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IAENG International Journal of Computer Science 2025年第5期52卷 1344-1364页

作者： Xie, Tao Xiao, Zhongchuang He, Xiaojun Fan, Yinchun Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Jiaotong University's School of Electromechanical and Vehicle Engineering Chongqing400065 China SINOPEC Sales Co Ltd's Chongqing EJI branch Chongqing400065 China

To improve the accuracy of short-term power load forecasting, a short-term power load forecasting method based on Transformer with fused CNN-BiGRU is proposed. First, the best input data sequence is selected using the Partial Autocorrelation Function (PACF). Next, the importance scores and rankings of the feature data are obtained through the gradient boosting tree algorithm of CatBoost, and the optimal input features are selected. Then, the feature data and load data are combined. Finally, the combined data is used as input for the Transformer fused with CNN-BiGRU. In model training and forecasting, a hybrid forecasting strategy is employed, incorporating elements of multi-step forecasting into single-step forecasting. For the data of each moment, personalized and independent model training is performed, along with forecasting that include hybrid elements. The model replaces the original word embedding and position encoding components of Transformer. It uses CNN-BiGRU to extract high-dimensional feature representations of latent feature information and relative positional information from the input data. The proposed model demonstrates higher forecasting accuracy through validation on two different datasets and comparison with other forecasting models. Additionally, two ablation experiments are conducted. Through systematic ablation experiments, we demonstrate that modifications to the Transformer input layer significantly improve model performance in time series tasks. These results validate the rationality and effectiveness of the proposed approach. The ablation experiments on the method of PACF selecting the optimal input data sequence and CatBoost filtering the optimal input feature data, as well as the hybrid forecasting strategy, further verify the effectiveness and rationality of the data selection methods and forecasting strategies used in this study for short-term power load forecasting. Moreover, to eliminate the zigzagging jitter phenomenon in the foreca

关键词： CatBoost hybrid forecasting strategy PACF Transformer fuses CNN-BiGRU

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Utilizing semantically enhanced self-supervised graph convolution and multi-head attention fusion for herb recommendation

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Artificial Intelligence in Medicine 2025年 164卷 103112-103112页

作者： Tang, Xianlun Tang, Yuze Liu, Xinran Zhang, Haochuan Dang, Xiaoyuan Wang, Ying Xu, Zihui Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing400065 China School of Intelligent Engineering Chongqing College of Mobile Communication Chongqing401520 China Big Data and Internet of Things School Chongqing Vocational Institute of Engineering Chongqing402260 China Xinqiao Hospital Army Medical University Chongqing400037 China

Traditional Chinese herbal medicine has long been recognized as an effective natural therapy. Recently, the development of recommendation systems for herbs has garnered widespread academic attention, as these systems significantly impact the application of traditional Chinese medicine. However, existing herb recommendation systems are limited by data sparsity, insufficient correlation between prescriptions, and inadequate representation of symptoms and herb characteristics. To address these issues, this paper introduces an approach to herb recommendation based on semantically enhanced self-supervised graph convolution and multi-head attention fusion (BSGAM). This method involves efficient embedding of entities following fine-tuning of BERT;leveraging the attributes of herbs to optimize feature representation through a residual graph convolution network and self-supervised learning;and ultimately employing a multi-head attention mechanism for feature integration and recommendation. Experiments conducted on a publicly available traditional Chinese medicine prescription dataset demonstrate that our method achieves improvements of 6.80%, 7.46%, and 6.60% in F1-Score@5, F1-Score@10, and F1-Score@20, respectively, compared to baseline methods. These results confirm the effectiveness of our approach in enhancing the accuracy of herb recommendations. © 2025 Elsevier B.V.

关键词： Self-supervised learning

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Position Tracking control for PMSM System Based on Continuous Sliding Mode control and Sliding Mode Observer 41

Position Tracking Control for PMSM System Based on Continuou...

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第41届中国控制会议

作者： Qian Wan Huiming Wang Xia Sun Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing Institute of Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this article,a robust finite-time control strategy using continuous terminal sliding mode control(CTSMC) and sliding mode observer(SMO) is proposed to solve the problem of position tracking of permanent magnet synchronous motor(PMSM) servo *** control performance of such a system is always affected by internal and external *** this reason,a continuous sliding mode control(CSMC) method with fast convergence and strong anti-disturbance is firstly ***,considering that the disturbance of the actual system is generally in various forms and time-varying,to further improve the disturbance rejection ability of the system,an SMO based CTSMC method is proposed,where the SMO is devised to estimate time-varying disturbances and the estimations are employed to the position controller through feedforward compensation for disturbance *** present control method can not only improve the tracking rate,but also enhance the disturbance suppression ability of the *** with the PD control,CSMC,and CTSMC,simulation results demonstrate that the proposed control method can achieve the promised control performance for the studied system.

关键词： Continuous terminal sliding mode(CTSM) sliding mode observer(SMO) position control permanent magnet synchronous motor(PMSM)

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A control System of Cell Electroporation induced by Double Nanosecond Pulsed Electric Field 3

A Control System of Cell Electroporation induced by Double N...

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2020 3rd International Symposium on Power Electronics and control Engineering, ISPECE 2020

作者： Peng, Hao Zhang, Lin Liu, Xin Guo, Fei Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing China

A feedback linearization control system, which is used to study and analyze cell electroporation induced by double nanosecond pulsed electric field, is proposed in this paper. Then the effects of the control system which controls the process of electroporation induced by unipolar or bipolar double pulse are investigated to achieve controllability of electroporation. The simulation results show that the output of the control system, namely, the transmembrane potential can reach a stable value of 0.78 (-0.78)V, the radius increases linearly from 0.86nm to 0.88nm and the pore density increases linearly to 2.072×108m-2 (2.077×108m-2) when the input of the control system, the expected transmembrane potential is set to 0.79 (-0.79) V, and these results are in well agreement with the simulation result obtained by computing the related equations of electroporation, demonstrating that the control system developed in study can well regulate cell electroporation under the action of double nanosecond pulsed electric field. Overall, the control system can help the researcher to obtain the corresponding output electroporation parameters by directly inputting the expected transmembrane potential. © Published under licence by IOP Publishing Ltd.

关键词： Feedback linearization

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Load peak-valley shape clustering and drift analysis for improving temporal pattern representation

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Sustainable Energy, Grids and Networks 2025年 43卷

作者： Yiwei Ma Yimeng Shen Xianlun Tang Dong Yan School of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 China Department of Electrical Engineering School of Automation and Industrial Internet Chongqing University of Posts and Telecommunications Chongqing 400065 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing 400065 China

Load shape pattern clustering is an important foundation for developing appropriate tariff design and load management to achieve more economical and reliable benefits. However, the existing load shape pattern clustering methods mainly focus on the whole load shape and various clustering algorithms, which do not consider the peak-valley shape features and distribution drift issue of the load shapes. Therefore, peak-valley shape pattern clustering and drift measurement of daily load shapes are proposed to solve this problem. To accurately reveal the peak-valley electricity consumption behaviors, load peak-valley shape models and a hybrid distance measurement are proposed to obtain more representative temporal patterns that have more compact peak-valley shape distributions. Moreover, two measurement models for power drift and time drift are proposed to analyze the significant drift problem between daily load peak-valley shape patterns. The results showed that the proposed method outperformed other methods, as it not only achieved the best clustering effectiveness scores, such as DBI, WAS, CHI, SC, and DI scores of 0.5537, 0.2633, 502.3634, 0.8872, and 1.4730, respectively, but also accurately obtained the time drift values between different modes, such as the maximum backward shift of the two peak times by 90 and 150 minutes, and the maximum backward shift of the valley time by 45 and 60 minutes, respectively.

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Linear active disturbance rejection control for load frequency control with wind power and energy storage 21

Linear active disturbance rejection control for load frequen...

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5th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2021

作者： Qin, Feng Chen, Yifan Chen, Gonggui Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781450384322

In this paper, linear active disturbance rejection control (LADRC) is adopted and designed for load frequency control (LFC). The biogeography-based optimization (BBO) algorithm is used to optimize parameters of LADRC, and the performance of BBO is improved by introducing the strategy of guidance and update. A two-area multi-source system with wind power and energy storage are designed, and the proposed LADRC optimized by improved biogeography-based optimization (IBBO) algorithm is tested in the designed system, and those system responses show that under the control of IBBO LADRC, controlled system has good dynamic performance and robustness. Those studies in this article show that the proposed method has excellent control performance and it is effective in solving the LFC problem. © 2021 ACM.

关键词： Wind power

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Improved GSA based on KHA and PSO algorithm for short-term hydrothermal scheduling 4

Improved GSA based on KHA and PSO algorithm for short-term h...

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4th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2019

作者： Xiao, Xiong Gao, Miao Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing400065 China

ISBN: (纸本)9781728119076

The short-term hydrothermal scheduling(STHS) is a complex non-linear and time-varying optimization problem in power system, and it is non-convex when the effect of valve point is considered, which increases the difficulty of intelligent algorithm optimization. Many methods have been used to solve this problem but still have some spaces in improving the quality of the solutions. This paper proposes an improved gravitational search algorithm(GSA) based on krill herd algorithm(KHA) and particle swarm optimization(PSO) algorithm(KHA-PSO-GSA) to deal with the problem of STHS. Introducing the random parameters into the memory characteristics of speed update in PSO and according the times when the global optimal value remains unchanged to improve the mutation strategy of default individuals in KHA. Two standard test cases of four hydro power plants and three thermal power plants system are taken to verify the abilities of the proposed method. The results show that the proposed KHA-PSO-GSA is stronger than KHA, GSA and PSO-GSA in optimizing local and global capabilities. At the same time, KHA-PSO-GSA has a better solution in fuel cost and power transmission loss than other article methods. © 2019 IEEE.

关键词： Learning algorithms

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Facial Expression Recognition Method Based on Sparse Batch Normalization CNN 37

Facial Expression Recognition Method Based on Sparse Batch N...

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37th Chinese control Conference, CCC 2018

作者： Cai, Jun Chang, Ouan Tang, Xian-Lun Xue, Can Wei, Chang Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9789881563941

In order to overcome the problem that the convolution neural network facial expression recognition model training is prone to gradient disappear or gradient explode and the fully connected layers with highly complex connection leads to the over-fitting. A sparse batch normalization convolution neural network model is proposed. In the early convolution process, the larger convolution kernels are used continuously to integrate the facial expression features. Batch normalization is applied to the middle layer of the model to adjust the data size and distribution so that the model can be trained quickly. At the fully connected layers, a low retention ratio Dropout connection forms reduce model calculations and over-fitting risk. The performance of the model is validated on the JAFFE dataset and the CK + dataset, respectively, and the recognition accuracy is over 95%. The model has better recognition performance than the common deep learning model and traditional methods. © 2018 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Neural network models

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Robust Tracking control of Flexible-Joint Robot Subject to Time-Varying Disturbances

Robust Tracking Control of Flexible-Joint Robot Subject to T...

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2022 Chinese Automation Congress, CAC 2022

作者： Wang, Xuechuang Wang, Huiming Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing400065 China

ISBN: (纸本)9781665465335

In this brief, a robust perturbance suppression control strategy is put forward to solve the trajectory tracking issue for the flexible-joint robot (FJR). It is well known that a variety of disturbances, including parametric perturbations, unmodeled dynamics, and model errors, consistently impair the FJR's ability to maintain stability. To reject the adverse impacts of disturbances and obtain high-precision tracking performance, an effective method for disturbance suppression based on an output feedback controller and a generalized proportional integral observer (GPIO) is applied. The output feedback controller is constructed based on the observed values made by the GPIO, which is designed to observe unmeasurable states and the unknown lumped perturbance. The outcomes demonstrate that the suggested control scheme outperforms the traditional linear active disturbance rejection control (LADRC) scheme in respect of tracking and anti-disturbance performances. © 2022 IEEE.

关键词： Disturbance rejection

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Cooperative Hierarchical Coverage control of Multi-Agent systems with Non-holonomic Constraints in Poriferous Environments

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IEEE Transactions on control of Network systems 2025年

作者： Zou, Jiayu Zhang, Hai-Tao Zhai, Chao Xing, Ning Ma, Yong Liu, Xingjian Huazhong University of Science and Technology School of Artificial Intelligence and Automation The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China China University of Geosciences School of Automation China The MOE Engineering Research Center of Intelligent Technology for Geo-Exploration Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Academician Workstation of China COSCO Shipping Corporation Ltd. Shanghai200120 China Wuhan University of Technology Sanya Science and Education Innovation Park Sanya572000 China Wuhan University of Technology Chongqing Research Institute Chongqing401120 China

It has long posed a challenging task to optimally deploy multi-agent systems (MASs) to cooperatively coverage poriferous environments in real cooperative detection applications. In response to this challenge, this paper proposes a hierarchical coverage control (HCC) protocol for MASs to perform sector-based coverage operations. First, a distributed Voronoi partition-based sweep-and-assign protocol, combined with a sectorial partition method, is developed, enabling the segmentation of a whole poriferous region into multiple sectorial sub-subregions. Following this procedure, an extreme search scheme is developed to determine the optimal amount of agents for each subregion. After this allocation, a distributed controller is proposed to deploy the agents, considering non-holonomic constraints, into designated niche positions. Additionally, sufficient conditions are derived to guarantee the asymptotical stability of the present HCC. The analytical challenge of the present study stems from the non-convex characteristics inherent in the loss function induced by porous environments. This non-convexity renders the task of globally optimizing the assignment of MASs intractable. Finally, numerical simulations are conducted to validate the effectiveness of the present coverage control approach. © 2014 IEEE.

关键词： Nonlinear control systems

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