀ Sraegy Ris Transacion Ris FIGURE 1. Tool ousourcing ris source fishbone diagram ⑴------Lacing of effecive incenive mechanism ⑵------Compeiion mechanism is no sufficien ⑶------Business process in disorder ⑷----- Lacing of effecive ool managemen performance esimaion sysem ⑸------Culure difference and unsaisfacory communicaion ⑹------Lacing of execuable service level agreemen ⑺------Lacing of recurren ob examinaion ⑻------Vendor supervision is deficien ⑼------Deerminaion of ousourcing limi is indisinc ⑽------Lacing of mare mauriy analysis ⑾------Key business idenificaion is no sufficien ⑿------Conrac clause is no perfec ⒀------Lacing of professional ousourcing eam According o he principle of auheniciy, comprehensiveness, scienific propery and fairness for indicaor sysem, aing ino accoun of sensiiviy and dynamic of he indicaors, and simulaneously, every index could be complemenary and could no be reduplicaive o comprehensively reflec ool ousourcing ris siuaion. Consequenly, every forewarning module has several represenaive indicaors and all indicaors consruc ool ousourcing ris forewarning indicaors sysem. The figure 2 is he ool ousourcing ris forewarning indicaors sysem.

TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK 61 3.3. Tool Ousourcing Ris Evaluaion Indicaor Sysem. According o he ool ousourcing ris source fishbone diagram, ool ousourcing ris, for insance, he sraegy ris, ransacion ris, managemen ris, relaionship ris and ou-of-conrol ris, can be embodied by 13 differen indicaors. Every indicaor has differen score form 1 o 7, 7 means he mos imporan, 1 means he leas imporan, as following able 2. Tool Ousourcing Ris Managemen Ris Transacion Ris Sraegy Ris Relaionship Ris Ou-of-Conrol Ris X1 X4 X6 X9 X11 X2 X5 X7 X10 X12 X3 X8 X13 FIGURE 2. Tool ousourcing ris indicaors forewarning sysem X1 ------ Tool Performance Esimaion Sysem X 2 ------ Business Process X 3 ------ Culure Communicaion Degree X 4 ------ Conrac Clause Perfec Degree X 5 ------ Ousourcing Team Specializaion Degree X 6 ------ Ousourcing Scope Deerminaion Accuracy X 7 ------ Ousourcing Mare Mauriy X 8 ------ Key Business Idenificaion X 9 ------ Effecive Incenive Mechanism X10 -----Compeiion mechanism X11 -----Service Level Agreemen X12 -----Job Assessmen X13 -----Vendor Supervision

62 SANFA CAI, LIN ZHAO AND ZHIZHI ZHANG TABLE 2-1. Tool ousourcing ris quaniaive index Relaed Facors 1 2 3 4 5 6 7 Tool Performance Esimaion Sysem X1 Business Process X2 Culure Communicaion Degree X3 Conrac Clause Perfec Degree X4 Ousourcing Team Specializaion Degree X5 Ousourcing Scope Deerminaion Accuracy X6 Ousourcing Mare Mauriy X7 Key Business Idenificaion X8 TABLE 2-2. Tool ousourcing ris quaniaive index Relaed Facors 1 2 3 4 5 6 7 Effecive Incenive Mechanism X9 Compeiion Mechanism X10 Service Level Agreemen X11 Job Examinaion X12 Vendor Supervision X13 4. Evaluaion of Tool Ris by BP Neural Newor Model. 4.1. Consrucing he Tool Ousourcing Ris Forewarning Model. This paper is o operae he neural newor design procedure by MATLAB. When designing BP neural newor, he following problems should be aen ino consideraion: deermining he newor s opological srucure, neuron s ransmission funcion, newor s iniializaion (he iniializaion of connecion weighs and hreshold); raining samples normalizaion processing; raining parameers seing; sample daa inpu mode and so on Zhou and Kang (2005). The newor s opological srucure conains he number of hidden layer, newor inpu, hidden layer, oupu layer. (1) The number of hidden layer: BP neural newor is o calculae from inpu layer o oupu layer. Alhough he speed is faser when he number of hidden layer becomes more, i coss more ime in pracical applicaion. The speed can be improved by adding nodes number of hidden layer. Consequenly, when applying BP neural newor o forecas ool ousourcing ris, i is bes o choose 3-hierarchy BP neural newor wih only one hidden layer. (2) Decision of he inpu layer s uni number: According o ool ousourcing ris indicaor forewarning sysem, i is o inpu 13 indicaors. The facors of his model are all qualiaive facors. When inpuing nodes inpu, i is beer o limi he indicaor beween 0 and 7 in order o apply in newor model. (3) Decision of he hidden layer s uni number: The number of hidden layer nodes has an impac on neural newor performance. When he quaniy of hidden layer nodes is less,

TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK 63 learning capaciy is so limied ha i is oo difficul o sore all laws which raining samples conain. The qualiy of hidden layer nodes is so more ha i coss more newor raining imes and non-regular conens of he samples, for insance, noise and disrupion, could be sored, which has a bad generalizaion. According o he empirical formula i = n+ m+ a, i is he number of neurons in hidden layer, n means he number of neurons in inpu layer, m means he number of neurons of oupu layer, a is a consan which is from 0 o 1. Therefore, based on differen models wih differen numbers of neurons in hidden layer, he auhor is abou o respecively simulae, compare and hen deermine he mos suiable number of neurons in hidden layer. I is supposed o be 12. (4) Selec unie number of oupu layer: Selecion of oupu nodes corresponds o esimaion resuls. In he model he ulimae resul is an esimaion value, which is ool ousourcing ris s comprehensive esimaion value represening differen ris degrees. Hence he auhor chooses one oupu nodes. The auhor consrucs he enerprise ool ousourcing ris forewarning sysem by adoping 3-layer BP neural newor. The node number of inpu layer, hidden layer and oupu layer is respecively 13,12,1. (5) Selec neurons ransmission funcion: The hidden layer of his model adops angen S form neurons and he oupu layer of his model adops linear neurons, which can be approximae o any coninuous funcions. If he hidden layer conains enough neurons, i can be approximae o any disconinuous funcions which have limied breapoins. (6) Daa s normalizaion processing: Quaniaive indicaor daa can no be direcly aen ino esimaion in he researches of ool ousourcing ris. Because ool ousourcing ris forewarning sysem is a complicaed sysem, he indicaors aen ino ris esimaion are no only so many, bu also have differen properies, dimensions and magniudes. In order o compare differen quaniies which have differen dimensions, all daa need o be ransformed appropriaely, which is he dimensionless processing. TABLE 3-1. Samples normalizaion processing Index Sample1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 1 0.798 0.468 0.798 0.404 0.798 0.318 0.296 2 0.875 0.75 0.745 0.56 0.75 0.25 0.318 3 0.713 0.75 0.794 0.753 0.753 0.246 0.399 4 0.774 0.75 0.816 0.584 0.791 0.323 0.421 5 0.904 0.863 0.802 0.752 0.831 0.266 0.314 6 0.813 0.848 0.843 0.318 0.815 0.25 0.653 7 0.693 0.818 0.75 0.693 0.75 0.323 0.499 8 0.661 0.785 0.745 0.323 0.704 0.246 0.568 9 0.771 0.683 0.727 0.548 0.728 0.296 0.563 10 0.624 0.824 0.74 0.74 0.763 0.266 0.484 11 0.637 0.762 0.59 0.629 0.633 0.314 0.495 12 0.789 0.875 0.704 0.628 0.75 0.246 0.323 13 0.795 0.759 0.813 0.646 0.728 0.296 0.325

64 SANFA CAI, LIN ZHAO AND ZHIZHI ZHANG TABLE 3-2. Samples normalizaion processing Sample8 Sample9 Sample10 Sample11 Sample12 Sample13 Sample14 Sample15 0.798 0.56 0.246 0.798 0.468 0.798 0.404 0.798 0.56 0.435 0.266 0.875 0.75 0.745 0.56 0.75 0.875 0.375 0.323 0.713 0.75 0.794 0.753 0.753 0.875 0.603 0.296 0.774 0.75 0.816 0.584 0.791 0.777 0.463 0.325 0.904 0.863 0.802 0.752 0.831 0.845 0.653 0.25 0.813 0.848 0.843 0.318 0.815 0.75 0.318 0.246 0.693 0.818 0.75 0.693 0.75 0.704 0.499 0.25 0.661 0.785 0.745 0.323 0.704 0.814 0.421 0.318 0.771 0.683 0.727 0.548 0.728 0.84 0.657 0.266 0.624 0.824 0.74 0.74 0.763 0.715 0.484 0.314 0.637 0.762 0.59 0.629 0.633 0.875 0.539 0.323 0.789 0.875 0.704 0.628 0.75 0.625 0.495 0.296 0.795 0.759 0.813 0.646 0.728 4.2. Model Training and Verificaion. This paper chooses MATLAB 6.5 o rain and verify ool ousourcing BP neural newor forewarning model. The auhor collecs 15 quesionnaires from 15 differen ool ousourcing enerprises which paricipae in he 4h manufacuring enerprise ousourcing forum in he 12h 2008. The members of Ousourcing Commiee of Shanghai Science Managemen gave hese esimable values. The comprehensive value of ris raing is given by several leading expers hrough analyzing indicaor values, as well as empiricism and relaive heories. The auhor adops one-hidden-layer BP newor and uses ool ousourcing ris esimaion indicaor as inpu variable. The inpu variable is respecively X1, X2,..., X 13, and he inpu nodes number is 13. The value range of oupu nodes is from 0 o 7 and he number of oupu nodes is 1. The auhor divides he enerprise ool ousourcing ris grade. According o he closeness degree beween he oupu resuls and sandard value of enerprise ool ousourcing ris, he auhor udges he enerprise s ris grade. The Table 4.2 saes he enerprise ool ousourcing ris grade coefficien. TABLE 4. Enerprise ool ousourcing ris grade coefficien Enerprise Tool Ousourcing Ris Grade Enerprise Tool Ousourcing Ris Grade Coefficien Low ris 5.5-7 Low o moderae ris 4.5-5.5 Moderae ris 3.5-4.5 Moderae o high ris 2.5-3.5 High ris 0-2.5

TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK 65 When he auhor adops levenberg-marquard raining algorihm, he raining error and raining ime of newor gain a minimum value a he same ime. Hence rainlm raining funcion is used by his model. By indicaors normalizaion processing, all basic daa is ready for raining and es, which can be seen in able 3. The auhor uses he firs 12 couples daa of normalized daa able as learning raining daase o inpu he newor and hen maes he las 3 couples daa of normalized daa able as newor s es daase. The number of hidden layer nodes is 13 and he hreshold funcion is ansig funcion. Purelin linear funcion is adoped ino oupu layer. In he experimen, he learning rae n is 0.01 and he accepable error is 0.001. Training ime is 5000, using rainlm funcion. Afer compleing he neural newor raining, he las 3 couples sample daa are used o verify his model. When he newor raining is a sep 3, he newor error is 8.65568e-007, newor performance reaches he sandard. When raining he ris forecas model by Malab, i can direcly call raingda funcion o rain afer deermining inpu value and expeced oupu. Afer building an M file, he auhor inpu he following daa in inerface according o ool ousourcing ris evaluaion indicaor daa. p1=[0.661 0.500 0.703 0.610 0.688 0.705 0.875 0.702 0.734 0.584 0.570 0.707 0.601]'; p2=[0.798 0.750 0.750 0.731 0.800 0.750 0.750 0.745 0.820 0.759 0.637 0.668 0.644]'; p3=[0.867 0.815 0.794 0.797 0.858 0.728 0.693 0.785 0.693 0.664 0.590 0.711 0.771]'; p4=[0.266 0.315 0.334 0.314 0.325 0.410 0.250 0.246 0.323 0.343 0.250 0.296 0.356]'; p5=[0.734 0.810 0.750 0.608 0.654 0.748 0.568 0.661 0.590 0.613 0.484 0.539 0.495]'; p6=[0.798 0.875 0.713 0.774 0.904 0.813 0.693 0.661 0.771 0.624 0.637 0.789 0.795]'; p7=[0.468 0.750 0.750 0.750 0.863 0.848 0.818 0.785 0.683 0.824 0.762 0.875 0.759]'; p8=[0.798 0.745 0.794 0.816 0.802 0.843 0.750 0.745 0.727 0.740 0.590 0.704 0.813]'; p9=[0.404 0.560 0.753 0.584 0.752 0.318 0.693 0.323 0.548 0.740 0.629 0.628 0.646]'; p10=[0.798 0.750 0.753 0.791 0.831 0.815 0.750 0.704 0.728 0.763 0.633 0.750 0.728]'; p11=[0.318 0.250 0.246 0.323 0.266 0.250 0.323 0.246 0.296 0.266 0.314 0.246 0.296]'; p12=[0.296 0.318 0.399 0.421 0.314 0.653 0.499 0.568 0.563 0.484 0.495 0.323 0.325]'; p=[p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12]; =[5.303 5.928 6.010 2.484 5.144 6.073 6.089 6.097 4.616 6.044 2.233 3.484]; [n,min,max]=premnmx(); ne=newff(minmax(p),[13,1],{'ansig','purelin'},'rainlm'); ne.rainparam.epochs=5000; ne.rainparam.goal=0.001; ne=rain(ne,p,); a=sim(ne,p); [m,b,r]=posreg(a,); %forecasing ris p_es= [0.798 0.560 0.875 0.875 0.777 0.845 0.750 0.704 0.814 0.840 0.715 0.875 0.625 0.560 0.435 0.375 0.603 0.463 0.653 0.318 0.499 0.421 0.657 0.484 0.539 0.495 0.246 0.266 0.323 0.296 0.325 0.250 0.246 0.250 0.318 0.266 0.314 0.323 0.296]'; y=sim(ne,p_es)

66 SANFA CAI, LIN ZHAO AND ZHIZHI ZHANG And hen he daa is operaing in command window and appears in Malab display inerface:traingd, Performance goal me.when he above cue abou he arge has been achieved appears and he dynamic diagram of raining can be seen in he figure 3. FIGURE 3. Tool ousourcing evaluaion performance diagram based on BP neural newor TRAINLM, Epoch 3/5000, MSE 8.65568e-007/0.001, Gradien 0.0183008/1e-010 TRAINLM, Performance goal me. This saes ha his newor raining is successful. 4.3. Tesing he BP Model. The 3 couples es values is o verify newor s adapabiliy. Afer simulaing, he oupu resuls are as following: The oupu Y value is 6.0535, 3.939,2.1805.The able 5 is he error of newor simulaion resuls. The oupu resuls of newor model respecively have displayed ris grade, for insance, low ris, moderae ris, high ris. The error rae is less han 5%, which means his model can accuraely forecas ool ousourcing ris in accordance wih he indicaor sysem. The accurae model needs more raining samples in order o be convenien for newor learning, which maes he newor have a beer faul-olerance propery. In he pracice, Maximum error of less han 10% can mee he demand of accuracy. TABLE 5. Newor simulaion resul error Sample for Verificaion 1 Sample for Verificaion 2 Sample for Verificaion 3 Simulaion Resul 6.0536 3.939 2.1805 Pracical Evaluaion 6.195 4.010 2.276 Error 2.283% 1.771% 4.196%

TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK 67 5. Conclusions. The auhor consrucs ool ousourcing ris evaluaion indicaor sysem and ool ousourcing ris evaluaion model by BP neural newor model. The auhor adops 15 auo enerprises daa as raining sample, which can evaluae he enerprise s ool ousourcing managemen ris. Because he number of enerprise which maes ool ousource is so small and hese ool ousourcing enerprises are also in he beginning sage, he raining and forecas funcions maybe have a beer applicaion if he number of sample is much bigger. REFERENCES [1] C. Lonsdale (1999), Effecively managing verical relaionships: a ris managemen model for ousourcing, Supply Chain Managemen, vol. 4, no. 4, pp.176-183. [2] D. Zhang and Z. Huang (2005), The ousourcing decision model and is applicaion in he mechanical manufacure process, Indusrial Engineering and Managemen, vol. 10, no. 6, pp.104-108. [3] H. B. Whimore (2006), You've ousourced he operaion, bu have you ousourced he ris? Financial Execuive, vol. 22, no.11, pp.41-44. [4] K. Zhou and Y. Kang (2005), Neural Newor Model and MATLAB, Tsinghua Universiy Press, Beiing. [5] M. Kainz (2001), Tool-managemen in der Hochsen Ausbausufe, Wersa und Berieb, vol. 134, no. 6, pp.62-64. [6] R. Aron, E. K. Clemons and S. Reddi (2005), Jus righ ousourcing: undersanding and managing ris, Journal of Managemen Informaion Sysems, vol. 22, no. 2, pp.37-55. [7] S. Da (2005), Sudy on ool managemen, Tool Engineering, vol. 39, no. 5, pp.14-18. [8] S. F. Cai and L. Zhao (2009), Ris analysis in auomobile indusry during ool ousourcing managemen, Tool Engineering, vol. 43, no. 3, pp.24-28. [9] T. Peibone (2009), Ou of sigh, no ou of mind, Ris Managemen, vol. 56, no. 4, pp.89-91. [10] X. Wang, H.Wang and W. Wang (2000), Principle and Applicaion of Arificial Neural Newor, Norheasern Universiy Press, Shenyang.