The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). I...The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.展开更多
BACKGROUND The utility of hepatitis C virus(HCV)organs has increased after the Food and Drug Administration approval of direct acting anti-viral(DAA)medications for the HCV treatment.The efficacy of DAA in treating HC...BACKGROUND The utility of hepatitis C virus(HCV)organs has increased after the Food and Drug Administration approval of direct acting anti-viral(DAA)medications for the HCV treatment.The efficacy of DAA in treating HCV is nearly 100%.AIM To analyze the United Network for Organ Sharing(UNOS)database to compare the survival rates between the hepatitis C positive donors and negative recipients and hepatitis C negative donors and recipients.METHODS We analyzed the adult patients in UNOS database who underwent deceased donor liver transplant from January 2014 to December 2017.The primary endpoint was to compare the survival rates among the four groups with different hepatitis C donor and recipient status:(Group 1)Both donor and recipient negative for HCV(Group 2)Negative donor and positive recipient for HCV(Group 3)Positive donor and negative recipient for HCV(Group 4)Both positive donor and recipient for HCV.SAS 9.4 software was used for the data analysis.Kaplan Meier log rank test was used to analyze the estimated survival rates among the four groups.RESULTS A total of 24512 patients were included:Group 1:16436,Group 2:6174,Group 3:253 and Group 4:1649.The 1-year(Group 1:91.8%,Group 2:92.12%,Group 3:87%,Group 4:92.8%),2-year(Group 1:88.4%,Group 2:88.1%,Group 3:84.3%,Group 4:87.5%),3-year(Group 1:84.9%,Group 2:84.3%,Group 3:75.9%,Group 4:83.2%)survival rates showed no statistical significance among the four groups.Kaplan Meier log rank test did not show any statistical significance difference in the estimated survival rates between Group 3 vs all the other groups.CONCLUSION The survival rates in hepatitis C positive donors and negative recipients are similar as compared to both hepatitis C negative donors and recipients.This could be due to the use of DAA therapy with cure rates of nearly 100%.This study supports the use of hepatitis C positive organs in the selected group of recipients with and without HCV infection.Further long-term studies are needed to further validate these findings.展开更多
The interpenetrating network structure provides an interesting avenue to novel materials. Locally resonant phononic crystal （LRPC） exhibits excellent sound attenuation performance based on the periodical arrangement...The interpenetrating network structure provides an interesting avenue to novel materials. Locally resonant phononic crystal （LRPC） exhibits excellent sound attenuation performance based on the periodical arrangement of sound wave scatters. Combining the LRPC concept and interpenetrating network glassy structure, this paper has developed a new material which can achieve a wide band underwater strong acoustic absorption. Underwater absorption coefficients of different samples were measured by the pulse tube. Measurement results show that the new material possesses excellent underwater acoustic effects in a wide frequency range.Moreover, in order to investigate impacts of locally resonant units,some defects are introduced into the sample. The experimental result and the theoretical calculation both show that locally resonant units being connected to a network structure play an important role in achieving a wide band strong acoustic absorption.展开更多
Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many comp...Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many competing and complex hidden units, such as the long short-term memory （LSTM） and the gated recurrent unit （GRU）. We propose a gated unit for RNN, named as minimal gated unit （MCU）, since it only contains one gate, which is a minimal design among all gated hidden units. The design of MCU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MCU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN＇s applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU＇s properties theoretically and empirically.展开更多
An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated rec...An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.展开更多
With development of networked storage and its applications, united storage network (USN) combined with network attached storage (NAS) and storage area network (SAN) has emerged. It has such advantages as high performa...With development of networked storage and its applications, united storage network (USN) combined with network attached storage (NAS) and storage area network (SAN) has emerged. It has such advantages as high performance, low cost, good connectivity, etc. However the security issue has been complicated because USN responds to block I/O and file I/O requests simultaneously. In this paper, a security system module is developed to prevent many types of attacks against USN based on NAS head. The module not only uses effective authentication to prevent unauthorized access to the system data, but also checks the data integrity. Experimental results show that the security module can not only resist remote attacks and attacks from those who has physical access to the USN, but can also be seamlessly integrated into underlying file systems, with little influence on their performance.展开更多
A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). ...A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). The MUVFS offers a storage volume view for each authorized user who can access only the data in his own storage volume, the security scheme enables all users to encrypt and decrypt the data of their own storage view at client-side, and the USN server needs only to check the users’ identities and the data’s integrity. Experiments were performed to compare the sequential read, write and read/write rates of NFS+MUVFS+secure_module with those of NFS. The results indicate that the security of the USN is improved greatly with little influence on the system performance when the MUVFS and the security scheme are integrated into it.展开更多
In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simulta...In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simultaneous acquisition, a mechanism of multi-process andinter-process communication, the integrating problem of signal acquisition, the data dynamicmanagement and network-based configuration in the embedded condition monitoring system is solved. Itoffers the input function of monitoring information for network-based condition monitoring and afault diagnosis system.展开更多
Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, p...Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of par- ticular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the treatment plant was divided into two main subsystems including: Low TDS (Total Dissolved Solids) treatment unit and Biological unit (extended aeration). The multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant, located in Mahshahr—Iran that qualita- tive and quantitative characteristics of its units were used for training, calibration and validation of the neural model. Also, Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. The results of L-TDS unit showed good accuracy of the models in estimating qualitative profile of wastewater but results of biological unit did not have sufficient accuracy to being used. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of outputs.展开更多
A graphic processing unit (GPU)-accelerated biological species recognition method using partially connected neural evolutionary network model is introduced in this paper. The partial connected neural evolutionary netw...A graphic processing unit (GPU)-accelerated biological species recognition method using partially connected neural evolutionary network model is introduced in this paper. The partial connected neural evolutionary network adopted in the paper can overcome the disadvantage of traditional neural network with small inputs. The whole image is considered as the input of the neural network, so the maximal features can be kept for recognition. To speed up the recognition process of the neural network, a fast implementation of the partially connected neural network was conducted on NVIDIA Tesla C1060 using the NVIDIA compute unified device architecture (CUDA) framework. Image sets of eight biological species were obtained to test the GPU implementation and counterpart serial CPU implementation, and experiment results showed GPU implementation works effectively on both recognition rate and speed, and gained 343 speedup over its counterpart CPU implementation. Comparing to feature-based recognition method on the same recognition task, the method also achieved an acceptable correct rate of 84.6% when testing on eight biological species.展开更多
Nowadays, with regard to environmental issues, proper operation of wastewater treatment plants is of particular importance that in the case of inappropriate utilization, they will cause serious problems. Processes tha...Nowadays, with regard to environmental issues, proper operation of wastewater treatment plants is of particular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems and environmental engineers are dealing with them mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant, powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the multilayer perceptron (MLP) feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant located in Mahshahr—Iran that qualitative and quantitative characteristics of its units were used for training, calibration and evaluation of the neural model. Also, Principal Component Analysis technique was applied to modify and improve performance of generated models of neural networks. The results of this model showed good accuracy of the model in estimating qualitative pro- file of wastewater. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of output.展开更多
Inspired by the modulation mechanism of neuroendocrine-immune system(NEIs),a novel structure of artificial neural network(ANN) named NEI-NN and its learning method are presented.The NEI-NN includes two parts,i.e.,posi...Inspired by the modulation mechanism of neuroendocrine-immune system(NEIs),a novel structure of artificial neural network(ANN) named NEI-NN and its learning method are presented.The NEI-NN includes two parts,i.e.,positive subnetwork(PSN) and negative sub-network(NSN).The neuron functions of PSN and NSN are designed according to the increased and decreased secretion functions of hormone,respectively.In order to make the novel neural network learn quickly,the novel neuron based on some characteristics of NEIs is also redesigned.Besides the normal input signals,two control signals are considered in the proposed solution.One is the enable/disable signal,and the other is the slope control signal.The former can modify the structure of NEI-NN,and the later can regulate the evolutionary speed of NEINN.The NEI-NN can obtain the optimized network structure by using error back-propagation(BP) learning algorithm.Since the modeling of the beam pumping unit is very difficult by using the conventional method,the modeling of bean bump unit is chosen to examine the performance of the NEI-NN.The experiment results show that the optimized structure and learning speed of NEI-NN are better than those of the conventional neural network.展开更多
情绪识别是人机交互(HCI)与情感智能领域的重要前沿课题之一。然而,目前基于脑电(EGG)信号的情绪识别方法主要提取静态特征,无法挖掘情绪的动态变化特性,难以提升情绪识别能力。在基于EGG构建动态脑功能网络的研究中,常采用滑动窗口方法...情绪识别是人机交互(HCI)与情感智能领域的重要前沿课题之一。然而,目前基于脑电(EGG)信号的情绪识别方法主要提取静态特征,无法挖掘情绪的动态变化特性,难以提升情绪识别能力。在基于EGG构建动态脑功能网络的研究中,常采用滑动窗口方法,通过依次构建不同窗口内的功能连接网络以形成动态网络。但该方法存在主观设定窗长的问题,无法提取每个时间点情绪状态的连接模式,导致时间信息丢失和脑连接信息不完整。针对上述问题,提出动态线性相位测量(dyPLM)方法,该方法无需使用滑窗,即可自适应地在每个时间点构建情绪相关脑网络,更精准地刻画情绪的动态变化特性。此外,还提出一种卷积门控神经网络(CNGRU)情绪识别模型,该模型可进一步提取动态脑网络深层次特征,有效提高情绪识别准确性。在公开情绪识别脑电数据集DEAP(Database for Emotion Analysis using Physiological signals)上进行验证,所提方法四分类准确率高达99.71%,较MFBPST-3D-DRLF提高3.51百分点。在SEED(SJTU Emotion EEG Dataset)数据集上进行验证,所提方法三分类准确率达到99.99%,较MFBPST-3D-DRLF提高3.32百分点。实验结果证明了所提出的动态脑网络构建方法dyPLM和情绪识别模型CNGRU的有效性和实用性。展开更多
基金jointly supported by the National Science Foundation of China (Grant Nos. 42275007 and 41865003)Jiangxi Provincial Department of science and technology project (Grant No. 20171BBG70004)。
文摘The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.
文摘BACKGROUND The utility of hepatitis C virus(HCV)organs has increased after the Food and Drug Administration approval of direct acting anti-viral(DAA)medications for the HCV treatment.The efficacy of DAA in treating HCV is nearly 100%.AIM To analyze the United Network for Organ Sharing(UNOS)database to compare the survival rates between the hepatitis C positive donors and negative recipients and hepatitis C negative donors and recipients.METHODS We analyzed the adult patients in UNOS database who underwent deceased donor liver transplant from January 2014 to December 2017.The primary endpoint was to compare the survival rates among the four groups with different hepatitis C donor and recipient status:(Group 1)Both donor and recipient negative for HCV(Group 2)Negative donor and positive recipient for HCV(Group 3)Positive donor and negative recipient for HCV(Group 4)Both positive donor and recipient for HCV.SAS 9.4 software was used for the data analysis.Kaplan Meier log rank test was used to analyze the estimated survival rates among the four groups.RESULTS A total of 24512 patients were included:Group 1:16436,Group 2:6174,Group 3:253 and Group 4:1649.The 1-year(Group 1:91.8%,Group 2:92.12%,Group 3:87%,Group 4:92.8%),2-year(Group 1:88.4%,Group 2:88.1%,Group 3:84.3%,Group 4:87.5%),3-year(Group 1:84.9%,Group 2:84.3%,Group 3:75.9%,Group 4:83.2%)survival rates showed no statistical significance among the four groups.Kaplan Meier log rank test did not show any statistical significance difference in the estimated survival rates between Group 3 vs all the other groups.CONCLUSION The survival rates in hepatitis C positive donors and negative recipients are similar as compared to both hepatitis C negative donors and recipients.This could be due to the use of DAA therapy with cure rates of nearly 100%.This study supports the use of hepatitis C positive organs in the selected group of recipients with and without HCV infection.Further long-term studies are needed to further validate these findings.
基金supported by the National Natural Science Foundation of China (Grant No. 10832011)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KJCX2-YW-L08)
文摘The interpenetrating network structure provides an interesting avenue to novel materials. Locally resonant phononic crystal （LRPC） exhibits excellent sound attenuation performance based on the periodical arrangement of sound wave scatters. Combining the LRPC concept and interpenetrating network glassy structure, this paper has developed a new material which can achieve a wide band underwater strong acoustic absorption. Underwater absorption coefficients of different samples were measured by the pulse tube. Measurement results show that the new material possesses excellent underwater acoustic effects in a wide frequency range.Moreover, in order to investigate impacts of locally resonant units,some defects are introduced into the sample. The experimental result and the theoretical calculation both show that locally resonant units being connected to a network structure play an important role in achieving a wide band strong acoustic absorption.
基金supported by National Natural Science Foundation of China(Nos.61422203 and 61333014)National Key Basic Research Program of China(No.2014CB340501)
文摘Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many competing and complex hidden units, such as the long short-term memory （LSTM） and the gated recurrent unit （GRU）. We propose a gated unit for RNN, named as minimal gated unit （MCU）, since it only contains one gate, which is a minimal design among all gated hidden units. The design of MCU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MCU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN＇s applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU＇s properties theoretically and empirically.
基金funded by“The Pearl River Talent Recruitment Program”of Guangdong Province in 2019(Grant No.2019CX01G338)the Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019).
文摘An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.
文摘With development of networked storage and its applications, united storage network (USN) combined with network attached storage (NAS) and storage area network (SAN) has emerged. It has such advantages as high performance, low cost, good connectivity, etc. However the security issue has been complicated because USN responds to block I/O and file I/O requests simultaneously. In this paper, a security system module is developed to prevent many types of attacks against USN based on NAS head. The module not only uses effective authentication to prevent unauthorized access to the system data, but also checks the data integrity. Experimental results show that the security module can not only resist remote attacks and attacks from those who has physical access to the USN, but can also be seamlessly integrated into underlying file systems, with little influence on their performance.
文摘A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). The MUVFS offers a storage volume view for each authorized user who can access only the data in his own storage volume, the security scheme enables all users to encrypt and decrypt the data of their own storage view at client-side, and the USN server needs only to check the users’ identities and the data’s integrity. Experiments were performed to compare the sequential read, write and read/write rates of NFS+MUVFS+secure_module with those of NFS. The results indicate that the security of the USN is improved greatly with little influence on the system performance when the MUVFS and the security scheme are integrated into it.
文摘In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simultaneous acquisition, a mechanism of multi-process andinter-process communication, the integrating problem of signal acquisition, the data dynamicmanagement and network-based configuration in the embedded condition monitoring system is solved. Itoffers the input function of monitoring information for network-based condition monitoring and afault diagnosis system.
文摘Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of par- ticular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the treatment plant was divided into two main subsystems including: Low TDS (Total Dissolved Solids) treatment unit and Biological unit (extended aeration). The multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant, located in Mahshahr—Iran that qualita- tive and quantitative characteristics of its units were used for training, calibration and validation of the neural model. Also, Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. The results of L-TDS unit showed good accuracy of the models in estimating qualitative profile of wastewater but results of biological unit did not have sufficient accuracy to being used. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of outputs.
基金National Natural Science Foundation of China (No. 60975084)Natural Science Foundation of Fujian Province,China (No.2011J05159)
文摘A graphic processing unit (GPU)-accelerated biological species recognition method using partially connected neural evolutionary network model is introduced in this paper. The partial connected neural evolutionary network adopted in the paper can overcome the disadvantage of traditional neural network with small inputs. The whole image is considered as the input of the neural network, so the maximal features can be kept for recognition. To speed up the recognition process of the neural network, a fast implementation of the partially connected neural network was conducted on NVIDIA Tesla C1060 using the NVIDIA compute unified device architecture (CUDA) framework. Image sets of eight biological species were obtained to test the GPU implementation and counterpart serial CPU implementation, and experiment results showed GPU implementation works effectively on both recognition rate and speed, and gained 343 speedup over its counterpart CPU implementation. Comparing to feature-based recognition method on the same recognition task, the method also achieved an acceptable correct rate of 84.6% when testing on eight biological species.
文摘Nowadays, with regard to environmental issues, proper operation of wastewater treatment plants is of particular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems and environmental engineers are dealing with them mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant, powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the multilayer perceptron (MLP) feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant located in Mahshahr—Iran that qualitative and quantitative characteristics of its units were used for training, calibration and evaluation of the neural model. Also, Principal Component Analysis technique was applied to modify and improve performance of generated models of neural networks. The results of this model showed good accuracy of the model in estimating qualitative pro- file of wastewater. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of output.
基金the Key Project of the National Natural Science Foundation of China(No.61134009)National Natural Science Foundations of China(Nos.61473078,61271001)+5 种基金Program for Changjiang Scholars from the Ministry of Education,ChinaSpecialized Research Fund for Shanghai Leading Talents,ChinaProject of the Shanghai Committee of Science and Technology,China(No.13JC1407500)the Fundamental Research Funds for the Central Universities,China(No.09CX04026A)Excellent Youth and Middle Age Scientists Fund of Shandong Province,China(No.BS2010DX038)Fundamental Research Funds for the Central Universities,China(No.14CX02171A)
文摘Inspired by the modulation mechanism of neuroendocrine-immune system(NEIs),a novel structure of artificial neural network(ANN) named NEI-NN and its learning method are presented.The NEI-NN includes two parts,i.e.,positive subnetwork(PSN) and negative sub-network(NSN).The neuron functions of PSN and NSN are designed according to the increased and decreased secretion functions of hormone,respectively.In order to make the novel neural network learn quickly,the novel neuron based on some characteristics of NEIs is also redesigned.Besides the normal input signals,two control signals are considered in the proposed solution.One is the enable/disable signal,and the other is the slope control signal.The former can modify the structure of NEI-NN,and the later can regulate the evolutionary speed of NEINN.The NEI-NN can obtain the optimized network structure by using error back-propagation(BP) learning algorithm.Since the modeling of the beam pumping unit is very difficult by using the conventional method,the modeling of bean bump unit is chosen to examine the performance of the NEI-NN.The experiment results show that the optimized structure and learning speed of NEI-NN are better than those of the conventional neural network.
文摘情绪识别是人机交互(HCI)与情感智能领域的重要前沿课题之一。然而,目前基于脑电(EGG)信号的情绪识别方法主要提取静态特征,无法挖掘情绪的动态变化特性,难以提升情绪识别能力。在基于EGG构建动态脑功能网络的研究中,常采用滑动窗口方法,通过依次构建不同窗口内的功能连接网络以形成动态网络。但该方法存在主观设定窗长的问题,无法提取每个时间点情绪状态的连接模式,导致时间信息丢失和脑连接信息不完整。针对上述问题,提出动态线性相位测量(dyPLM)方法,该方法无需使用滑窗,即可自适应地在每个时间点构建情绪相关脑网络,更精准地刻画情绪的动态变化特性。此外,还提出一种卷积门控神经网络(CNGRU)情绪识别模型,该模型可进一步提取动态脑网络深层次特征,有效提高情绪识别准确性。在公开情绪识别脑电数据集DEAP(Database for Emotion Analysis using Physiological signals)上进行验证,所提方法四分类准确率高达99.71%,较MFBPST-3D-DRLF提高3.51百分点。在SEED(SJTU Emotion EEG Dataset)数据集上进行验证,所提方法三分类准确率达到99.99%,较MFBPST-3D-DRLF提高3.32百分点。实验结果证明了所提出的动态脑网络构建方法dyPLM和情绪识别模型CNGRU的有效性和实用性。
