The increasing adoption of smart devices and cloud services,coupled with limitations in local computing and storage resources,prompts numerous users to transmit private data to cloud servers for processing.However,the...The increasing adoption of smart devices and cloud services,coupled with limitations in local computing and storage resources,prompts numerous users to transmit private data to cloud servers for processing.However,the transmission of sensitive data in plaintext form raises concerns regarding users'privacy and security.To address these concerns,this study proposes an efficient privacy-preserving secure neural network inference scheme based on homomorphic encryption and secure multi-party computation,which ensures the privacy of both the user and the cloud server while enabling fast and accurate ciphertext inference.First,we divide the inference process into three stages,including the merging stage for adjusting the network structure,the preprocessing stage for performing homomorphic computations,and the online stage for floating-point operations on the secret sharing of private data.Second,we propose an approach of merging network parameters,thereby reducing the cost of multiplication levels and decreasing both ciphertext-plaintext multiplication and addition operations.Finally,we propose a fast convolution algorithm to enhance computational eficiency.Compared with other state-of-the-art methods,our scheme reduces the linear operation time in the online stage by at least 11%,significantly reducing inference time and communication overhead.展开更多
With the development of industrial production modernization, FMS and CIMS will become more and more popularized. For its control system is increasingly modeled, intellectualized and automatized, in order to raise the ...With the development of industrial production modernization, FMS and CIMS will become more and more popularized. For its control system is increasingly modeled, intellectualized and automatized, in order to raise the reliability and stability in the manufacturing process, the comprehensive monitoring and diagnosis aimed at cutting tool wear and chatter become more and more important and get rapid development. The paper tried to discuss of the intellectual status identification method based on acoustics-vibra characteristics of machining process, and propose that the working conditions may be taken as a core, complex fuzzy inference neural network model based on artificial neural network theory, and by using various kinds of modernized signal processing method to abstract enough characteristics parameters which will reflect overall processing status from machining acoustics-vibra signal as information source, to identify different working condition, and provide guarantee for automation and intelligence in machining process. The complex network is composed of NNw and NNs, Each of them is composed of BP model network, NNw is weight network at rule condition, NNs is decision-making network of each status. Y out is final inference result which is to take subordinate degree as weight from NNw, to weight reflecting result from NNs and obtain status inference of monitoring system. In the process of machining, the acoustics-vibor signal were gotten by the acoustimeter and the acceleration piezoelectricity detector, the date is analysed by the signal processing software in time and frequency domain, then form multi feature parameter vector of criterion pattern samples for the different stage of cutting chatter and acoustics-vibra multi feature parameter vector. The vector can give a accurate and comprehensive description for the cutting process, and have the characteristic which are speediness of time domain and veracity of frequency domain. The research works have been practically applied in identification of tool wear, cutting chatter, experiment results showed that it is practicable to identify the cutting chatter based on fuzzy neural network, and the new method based on fuzzy neural network can be applied to other state identification in machining process.展开更多
In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, n...In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.展开更多
Numerous neural network(NN)applications are now being deployed to mobile devices.These applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to th...Numerous neural network(NN)applications are now being deployed to mobile devices.These applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to the computing ability of mobile devices.Moreover,devices’life and performance depend on temperature.Hence,in many scenarios,such as industrial production and automotive systems,where the environmental temperatures are usually high,it is important to control devices’temperatures to maintain steady operations.In this paper,we propose a thermal-aware channel-wise heterogeneous NN inference algorithm.It contains two parts,the thermal-aware dynamic frequency(TADF)algorithm and the heterogeneous-processor single-layer workload distribution(HSWD)algorithm.Depending on a mobile device’s architecture characteristics and environmental temperature,TADF can adjust the appropriate running speed of the central processing unit and graphics processing unit,and then the workload of each layer in the NN model is distributed by HSWD in line with each processor’s running speed and the characteristics of the layers as well as heterogeneous processors.The experimental results,where representative NNs and mobile devices were used,show that the proposed method can considerably improve the speed of the on-device inference by 21%–43%over the traditional inference method.展开更多
基金Project supported by the National Natural Science Foundation of China(No.U22B2026 and 62572121)the ZTE Industry University Research Cooperation Project。
文摘The increasing adoption of smart devices and cloud services,coupled with limitations in local computing and storage resources,prompts numerous users to transmit private data to cloud servers for processing.However,the transmission of sensitive data in plaintext form raises concerns regarding users'privacy and security.To address these concerns,this study proposes an efficient privacy-preserving secure neural network inference scheme based on homomorphic encryption and secure multi-party computation,which ensures the privacy of both the user and the cloud server while enabling fast and accurate ciphertext inference.First,we divide the inference process into three stages,including the merging stage for adjusting the network structure,the preprocessing stage for performing homomorphic computations,and the online stage for floating-point operations on the secret sharing of private data.Second,we propose an approach of merging network parameters,thereby reducing the cost of multiplication levels and decreasing both ciphertext-plaintext multiplication and addition operations.Finally,we propose a fast convolution algorithm to enhance computational eficiency.Compared with other state-of-the-art methods,our scheme reduces the linear operation time in the online stage by at least 11%,significantly reducing inference time and communication overhead.
文摘With the development of industrial production modernization, FMS and CIMS will become more and more popularized. For its control system is increasingly modeled, intellectualized and automatized, in order to raise the reliability and stability in the manufacturing process, the comprehensive monitoring and diagnosis aimed at cutting tool wear and chatter become more and more important and get rapid development. The paper tried to discuss of the intellectual status identification method based on acoustics-vibra characteristics of machining process, and propose that the working conditions may be taken as a core, complex fuzzy inference neural network model based on artificial neural network theory, and by using various kinds of modernized signal processing method to abstract enough characteristics parameters which will reflect overall processing status from machining acoustics-vibra signal as information source, to identify different working condition, and provide guarantee for automation and intelligence in machining process. The complex network is composed of NNw and NNs, Each of them is composed of BP model network, NNw is weight network at rule condition, NNs is decision-making network of each status. Y out is final inference result which is to take subordinate degree as weight from NNw, to weight reflecting result from NNs and obtain status inference of monitoring system. In the process of machining, the acoustics-vibor signal were gotten by the acoustimeter and the acceleration piezoelectricity detector, the date is analysed by the signal processing software in time and frequency domain, then form multi feature parameter vector of criterion pattern samples for the different stage of cutting chatter and acoustics-vibra multi feature parameter vector. The vector can give a accurate and comprehensive description for the cutting process, and have the characteristic which are speediness of time domain and veracity of frequency domain. The research works have been practically applied in identification of tool wear, cutting chatter, experiment results showed that it is practicable to identify the cutting chatter based on fuzzy neural network, and the new method based on fuzzy neural network can be applied to other state identification in machining process.
文摘In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.
基金supported by the National Key R&D Program of China (No.2018AAA0100500)the National Natural Science Foundation of China (Nos.61972085,61872079,and 61632008)+5 种基金the Jiangsu Provincial Key Laboratory of Network and Information Security (No.BM2003201)Key Laboratory of Computer Network and Information Integration of Ministry of Education of China (No.93K-9)Southeast University China Mobile Research Institute Joint Innovation Center (No.R21701010102018)the University Synergy Innovation Program of Anhui Province (No.GXXT2020-012)partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization,the Fundamental Research Funds for the Central Universities,CCF-Baidu Open Fund (No.2021PP15002000)the Future Network Scientific Research Fund Project (No.FNSRFP-2021-YB-02).
文摘Numerous neural network(NN)applications are now being deployed to mobile devices.These applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to the computing ability of mobile devices.Moreover,devices’life and performance depend on temperature.Hence,in many scenarios,such as industrial production and automotive systems,where the environmental temperatures are usually high,it is important to control devices’temperatures to maintain steady operations.In this paper,we propose a thermal-aware channel-wise heterogeneous NN inference algorithm.It contains two parts,the thermal-aware dynamic frequency(TADF)algorithm and the heterogeneous-processor single-layer workload distribution(HSWD)algorithm.Depending on a mobile device’s architecture characteristics and environmental temperature,TADF can adjust the appropriate running speed of the central processing unit and graphics processing unit,and then the workload of each layer in the NN model is distributed by HSWD in line with each processor’s running speed and the characteristics of the layers as well as heterogeneous processors.The experimental results,where representative NNs and mobile devices were used,show that the proposed method can considerably improve the speed of the on-device inference by 21%–43%over the traditional inference method.
