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
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
Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems.Optical neural network(ONN)has the native advantages of high paralleliz...Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems.Optical neural network(ONN)has the native advantages of high parallelization,large bandwidth,and low power consumption to meet the demand of big data.Here,we demonstrate the dual-layer ONN with Mach-Zehnder interferometer(MZI)network and nonlinear layer,while the nonlinear activation function is achieved by optical-electronic signal conversion.Two frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the ONN.We successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power distribution.Efficient parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional ONN.This work provides a high-performance architecture for future parallel high-capacity optical analog computing.展开更多
Memristors are now becoming a prominent candidate to serve as the building blocks of non-von Neumann inmemory computing architectures.By mapping analog numerical matrices into memristor crossbar arrays,efficient multi...Memristors are now becoming a prominent candidate to serve as the building blocks of non-von Neumann inmemory computing architectures.By mapping analog numerical matrices into memristor crossbar arrays,efficient multiply accumulate operations can be performed in a massively parallel fashion using the physics mechanisms of Ohm’s law and Kirchhoff’s law.In this brief review,we present the recent progress in two niche applications:neural network accelerators and numerical computing units,mainly focusing on the advances in hardware demonstrations.The former one is regarded as soft computing since it can tolerant some degree of the device and array imperfections.The acceleration of multiple layer perceptrons,convolutional neural networks,generative adversarial networks,and long short-term memory neural networks are described.The latter one is hard computing because the solving of numerical problems requires high-precision devices.Several breakthroughs in memristive equation solvers with improved computation accuracies are highlighted.Besides,other nonvolatile devices with the capability of analog computing are also briefly introduced.Finally,we conclude the review with discussions on the challenges and opportunities for future research toward realizing memristive analog computing machines.展开更多
Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the ...Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the controller design for these quadrotors is considered the challenging issue of the day.In this work,an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor.The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output.Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value.The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it.The superiority of the proposed control method has been proven by comparison and simulation results.展开更多
Based on Immune Programming(IP), a novel Radial Basis Function (RBF) networkdesigning method is proposed. Through extracting the preliminary knowledge about the widthof the basis function as the vaccine to form the im...Based on Immune Programming(IP), a novel Radial Basis Function (RBF) networkdesigning method is proposed. Through extracting the preliminary knowledge about the widthof the basis function as the vaccine to form the immune operator, the algorithm reduces thesearching space of canonical algorithm and improves the convergence speed. The application ofthe RBF network trained with the algorithm in the modulation-style recognition of radar signalsdemonstrates that the network has a fast convergence speed with good performances.展开更多
Based on the scale-free network, an integrated systemic inflammatory response syndrome model with artificial immunity, a feedback mechanism, crowd density and the moving activities of an individual can be built. The e...Based on the scale-free network, an integrated systemic inflammatory response syndrome model with artificial immunity, a feedback mechanism, crowd density and the moving activities of an individual can be built. The effects of these factors on the spreading process are investigated through the model. The research results show that the artificial immunity can reduce the stable infection ratio and enhance the spreading threshold of the system. The feedback mechanism can only reduce the stable infection ratio of system, but cannot affect the spreading threshold of the system. The bigger the crowd density is, the higher the infection ratio of the system is and the smaller the spreading threshold is. In addition, the simulations show that the individual movement can enhance the stable infection ratio of the system only under the condition that the spreading rate is high, however, individual movement will reduce the stable infection ratio of the system.展开更多
As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although convent...As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although conventional CNN accelerators can reduce the computational time of learning and inference tasks,they tend to occupy large chip areas due to many multiply-and-accumulate(MAC)operators when implemented in complex digital circuits,incurring excessive power consumption.To overcome these drawbacks,this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter(ADC).This paper introduces the architecture of an analog convolutional kernel comprised of low-power ultra-small circuits for neural network accelerator chips.ADC is an essential component of the analog convolutional kernel used to convert the analog convolutional result to digital values to be stored in memory.This work presents the implementation of a highly low-power and area-efficient 12-bit Successive Approximation Register(SAR)ADC.Unlink most other SAR-ADCs with differential structure;the proposed ADC employs a single-ended capacitor array to support the preceding single-ended max-pooling circuit along with minimal power consumption.The SARADCimplementation also introduces a unique circuit that reduces kick-back noise to increase performance.It was implemented in a test chip using a 55 nm CMOS process.It demonstrates that the proposed ADC reduces Kick-back noise by 40%and consequently improves the ADC’s resolution by about 10%while providing a near rail-to-rail dynamic rangewith significantly lower power consumption than conventional ADCs.The ADC test chip shows a chip size of 4600μm^(2)with a power consumption of 6.6μW while providing an signal-to-noise-and-distortion ratio(SNDR)of 68.45 dB,corresponding to an effective number of bits(ENOB)of 11.07 bits.展开更多
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.
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Peng Xie acknowledges the support from the China Scholarship Council(Grant no.201804910829).
文摘Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems.Optical neural network(ONN)has the native advantages of high parallelization,large bandwidth,and low power consumption to meet the demand of big data.Here,we demonstrate the dual-layer ONN with Mach-Zehnder interferometer(MZI)network and nonlinear layer,while the nonlinear activation function is achieved by optical-electronic signal conversion.Two frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the ONN.We successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power distribution.Efficient parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional ONN.This work provides a high-performance architecture for future parallel high-capacity optical analog computing.
基金the National Key Research and Development Plan of MOST of China(2019YFB2205100,2016YFA0203800)the National Natural Science Foundation of China(No.61874164,61841404,51732003,61674061)Hubei Engineering Research Center on Microelectronics.
文摘Memristors are now becoming a prominent candidate to serve as the building blocks of non-von Neumann inmemory computing architectures.By mapping analog numerical matrices into memristor crossbar arrays,efficient multiply accumulate operations can be performed in a massively parallel fashion using the physics mechanisms of Ohm’s law and Kirchhoff’s law.In this brief review,we present the recent progress in two niche applications:neural network accelerators and numerical computing units,mainly focusing on the advances in hardware demonstrations.The former one is regarded as soft computing since it can tolerant some degree of the device and array imperfections.The acceleration of multiple layer perceptrons,convolutional neural networks,generative adversarial networks,and long short-term memory neural networks are described.The latter one is hard computing because the solving of numerical problems requires high-precision devices.Several breakthroughs in memristive equation solvers with improved computation accuracies are highlighted.Besides,other nonvolatile devices with the capability of analog computing are also briefly introduced.Finally,we conclude the review with discussions on the challenges and opportunities for future research toward realizing memristive analog computing machines.
基金authorities of East Tehran Branch,Islamic Azad University,Tehran,Iran,for providing support and necessary facilities
文摘Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the controller design for these quadrotors is considered the challenging issue of the day.In this work,an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor.The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output.Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value.The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it.The superiority of the proposed control method has been proven by comparison and simulation results.
文摘Based on Immune Programming(IP), a novel Radial Basis Function (RBF) networkdesigning method is proposed. Through extracting the preliminary knowledge about the widthof the basis function as the vaccine to form the immune operator, the algorithm reduces thesearching space of canonical algorithm and improves the convergence speed. The application ofthe RBF network trained with the algorithm in the modulation-style recognition of radar signalsdemonstrates that the network has a fast convergence speed with good performances.
基金Project supported by the Natural Science Foundation of the Education Department of Guizhou Province,China (Grant No.20090133)International Cooperative Foundation of Guizhou Province,China (Grant No.20117007)
文摘Based on the scale-free network, an integrated systemic inflammatory response syndrome model with artificial immunity, a feedback mechanism, crowd density and the moving activities of an individual can be built. The effects of these factors on the spreading process are investigated through the model. The research results show that the artificial immunity can reduce the stable infection ratio and enhance the spreading threshold of the system. The feedback mechanism can only reduce the stable infection ratio of system, but cannot affect the spreading threshold of the system. The bigger the crowd density is, the higher the infection ratio of the system is and the smaller the spreading threshold is. In addition, the simulations show that the individual movement can enhance the stable infection ratio of the system only under the condition that the spreading rate is high, however, individual movement will reduce the stable infection ratio of the system.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by theKorea government(MSIT)(No.2022R1A5A8026986)and supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020-0-01304,Development of Self-learnable Mobile Recursive Neural Network Processor Technology)+3 种基金It was also supported by the MSIT(Ministry of Science and ICT),Korea,under the Grand Information Technology Research Center support program(IITP-2022-2020-0-01462)supervised by the“IITP(Institute for Information&communications Technology Planning&Evaluation)”supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1F1A1061314)In addition,this work was conducted during the research year of Chungbuk National University in 2020.
文摘As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although conventional CNN accelerators can reduce the computational time of learning and inference tasks,they tend to occupy large chip areas due to many multiply-and-accumulate(MAC)operators when implemented in complex digital circuits,incurring excessive power consumption.To overcome these drawbacks,this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter(ADC).This paper introduces the architecture of an analog convolutional kernel comprised of low-power ultra-small circuits for neural network accelerator chips.ADC is an essential component of the analog convolutional kernel used to convert the analog convolutional result to digital values to be stored in memory.This work presents the implementation of a highly low-power and area-efficient 12-bit Successive Approximation Register(SAR)ADC.Unlink most other SAR-ADCs with differential structure;the proposed ADC employs a single-ended capacitor array to support the preceding single-ended max-pooling circuit along with minimal power consumption.The SARADCimplementation also introduces a unique circuit that reduces kick-back noise to increase performance.It was implemented in a test chip using a 55 nm CMOS process.It demonstrates that the proposed ADC reduces Kick-back noise by 40%and consequently improves the ADC’s resolution by about 10%while providing a near rail-to-rail dynamic rangewith significantly lower power consumption than conventional ADCs.The ADC test chip shows a chip size of 4600μm^(2)with a power consumption of 6.6μW while providing an signal-to-noise-and-distortion ratio(SNDR)of 68.45 dB,corresponding to an effective number of bits(ENOB)of 11.07 bits.
