The Experts below are selected from a list of 4938 Experts worldwide ranked by ideXlab platform
Xianmin Zhang - One of the best experts on this subject based on the ideXlab platform.
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proximate analysis based prediction of gross calorific value of coals a comparison of support vector machine alternating conditional expectation and artificial neural network
2015Co-Authors: Qihong Feng, Jiyuan Zhang, Xianmin ZhangAbstract:Abstract The gross calorific value (GCV) of coal is important in both the direct use and conversion into other fuel forms of coals. The measurement of GCV usually requires sophisticated bomb calorimetric experimental apparatus and expertise, whereas proximate analysis is much cheaper, easier and faster to conduct. This paper presents the application of three regression models, i.e., support vector machine (SVM), alternating conditional expectation (ACE) and back propagation neural network (BPNN) to predict the GCV of coals based on proximate analysis information. Analytical data of 76 Chinese coal samples, with a large variation in rank were acquired and used as input into these models. The modeling results show that: 1) all three methods are generally capable of tracking the variation trend of GCV with the proximate analysis parameters; 2) SVM performs the best in terms of generalization capability among the models investigated; 3) BPNN has the potential to outperform SVM in the training stage and ACE in both training and testing stages; however, its prediction accuracy is dramatically affected by the model parameters including Hidden Neuron number, learning rate and initial weights; 4) ACE performs slightly better with respect to the generalization capability than does BPNN, on an averaged scale.
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proximate analysis based prediction of gross calorific value of coals a comparison of support vector machine alternating conditional expectation and artificial neural network
2015Co-Authors: Qihong Feng, Jiyuan Zhang, Xianmin ZhangAbstract:Abstract The gross calorific value (GCV) of coal is important in both the direct use and conversion into other fuel forms of coals. The measurement of GCV usually requires sophisticated bomb calorimetric experimental apparatus and expertise, whereas proximate analysis is much cheaper, easier and faster to conduct. This paper presents the application of three regression models, i.e., support vector machine (SVM), alternating conditional expectation (ACE) and back propagation neural network (BPNN) to predict the GCV of coals based on proximate analysis information. Analytical data of 76 Chinese coal samples, with a large variation in rank were acquired and used as input into these models. The modeling results show that: 1) all three methods are generally capable of tracking the variation trend of GCV with the proximate analysis parameters; 2) SVM performs the best in terms of generalization capability among the models investigated; 3) BPNN has the potential to outperform SVM in the training stage and ACE in both training and testing stages; however, its prediction accuracy is dramatically affected by the model parameters including Hidden Neuron number, learning rate and initial weights; 4) ACE performs slightly better with respect to the generalization capability than does BPNN, on an averaged scale.
Qihong Feng - One of the best experts on this subject based on the ideXlab platform.
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proximate analysis based prediction of gross calorific value of coals a comparison of support vector machine alternating conditional expectation and artificial neural network
2015Co-Authors: Qihong Feng, Jiyuan Zhang, Xianmin ZhangAbstract:Abstract The gross calorific value (GCV) of coal is important in both the direct use and conversion into other fuel forms of coals. The measurement of GCV usually requires sophisticated bomb calorimetric experimental apparatus and expertise, whereas proximate analysis is much cheaper, easier and faster to conduct. This paper presents the application of three regression models, i.e., support vector machine (SVM), alternating conditional expectation (ACE) and back propagation neural network (BPNN) to predict the GCV of coals based on proximate analysis information. Analytical data of 76 Chinese coal samples, with a large variation in rank were acquired and used as input into these models. The modeling results show that: 1) all three methods are generally capable of tracking the variation trend of GCV with the proximate analysis parameters; 2) SVM performs the best in terms of generalization capability among the models investigated; 3) BPNN has the potential to outperform SVM in the training stage and ACE in both training and testing stages; however, its prediction accuracy is dramatically affected by the model parameters including Hidden Neuron number, learning rate and initial weights; 4) ACE performs slightly better with respect to the generalization capability than does BPNN, on an averaged scale.
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proximate analysis based prediction of gross calorific value of coals a comparison of support vector machine alternating conditional expectation and artificial neural network
2015Co-Authors: Qihong Feng, Jiyuan Zhang, Xianmin ZhangAbstract:Abstract The gross calorific value (GCV) of coal is important in both the direct use and conversion into other fuel forms of coals. The measurement of GCV usually requires sophisticated bomb calorimetric experimental apparatus and expertise, whereas proximate analysis is much cheaper, easier and faster to conduct. This paper presents the application of three regression models, i.e., support vector machine (SVM), alternating conditional expectation (ACE) and back propagation neural network (BPNN) to predict the GCV of coals based on proximate analysis information. Analytical data of 76 Chinese coal samples, with a large variation in rank were acquired and used as input into these models. The modeling results show that: 1) all three methods are generally capable of tracking the variation trend of GCV with the proximate analysis parameters; 2) SVM performs the best in terms of generalization capability among the models investigated; 3) BPNN has the potential to outperform SVM in the training stage and ACE in both training and testing stages; however, its prediction accuracy is dramatically affected by the model parameters including Hidden Neuron number, learning rate and initial weights; 4) ACE performs slightly better with respect to the generalization capability than does BPNN, on an averaged scale.
Shuihua Wang - One of the best experts on this subject based on the ideXlab platform.
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fractal dimension estimation for developing pathological brain detection system based on minkowski bouligand method
2016Co-Authors: Yudong Zhang, Xianqing Chen, Tianming Zhan, Zhuqing Jiao, Zhimin Chen, Lanting Fang, Yiding Lv, Shuihua WangAbstract:It is of enormous significance to detect abnormal brains automatically. This paper develops an efficient pathological brain detection system based on the artificial intelligence method. We first extract brain edges by a Canny edge detector. Next, we estimated the fractal dimension using box counting method with grid sizes of 1, 2, 4, 8, and 16, respectively. Afterward, we employed the single-Hidden layer feedforward neural network. Finally, we proposed an improved particle swarm optimization based on three-segment particle representation, time-varying acceleration coefficient, and chaos theory. This three-segment particle representation encodes the weights, biases, and number of Hidden Neuron. The statistical analysis showed the proposed method achieves the detection accuracies of 100%, 98.19%, and 98.08% over three benchmark data sets. Our method costs merely 0.1984 s to predict one image. Our performance is superior to the 11 state-of-the-art approaches.
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a multilayer perceptron based smart pathological brain detection system by fractional fourier entropy
2016Co-Authors: Yudong Zhang, Yi Sun, Preetha Phillips, Ge Liu, Xingxing Zhou, Shuihua WangAbstract:This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal Hidden Neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE?+?KC-MLP?+?ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods.
Jiyuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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proximate analysis based prediction of gross calorific value of coals a comparison of support vector machine alternating conditional expectation and artificial neural network
2015Co-Authors: Qihong Feng, Jiyuan Zhang, Xianmin ZhangAbstract:Abstract The gross calorific value (GCV) of coal is important in both the direct use and conversion into other fuel forms of coals. The measurement of GCV usually requires sophisticated bomb calorimetric experimental apparatus and expertise, whereas proximate analysis is much cheaper, easier and faster to conduct. This paper presents the application of three regression models, i.e., support vector machine (SVM), alternating conditional expectation (ACE) and back propagation neural network (BPNN) to predict the GCV of coals based on proximate analysis information. Analytical data of 76 Chinese coal samples, with a large variation in rank were acquired and used as input into these models. The modeling results show that: 1) all three methods are generally capable of tracking the variation trend of GCV with the proximate analysis parameters; 2) SVM performs the best in terms of generalization capability among the models investigated; 3) BPNN has the potential to outperform SVM in the training stage and ACE in both training and testing stages; however, its prediction accuracy is dramatically affected by the model parameters including Hidden Neuron number, learning rate and initial weights; 4) ACE performs slightly better with respect to the generalization capability than does BPNN, on an averaged scale.
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proximate analysis based prediction of gross calorific value of coals a comparison of support vector machine alternating conditional expectation and artificial neural network
2015Co-Authors: Qihong Feng, Jiyuan Zhang, Xianmin ZhangAbstract:Abstract The gross calorific value (GCV) of coal is important in both the direct use and conversion into other fuel forms of coals. The measurement of GCV usually requires sophisticated bomb calorimetric experimental apparatus and expertise, whereas proximate analysis is much cheaper, easier and faster to conduct. This paper presents the application of three regression models, i.e., support vector machine (SVM), alternating conditional expectation (ACE) and back propagation neural network (BPNN) to predict the GCV of coals based on proximate analysis information. Analytical data of 76 Chinese coal samples, with a large variation in rank were acquired and used as input into these models. The modeling results show that: 1) all three methods are generally capable of tracking the variation trend of GCV with the proximate analysis parameters; 2) SVM performs the best in terms of generalization capability among the models investigated; 3) BPNN has the potential to outperform SVM in the training stage and ACE in both training and testing stages; however, its prediction accuracy is dramatically affected by the model parameters including Hidden Neuron number, learning rate and initial weights; 4) ACE performs slightly better with respect to the generalization capability than does BPNN, on an averaged scale.
Yudong Zhang - One of the best experts on this subject based on the ideXlab platform.
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fractal dimension estimation for developing pathological brain detection system based on minkowski bouligand method
2016Co-Authors: Yudong Zhang, Xianqing Chen, Tianming Zhan, Zhuqing Jiao, Zhimin Chen, Lanting Fang, Yiding Lv, Shuihua WangAbstract:It is of enormous significance to detect abnormal brains automatically. This paper develops an efficient pathological brain detection system based on the artificial intelligence method. We first extract brain edges by a Canny edge detector. Next, we estimated the fractal dimension using box counting method with grid sizes of 1, 2, 4, 8, and 16, respectively. Afterward, we employed the single-Hidden layer feedforward neural network. Finally, we proposed an improved particle swarm optimization based on three-segment particle representation, time-varying acceleration coefficient, and chaos theory. This three-segment particle representation encodes the weights, biases, and number of Hidden Neuron. The statistical analysis showed the proposed method achieves the detection accuracies of 100%, 98.19%, and 98.08% over three benchmark data sets. Our method costs merely 0.1984 s to predict one image. Our performance is superior to the 11 state-of-the-art approaches.
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a multilayer perceptron based smart pathological brain detection system by fractional fourier entropy
2016Co-Authors: Yudong Zhang, Yi Sun, Preetha Phillips, Ge Liu, Xingxing Zhou, Shuihua WangAbstract:This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal Hidden Neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE?+?KC-MLP?+?ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods.