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A comprehensive framework for decoding salty taste information from electroencephalography signals:distinguishing brain reactions to saltiness of comparable intensity

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Food Science and Human Wellness 2025年第4期14卷 1538-1545页

作者： Jingjing Liu Yifei Xu Xingwang Lian Tianming Liu Haohao Ning Xi Jiang Shixin Yu Shikun Liu Lu Huang Xiaojun Sun Jiangyong Li Dongfu Xu School of Automation Engineering Northeast Electric Power UniversityJilin 132012China Department of Computer Science the University of GeorgiaAthens 30602USA School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731China

The perception of salty taste is crucial for individuals to make healthy food ***,the brain electrophysiological signals underlying salty taste perception have been poorly *** this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response *** this method to the EEG processing workflow can form a complete data processing *** results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the ***,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.

关键词： Saltiness Salty perception Electroencephalography Stimulus paradigm

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An Improved Convolutional Networks Model for Carbon Emission Prediction in Power Systems

An Improved Convolutional Networks Model for Carbon Emission...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Cai, Dongsheng Acen, Caroline Ukwuoma, Chiagoziem C. Zhang, Nan Liu, Wei Zhang, Zhenyuan Huang, Qi College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Computing and Cyber Security Chengdu University of Technology Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350352290

In response to the imperative need for carbon reduction, tracking the carbon footprint of power systems has emerged as a critical concern. Traditional methods have struggled to accurately capture the nuances of carbon emissions, necessitating a fresh approach. To address this gap, the study introduces a pioneering deep learning-based model. We contend that the rapid advancement of deep learning offers a solution by harnessing neural network models, particularly the temporal convolutional networks (TCN) and gated recurrent units (GRU). These models are adept at handling the intricate patterns and dependencies inherent in carbon emission data, which exhibit both short-term fluctuations and long-term trends. Sequel to that, we propose a novel TCN-GRU-ATT neural network model designed explicitly for carbon footprint data. This innovative model combines TCN, GRU, and attention mechanisms to comprehensively capture both short-term variations and long-term dependencies within carbon emission patterns. Using power generation data from a gas-fired unit within the power grid as a foundation, the model calculates carbon emissions across various time intervals. Results demonstrate the model's exceptional performance, with a low Mean Absolute Percentage Error (MAPE) of 1.7123%, a Root Mean Square Error (RMSE) of 8.8436 kg/CO2, and a high Coefficient of Determination R2 of 0.9802. © 2024 IEEE.

关键词： Low emission

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Seismic landslide detection based on Transformer and RNN algorithm

Seismic landslide detection based on Transformer and RNN alg...

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2023 IEEE International Conference on Sensors, Electronics and Computer engineering, ICSECE 2023

作者： Yang, Qingru Zhang, Xin Xie, Yuxuan Huang, Shuhong Kong, Junfeng Zhang, Xiaoyu Chengdu China Chengdu University of Technology College of Mechanical and Electrical Engineering Chengdu China Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu China

ISBN: (纸本)9798350313734

This paper proposes a seismic landslide detection method based on Transformer and RNN algorithm. This method uses seismic data and topographic data as inputs, encodes the data through the Transformer model, and then inputs the encoded data into the RNN model for classification. In the training process, we use the cross entropy loss function and Adam optimizer. To verify the effectiveness of the method, we used public data sets for experiments, and the results show that our proposed method can significantly improve the accuracy of seismic landslide detection. In addition, we interpret the results by visualization method and analyze the performance of the model under different parameters. Our method is expected to provide effective support for earthquake landslide prediction and early warning. © 2023 IEEE.

关键词： Seismology

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Multi-Agent Deep Deterministic Policy Gradient-Based Bidding Strategy in Electricity Market 11th

Multi-Agent Deep Deterministic Policy Gradient-Based Bidding...

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11th Frontier Academic Forum of Electrical engineering, FAFEE 2024

作者： Zhang, Shiwei Chen, Shuheng Zhou, Rui Han, Yang Liu, Qunying School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819788118

Bidding strategy and Nash equilibrium (NE) research are crucial for electricity retailers to increase their profits in a deregulated electricity market. However, traditional game theory and reinforcement learning methods have limitations in solving NE problems due to incomplete information and the curse of dimensionality. In this paper, an agent-based model of electricity retailers that uses the multi-agent deep deterministic policy gradient algorithm has been proposed to approximate the NE under an incomplete information environment. The effectiveness of the proposed model is verified in the 3-bus and IEEE 30-bus system. The numerical results show that the proposed algorithm can converge to the NE of a complete information environment under an incomplete information environment. Compared with the NE obtained through game theory, the proposed the agent-based model owns high accuracy with a tiny error margin. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Nash equilibrium

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Moving Object Tracking based on Kernel and Random-coupled Neural Network 6

Moving Object Tracking based on Kernel and Random-coupled Ne...

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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Chen, Yiran Liu, Haoran Liu, Mingzhe Liu, Yanhua Wang, Ruili Li, Peng Chengdu University of Technology Chengdu China Wenzhou University of Technology Chengdu China Massey University Palmerston North New Zealand Southwestern Institute of Physics Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China

ISBN: (纸本)9798400712739

Moving object tracking on cost-effective hardware is a crucial need in numerous research and industrial applications. However, current deep learning-based tracking algorithms usually prioritize exceptional performance at the expense of increased computational load. Due to the unavailability of expensive GPUs for many tracking tasks, these popular trackers often fall short in providing robust tracking capabilities with affordable computational resources. This study introduces RCNNshift, a kernel-based tracker that relies on feature extraction from a random-coupled neural network. This visual cortex inspired neural model can extract image features without requiring cumbersome pre-training or deep neural connections. By utilizing an enhanced one-dimensional feature representation, RCNNshift demonstrates superior performance compared to other kernel-based object tracking methods, even those employing higher-dimensional feature spaces. Its improvement in precision and success plots of OPE, compared to the Meanshift and Camshift in the HSV and RGB color spaces, exceeds over 160% and 190% respectively. Comparative experiments have validated the robustness of RCNNshift, showcasing its superior performance over various kernel-based and particle filter trackers. Its combination of robustness and computational efficiency makes RCNNshift an ideal choice for mid to low-end object tracking tasks such as surveillance and underwater tracking. The source code is available at https://***/HaoranLiu507/RCNNshift. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Object detection

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SIMULATION AND EXPERIMENTAL STUDY OF LOW-ENERGY FLUORESCENCE X-RAYS 10

SIMULATION AND EXPERIMENTAL STUDY OF LOW-ENERGY FLUORESCENCE...

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10th International Symposium on Test automation and Instrumentation, ISTAI 2024

作者： Dai, Derong Guo, Siming Ding, Pingping Wang, Zhen Li, Zhiwei Li, Wen College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China National Institute of Metrology Beijing China

ISBN: (纸本)9781837242672

Fluorescence X-rays have the advantages of good monochromaticity, high flux and stable dose rate. Carried out simulation and experimental studies of fluorescence X-rays in the (4.5 ~ 25.3) KeV energy range. The energy spectra produced by different radiators were simulated using Monte Carlo simulation software to obtain the theoretical spectra. The Kp and L fluorescence X-rays were also filtered in the simulation by choosing different materials of secondary filter materials for each radiator. Measurement experiments were carried out using a HPGe detector to obtain experimental spectra. The simulation results match the experimental results, verifying the correctness of the experimental measurements. The fluorescence yield and Ka fluorescence purity of the radiator were calculated, in which the fluorescence yield of Zn reached percent 95.2 and the purity of Ka fluorescence of Cd reached percent 82.8. The factors affecting the fluorescence yield experiment are the secondary filter corresponding to the radiator, which can be subsequently optimized in the selection of secondary filter. © The Institution of engineering & technology 2024.

关键词： Radiators

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Pyramid Bayesian Method for Model Uncertainty Evaluation of Semantic Segmentation in Autonomous Driving

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Automotive Innovation 2022年第1期5卷 70-78页

作者： Yang Zhao Wei Tian Hong Cheng School of Automation Engineering University of Electronic Science and Technology of ChinaChengduChina

With the fast-developing deep learning models in the field of autonomous driving,the research on the uncertainty estima-tion of deep learning models has also ***,a pyramid Bayesian deep learning method is proposed for the model uncertainty evaluation of semantic *** segmentation is one of the most important perception problems in understanding visual scene,which is critical for autonomous *** study to optimize Bayesian SegNet for uncertainty *** paper first simplifies the network structure of Bayesian SegNet by reducing the number of MC-Dropout layer and then introduces the pyramid pooling module to improve the performance of Bayesian *** and mPAvPU are used as evaluation matrics to test the proposed method on the public Cityscapes *** experimental results show that the proposed method improves the sampling effect of the Bayesian SegNet,shortens the sampling time,and improves the network performance.

关键词： Semantic Segmentation Bayesian SegNet Uncertainty Evaluation

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Dynamic Event-Triggered Consensus Control for Input Constrained Multi-Agent Systems With a Designable Minimum Inter-Event Time

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 649-660页

作者： Meilin Li Yue Long Tieshan Li Hongjing Liang C.L.Philip Chen the School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China IEEE the Yangtze Delta Region Institute University of Electronic Science and Technology of ChinaHuzhou 313000 the Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing 100089China the School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006

This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first ***,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale *** is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event *** addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable ***,the results are extended to regional consensus of the MASs with the bounded control *** simulations show the effectiveness of the proposed approach.

关键词： Constrained input designable minimum inter-event time directed communication topology dynamic event-triggered mechanism MASs consensus control

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A Fast dynamic droop control strategy based on output impedance for DC microgrids 10

A Fast dynamic droop control strategy based on output impeda...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Chen, Zhangyong Liu, Yunyan Wang, Tieqi University of Electronic Science and Technology of China School of Automation Engineering Chengdu China

ISBN: (纸本)9798350351330

With the development of renewable and distributed energy sources, DC microgrids(Mgs) are widely used in various microgrid fields due to their advantages of no AC-DC conversion, high efficiency, and no harmonic, synchronization, and reactive power problems. In this paper, a dynamic droop control is proposed, where an additional dynamic droop term is added to the conventional droop control to form a two-degree-of-freedom droop structure, which improves the dynamic tracking performance of the system without affecting the power regulation of the conventional droop control. In this paper, the output impedance of the conventional droop control system is firstly pushed to in detail to demonstrate that the droop coefficient of the original droop system affects both the linearized power regulation performance and the dynamic performance. At the same time, the output impedance of the system of the proposed method is also pushed to the comparison of the output impedance of this method, and the dynamic parameters of the original system are improved by reasonable design parameters. Finally, simulations and experiments verify the correctness of the proposed method. © 2024 IEEE.

关键词： Reactive power

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Parameter Identification of a Fractional-Order Human-Exoskeleton Coupling Model with the Segmented Form for the Lower Limb 14

Parameter Identification of a Fractional-Order Human-Exoskel...

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14th International Conference on Information Science and technology, ICIST 2024

作者： Chen, Zhenlei Li, Tieshan Yan, Yao Guo, Qing School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350353334

This paper presents a human-exoskeleton coupling model for estimating the human-exoskeleton coupling force. Specifically, to enhance the model's ability to characterize the complex mechanical characteristics of human-exoskeleton coupling, the fractional-order differential term is introduced. Moreover, the coupling model adopts a segmented form to characterize the different mechanical characteristics of front-side and back-side compression in the human-exoskeleton coupling. Using experimental data measured by the self-developed humanexoskeleton coupling measurement platform, the corresponding model parameters for 10 adult male and 10 adult female volunteers (Age: 23.65 ± 4.03 years, Height: 165.60 ± 8.32 cm, Weight: 62.35 ± 14.09kg) were identified via Neighborhood Field Optimization (NFO) and Least Squares (LS) methods. Based on the analysis of the estimated root mean square error (RMSE) of the coupling force, it was verified that the coupling force can be estimated accurately based on the proposed fractional-order human-exoskeleton coupling model with the segmented form. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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