In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantl...In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.展开更多
Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two tim...Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two time-division based distributed sensor scheduling schemes are proposed to deal with ISI by scheduling sensors periodically and adaptively respectively. Extended Kalman filter (EKF) is used as the tracking algorithm in distributed manner. Simulation results show that the adaptive sensor scheduling scheme can achieve superior tracking accuracy with faster tracking convergence speed.展开更多
The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This pap...The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This paper develops a vibration isolation system of micro-manufacturing platform. The brains of many kinds of birds can isolate vibrations well, such as woodpecker’s brain. When a woodpecker pecks the wood at the speed as 1.6 times as the velocity of sound, its brain will tolerate the wallop 1 500 times of the weight of itself without any damage. The isolation mechanics and organic texture of woodpecker’s brain that has good isolation characteristics were studied. A structure model of vibration isolation system for the micro-manufacturing platform is established based on the bionics of the bird’s brain vibration isolation mechanism. In order to isolate effectively the high frequency vibrations from the ground, a rubber layer is used to isolate vibrations passively between the micro-manufacturing platform’s pedestal and the ground. This layer corresponds to the cartilage and muscles in the outer meninges of the bird’s brain. The active vibration isolation technique is adopted to isolate vibrations between the micro-manufacturing platform and the pedestal. Air springs are used as elastic components, which correspond to the interspaces between the outer meninges and the encephala of the bird’s brain. Actuators are made of giant magnetostrictive material, and it corresponds to the nerves and neural muscles linking the meninges and the encephala. The actuators and air springs are arranged vertically in parallel to make use of the giant magnetostrictive actuators effectively. The air springs support almost all weight of the micro-manufacturing platform and the giant magnetostrictive actuators support almost no weight. In order to realize high performance to isolate complex micro-vibration, the control method using a three-layer neural network is presented. This vibration control system takes into account the floor disturbance and the direct disturbance acting on the micro-manufacturing platform. The absolute acceleration of the micro-manufacturing platform is used as the performance index of vibration control. The performance of the control system is tested by numerical simulation. Simulation results show that the active vibration isolation system has good isolation performance against the floor disturbance and the direct disturbance acting on the micro-manufacturing platform in all the frequency range.展开更多
On the basis of the gain-scheduled H∞ design strategy, a novel active fault-tolerant control scheme is proposed. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine...On the basis of the gain-scheduled H∞ design strategy, a novel active fault-tolerant control scheme is proposed. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine parameter dependence, a reconfigurable robust H∞ linear parameter varying controller is developed. The designed controller is a function of the fault effect factors that can be derived online by using a well-trained neural network. To demonstrate the effectiveness of the proposed method, a double inverted pendulum system, with a fault in the motor tachometer loop, is considered.展开更多
A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spect...A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.展开更多
Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependenc...Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.展开更多
Today the cycle time of the product develop is requ ir ed to be shortened. At the same time the requirement of the customers becomes mo re and more diverse and complex. The capability of the develop unit is limited b ...Today the cycle time of the product develop is requ ir ed to be shortened. At the same time the requirement of the customers becomes mo re and more diverse and complex. The capability of the develop unit is limited b ecause of the existence of heterogeneous systems and distributed environments. I n this paper, we bring forward a new approach to solve the problem in product de velopment process. We also settle part key technologies in it. A great deal of information from all kinds of sources in the distributed develop ment process is interweaved. The solution to organize the workflow and manage th e information in the process is called for anxiously. We use a new approach that is asynchronous and synchronous coupling product development approach based on the network. The approach extends the develop process from the time axis. Then t he activities in the process are organized from the asynchronous and synchronous aspects. The state of every activity projects at the ASN (active semantic netwo rk). The ASN includes decision system, intelligent agent, user interface and net work. The ASN decides the types and states of the activities and deals with the couple relationship among them. The knowledge stored in ASN is open to all users through the relative interfaces. Every specialist keeps contact with their user s relying on collaborative platform implements CSCW (computer support collaborat ive work) that integrated product/process design and development. The lack of gl obal communication in product development process can be prevented in the most d egree. The key technologies that exist in the asynchronous and synchronous coupling pro duct develop approach include: integrated development structure, orderly organiz ation of information, transparent management of process, agile transfer of infor mation and rapid prototype. The development process can be completed quickly by these technologies. The technologies involve wide content. In this paper, we dis cuss some key technologies. We validate the approach by the projectrapid response manufacturing a pplication in the distributed environment. The expensive device, high technology and low using lead to RE (Rapid engineering) and RP (Rapid prototype) service a pplication by the network. RE and RP develop rapidly due to the accelerated prod uct development process. RE and RP application service platform is built in the project.展开更多
The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theor...The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.展开更多
构造一种适用于反向传播(backpropagation,BP)神经网络的新型激活函数Lfun(logarithmic series function),并使用基于该函数的BP神经网络进行机床能耗状态的预测。首先,分析Sigmoid系列和ReLU系列激活函数的特点和缺陷,结合对数函数,构...构造一种适用于反向传播(backpropagation,BP)神经网络的新型激活函数Lfun(logarithmic series function),并使用基于该函数的BP神经网络进行机床能耗状态的预测。首先,分析Sigmoid系列和ReLU系列激活函数的特点和缺陷,结合对数函数,构造了一种非线性分段含参数激活函数。该函数可导且光滑、导数形式简单、单调递增、输出均值为零,且通过可变参数使函数形式更灵活;其次,通过数值仿真实验在公共数据集上将Lfun函数与Sigmoid、ReLU、tanh、Leaky_ReLU和ELU函数的性能进行对比;最后,使用基于Lfun函数的BP神经网络进行机床能耗状态的预测。实验结果表明,使用Lfun函数的BP神经网络相较于使用其他几种常用激活函数的网络具有更好的性能。展开更多
文摘In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.
基金Supported by Science & Engineering Research Council of Singnpore (0521010037)
文摘Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two time-division based distributed sensor scheduling schemes are proposed to deal with ISI by scheduling sensors periodically and adaptively respectively. Extended Kalman filter (EKF) is used as the tracking algorithm in distributed manner. Simulation results show that the adaptive sensor scheduling scheme can achieve superior tracking accuracy with faster tracking convergence speed.
文摘The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This paper develops a vibration isolation system of micro-manufacturing platform. The brains of many kinds of birds can isolate vibrations well, such as woodpecker’s brain. When a woodpecker pecks the wood at the speed as 1.6 times as the velocity of sound, its brain will tolerate the wallop 1 500 times of the weight of itself without any damage. The isolation mechanics and organic texture of woodpecker’s brain that has good isolation characteristics were studied. A structure model of vibration isolation system for the micro-manufacturing platform is established based on the bionics of the bird’s brain vibration isolation mechanism. In order to isolate effectively the high frequency vibrations from the ground, a rubber layer is used to isolate vibrations passively between the micro-manufacturing platform’s pedestal and the ground. This layer corresponds to the cartilage and muscles in the outer meninges of the bird’s brain. The active vibration isolation technique is adopted to isolate vibrations between the micro-manufacturing platform and the pedestal. Air springs are used as elastic components, which correspond to the interspaces between the outer meninges and the encephala of the bird’s brain. Actuators are made of giant magnetostrictive material, and it corresponds to the nerves and neural muscles linking the meninges and the encephala. The actuators and air springs are arranged vertically in parallel to make use of the giant magnetostrictive actuators effectively. The air springs support almost all weight of the micro-manufacturing platform and the giant magnetostrictive actuators support almost no weight. In order to realize high performance to isolate complex micro-vibration, the control method using a three-layer neural network is presented. This vibration control system takes into account the floor disturbance and the direct disturbance acting on the micro-manufacturing platform. The absolute acceleration of the micro-manufacturing platform is used as the performance index of vibration control. The performance of the control system is tested by numerical simulation. Simulation results show that the active vibration isolation system has good isolation performance against the floor disturbance and the direct disturbance acting on the micro-manufacturing platform in all the frequency range.
文摘On the basis of the gain-scheduled H∞ design strategy, a novel active fault-tolerant control scheme is proposed. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine parameter dependence, a reconfigurable robust H∞ linear parameter varying controller is developed. The designed controller is a function of the fault effect factors that can be derived online by using a well-trained neural network. To demonstrate the effectiveness of the proposed method, a double inverted pendulum system, with a fault in the motor tachometer loop, is considered.
基金supported by the KERI Primary Research Program through the Korea Research Council for Industrial Science & Technology funded by the Ministry of Science,ICT and Future Planning (No.15-12-N0101-46)
文摘A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.
文摘Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.
文摘Today the cycle time of the product develop is requ ir ed to be shortened. At the same time the requirement of the customers becomes mo re and more diverse and complex. The capability of the develop unit is limited b ecause of the existence of heterogeneous systems and distributed environments. I n this paper, we bring forward a new approach to solve the problem in product de velopment process. We also settle part key technologies in it. A great deal of information from all kinds of sources in the distributed develop ment process is interweaved. The solution to organize the workflow and manage th e information in the process is called for anxiously. We use a new approach that is asynchronous and synchronous coupling product development approach based on the network. The approach extends the develop process from the time axis. Then t he activities in the process are organized from the asynchronous and synchronous aspects. The state of every activity projects at the ASN (active semantic netwo rk). The ASN includes decision system, intelligent agent, user interface and net work. The ASN decides the types and states of the activities and deals with the couple relationship among them. The knowledge stored in ASN is open to all users through the relative interfaces. Every specialist keeps contact with their user s relying on collaborative platform implements CSCW (computer support collaborat ive work) that integrated product/process design and development. The lack of gl obal communication in product development process can be prevented in the most d egree. The key technologies that exist in the asynchronous and synchronous coupling pro duct develop approach include: integrated development structure, orderly organiz ation of information, transparent management of process, agile transfer of infor mation and rapid prototype. The development process can be completed quickly by these technologies. The technologies involve wide content. In this paper, we dis cuss some key technologies. We validate the approach by the projectrapid response manufacturing a pplication in the distributed environment. The expensive device, high technology and low using lead to RE (Rapid engineering) and RP (Rapid prototype) service a pplication by the network. RE and RP develop rapidly due to the accelerated prod uct development process. RE and RP application service platform is built in the project.
基金supported by the National Natural Science Foundation of China(6077504760835004)+2 种基金the National High Technology Research and Development Program of China(863 Program)(2007AA04Z244 2008AA04Z214)the Graduate Innovation Fundation of Hunan Province(CX2010B132)
文摘The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.
基金Supported by National Natural Science Foundation of China （60974129, 70931002）, Natural Science Foundation of Jiangsu Province （BK2008188, BK2009388）, and Science Foundation of Nanjing University of Science and Technology （AB41972）
文摘构造一种适用于反向传播(backpropagation,BP)神经网络的新型激活函数Lfun(logarithmic series function),并使用基于该函数的BP神经网络进行机床能耗状态的预测。首先,分析Sigmoid系列和ReLU系列激活函数的特点和缺陷,结合对数函数,构造了一种非线性分段含参数激活函数。该函数可导且光滑、导数形式简单、单调递增、输出均值为零,且通过可变参数使函数形式更灵活;其次,通过数值仿真实验在公共数据集上将Lfun函数与Sigmoid、ReLU、tanh、Leaky_ReLU和ELU函数的性能进行对比;最后,使用基于Lfun函数的BP神经网络进行机床能耗状态的预测。实验结果表明,使用Lfun函数的BP神经网络相较于使用其他几种常用激活函数的网络具有更好的性能。
