A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In con...A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.展开更多
In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier ...In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier Transform(FFT)algorithms,convolution,image processing algorithms,etcetera.In the domain of digital signal processing,the use of normalization architecture is very vast.The main objective of using normalization is to performcomparison and shift operations.In this research paper,an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer,Adder etc.The proposed normalization algorithm is further used in designing an 8×8 bit Signed Floating-Point Multiply-Accumulate(SFMAC)architecture.Since the SFMAC can accept an 8-bit significand and a 3-bit exponent,the input to the said architecture can be somewhere between−(7.96872)_(10) to+(7.96872)_(10).The proposed architecture is designed and implemented using the Cadence Virtuoso using 90 and 130 nm technologies(in Generic Process Design Kit(GPDK)and Taiwan Semiconductor Manufacturing Company(TSMC),respectively).To reduce the power consumption of the proposed normalization architecture,techniques such as“block enabling”and“clock gating”are used rigorously.According to the analysis done on Cadence,the proposed architecture uses the least amount of power compared to its current predecessors.展开更多
Autism Spectrum Disorder(ASD)is a complex neurodevelopmental condition that causes multiple challenges in behavioral and communication activities.In the medical field,the data related to ASD,the security measures are ...Autism Spectrum Disorder(ASD)is a complex neurodevelopmental condition that causes multiple challenges in behavioral and communication activities.In the medical field,the data related to ASD,the security measures are integrated in this research responsibly and effectively to develop the Mobile Neuron Attention Stage-by-Stage Network(MNASNet)model,which is the integration of both Mobile Network(MobileNet)and Neuron Attention Stage-by-Stage.The steps followed to detect ASD with privacy-preserved data are data normalization,data augmentation,and K-Anonymization.The clinical data of individuals are taken initially and preprocessed using the Z-score Normalization.Then,data augmentation is performed using the oversampling technique.Subsequently,K-Anonymization is effectuated by utilizing the Black-winged Kite Algorithm to ensure the privacy of medical data,where the best fitness solution is based on data utility and privacy.Finally,after improving the data privacy,the developed approach MNASNet is implemented for ASD detection,which achieves highly accurate results compared to traditional methods to detect autism behavior.Hence,the final results illustrate that the proposed MNASNet achieves an accuracy of 92.9%,TPR of 95.9%,and TNR of 90.9%at the k-samples of 8.展开更多
双有源桥变换器因其优异的功率密度和双向功率传输能力,在众多工业应用中得到广泛关注。随着电力电子设备对能效和可靠性要求的不断提高,双有源桥变换器的电流应力已成为衡量其性能的关键指标之一。过大的电流应力不仅会导致功率器件损...双有源桥变换器因其优异的功率密度和双向功率传输能力,在众多工业应用中得到广泛关注。随着电力电子设备对能效和可靠性要求的不断提高,双有源桥变换器的电流应力已成为衡量其性能的关键指标之一。过大的电流应力不仅会导致功率器件损耗增加,系统效率下降,还会影响变换器的可靠性和使用寿命。针对上述问题,提出了一种基于TabNet-LN-LSTM协同预测与粒子群优化的电流应力优化方法。该方法通过利用TabNet和层归一化长短期记忆神经网络(Long-short term memory neural network with layer normalization,LN-LSTM)协同构建电感电流时序预测模型,并结合粒子群优化算法对双有源桥变换器在不同运行工况下的电流应力进行优化。通过算法试验和硬件试验证明,所提方法不仅能够精确预测电感电流波形,其预测波形与硬件实测波形相比,其平均绝对误差仅为0.3525,决定系数高达97.17%;同时,能够有效降低双有源桥变换器的电流应力,进一步提升系统的整体效能和可靠性。展开更多
A novel adaptive algorithm of IIR lattice notch filter realized by all-pass filter is presented. The time-averaged estimation of cross correlation of the present instantaneous input signal and the past output signal i...A novel adaptive algorithm of IIR lattice notch filter realized by all-pass filter is presented. The time-averaged estimation of cross correlation of the present instantaneous input signal and the past output signal is used to update the step-size, leading to a considerably improved convergence rate in a low SNR situation and reduced steady-state bias and MSE. The theoretical expression for steady-state bounds on the step-size is derived, and the influence factors on the stable performance of the algorithm theoretically are analyzed. A normalized power factor is then introduced to control variation of step-size in its steady-state bounds. This technique prevents divergence due to the influence of large power input signal and improves robustness. Numerical experiments are performed to demonstrate superiority of the proposed method.展开更多
The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorit...The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.展开更多
A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (...A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (SMI) and the Normalized Least Mean Square (NLMS) algorithms is described. Simulation results showed that the less complexity MI-NLMS yields 15 dB improvements in interference suppression and 5 dB gain enhancement over LMS algorithm, converges from the initial iteration and achieves 24% BER improvements at cochannel interference equal to 5. For the case of 4-element uniform linear array antenna, MI-NLMS achieved 76% BER reduction over LMS algorithm.展开更多
基金National Natural Science Foundation of China(No.61203184)
文摘A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.
基金This work was supported by Research Support Fund(RSF)of Symbiosis International(Deemed University),Pune,India。
文摘In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier Transform(FFT)algorithms,convolution,image processing algorithms,etcetera.In the domain of digital signal processing,the use of normalization architecture is very vast.The main objective of using normalization is to performcomparison and shift operations.In this research paper,an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer,Adder etc.The proposed normalization algorithm is further used in designing an 8×8 bit Signed Floating-Point Multiply-Accumulate(SFMAC)architecture.Since the SFMAC can accept an 8-bit significand and a 3-bit exponent,the input to the said architecture can be somewhere between−(7.96872)_(10) to+(7.96872)_(10).The proposed architecture is designed and implemented using the Cadence Virtuoso using 90 and 130 nm technologies(in Generic Process Design Kit(GPDK)and Taiwan Semiconductor Manufacturing Company(TSMC),respectively).To reduce the power consumption of the proposed normalization architecture,techniques such as“block enabling”and“clock gating”are used rigorously.According to the analysis done on Cadence,the proposed architecture uses the least amount of power compared to its current predecessors.
文摘Autism Spectrum Disorder(ASD)is a complex neurodevelopmental condition that causes multiple challenges in behavioral and communication activities.In the medical field,the data related to ASD,the security measures are integrated in this research responsibly and effectively to develop the Mobile Neuron Attention Stage-by-Stage Network(MNASNet)model,which is the integration of both Mobile Network(MobileNet)and Neuron Attention Stage-by-Stage.The steps followed to detect ASD with privacy-preserved data are data normalization,data augmentation,and K-Anonymization.The clinical data of individuals are taken initially and preprocessed using the Z-score Normalization.Then,data augmentation is performed using the oversampling technique.Subsequently,K-Anonymization is effectuated by utilizing the Black-winged Kite Algorithm to ensure the privacy of medical data,where the best fitness solution is based on data utility and privacy.Finally,after improving the data privacy,the developed approach MNASNet is implemented for ASD detection,which achieves highly accurate results compared to traditional methods to detect autism behavior.Hence,the final results illustrate that the proposed MNASNet achieves an accuracy of 92.9%,TPR of 95.9%,and TNR of 90.9%at the k-samples of 8.
文摘双有源桥变换器因其优异的功率密度和双向功率传输能力,在众多工业应用中得到广泛关注。随着电力电子设备对能效和可靠性要求的不断提高,双有源桥变换器的电流应力已成为衡量其性能的关键指标之一。过大的电流应力不仅会导致功率器件损耗增加,系统效率下降,还会影响变换器的可靠性和使用寿命。针对上述问题,提出了一种基于TabNet-LN-LSTM协同预测与粒子群优化的电流应力优化方法。该方法通过利用TabNet和层归一化长短期记忆神经网络(Long-short term memory neural network with layer normalization,LN-LSTM)协同构建电感电流时序预测模型,并结合粒子群优化算法对双有源桥变换器在不同运行工况下的电流应力进行优化。通过算法试验和硬件试验证明,所提方法不仅能够精确预测电感电流波形,其预测波形与硬件实测波形相比,其平均绝对误差仅为0.3525,决定系数高达97.17%;同时,能够有效降低双有源桥变换器的电流应力,进一步提升系统的整体效能和可靠性。
文摘目前贝叶斯网络(Bayesian networks,BN)的传统结构学习算法在处理高维数据时呈现出计算负担过大、在合理时间内难以得到期望精度结果的问题.为了在高维数据下学习稀疏BN的最优结构,本文提出了一种学习稀疏BN最优结构的改进K均值分块学习算法.该算法采用分而治之的策略,首先采用互信息作为节点间距离度量,利用融合互信息的改进K均值算法对网络分块;其次,使用MMPC(Max-min parent and children)算法得到整个网络的架构,根据架构找到块间所有边的可能连接方向,从而找到所有可能的图结构;之后,对所有图结构依次进行结构学习;最终利用评分找到最优BN.实验证明,相比现有分块结构学习算法,本文提出的算法不仅习得了网络的精确结构,且学习速度有一定提高;相比非分块经典结构学习算法,本文提出的算法在保证精度基础上,学习速度大幅提高,解决了非分块经典结构学习算法无法在合理时间内处理高维数据的难题.
文摘A novel adaptive algorithm of IIR lattice notch filter realized by all-pass filter is presented. The time-averaged estimation of cross correlation of the present instantaneous input signal and the past output signal is used to update the step-size, leading to a considerably improved convergence rate in a low SNR situation and reduced steady-state bias and MSE. The theoretical expression for steady-state bounds on the step-size is derived, and the influence factors on the stable performance of the algorithm theoretically are analyzed. A normalized power factor is then introduced to control variation of step-size in its steady-state bounds. This technique prevents divergence due to the influence of large power input signal and improves robustness. Numerical experiments are performed to demonstrate superiority of the proposed method.
基金Project(50905037) supported by the National Natural Science Foundation of ChinaProject(20092304120014) supported by Specialized Research Fund for the Doctoral Program of Higher Education of China+2 种基金 Project(20100471021) supported by the China Postdoctoral Science Foundation Project(LBH-Q09134) supported by Heilongjiang Postdoctoral Science-Research Foundation,China Project (HEUFT09013) supported by the Foundation of Harbin Engineering University,China
文摘The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.
基金Project supported by the IRPA Secretariat, Ministry of Science,Technology and Environment of Malaysia (No. 04-02-02-0029) andthe Zamalah Scheme
文摘A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (SMI) and the Normalized Least Mean Square (NLMS) algorithms is described. Simulation results showed that the less complexity MI-NLMS yields 15 dB improvements in interference suppression and 5 dB gain enhancement over LMS algorithm, converges from the initial iteration and achieves 24% BER improvements at cochannel interference equal to 5. For the case of 4-element uniform linear array antenna, MI-NLMS achieved 76% BER reduction over LMS algorithm.
