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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, BingTong Zhang, ZheYuan Zhou, Ju Wang, HanPu Chen, Tong Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing Chongqing400715 China Southwest University School of Electronic and Information Engineering Chongqing400715 China

ISBN: (纸本)9798350337488

The generation of micro-expression (ME) is unconscious and unaffected by human subjective consciousness. It can reveal hidden emotions in humans, which has important applications in the domains of psychological diagnosis, lie detection, and so on. ME detection, which aims to locate ME apex frames in long videos, is the foundation of ME recognition. In this paper, we propose a ME detection method based on One-Class Classification (OCC) theory for long videos. The method is the first to use neutral expression frames to detect MEs, which addresses the problem of insufficient training samples in ME detection. We design a geometric motion feature based on the prior knowledge from the FACS and utilize it in conjunction with the features extracted by CNN to train separate one-class classifiers for ME detection. Finally, we evaluate the proposed method on publicly available datasets CASME I, CASME II, SAMM, and a mixed database comprising data from the three datasets, to verify the feasibility of the proposed method in both single-database and cross-database ME detection. The experimental results show that our method outperforms three other ME detection methods, demonstrating its ability to more accurately locate ME apex frames in long videos. © 2023 IEEE.

关键词： Micro-expression Detection OCC One-Class Classifier

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A novel torus-based optical networks-on-chip exploiting double path communication algorithm 2

A novel torus-based optical networks-on-chip exploiting doub...

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2nd International Conference on Electronic information Engineering, Big Data, and Computer Technology, EIBDCT 2023

作者： Dai, Li Su, Ye Xie, Yiyuan School of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9781510664968

Optical networks-on-chip (ONoCs) using optical media to interconnect cores, have the advantages of high bandwidth, low power consumption and low latency. At present, mesh, torus and fat-tree are the most widely used topologies in ONoCs. Torus structure has the advantages of higher connectivity due to its annular structure. However, the traditional torus structure still has the problem of high end-to-end (ETE) delay. This paper proposes a new double-path ONoCs based on torus topology, named DPtorus. The DPtorus consists of two layers: one layer is a torus network divided into unit clusters, the other layer is an all-pass optical router. The dual-layer network is connected with each other by optical vias, so that there are two communication pathes between the source node and the target node in the network can be selected. The One is inter-cluster communication and the other is non-cluster communication. According to the comparison of the number of hop of the two communication paths, the communication path with fewer hops between the nodes is preferably selected to reduce the communication cost of the data packet. The network simulation results show that the DPtorus reduces latency and improves throughput compared to the torus network, resulting in better performance and enhanced reliability of network communications for the DPtorus. © 2023 SPIE.

关键词： Topology

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A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1325-1335页

作者： Chentao Xu Xing He the Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China

A fully distributed microgrid system model is presented in this *** the user side,two types of load and plug-in electric vehicles are considered to schedule energy for more *** charging and discharging states of the electric vehicles are represented by the zero-one variables with more *** solve the nonconvex optimization problem of the users,a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm is designed and its convergence speed is faster.A distributed algorithm with a new approach to deal with the inequality constraints is used to solve the convex optimization problem of the generators which can protect their *** results and comparative experiments show that the model and algorithms are effective.

关键词： Differential evolution algorithm distributed algorithm electric vehicle neural network zero-one variable.

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Distributed Model Predictive Control Framework for Consensus of Multi-Agent System 20

Distributed Model Predictive Control Framework for Consensus...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Li, Huaqing Zheng, Lifeng Wang, Huiwei Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering No. 2 Tiansheng Road Beibei District Chongqing China

ISBN: (纸本)9798350320831

Considering the widespread applications of the model predictive control (MPC), such as industrial process control and robotic operations, this paper proposes an MPC-based asynchronous distributed algorithm to solve the consensus problem of multi-agent system (MAS) by using the operator splitting method. The proposed algorithm is asynchronous, which means that all agents do not require to update and communicate simultaneously. When the positive uncoordinated step sizes satisfy the given condition, the optimal solution can be worked out by using the proposed algorithm. Moreover, all agents are allowed to set the step sizes locally based on local information. Numerical simulation validates the feasibility and effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Model predictive control

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Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks 57

Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-B...

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57th Annual Conference on information Sciences and Systems, CISS 2023

作者： Wang, Fei Zhang, Xi Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

ISBN: (纸本)9781665451819

We propose the novel scheme to solve a multi-objective optimization problem over an unmanned aerial vehicle (UAV) communications system to jointly minimize the energy consumption of the UAV and ground users (GUs). In particular, the UAV communicates with multiple GUs using active intelligent reflecting surfaces (IRSs), which can actively amplify and thus significantly enhance the strengths of reflected signals. We develop an energy minimization scheme based on the multi-objective hierarchical deep reinforcement learning (DRL), by decomposing the formulated optimization problem into two-layered subproblems. By solving the upper-level subproblem, we derive the optimal UAV trajectory and GUs scheduling strategies to minimize the UAV's energy consumption. By solving the lower-level subproblem, we obtain the IRS's phase shifts and amplification factors and GUs' transmit/receive beamforming to minimize the GUs' energy consumption. Finally, we validate and evaluate the proposed schemes through simulations, which show that the UAV's and GUs' energy consumption can be significantly reduced by using the active IRS, when the thermal noise powers at the IRS are much smaller than those at the UAV and GUs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G THz Mobile Wireless Networks

IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G ...

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2023 IEEE International Conference on Communications, ICC 2023

作者： Wang, Fei Zhang, Xi Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&m University College StationTX77843 United States

ISBN: (纸本)9781538674628

intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Joint Global and Local Feature Learning Based on Facial StO2 for Stress Recognition 19

Joint Global and Local Feature Learning Based on Facial StO2...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Dong Liu, Xinyu Chen, Tong Peng, Dairong Wang, Jiaxiu Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Chongqing China

ISBN: (纸本)9798350346558

Stress is an emotional state that is inevitable in social life, it is of great importance to accurately identify different types of stress. In this paper, we use human facial tissue oxygen saturation (StO2) to identify four states of an individual's baseline, emotional stress, high-intensity physical stress and low-intensity physical stress. For the stress classification, we proposed a network called GLNet that combines global and local StO2 features. Specifically, GLNet learns local features from facial regions that provide the effective stress information and global features from the whole face. Then, the two features are fused at decision-level and classified. In addition, a new data augmentation method was proposed to address the data imbalance, which generates new data by constructing a mixed network called MixNet that fuses the depth features of multiple data. Experimental results show that MixNet can effectively alleviate the problem of data imbalance and GLNet combined with the data expanded by MixNet achieved the best performance compared to previous methods of StO2 stress recognition. © 2022 IEEE.

关键词： Face recognition

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Diffusion Recursive Minimum Error Entropy Algorithm 22

Diffusion Recursive Minimum Error Entropy Algorithm

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6th International Conference on Digital Signal processing, ICDSP 2022

作者： Cai, Peng Lin, Dongyuan Yan, Wenxing Wang, Shiyuan Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9781450395809

Distributed estimation algorithms under the mean square error (MSE) criterion have received a lot of attention due to their simplicity and good estimation performance for distributed estimates in Gaussian noise environments. However, when the noise does not obey the Gaussian distribution, the performance of these algorithms can degrade seriously. In this paper, a robust distributed estimation algorithm, called the diffusion recursive minimum error entropy (DRMEE), is proposed by combining the diffusion strategy and the minimum error entropy (MEE) criterion. Since MEE criterion has been proved to be insensitive to many types of non-Gaussian noise models, the proposed algorithm is expected to improve the robustness of distributed estimation algorithms under the MSE criterion, significantly. The superior performance of DRMEE is confirmed by simulation results in the scenario of system identification with two multi-peak distribution noise environments. © 2022 ACM.

关键词： Diffusion

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A hybrid neurodynamics algorithm for solving distributed nonconvex nonsmooth load sharing problems in power systems

A hybrid neurodynamics algorithm for solving distributed non...

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2022 International Conference on intelligent Systems and Computational Intelligence, ICISCI 2022

作者： Yang, Jianyu He, Xing Southwest University Chongqing Key Laboratory Of Nonlinear Circuits And Intelligent Information Processing College Of Electronic And Information Engineering Chongqing400715 China

ISBN: (数字)9781665456197

ISBN: (纸本)9781665456197

In this paper, a hybrid neurodynamics optimizatio (HNO) algorithm is proposed, which is a distributed convex optimization algorithm combined with stochastic neurodynamics. The state solution of the HNO algorithm can avoid falling into local optimal solution by adding neural white noise. The results of numerical experiments show that HNO algorithm has better global optimization ability. In addition, we build a distributed load share (DLS) model. Different from the current model, which only focuses on the quadratic economic cost function, we not only consider the valve point effect and the environmental cost function of the fuel generator, but also consider the constraints of the transmission line which connects the bus. A distributed non-convex nonsmooth optimization problem is constructed. To solve this problem, we first smoothed each local objective function, then dealt with nonconvex constraints utilizing penalty function, and transformed the original problem into a distributed nonconvex optimization problem. Finally, the HNO algorithm is used to solve the problem. The optimization values of each variable in the power grid show that the power grid can operate in an optimal state under the condition of power limitation. © 2022 IEEE.

关键词： Global optimization

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Random Fourier Features Based Kernel Risk Sensitive Loss Algorithm with Adaptive Moment Estimation Optimization Technology 22

Random Fourier Features Based Kernel Risk Sensitive Loss Alg...

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6th International Conference on Digital Signal processing, ICDSP 2022

作者： Wang, Daili Zheng, Yunfei Cai, Peng Liu, Qiang Shen, Minglin Wang, Shiyuan Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University China

ISBN: (纸本)9781450395809

Random Fourier features based kernel risk sensitive loss (RFFKRSL) is a popular nonlinear adaptive filtering algorithm developed in the random Fourier features space. The most attractive feature of such algorithm is that it would not cause the issue of linearly increasing network structure like the well-known kernel adaptive filtering algorithms, while having the ability to curb the negative influence induced by non-Gaussian noises. The stochastic gradient descent (SGD) method, however, is a default choice to determine the filtering coefficients of RFFKRSL, which can result in an undesirable convergence performance of the algorithm in many cases. To address this issue, two alternative optimization technologies, including adaptive moment estimation (Adam) and its extended version, i. e., Nesterov-accelerated Adam (Nadam), have been adopted to re-derive RFFKRSL. For simplicity, the proposed two algorithms are named as RFFKRSL with Adam (AdamRFFKRSL) and RFFKRSL with Nadam (NadamRFFKRSL), respectively. Although Adam and Nadam are common to deep neural network based learning methods, their applications to adaptive filtering are seldom to be seen, and the combination of them with RFFKRSL may open a new way to design nonlinear adaptive filtering algorithms that have been built with SGD method. Simulations on two time series prediction tasks are reported to demonstrate the desirable performance of the proposed algorithms. © 2022 ACM.

关键词： Stochastic systems

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