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International Conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Wenge Jiang Guangyuan Liu Xingcong Zhao Fu Yang School of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China

Many of the current emotion recognitions are intra-subject, the reason is that recognize cross-subject emotions have always been difficult. In this paper, we proposed a decision tree classifier based on sequential backward selection (DT-SBS), and "leave-one-subject-out" verification strategy was used for evaluating performance of cross-subject emotion recognition. Power spectral density (PSD) was extracted from EEG signals as feature set. The emotion recognition performance of the method we proposed was examined on EEG data of a group of subjects. The pictures were utilized to stimulate the emotion of 12 subjects and we collected their EEG signals (PEEG), the mean recognition accuracy of 65.8%. The dataset for emotion analysis using physiological signals (DEAP) was utilized to verify the method we proposed in this paper and the mean recognition accuracy of 65.2%. Compared with the performance reported in the existing literature, the method we proposed in this paper was effective in cross-subject emotion recognition. In addition, the emotion recognition performance of different rhythms were explored, we found that the mean recognition accuracy of PSD in the gamma rhythm achieved the best mean recognition accuracy compared with the other rhythms; the high frequency bands contained higher emotional information than the low frequency bands.

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Privacy-Preserving Fuzzy Multi-keyword Search for Multiple Data Owners in Cloud Computing

Privacy-Preserving Fuzzy Multi-Keyword Search for Multiple D...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Lvhao Chen Xiaofeng Liao Nankun Mu Jiahui Wu Junqing Junqing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Electronic and Information Engineering Southwest University Chongqing P.R. China Computer Science Chongqing University Chongqing P. R. China Le Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Electronic and Information Engineering Southwest University Chongqing P.R. China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

With cloud computing's development, more users are decide to store information on the cloud server. Owing to the cloud server's insecurity, many documents should be encrypted to avoid information leakage before being sent to the cloud. Nevertheless, it leads to the problem that plaintext search techniques can not be directly applied to the ciphertext search. In this case, many searchable encryption schemes based on single data owner model have been proposed. But, the actual situation is that users want to do research with encrypted documents originating from various data owners. This paper puts forward a privacy-preserving scheme that is based on fuzzy multi-keyword search (PPFMKS) for multiple data owners. For the sake of espousing fuzzy multi-keyword and accurate search, secure indexes on the basis of Locality-Sensitive Hashing (LSH) and Bloom Filter (BF)are established. To guarantee the search privacy under multiple data owners model, a new encryption method allowing that different data owners have diverse keys to encrypt files is proposed. This method also solves the high cost caused by inconvenience of key management.

关键词： Cloud computing Servers Indexes Encryption Data privacy

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An adaptive malicious punishment over secure distributed estimation under attacks 4

An adaptive malicious punishment over secure distributed est...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Hua, Yi Hu, Limei Chen, Feng Department of Electronic and Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China

ISBN: (纸本)9781538683392

In this paper, we propose an adaptive malicious punishment DLMS algorithm to mitigate the adversarial attack. Two types of attacks are proposed in the WSNs. The adaptive threshold is designed to detect attacks. Then corresponding weight of malicious nodes can be reduced, due to the punishing factor. The simulations demonstrate that the proposed algorithm is robust to the adversarial attacks. © 2018 IEEE.

关键词： Component Detecting malicious nodes DLMS The adaptive threshold The malicious attack The punishing weight

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Association Rules Hiding via Multi-Objective Differential Evolution Algorithm

Association Rules Hiding via Multi-Objective Differential Ev...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Nankun Mu Fan Yang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing P.R. China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

Association rules (ARs) mining has been widely used in discovering interesting patterns from massive data as a key data mining technique. However, this mining technique may lead to the disclosure of sensitive ARs, and thus, the ARs hiding strategy is very important and necessary. In this paper, a novel ARs hiding method (DEvoArH) is proposed for hiding the ARs. Noting that there are three side-effects in ARs hiding (ARH), i.e., the hiding failure rate, the lost rules rate, and the ghost rules rate, a multi-objective differential evolution algorithm is carefully designed. Specifically, the Pareto-optimal solutions are defined as the elite set, and then each solution is capable of learning from the elite set with a certain probability. Considering the convergence speed and search ability, a directional mutation and random mutate round is introduced into the mutation operator. Besides, to further decrease the data change rate, a database pre-processing mechanism is proposed to filter the unrelated data before the hiding process. Finally, the results of the comparison experiments also demonstrate the superiority of the proposed ARH method in terms of the three side-effects and efficiency.

关键词： association rules hiding multi-objective optimal evolutionary algorithm

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TSW-FD: A Novel Temporal and Spatial Domain Weight Analysis of Feature Difference for Micro-Expression Spotting

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Zhihao Zhang Fan Mo Ke Zhao Tong Chen Xiaolan Fu State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy of Sciences Beijing 100101 China Department of Psychology University of Chinese Academy of Sciences Beijing 100049 China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Southwest University Chongqing 400715 China Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology Chongqing 400715 China

The micro-expression spotting has recently attracted increasing attention from psychology and computer vision community, since embraced in the second facial Micro-Expression Grand Challenge (MEGC 2019). Different from the original feature difference (FD) analysis, in this paper, we proposed a novel temporal and spatial domain weight analysis of feature difference (TSW-FD) to achieve micro-expression spotting. The experimental results showed that TSW-FD improved 17.86% and 24.21% in F1-Score comparing to the FD in CASME II and SMIC-E-HS.

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The Design of PID Controller Based on Improved Bird Swarm Algorithm 5

The Design of PID Controller Based on Improved Bird Swarm Al...

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5th International Conference on information, Cybernetics, and Computational Social Systems, ICCSS 2018

作者： Qiao, Dewen Mu, Nankun Liao, Xiaofeng Wang, Huiwei Yang, Fan College of Electronic and Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China

ISBN: (纸本)9781538670880

The proportional - integral - derivative(PID)controller has been widely employed in industrial control systems. However, the PID parameters obtained by Ziegler-Nichols(Z-N)method are difficult to show superior performance. Based on the improved bird swarm algorithm(IBSA), a method of parameter optimization of PID controller is proposed in this paper. We improve the three parameters C and S respectively. The ω is the inertia coefficient added to the bird swarm algorithm(BSA). The C and S are cognitive coefficient and social coefficient of BSA respectively. In addition, the PID controller using IBSA is also called IBSA-PID controller. In order to validate the superiority of IBSA, we compare IBSA with particle swarm optimization(PSO), genetic algorithm(GA)and bird swarm algorithm(BSA)under the aid of integrated time and absolute error(ITAE). Experimental results validate that IBSA has superior performance than other algorithms on the aspect of convergence, robust, and time cost. © 2018 IEEE.

关键词： Three term control systems

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A recurrent neural network for optimal energy management considering the battery cycle-life in smart grid

A recurrent neural network for optimal energy management con...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Miao Sun Xing He School of Microelectronics Fudan University Shanghai China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronics and Information Engineering Southwest University Chongqing China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

This paper proposes a combined battery energy management (CBEM) model against the background of smart grid, which focus on the absorptive ability of Battery Energy Storage System (BESS) distinguishing to the traditional energy management. To solve the CBEM model, an one-layer recurrent neural network (RNN) is recommended. Furthermore, by considering the peak and valley periods division prices, the generators absorption and the distribution network bought from power grid are optimized. Meanwhile, the emission cost is considered in total cost and there is a corresponding constraint to control the emission and the outputs of generators. Finally, the effectiveness of the CBEM model is verified on Matlab. With the fluctuation of electrical price, the charge model and discharge model of BESS are showed. Results indicate that this model performs well in cutting the load in peak time and storage energy in valley time.

关键词： BESS cycle-life cost emission cost control recurrent neural network energy management recurrent neural nets Energy management Smart grids Power grids Battery Cycle Life energy storage battery energy storage system

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Relay selections for cooperative underlay CR systems with energy harvesting

Relay selections for cooperative underlay CR systems with en...

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作者： Ye, Jia Liu, Zhedong Zhao, Hui Pan, Gaofeng Ni, Qiang Alouini, Mohamed-Slim Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom

In this paper, we consider a cooperative underlay cognitive radio system in which one secondary user (S) transmits its data to a secondary receiver with the assistance of multiple relays. In the considered underlay spectrum sharing scenario, the transmit power levers at S and forwarding relays are adjusted simultaneously according to a given threshold interference power that the primary user can tolerate. We also consider that relays adopt threshold decode-and-forward relay scheme and harvest energy from the primary transmitter with finite energy storage capacity under time switching scheme. Three best relay selection (RS) schemes, optimal source-relay link with wireless energy harvesting (WEH), optimal relay-destination link with WEH and optimal source-relay-destination link with WEH schemes, are proposed and studied. Closed-form analytical expressions for the outage probability of these three best RS schemes are derived, respectively. Finally, our analytical results are verified by Monte-Carlo simulations. © 2015 IEEE.

关键词： Energy harvesting

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Reversible Data Hiding Based on Improved Rhombus Prediction Method

Reversible Data Hiding Based on Improved Rhombus Prediction ...

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International Conference on intelligent Control and information processing (ICICIP)

作者： Simi Lu Xiaofeng Liao Nankun Mu Jiahui Wu Junqing Le Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Electronic and Information Engineering Southwest University Chongqing P.R. China Computer Science Chongqing University Chongqing P. R. China

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Reversible data hiding(RDH) is a research hotspot in the field of information hiding. Among them, RDH based on histogram shift(HS) is a high performance algorithm. Accurate pixel prediction can reduce image distortion while maintaining high capacity. Therefore, this paper proposes an RDH algorithm based on the improved rhombus prediction method. Experiments show that the improved rhombus prediction method can predict pixels more accurately, and the generated prediction error histogram is more compact and clear. The proposed RDH algorithm has a higher embedding capacity and a lower distortion rate.

关键词： intelligent control information processing Airplanes

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S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimization

arXiv

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arXiv 2019年

作者： Li, Huaqing Zheng, Lifeng Wang, Zheng Yan, Yu Feng, Liping Guo, Jing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Department of Computer Science Xinzhou Teachers University Shanxi Xinzhou034000 China

In this paper, we study convex optimization problems where agents of a network cooperatively minimize the global objective function which consists of multiple local objective functions. The intention of this work is to solve large-scale optimization problems where the local objective function is complicated and numerous. Different from most of the existing works, the local objective function of each agent is presented as the average of finite instantaneous functions. Integrating the gradient tracking algorithm with stochastic averaging gradient technology, a distributed stochastic gradient tracking (termed as S-DIGing) algorithm is proposed. At each time instant, only one randomly selected gradient of an instantaneous function is computed and applied to approximate the local batch gradient for each agent. Based on a novel primal-dual interpretation of the S-DIGing algorithm, it is shown that the S-DIGing algorithm linearly converges to the global optimal solution when step-size do not exceed an explicit upper bound and the instantaneous functions are strongly convex with Lipschitz continuous gradients. Numerical experiments are presented to demonstrate the practicability of the S-DIGing algorithm and correctness of the theoretical results. Copyright © 2019, The Authors. All rights reserved.

关键词： Consensus algorithm

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