In order to effectively solve the problems of low accuracy and large amount of calculation of current human behavior recognition,a behavior recognition algorithm based on squeeze-and-excitation network(SENet) combined...In order to effectively solve the problems of low accuracy and large amount of calculation of current human behavior recognition,a behavior recognition algorithm based on squeeze-and-excitation network(SENet) combined with 3 D Inception network(I3 D) and gated recurrent unit(GRU) network is proposed.The algorithm first expands the Inception module to three-dimensional,and builds a network based on the three-dimensional module,and expands SENet to three-dimensional,making it an attention mechanism that can pay attention to the three-dimensional channel.Then SENet is introduced into the 13 D network,named SE-I3 D,and SENet is introduced into the CRU network,named SE-GRU.And,SE-13 D and SE-GRU are merged,named SE-13 D-GRU.Finally,the network uses Softmax to classify the results in the UCF-101 dataset.The experimental results show that the SE-I3 D-GRU network achieves a recognition rate of 93.2% on the UCF-101 dataset.展开更多
The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dim...The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network(3D-CNN),which can extract two-di-mensional features in spatial domain and one-dimensional features in time domain,simultaneously.The network structure design is based on the deep learning framework Keras,and the discarding method and batch normalization(BN)algorithm are effectively combined with three-dimensional vis-ual geometry group block(3D-VGG-Block)to reduce the risk of overfitting while improving training speed.Aiming at the problem of the lack of samples in the data set,two methods of image flipping and small amplitude flipping are used for data amplification.Finally,the recognition rate on the data set is as high as 69.11%.Compared with the current international average micro-expression recog-nition rate of about 67%,the proposed algorithm has obvious advantages in recognition rate.展开更多
Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algorithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neural networks(CNN)to ex...Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algorithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neural networks(CNN)to extract spatial features of micro-expressions,and long short-term memory network(LSTM)to extract time domain features.CNN and LSTM are combined as the basis of micro-expression recognition.In many CNN structures,the visual geometry group(VGG)using a small convolution kernel is finally selected as the pre-network through comparison.Due to the difficulty of deep learning training and over-fitting,the dropout method and batch normalization method are used to solve the problem in the VGG network.Two data sets CASME and CASME II are used for test comparison,in order to solve the problem of insufficient data sets,randomly determine the starting frame,and a fixedlength frame sequence is used as the standard,and repeatedly read all sample frames of the entire data set to achieve trayersal and data amplification.Finallv.a hieh recognition rate of 67.48% is achieved.展开更多
为了获得更准确的静止无功发生器(static var generator,SVG)模型参数以满足风电并网系统安全稳定运行的要求,提出一种计及风电场随机特性的SVG模型参数智能辨识方法。首先,通过分析SVG的动作特性建立其数学模型。然后,研究了风电场随...为了获得更准确的静止无功发生器(static var generator,SVG)模型参数以满足风电并网系统安全稳定运行的要求,提出一种计及风电场随机特性的SVG模型参数智能辨识方法。首先,通过分析SVG的动作特性建立其数学模型。然后,研究了风电场随机特性对辨识结果的影响途径和机理。最后,针对风电场随机特性引起的辨识结果不准确问题,提出一种考虑风电场随机特性的SVG模型参数多方式混合辨识方法,为准确辨识风电场SVG模型参数提供了新的方法。参数辨识仿真实验结果验证了所提方法的可行性。展开更多
Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emis...Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.展开更多
To meet the considerably increase of mobile data traffic and wireless communication connections around 2020,the 5th generation(5G)mobile network will necessarily consider more frequency bands,enabling technologies and...To meet the considerably increase of mobile data traffic and wireless communication connections around 2020,the 5th generation(5G)mobile network will necessarily consider more frequency bands,enabling technologies and diversified key performance indicators and test environments comparing with existing network,for example Long Term Evolution.More specifically,the obvious difference between 5G and previous wireless communication system are not only included eMBB(enhance mobile broadband) usage scenario,but also introduced mMTC(massive machine type communications)and URLLC(ultra-reliable and low latency communications) usage scenarios.Hence,in order to evaluate 5G related technologies,some novel test environments will be widely discussed,as well as,certain new key performance indicators will be drawn into5 G evaluation methodology for satisfied new requirements.We will discuss characteristic of these 4 candidate test environments,such as indoor isolated environment,high speed train environment;and the definition of 15 keys performance indicator will be explained and clarified,for example,Throughput,Network Energy Efficiency,Device Connection Density and so on.Furthermore,high-level assessment method of each test environment also will be initially considered.It is notable that an initial evaluation of indoor isolated environment also can be found,which the results show that there are 3 times average cell spectral efficiency than IMT-advanced's in same test environment.展开更多
基金Supported by the Shaanxi Province Key Research and Development Project(No.2021 GY-280)the Natural Science Foundation of Shaanxi Province(No.2021JM-459)the National Natural Science Foundation of China(No.61772417,61634004,61602377).
文摘In order to effectively solve the problems of low accuracy and large amount of calculation of current human behavior recognition,a behavior recognition algorithm based on squeeze-and-excitation network(SENet) combined with 3 D Inception network(I3 D) and gated recurrent unit(GRU) network is proposed.The algorithm first expands the Inception module to three-dimensional,and builds a network based on the three-dimensional module,and expands SENet to three-dimensional,making it an attention mechanism that can pay attention to the three-dimensional channel.Then SENet is introduced into the 13 D network,named SE-I3 D,and SENet is introduced into the CRU network,named SE-GRU.And,SE-13 D and SE-GRU are merged,named SE-13 D-GRU.Finally,the network uses Softmax to classify the results in the UCF-101 dataset.The experimental results show that the SE-I3 D-GRU network achieves a recognition rate of 93.2% on the UCF-101 dataset.
基金Supported by the Shaanxi Province Key Research and Development Project(No.2021GY-280)Shaanxi Province Natural Science Basic Re-search Program Project(No.2021JM-459)+1 种基金the National Natural Science Foundation of China(No.61834005,61772417,61802304,61602377,61634004)the Shaanxi Province International Science and Technology Cooperation Project(No.2018KW-006).
文摘The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network(3D-CNN),which can extract two-di-mensional features in spatial domain and one-dimensional features in time domain,simultaneously.The network structure design is based on the deep learning framework Keras,and the discarding method and batch normalization(BN)algorithm are effectively combined with three-dimensional vis-ual geometry group block(3D-VGG-Block)to reduce the risk of overfitting while improving training speed.Aiming at the problem of the lack of samples in the data set,two methods of image flipping and small amplitude flipping are used for data amplification.Finally,the recognition rate on the data set is as high as 69.11%.Compared with the current international average micro-expression recog-nition rate of about 67%,the proposed algorithm has obvious advantages in recognition rate.
基金Shaanxi Province Key Research and Development Project(No.2021 GY-280)Shaanxi Province Natural Science Basic Research Program Project(No.2021JM-459)+1 种基金National Natural Science Foundation of China(No.61834005,61772417,61802304,61602377,61634004)Shaanxi Province International Science and Technology Cooperation Project(No.2018KW-006)。
文摘Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algorithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neural networks(CNN)to extract spatial features of micro-expressions,and long short-term memory network(LSTM)to extract time domain features.CNN and LSTM are combined as the basis of micro-expression recognition.In many CNN structures,the visual geometry group(VGG)using a small convolution kernel is finally selected as the pre-network through comparison.Due to the difficulty of deep learning training and over-fitting,the dropout method and batch normalization method are used to solve the problem in the VGG network.Two data sets CASME and CASME II are used for test comparison,in order to solve the problem of insufficient data sets,randomly determine the starting frame,and a fixedlength frame sequence is used as the standard,and repeatedly read all sample frames of the entire data set to achieve trayersal and data amplification.Finallv.a hieh recognition rate of 67.48% is achieved.
文摘为了获得更准确的静止无功发生器(static var generator,SVG)模型参数以满足风电并网系统安全稳定运行的要求,提出一种计及风电场随机特性的SVG模型参数智能辨识方法。首先,通过分析SVG的动作特性建立其数学模型。然后,研究了风电场随机特性对辨识结果的影响途径和机理。最后,针对风电场随机特性引起的辨识结果不准确问题,提出一种考虑风电场随机特性的SVG模型参数多方式混合辨识方法,为准确辨识风电场SVG模型参数提供了新的方法。参数辨识仿真实验结果验证了所提方法的可行性。
基金the financial support from the National Natural Science Foundation of China(62021003,61890930-5,61903012,62073006)Beijing Natural Science Foundation(42130232)the National Key Research and Development Program of China(2021ZD0112301,2021ZD0112302)。
文摘Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.
文摘To meet the considerably increase of mobile data traffic and wireless communication connections around 2020,the 5th generation(5G)mobile network will necessarily consider more frequency bands,enabling technologies and diversified key performance indicators and test environments comparing with existing network,for example Long Term Evolution.More specifically,the obvious difference between 5G and previous wireless communication system are not only included eMBB(enhance mobile broadband) usage scenario,but also introduced mMTC(massive machine type communications)and URLLC(ultra-reliable and low latency communications) usage scenarios.Hence,in order to evaluate 5G related technologies,some novel test environments will be widely discussed,as well as,certain new key performance indicators will be drawn into5 G evaluation methodology for satisfied new requirements.We will discuss characteristic of these 4 candidate test environments,such as indoor isolated environment,high speed train environment;and the definition of 15 keys performance indicator will be explained and clarified,for example,Throughput,Network Energy Efficiency,Device Connection Density and so on.Furthermore,high-level assessment method of each test environment also will be initially considered.It is notable that an initial evaluation of indoor isolated environment also can be found,which the results show that there are 3 times average cell spectral efficiency than IMT-advanced's in same test environment.
