Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communi...Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communication has evolved into an increasingly prominent area of research in recent years.Here,we demonstrate DSP-free coherent optical transmission by analog signal processing in frequency synchronous optical network(FSON)architecture,which supports polarization multiplexing and higher-order modulation formats.The FSON architecture that allows the numerous laser sources of optical transceivers within a data center can be quasi-synchronized by means of a tree-distributed homology architecture.In conjunction with our proposed pilot-tone assisted Costas loop for an analog coherent receiver,we achieve a record dual-polarization 224-Gb/s 16-QAM 5-km mismatch transmission with reset-free carrier phase recovery in the optical domain.Our proposed DSP-free analog coherent detection system based on the FSON makes it a promising solution for next-generation,low-power,and high-capacity coherent data center interconnects.展开更多
Objective To evaluate the clinical efficacy of different acupuncture-related therapies in treating postoperative pain in patients with osteoporotic vertebral compression fractures(OVCFs)after percutaneous kyphoplasty(...Objective To evaluate the clinical efficacy of different acupuncture-related therapies in treating postoperative pain in patients with osteoporotic vertebral compression fractures(OVCFs)after percutaneous kyphoplasty(PKP)or percutaneous vertebroplasty(PVP)using a network meta-analysis.Methods A systematic search was conducted in PubMed,Cochrane Library,Embase,Web of Science,China National Knowledge Infrastructure,Wanfang Database,Chinese Scientific Journal Database,and Chinese Biomedical Literature Database(SinoMed)from their inception to January 15,2025.Outcome measures included the Visual Analog Scale(VAS)score,Oswestry Disability Index(ODI)score,and overall efficacy rate.Literature screening,data extraction,and risk-of-bias assessment were independently performed by two researchers.Data analysis was conducted using Stata 17.0 software.Results A total of 35 randomized controlled trials involving 2860 patients were included.The data analysis revealed that,in terms of improving VAS and ODI scores,the top three effective therapies were Fu's subcutaneous needling,wrist-ankle acupuncture,and acupotomy.For the overall efficacy rates in pain treatment,the top three therapies were wrist-ankle acupuncture,warm acupuncture and moxibustion,and Fu's subcutaneous needling.Based on the combined results across the three outcome measures,Fu's subcutaneous needling was found to be the most effective in relieving pain and improving lumbar function.Conclusion Fu's subcutaneous needling,wrist-ankle acupuncture,warm acupuncture and moxibustion,and acupotomy were all effective in treating postoperative pain post-PKP/PVP and improving lumbar function.However,further high-quality,large-sample studies are required to confirm these findings.展开更多
Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit feature...Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.展开更多
The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studled. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the ...The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studled. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation（BP） neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.展开更多
Cognitive radio and cooperative communication can greatly improve the spectrum efficiency in wireless communications.We study a cognitive radio network where two secondary source terminals exchange their information w...Cognitive radio and cooperative communication can greatly improve the spectrum efficiency in wireless communications.We study a cognitive radio network where two secondary source terminals exchange their information with the assistance of a relay node under interference power constraints.In order to enhance the transmit rate and maintain fairness between two source terminals,a practical 2-phase analog network coding protocol is adopted and its optimal power allocation algorithm is proposed.Numerical results verify the superiority of the proposed algorithm over the conventional direct transmission protocol and 4-phase amplify-and-forward relay protocol.展开更多
One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorit...One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.展开更多
Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of...Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.展开更多
Objective:Used extensively to treat cardiovascular disease,Danqi analogous formulas(DQAF)include prescriptions for Danqi(DQ),Fufang Danshen(FFDS)and Qishen Yiqi(QSYQ).Differences in prescription compatibility result i...Objective:Used extensively to treat cardiovascular disease,Danqi analogous formulas(DQAF)include prescriptions for Danqi(DQ),Fufang Danshen(FFDS)and Qishen Yiqi(QSYQ).Differences in prescription compatibility result in varying emphases of DQAF in clinical application.Methods and results:Based on network analysis in this study,common and distinct mechanisms of DQAF actions on cardiovascular disease were analyzed at a systemic level.Components etargetsepathways models were developed by Cytoscape(http://www.cytoscape.org/);whereby,target information for active compounds was obtained based on the PharmMapper database(http://59.78.96.61/pharmmapper/),which was further used to search pathways using the Kyoto Encyclopedia of Genes and Genomes database(http://www.genome.jp/kegg/).Based on target and network analyses,we discovered RBP4 is a potential common target of DQAF,while mitogen-activated protein kinase 1(MAPK1)and glutathione S-transferase P were potential targets of FFDS and QSYQ,respectively.Furthermore,the potential of DQAF to treat cardiovascular disease occurs through effects on the endocrine,immune,and digestive systems,in addition to lipid,sugar and amino acid metabolic pathways.Whereas FFDS exhibits effects on Toll-like receptor,transforming growth factor beta and MAPK signaling pathways;QSYQ exerts effects on cyclic adenosine monophosphate signaling,as well as metabolism of glutathione and arachidonic acid.展开更多
A new method for analyzing the stabilities of analog electronic neural networks ispresented.The energy functions with clear physical meaning are derived by introducing the staticequivalent circuit models,which has exp...A new method for analyzing the stabilities of analog electronic neural networks ispresented.The energy functions with clear physical meaning are derived by introducing the staticequivalent circuit models,which has expanded the Tellegen Theorem for application on circuitanalysis.The method used to derive the energy functions of nets from first order differentialequations is valid for all first order continuous autonomous systems.The stability analysis ofcellular neural networks is made by the use of the stationary cocontent theorem.Some resultsare instructive for the network implementation on circuits.展开更多
Network coding (NC), which works in the network layer, is an effective technology to improve the network throughput, by allowing the relay to encode the information from different users and ensuring the destination to...Network coding (NC), which works in the network layer, is an effective technology to improve the network throughput, by allowing the relay to encode the information from different users and ensuring the destination to retrieve the desired information. Employing network coding technique in a cooperative network can improve the network performance further. In this paper, we introduce analog network coding (ANC) to a simple two-user cooperative diversity network, which adopts amplify-and-forward (AF) mode and all nodes use multiple antennas. The impact of the number of antenna on the system achievable rate is investigated. And the bit error rate (BER) performances of the traditional relay cooperative network and the cooperative network based on analog network coding under different propagation conditions are discussed. The simulation results show that the performance of the traditional cooperative network has improved significantly due to the employ of network coding.展开更多
In order to improve the speed and accuracy of analog circuit fault diagnosis,using Back Propagation Neural Network(BPNN),a new method is proposed based on Particle Swarm Optimization(PSO)to adjust weights of BP neural...In order to improve the speed and accuracy of analog circuit fault diagnosis,using Back Propagation Neural Network(BPNN),a new method is proposed based on Particle Swarm Optimization(PSO)to adjust weights of BP neural network.The model can not only overcome the limitations of the slow convergence and the local extreme values by basic BP algorithm,but also improve the learning ability and generalization ability with a higher precision.The response signals of analog circuit is preprocessed by Wavelet Packet Transform(WPT)as the fault feature.The simulation result shows that the proposed method has higher diagnostic accuracy and faster convergence speed,which is effective for fault location.展开更多
At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material form...At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network （ANN） model of FGH96 superalloy during hot deformation is established by using back propagation （BP） network. Then according to electrical analogy theory, its analog-circuit （AC） model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy＇s dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.展开更多
Digital circuit and analog circuit courses are basic courses for students of science and engineering universities. Among them,the practical courses are of great significance for students to master the knowledge of ele...Digital circuit and analog circuit courses are basic courses for students of science and engineering universities. Among them,the practical courses are of great significance for students to master the knowledge of electronics. In order to make teachers teaching more efficiently and students studying more quickly,how to update the experimental course in teaching reform is the key point. This paper analyzing the present situation of teaching in the digital circuit and analog circuit courses,the teaching questions in universities. On the basis of it,the innovation measures of experimental teaching methods and contents are discussed. Our school tries to introduce the UltraLab network experiment platform,reform and optimize the teaching methods of related courses.And it’ s accelerating the construction and development of emerging engineering education’ s process,reducing effectively the teacher’s time for managing in equipment,improving the students’ ability to use instruments.展开更多
In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to m...In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.展开更多
基于忆阻器阵列的类脑电路为实现高能效神经网络计算提供了极具潜力的技术路线.然而,现有方案通常需要使用大量的模数转换过程,成为计算电路能效进一步提升的瓶颈.因此,提出了一种基于1T1R(1 Transistor 1 Resistor)忆阻器交叉阵列与CMO...基于忆阻器阵列的类脑电路为实现高能效神经网络计算提供了极具潜力的技术路线.然而,现有方案通常需要使用大量的模数转换过程,成为计算电路能效进一步提升的瓶颈.因此,提出了一种基于1T1R(1 Transistor 1 Resistor)忆阻器交叉阵列与CMOS(Complementary Metal-Oxide-Semiconductor)激活函数的全模拟神经网络架构,以及与其相关的训练优化方法 .该架构采用1T1R忆阻器交叉阵列来实现神经网络线性层中的模拟计算,同时利用CMOS非线性电路来实现神经网络激活层的模拟计算,在全模拟域实现神经网络大幅减少了模数转换器的使用,优化了能效和面积成本.实验结果验证了忆阻器作为神经网络权重层的可行性,同时设计多种CMOS模拟电路,在模拟域实现了多种非线性激活函数,如伪ReLU(Rectified Linear Unit)、伪Sigmoid、伪Tanh、伪Softmax等电路.通过定制化训练方法来优化模拟电路神经网络的训练过程,解决了实际非线性电路的输出饱和条件下的训练问题.仿真结果表明,即使在模拟电路的激活函数与理想激活函数不一致的情况下,全模拟神经网络电路在MNIST(Modified National Institute of Standards and Technology)手写数字识别任务中的识别率仍然可以达到98%,可与基于软件的标准网络模型的结果相比.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62405250 and 62471404)the China Postdoctoral Science Foundation(Grant No.2024M762955)+1 种基金the Key Project of Westlake Institute for Optoelectronics(Grant No.2023GD003)the Optical Com-munication and Sensing Laboratory,School of Engineering,Westlake University.
文摘Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communication has evolved into an increasingly prominent area of research in recent years.Here,we demonstrate DSP-free coherent optical transmission by analog signal processing in frequency synchronous optical network(FSON)architecture,which supports polarization multiplexing and higher-order modulation formats.The FSON architecture that allows the numerous laser sources of optical transceivers within a data center can be quasi-synchronized by means of a tree-distributed homology architecture.In conjunction with our proposed pilot-tone assisted Costas loop for an analog coherent receiver,we achieve a record dual-polarization 224-Gb/s 16-QAM 5-km mismatch transmission with reset-free carrier phase recovery in the optical domain.Our proposed DSP-free analog coherent detection system based on the FSON makes it a promising solution for next-generation,low-power,and high-capacity coherent data center interconnects.
基金supported by the National Natural Science Foundation of China(82305273)the Central High-Level Clinical Research Fund for Traditional Chinese Medicine Hospitals(DZMG-QNGG0010).
文摘Objective To evaluate the clinical efficacy of different acupuncture-related therapies in treating postoperative pain in patients with osteoporotic vertebral compression fractures(OVCFs)after percutaneous kyphoplasty(PKP)or percutaneous vertebroplasty(PVP)using a network meta-analysis.Methods A systematic search was conducted in PubMed,Cochrane Library,Embase,Web of Science,China National Knowledge Infrastructure,Wanfang Database,Chinese Scientific Journal Database,and Chinese Biomedical Literature Database(SinoMed)from their inception to January 15,2025.Outcome measures included the Visual Analog Scale(VAS)score,Oswestry Disability Index(ODI)score,and overall efficacy rate.Literature screening,data extraction,and risk-of-bias assessment were independently performed by two researchers.Data analysis was conducted using Stata 17.0 software.Results A total of 35 randomized controlled trials involving 2860 patients were included.The data analysis revealed that,in terms of improving VAS and ODI scores,the top three effective therapies were Fu's subcutaneous needling,wrist-ankle acupuncture,and acupotomy.For the overall efficacy rates in pain treatment,the top three therapies were wrist-ankle acupuncture,warm acupuncture and moxibustion,and Fu's subcutaneous needling.Based on the combined results across the three outcome measures,Fu's subcutaneous needling was found to be the most effective in relieving pain and improving lumbar function.Conclusion Fu's subcutaneous needling,wrist-ankle acupuncture,warm acupuncture and moxibustion,and acupotomy were all effective in treating postoperative pain post-PKP/PVP and improving lumbar function.However,further high-quality,large-sample studies are required to confirm these findings.
基金the National Natural Science Fundation of China (60372001 90407007)the Ph. D. Programs Foundation of Ministry of Education of China (20030614006).
文摘Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method.
基金This project was supported by the National Nature Science Foundation of China(60372001)
文摘The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studled. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation（BP） neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.
基金Acknowledgements The work was supported by National Natural Science Foundation of China （Grant No.60972008）. The corresponding author is Jiang Wei.
文摘Cognitive radio and cooperative communication can greatly improve the spectrum efficiency in wireless communications.We study a cognitive radio network where two secondary source terminals exchange their information with the assistance of a relay node under interference power constraints.In order to enhance the transmit rate and maintain fairness between two source terminals,a practical 2-phase analog network coding protocol is adopted and its optimal power allocation algorithm is proposed.Numerical results verify the superiority of the proposed algorithm over the conventional direct transmission protocol and 4-phase amplify-and-forward relay protocol.
基金Pre-research Projects Fund of the National Ar ming Department,the 11th Five-year Projects
文摘One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.
基金National Natural Science Foundation of China(No.61371024)Aviation Science Fund of China(No.2013ZD53051)+2 种基金Aerospace Technology Support Fund of Chinathe Industry-Academy-Research Project of AVIC,China(No.cxy2013XGD14)the Open Research Project of Guangdong Key Laboratory of Popular High Performance Computers/Shenzhen Key Laboratory of Service Computing and Applications,China
文摘Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.
基金support of this work by the National Natural Science Foundation of China(No.81430094 and 81173522)the National Key Technology R&D Program(2008BAI51B01).
文摘Objective:Used extensively to treat cardiovascular disease,Danqi analogous formulas(DQAF)include prescriptions for Danqi(DQ),Fufang Danshen(FFDS)and Qishen Yiqi(QSYQ).Differences in prescription compatibility result in varying emphases of DQAF in clinical application.Methods and results:Based on network analysis in this study,common and distinct mechanisms of DQAF actions on cardiovascular disease were analyzed at a systemic level.Components etargetsepathways models were developed by Cytoscape(http://www.cytoscape.org/);whereby,target information for active compounds was obtained based on the PharmMapper database(http://59.78.96.61/pharmmapper/),which was further used to search pathways using the Kyoto Encyclopedia of Genes and Genomes database(http://www.genome.jp/kegg/).Based on target and network analyses,we discovered RBP4 is a potential common target of DQAF,while mitogen-activated protein kinase 1(MAPK1)and glutathione S-transferase P were potential targets of FFDS and QSYQ,respectively.Furthermore,the potential of DQAF to treat cardiovascular disease occurs through effects on the endocrine,immune,and digestive systems,in addition to lipid,sugar and amino acid metabolic pathways.Whereas FFDS exhibits effects on Toll-like receptor,transforming growth factor beta and MAPK signaling pathways;QSYQ exerts effects on cyclic adenosine monophosphate signaling,as well as metabolism of glutathione and arachidonic acid.
文摘A new method for analyzing the stabilities of analog electronic neural networks ispresented.The energy functions with clear physical meaning are derived by introducing the staticequivalent circuit models,which has expanded the Tellegen Theorem for application on circuitanalysis.The method used to derive the energy functions of nets from first order differentialequations is valid for all first order continuous autonomous systems.The stability analysis ofcellular neural networks is made by the use of the stationary cocontent theorem.Some resultsare instructive for the network implementation on circuits.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 60872016)
文摘Network coding (NC), which works in the network layer, is an effective technology to improve the network throughput, by allowing the relay to encode the information from different users and ensuring the destination to retrieve the desired information. Employing network coding technique in a cooperative network can improve the network performance further. In this paper, we introduce analog network coding (ANC) to a simple two-user cooperative diversity network, which adopts amplify-and-forward (AF) mode and all nodes use multiple antennas. The impact of the number of antenna on the system achievable rate is investigated. And the bit error rate (BER) performances of the traditional relay cooperative network and the cooperative network based on analog network coding under different propagation conditions are discussed. The simulation results show that the performance of the traditional cooperative network has improved significantly due to the employ of network coding.
基金supported the Science and Technology Research Project of Liaoning Provincial Department of Education
文摘In order to improve the speed and accuracy of analog circuit fault diagnosis,using Back Propagation Neural Network(BPNN),a new method is proposed based on Particle Swarm Optimization(PSO)to adjust weights of BP neural network.The model can not only overcome the limitations of the slow convergence and the local extreme values by basic BP algorithm,but also improve the learning ability and generalization ability with a higher precision.The response signals of analog circuit is preprocessed by Wavelet Packet Transform(WPT)as the fault feature.The simulation result shows that the proposed method has higher diagnostic accuracy and faster convergence speed,which is effective for fault location.
文摘At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network （ANN） model of FGH96 superalloy during hot deformation is established by using back propagation （BP） network. Then according to electrical analogy theory, its analog-circuit （AC） model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy＇s dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.
基金supported by University-level Teaching Reform Project of New Engineering,Beijing University of Chemical Technology(xgk2017040436)Teaching Reform Project of School of International Teaching,Beijing University of Chemical Technology(siejg201713)
文摘Digital circuit and analog circuit courses are basic courses for students of science and engineering universities. Among them,the practical courses are of great significance for students to master the knowledge of electronics. In order to make teachers teaching more efficiently and students studying more quickly,how to update the experimental course in teaching reform is the key point. This paper analyzing the present situation of teaching in the digital circuit and analog circuit courses,the teaching questions in universities. On the basis of it,the innovation measures of experimental teaching methods and contents are discussed. Our school tries to introduce the UltraLab network experiment platform,reform and optimize the teaching methods of related courses.And it’ s accelerating the construction and development of emerging engineering education’ s process,reducing effectively the teacher’s time for managing in equipment,improving the students’ ability to use instruments.
文摘In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.
文摘基于忆阻器阵列的类脑电路为实现高能效神经网络计算提供了极具潜力的技术路线.然而,现有方案通常需要使用大量的模数转换过程,成为计算电路能效进一步提升的瓶颈.因此,提出了一种基于1T1R(1 Transistor 1 Resistor)忆阻器交叉阵列与CMOS(Complementary Metal-Oxide-Semiconductor)激活函数的全模拟神经网络架构,以及与其相关的训练优化方法 .该架构采用1T1R忆阻器交叉阵列来实现神经网络线性层中的模拟计算,同时利用CMOS非线性电路来实现神经网络激活层的模拟计算,在全模拟域实现神经网络大幅减少了模数转换器的使用,优化了能效和面积成本.实验结果验证了忆阻器作为神经网络权重层的可行性,同时设计多种CMOS模拟电路,在模拟域实现了多种非线性激活函数,如伪ReLU(Rectified Linear Unit)、伪Sigmoid、伪Tanh、伪Softmax等电路.通过定制化训练方法来优化模拟电路神经网络的训练过程,解决了实际非线性电路的输出饱和条件下的训练问题.仿真结果表明,即使在模拟电路的激活函数与理想激活函数不一致的情况下,全模拟神经网络电路在MNIST(Modified National Institute of Standards and Technology)手写数字识别任务中的识别率仍然可以达到98%,可与基于软件的标准网络模型的结果相比.
