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检索条件"机构=Key Laboratory of Nonlinear Circuits and Intelligent Information Processing"

共 185 条记录，以下是31-40 订阅

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Evolution of facial tissue oxygen saturation and detection of human physical stress

Evolution of facial tissue oxygen saturation and detection o...

引用

2020 Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2020

作者： Liu, Xinyu Xiao, Xiao Cao, Ranlei Chen, Tong School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

ISBN: (纸本)9781728160665

Physical stress has a direct impact on human health, which makes the detection of physical stress necessary in daily life. In this paper, we extracted tissue oxygen saturation (StO2) by hyperspectral imaging to detect and distinguish physical stress from relaxation state remotely. During the physical stress experiment, we observed that the facial StO2 right after the stress can be either increased and decreased comparing to the baseline, the StO2 after two minutes recovered towards the baseline. This changes of StO2 is reported for the first time. Based on the findings, we also located some useful ROIs on face for the detection of human physical stress. © 2020 IEEE.

关键词： Hyperspectral imaging

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A Distributed Dynamic Resource Allocation Strategy in A Large-scale Micro-grids Group 6

A Distributed Dynamic Resource Allocation Strategy in A Larg...

引用

6th International Conference on information, Cybernetics, and Computational Social Systems, ICCSS 2019

作者： Fu, Zao He, Xing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9781728158570

In this paper, a dynamic economic dispatch problem (DEDP) with renewable energy generators is studied. With the purpose of minimizing the cost of DEDP and optimally allocate the load demand among the interconnected generators considering ramp-rate limits and generation limits, an effective continuous-time distributed algorithm based on interior point methods and primal-dual methods is proposed. In order to overcome the drawbacks of the renewable energy, a parallel system composed with flywheel energy storage systems and renewable energy generators is proposed to work in a constant power output mode. In addition, the using of switched systems makes the optimal solution more accurate. The excellent performance of the proposed algorithm is verified by IEEE 30-bus system with the REG based simulation. © 2019 IEEE.

关键词： Continuous time systems

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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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Effects of acute moderate intensity exercise on emotion based on beta power in EEG 3

Effects of acute moderate intensity exercise on emotion base...

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3rd International Conference on Computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - The 11th International Conference on information and Multimedia Technology, ICIMT 2019

作者： Gao, Pengfei Long, Zhengji Xiao, Zhangyan Yang, Fu Zhang, Jihai Liu, Guangyuan School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Processing Chongqing China

ISBN: (纸本)9781450376273

According to previous studies, high anxiety leads to an increase in beta power, while pleasure and relaxation of emotion reduces beta power. In this experiment, the International Standard Picture Stimulation Library(IAPS) was used to induce positive and negative emotions of subjects, and EEG signals were collected when subjects were looking at pictures. Comparing the beta power of positive and negative EEG induced by before and after exercise, we found that the power of beta stimulated by positive and negative pictures before exercise is generally greater than that after exercise. It indicates that subjects were happier when facing positive stimuli than before exercise, and less negative emotions were produced when facing negative stimuli than before exercise. In short, acute moderate intensity exercise can improve people's negative emotion in the face of negative stimuli. The purpose of this study is to explore the mechanism of the effect of acute moderate intensity exercise on emotion by analyzing beta power in late positive potential (LPP). © 2019 Association for Computing Machinery.

关键词： Facings

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Association Rules Hiding via Multi-Objective Differential Evolution Algorithm 6

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

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6th International Conference on information, Cybernetics, and Computational Social Systems, ICCSS 2019

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

ISBN: (纸本)9781728158570

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. © 2019 IEEE.

关键词： Association rules

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Diffusion Recursive Minimum Error Entropy Algorithm 22

Diffusion Recursive Minimum Error Entropy Algorithm

引用

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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Robust Multi-kernel Generalized Maximum Correntropy Filters 3

Robust Multi-kernel Generalized Maximum Correntropy Filters

引用

3rd IEEE International Conference on Electronic information and Communication Technology, ICEICT 2020

作者： Sun, Qiaona Zhang, Tao Huang, Xuewei Wang, Shiyuan College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9781728190457

The traditional multi-kernel filtering algorithms are proposed by combining multiple kernel with the second similarity measure. Nevertheless, these multi-kernel filters suffer from both heavy computation burden and divergence for impulsive noise environments. To deal with these challenges for online applications, the multi-kernel generalized maximum correntropy (MKGMC) algorithm is developed for improving both precision and stability under the generalized maximum correntropy criterion. To alleviate the complex network structure of MKGMC, using the vector quantization strategy in MKGMC develops quantized MKGMC (QMKGMC) to reduce the computational and spatial complexity. From the viewpoints of precision and stability, the proposed algorithms outperform other typical compared algorithms on time series prediction. © 2020 IEEE.

关键词： Impulse noise

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The Robust Kernel Conjugate Gradient Least Mean p-Power Algorithm∗ 2

The Robust Kernel Conjugate Gradient Least Mean p-Power Algo...

引用

2nd China Symposium on Cognitive Computing and Hybrid Intelligence, CCHI 2019

作者： Huang, Xuewei Xiong, Kui Wang, Lin Wang, Shiyuan College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9781728140919

The traditional kernel adaptive filter (KAF) based on the minimum mean square error (MMSE) criterion suffers from poor filtering accuracy and slow convergence rate in Gaussian noises. To this end, the recursive KAF based on the least squares (LS) criterion can improve filtering accuracy and convergence rate in Gaussian noises with the increase of computational and storage burdens. However, both quadratic similarity measures based filters may suffer instability in non-Gaussian noises. To address these issues, a robust kernel conjugate gradient least mean p-power (KCGLMP) algorithm based on the mean p- power error (MPE) criterion is therefore proposed by combining the conjugate gradient optimization method with kernel trick. The proposed KCGLMP algorithm improves filtering accuracy and computational efficiency, simultaneously. Simulation results confirm the superiorities of the proposed KCGLMP algorithm over other conventional KAFs in non-Gaussian noises. © 2019 IEEE.

关键词： Computational efficiency

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The nyström kernel adagrad least mean square algorithm using K-means sampling 4

The nyström kernel adagrad least mean square algorithm usin...

引用

4th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2021

作者： Huang, Xuewei Wang, Daili Qi, Letian Wang, Shiyuan College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9781665432030

The kernel least mean square (KLMS) using the stochastic gradient descent (SGD) method has been proposed successfully in a Gaussian noise. However, KLMS suffers from performance degradation in terms of convergence rate and filtering accuracy. To this end, an adaptive gradient (Adagrad) optimization algorithm is used to find the solution to the least mean square of error, and the Nyström method is applied into KLMS to reduce its computational and spatial complexity, generating an online Nyström kernel Adagrad least mean square (NKALMS) algorithm. To further improve the filtering accuracy of NKALMS, another online Nyström kernel Adagrad least mean square algorithm using k-means sampling (NKALMS-K) is developed in this paper. Monte Carlo simulations on time series prediction demonstrate that the NKALMS-K algorithm outperforms KLMS and its extensions in the presence of Gaussian noise from the aspects of accuracy and computational efficiency. ©2021 IEEE

关键词： Computational efficiency

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The Kernel Recursive Generalized Cauchy Kernel Loss Algorithm 6

The Kernel Recursive Generalized Cauchy Kernel Loss Algorith...

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6th International Conference on information, Cybernetics, and Computational Social Systems, ICCSS 2019

作者： Shi, Wei Xiong, Kui Wang, Shiyuan College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9781728158570

As a nonlinear measure similarity developed in a kernel space, the generalized correntropic loss (GC-Loss) has been successfully used for signal processing and machine learning in non-Gaussian situations thanks to its ability of extracting high-order statistical properties of data. However, a highly non-convex performance surface in GC-Loss leads to poor optimization performance. To address this issue, we propose a new similarity measure developed in the kernel space by combining a generalized Gaussian distributed kernel function into the Cauchy loss. By minimizing the proposed loss function and using the kernel method, a novel kernel recursive generalized Cauchy kernel loss (KRGCKL) algorithm in the reproducing kernel Hilbert space (RKHS) is proposed. Simulations on different examples under non-Gaussian noises show the superiorities of KRGCKL over other representative algorithms in terms of filtering accuracy and robustness to large outliers. © 2019 IEEE.

关键词： Gaussian noise (electronic)

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