Artificial Neural Networks - Industrial and Control Engineering Applications 234 OBJECTIVE SAMPLE INPUT DATA RESULTS REFERENCE P storage time, spoiled meat Ground beef, pork n=20 Electronic nose Successful Winquist et al., 1993 P Meat freshness Chicken Electronic nose Successful prediction of storage time Galdikas et al., 2000 P Bacterial growth (L. sake) Cooked meat products T, a w , CO 2 Max. specific growth rate R 2 =0.94, RMSE=0.011 Lag phase λ R 2 =0.97, RMSE=6.70 Lou & Nakai, 2001 P Bacterial growth (L. monocytogenes) Meat broth Fluctuating conditions (T, pH, NaCl, a w ) ANN can be used to describe/predict bacterial growth in dynamic conditions Cheroutre- Vialette & Lebert, 2002 P Internal temperature estimation Chicken n=85 IR and laser range imaging R 2 =0.94-0.96 Ma & Tao, 2005 P Shelf-life estimation Cooked meat products T, pH, NaCl, NaNO 2 Error, bias and accuracy factors show successful validation Zurera-Cosano et al., 2005 C Identification of spoiled meat Bovine LD n=156 Electronic nose 83-100% correctness Panigrahi et al., 2006 P Survivival of Escherichia coli Fermented sausage pH, a w , iso- thiocyanate concentration Accurate ANN based models Palanichamy et al., 2008 C,P Meat spoilage identification Bovine LD n=156 Electronic nose Sorting accuracy >90% Microbial count R 2 >0.70 Balasubramanian et al., 2009 C,P Spoilage identification Beef fillets n=74 FT-IR spectroscopy Sorting accuracy 81-94% Satisfactory prediction of microbial counts Argyri et al., 2010 LD – longissimus dorsi; R 2 – coefficient of determination; r – correlation coefficient; P – prediction; C – classification; IR – infrared. Table 3. Application of ANN for spoilage or storage time prediction Application of Artificial Neural Networks in Meat Production and Technology 235 7. Various other applications of ANN in meat science and technology In addition to the mentioned subjects of interest for ANN application in meat science there are various other applications related to meat technology issues (Table 4). These involve identification of animal species in ground meat mixtures (Winquist et al., 1993) or fat tissue (Beattie et al., 2007), recognition of animal origin (distinction between Iberian and Duroc OBJECTIVE SAMPLE INPUT DATA RESULTS REFERENCE Species recognition Ground beef, pork, n=20 Electronic nose Successful Winquist et al., 1993 Visual guidance of evisceration Pig carcasses Computer vision Efficient ANN based system Christensen et al., 1996 Lean tissue extraction (image segmentation) Bovine LD n=60 Computer vision (hybrid image) Better efficiency and robustness of ANN based system Hwang et al., 1997 Fermentation monitoring Sausage Electronic nose Lowest error in case of ANN compared to regression Eklöv et al., 1998 Estimation of meat internal T Cooked chicken meat IR imaging Great potential for monitoring of meat doneness (error of ±1°C) Ibarra et al., 2000 Determination of RN - phenotype Pig n=96 NIR spectroscopy 96% correctness Josell et al., 2000 Identification of feeding and ripening time Pig; dry- cured ham Electronic nose Best prediction for N at 250°C; misclassified hams ≈8% Santos et al., 2004 Species recognition on adipose tissue Lamb, beef chicken,pork n=255 Raman spectroscopy >98% correctness Beattie et al., 2007 P Cooking shrinkage Bovine TB n=25 Computer vision technique r=0.52-0.75 Zheng et al., 2007 Walk-through weighing Pigs Machine vision relative error ≈3% Wang et al., 2008 Differentiation of Iberian and Duroc Pigs n=30 VIS-NIR spectroscopy >95% correctness del Moral et al., 2009 LD – longissimus dorsi; TB – triceps brachii; R 2 – coefficient of determination; r – correlation coefficient; P – prediction; C – classification; VIS – visible; NIR – near infrared; IR - infrared. Table 4. Other applications of ANN in meat science and technology Artificial Neural Networks - Industrial and Control Engineering Applications 236 pigs) as affected by rearing regime and/or breed (del Moral et al., 2009), hybrid image processing for lean tissue extraction (Hwang et al., 1997), detection of RN - phenotype in pigs (Josell et al., 2000), the “walk-through” weighing of pigs (Wang et al., 2008), the efficiency of ANN for visual guidance of pig evisceration at the slaughter line (Christensen et al., 1996) and the use of ANN for the processing control of meat products (Eklöv et al., 1998; Ibarra et al., 2000; Santos et al., 2004). Again, in the majority of studies, ANN approach was an instrument to deal with the complex output signal of novel technologies applied. Again, based on the literature reports, supervised learning strategy of ANN (BP-ANN, RBF) was applied in the majority of studies. There were also a few studies where unsupervised learning has been tested (Winquist et al., 1993; Beattie et al., 2007). A bibliographic overview given in Table 4 demonstrates the efficiency and successful classification rate of ANN based systems. 8. Conclusions and future perspectives The existing research work of ANN application in meat production and technology provided many useful results for its application, the majority of them in association with novel technologies. Among interesting ideas that have not been encountered in the literature review is the combination of ANN with bio-sensing technology. ANN shows great potential for carcass and meat (product) quality evaluation and monitoring under industrial conditions or bacterial growth and shelf-life estimation. 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European Food Research and Technology, 221, 5, 717-724, ISSN: 1438-2377 Part 4 Electric and Power Industry 12 State of Charge Estimation of Ni-MH battery pack by using ANN Chang-Hao Piao 1,2,3 , Wen-Li Fu 1,3 , Jin-Wang 3 , Zhi-Yu Huang 1,3 and Chongdu Cho 4 1 Chongqing University of Posts and Telecommunications (Key Laboratory of Network Control & Intelligent Instrument), 2 Chongqing Changan New Energy Automobile CO, LTD, 3 Chongqing University of Posts and Telecommunications(Research Institution of Pattern Recognition and Application), 4 INHA University of Korea (Department of mechanical Engineering) 1,2,3 China 4 Korea 1. Introduction 1.1 Background and significance of the research Currently, the world's fuel vehicle is growing by the rate of 30 million per year. It is estimated that the total amount of the world's fuel vehicle for the whole year will reach one billion. The sharp increase demand in oil’s resources, further aggravate the shortage of oil resources in the world [1-2]. Fuel vehicle exhaust emission is the main source of urban air pollution today, and the negative impact on the environment is enormous. Environment is closely related to the survival and development of human society. In the case of the energy shortage and environmental protection urgent need to improve, governments invest enormous human and material resources to seek new solutions. This is also bringing the development of electric vehicle [3-6]. As power source and energy storage of HEV, battery is the main factors of impacting on the driving range and driving performance of HEV [7-8]. At present, the most important question is the capacity and battery life issues with HEV application. Only estimate SOC as accurate as possible can we ensure the realization of fast charging and balanced strategy. The purpose of that is to prevent over charge or discharge from damaging battery, and improve battery life. This also has practical significance in increasing battery safety and reducing the battery cost [9]. How accurate tracking of the battery SOC, has been the nickel-hydrogen battery’s researchers concerned about putting in a lot of energy to study. Currently, it is very popular to estimate the SOC with Ampere hours (Ah) algorithm as this method is easy to apply in HEV. The residual capacity is calculated by initial capacity minus capacity discharged. But Ah algorithm has two shortcomings. First, it is impossible to forecast the initial SOC. Second, the accumulated error cannot be ignored with the test time growing [10]. The researchers also used a new method that the battery working conditions will be divided into [...]... Vol.26, No.11, (2006) page numbers (2 18- 220), ISSN 1006-93 48 2 58 Artificial Neural Networks - Industrial and Control Engineering Applications [22] Wen Xin; Zhou Lu (2000) MATLAB Neural network application and design, Science and Technology Press, ISBN 7030 084 802, Bei Jing [23] Gao Juan (2003) Artificial neural network theory and simulation, Machinery Industry Press, ISBN 9 787 111125914, Bei Jing [24] Lin C,... the network 4 as Neural Network which predicts the car battery SOC Network structure of the network 4 as shown in Fig .8 And the weight of concealment level to output level is middle line of data in Fig .8 The weight of input level to conceals between the level as shown in Table 3 Fig 8 Actual structure of neural network 254 Artificial Neural Networks - Industrial and Control Engineering Applications Hidden... checking sample training sample 17.3% 29 .8% 9.7% 13.3% 7.5% 8. 2% 9.5% 6.7% 8. 4% 7.5% checking sample 23.7% 7.1% 4.7% 4.2% 4.3% Table 1 Different network’s average error 252 Artificial Neural Networks - Industrial and Control Engineering Applications 4.1.2 Test and result When you are sure the neural network which you have got is the best, use the validation sample and training sample to test the tracking... time, according to the comparison of the different neural networks, we can avoid over-training network Simulation of the samples 256 Artificial Neural Networks - Industrial and Control Engineering Applications indicate that artificial neural network built by experiment can accurately predict the SOC of the nickel hydrogen power battery of hybrid automobile and the self-adaptive is good These features make... observer For the purpose of frequency tracking, the speed of response and tracking ability are of particular importance After studying various choices, the following case is considered P = [0.2 48, 0.0513,0.173,0.046,0.0674,0.0434,0.00916, 0.0236, −0.000415,0.113,0. 080 2]T (37) 2 68 Artificial Neural Networks - Industrial and Control Engineering Applications To estimate fundamental frequency, the approach is... a Hamming filter is used to 260 Artificial Neural Networks - Industrial and Control Engineering Applications smoothen the response and cancel high-frequency noises The most distinguishing features of the proposed method are the reduction in the size of observation state vector required by a simple adaptive linear neural network (ADALINE) and increase in the accuracy and convergence speed under transient... rate is also rising 2 48 Artificial Neural Networks - Industrial and Control Engineering Applications It adopts the training and emulating alternate work model to avoid the net excess training After the training samples achieve an net training, it keep the net weight value and threshold constant, validation samples data is used as the net input, running the net in forward direction and examining the average... (k) T (7) 262 Artificial Neural Networks - Industrial and Control Engineering Applications where α is the constant learning parameter and e ( k ) = y ( k ) − d ( k ) is the error When perfect learning is attained, the error is reduced to zero and the desired output becomes T equal to d ( k ) = W0 × X ( k ) , where W0 is the weight vector after the complete algorithm convergence Thus, the neural model...244 Artificial Neural Networks - Industrial and Control Engineering Applications static, resume, three states of charge and discharge Then estimate on the three state of SOC separately It can disperse and eliminates the factors that affect the SOC value in the estimation process Particularly in the charge-discharge state, they improve Ah algorithm... as: 266 Artificial Neural Networks - Industrial and Control Engineering Applications An1 ( kTs ) = Ah1 ( kTs ) abs ( Ah1 ( kTs )) (29) where abs( x ) stands for absolute value of x By using the first-order discrete differentiator, f 1 is obtained as: ⎛ ⎞ ⎛ An1 ( kTs ) − An1 ( kTs − Ts ) ⎞ 1 f 1 ( kTs ) = ⎜ ⎟⋅⎜ ⎟ Ts ⎠ ⎝ j 2π ⋅ An1 ⋅ k ⋅ Ts ⎠ ⎝ (30) It can be seen that observation matrix size and the . 4. Other applications of ANN in meat science and technology Artificial Neural Networks - Industrial and Control Engineering Applications 236 pigs) as affected by rearing regime and/ or breed. analysis and neural network. Journal of Food Engineering, 79, 4, 1243-1249, ISSN: 0260 -87 74 Zou, J., Han, Y. & So, S S. (20 08) . Overview of artificial neural networks. In: Artificial neural networks. applied for mapping and interpretation of IR spectra. In: Artificial neural networks : methods and applications. Livingstone, D. (Ed.), 45-60, Humana Press, ISBN: 9 78- 1- 588 29-7 18- 1, New York Palanichamy,