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A Decomposition and Reconstruction Based Hybrid Time Series Model for Short-Term Wind Power forecasting

Journal of advanced Computational Intelligence and intelligent Informatics 2025年第3期29卷 592-605页

作者： Ding, Min Lv, Ji Zhou, Sibei Li, Junhao Fang, Zhijian Yokoyama, Ryuichi School of Automation China University of Geosciences No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education No 388 Lumo Road Hongshan District Hubei Wuhan430074 China School of Environment and Energy Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo169-8555 Japan

The intermittency, uncontrollability, and variability of wind power affect the economical operation and reliable delivery of the power system. To ensure the smooth integration of wind power into the grid, an accurate wind power forecast is essential. In this paper, we propose a short-term wind power prediction model based on decomposition and reconstruction to improve the accuracy of wind power forecasts. This model has the following characteristics: (i) wind power time series are decomposition into multiple components through the application of a decomposition method that combines fully integrated empirical mode decomposition with adaptive noise algorithm;(ii) the components are clustered using ķ-means clustering based on dynamic time warping. According to the similarity of the complexity and lag of components, they are divided into three classes;(iii) three different prediction models, including seasonal autoregressive integrated moving average model (SARIMA), long short-term memory network (LSTM) and Bi-directional long short-term memory network (BiLSTM), predict three types of components respectively. Finally, to illustrate the capability of the model, we compare its performance with three typical models. The results demonstrate that the proposed method exceeds the baseline models in regard to prediction performance. © Fuji Technology Press Ltd.

关键词： Electric power transmission networks

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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Remote Sensing Image Classification Based on Lightweight Network and Pruning

Remote Sensing Image Classification Based on Lightweight Net...

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Chinese automation Congress (CAC)

作者： Zexi Wang Wei Xue Kehui Chen Shaopeng Ma School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

In recent years, deep neural network has continuously improved the performance of remote sensing image classification. Though The deep neural network model is powerful, it is difficult to deploy on resource-constrained hardware platforms such as mobile terminal devices and embedded devices due to the large number of network parameters. In this paper, a novel lightweight network (LW-Net) model and a network pruning method are used for remote sensing image classification. This LW-Net model adopts a net block unit to obtain more characteristic graphs with less computational complexity. The proposed network pruning method uses the sparsity regularization on the influence factor in BN layer to automatically identified and pruned unimportant channels to make the model structure simpler. Experimental results demonstrate compared with traditional deep neural networks, the proposed model with the network pruning method can greatly reduce the computational complexity and model parameters with a little accuracy loss.

关键词： Deep learning Training Performance evaluation Runtime Computational modeling Neural networks Sensors

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Vital Signs Detection Based on Scale-Space Representation and Empirical Wavelet Transform for FMCW Radar

Vital Signs Detection Based on Scale-Space Representation an...

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Chinese automation Congress (CAC)

作者： Dongchang Wu Wei Xue Xinya Yang Tin Li School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

With the development of radar technology, frequency modulated continuous wave (FMCW) radar has been used for non-contact vital signs detection. In order to suppress the environmental noise and interference of breathing harmonics on heartbeat signal, this paper proposes a new vital sign detection method based on scale-space representation (SSR) and empirical wavelet transform (EWT) for the FMCW radar. First, a low-pass filter is set for the high-frequency noise elimination outside the frequency range of vital sign signals. Second, SSR is used to adaptively segment the spectrum of signals to obtain the initial frequency boundaries, and the kurtosis of the spectrum is applied to segment the spectrum again and obtain the final frequency boundaries to further eliminate noise. Third, the empirical wavelet filter bank is constructed by using the determined boundaries and EWT is used to decompose the signals to several components. Finally, according to the frequency ranges of components and the correlation between the components and vital sign signals, the components are selected to reconstruct the breathing and heartbeat signals. The experimental results show that the proposed method achieves a better detection performance than the classical EWT and empirical mode decomposition (EMD).

关键词： Frequency modulation Correlation Source separation Heart beat Working environment noise Radar detection Filter banks

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Exponential Synchronization of Chaotic Lur'e systems using Sampled-Data PI control

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IFAC-PapersOnLine 2023年第2期56卷 9787-9792页

作者： Hong-Zhang Wang Xing-Chen Shangguan Du Xiong Chuan-Ke Zhang School of Automation China University of Geosciences Wuhan 430074 P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 P. R. China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 P. R. China

In this paper, an exponential synchronization design method for chaotic Lur'e systems is developed. Firstly, a sampled-data based proportional-integral (PI) type controller is used in the slave system to achieve the synchronization. Next, the synchronization problem of the chaotic Lur'e system is transformed into the stabilization problem of the synchronization error system. Then, by introducing virtual vectors for reconstruction, a new augmented error system is obtained. As a result, the synchronization design based on sampled-data PI type controller can be achieved using the existing sampling system method. And based on this, a criterion for exponential synchronization design is obtained using the Lyapunov functional method. Finally, the effectiveness of the developed method is demonstrated by numerical example.

关键词： Chaotic Lur'e systems sampled-data PI control exponential synchronization system augmentation method

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Adaptive Backstepping Sliding Mode control for Underwater Glider Trajectory Tracking Based on Nonlinear Disturbance Observer

Adaptive Backstepping Sliding Mode Control for Underwater Gl...

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Chinese automation Congress (CAC)

作者： Zhenjia Li Weihua Cao Chao Gan Mingming Wen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Guangzhou Marine Geological Survey Guangzhou China

An adaptive backstepping dynamic surface sliding mode controller based on nonlinear disturbance observer (NDOABSMC) is designed to track zigzag motion of underwater glider (UG). Firstly, a nonlinear disturbance observer is devised to observe ocean current. Then, the backstepping sliding mode control is used to devise motion attitude controller of UG to ensure that UG can track the target trajectory quickly. Additionally, the stability of UG's control system is analyzed. In the end, the proposed control strategy is compared with other approaches.

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Obstacle Detection of Unmanned Surface Vessel based on Faster RCNN

Obstacle Detection of Unmanned Surface Vessel based on Faste...

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Industrial Cyber-Physical systems (ICPS)

作者： Jiahe Cai Sheng Du Chengda Lu Bo Xiao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Guangzhou Marine Geological Survey Guangzhou China

With the development of unmanned surface vessel in the world today, obstacle avoidance using environmental information is the basis to ensure its high maneuvering performance and safety. However, directly using standard algorithms will lead to missing and wrong identification severely for characteristics of marine obstacles. This paper adds a multi-scale feature extraction layer of dilation convolution and group convolution to Faster Region based Convolutional Neural Network (Faster-RCNN), the baseline model, and changes the classification algorithm to improve its robustness and accuracy. Soft-Non Maximum Suppression (Soft-NMS) is used to enhance the prediction effects further. After improvements, the mean average precision value increases by 3.35%, and the final loss value decreases by 0.20. Given the phenomenon of missing and misidentification in the prediction by the baseline model, the results of our new model show outstanding performance.

关键词： Convolution Predictive models Feature extraction Prediction algorithms Robustness Classification algorithms Safety

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Reinforcement Learning-Enhanced Dynamic Multi-Objective Evolutionary Algorithm for Cooperative Shovel Scheduling in Open-Pit Mining

Reinforcement Learning-Enhanced Dynamic Multi-Objective Evol...

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Chinese automation Congress (CAC)

作者： Zihao Zhang Changhe Li Shengxiang Yang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation China University of Geosciences Ministry of Education Wuhan China School of Artificial Intelligence Anhui University of Science & Technology Hefei China School of Computer Science and Informatics De Montfort University Leicester UK

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm.

关键词： Q-learning Ores Heuristic algorithms Process control Evolutionary computation Production Dynamic scheduling Real-time systems Windows Optimization

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A distributed high-density resistivity meter working on a two-wire cable 3

A distributed high-density resistivity meter working on a tw...

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

作者： Dong, Xiquan Luo, Wang Wang, Hongpeng Ge, Jian Liu, Huan Dong, Haobin School of Automation China University of Geosciences Wuhan Hubei430074 China Hubei Key Laboratory of Advance Control and Intelligent Automation for Complex Systems Wuhan Hubei430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan Hubei430074 China

High-density resistivity meter is a common instrument used in shallow geophysical exploration. At present, the structure of the centralized high-density resistivity meter is bulky, while the distributed instrument needs more than 7 core cables, and the long measuring line needs to relay power supply to the intelligent electrode to provide enough voltage, so it is not convenient to use in the field. Aiming at the above problems, a new type of distributed high-density meter is developed, which can realize the power supply, communication, and measurement functions of distributed high-density meter and carry out multi-channel measurement through the time-division multiplexing of a two-wire cable and the short-time power supply of supercapacitor. Finally, the performance of the instrument was verified by experiments. © Published under licence by IOP Publishing Ltd.

关键词： Cables

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Modeling and Analysis of Mtj Array with Shielding Layer

SSRN

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SSRN 2023年

作者： Qu, Ying-Hui Xiong, Jing-Ya Zuo, Chao Dong, Kai-Feng Song, Jun-Lei Mo, Wen-Qin Hui, Ya-Juan Jin, Fang School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Geological Exploration Intelligent Technology Engineering Research Center The Ministry of Education Wuhan Second Ship Design and Research Institute Hubei Province Wuhan China

In STT-MRAM, data is stored in a magnetic tunnel structure (MTJ) containing multiple ferromagnetic layers. Each ferromagnetic layer generates a stray field, which affects the critical switching current, switching time, thermal stability, and the density of the MTJ array. These factors have a direct impact on the performance and storage capacity of STT-MRAM. Previously, we have studied that hexagonal MTJ arrays can improve storage density. However, this improvement also leads to increased intracellular and intercellular stray fields, which in turn affects the device's performance. This paper aims to reduce the intercellular stray field by covering each MTJ unit with a soft magnetic shielding layer and investigates the performance of the MTJ array with a shielding layer. Firstly, a simulation model of a single MTJ stack is built using the simulation software COMSOL. Then, based on modeling the MTJ stack, square and hexagonal MTJ arrays are established. The intercellular magnetic coupling coefficient (Ψ) is quoted, and Ψ=2% is defined as the threshold below which the intercellular magnetic coupling can be considered negligible. The results indicate that the soft magnetic shielding layer can effectively shield the stray field, reducing its impact on MTJ performance. Specifically, a thicker shielding layer with higher magnetic permeability provides better shielding efficacy. However, comparing square and hexagonal MTJ array after magnetic shielding reveals a larger stray field between the hexagonal array. © 2023, The Authors. All rights reserved.

关键词： Ferromagnetic materials

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