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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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Enhanced SVSF for SoC Estimation of Lithium Ion Batteries

Enhanced SVSF for SoC Estimation of Lithium Ion Batteries

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Meriem Ben Lazreg Sabeur Jemmali Bilal Manai Mahmoud Hamouda LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d'Ingénieurs de Sousse Sousse Tunisie IntelliBatteries Company SoftTech Firm Incubator Technopole of Sousse Sousse Tunisia Université de Sousse Sousse Tunisie

The last decade has witnessed a spike in Lithium-ion batteries usage. This fact is due to the battery interesting characteristics. This technology needs careful monitoring to avoid overcharge and deep discharge and ensure user safety. This paper proposes an enhanced version of the Smooth Variable Structure Filter (ESVSF) to estimate the State of Charge (SoC) of lithium ion batteries. The ESVSF algorithm proposes a variable boundary layer with an online and dynamic covariance error determination. This technique is validated on LCO batteries of nominal capacity 2.1Ah and validated on a Random Walk (RW) discharge profile. The correct operation of the proposal is confirmed throw simulation results.

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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IEEE International Conference on Industrial technology (ICIT)

作者： Nanxin Huang Chi Xu 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

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models.

关键词： Training Solid modeling Convolution Service robots Pose estimation Zero shot learning Virtual reality Benchmark testing Feature extraction Robustness

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation

Efficient Facial Expression Recognition Network Based on Spo...

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IEEE International Conference on Industrial technology (ICIT)

作者： Xin Wen Zhen-Tao Liu Dan Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation.

关键词： Knowledge engineering Training Human computer interaction Accuracy Face recognition Computational modeling Noise Neural networks Interference Data models

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Bit Bounce Detection for Drilling Process Based on Multi-Feature Graph and Graph Convolutional Network 49

Bit Bounce Detection for Drilling Process Based on Multi-Fea...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Zhang, Peng Hu, Wenkai Cao, Weihua Wu, Min Wang, Jiandong School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China College of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Province Qingdao China

ISBN: (纸本)9798350331820

Prompt detection of bit bounce can prevent serious incidents and is of great importance for safe and efficient deep geological drilling. In the early stage of bit bounce, signal changes are relatively weak. In addition, there are differences in the topological relationships of samples at different time instances in normal state and bit bounce. These factors present a challenge to timely and accurate bit bounce detection. Therefore, this paper proposes a bit bounce detection method based on multi-feature graph and graph convolution networks. A multi-feature graph construction method using process variables, mean value, Mahalanobis distance, and Euclidean distance is proposed, and a two-layer graph convolutional network is designed to realize deep feature extraction and incident detection. The effectiveness and superiority of the proposed method are demonstrated by a real drilling industrial case. © 2023 IEEE.

关键词： Infill drilling

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Impact of hot carrier on RF N-LDMOS reliability for radar application after pulsed life test 2

Impact of hot carrier on RF N-LDMOS reliability for radar ap...

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2nd IEEE International Conference on Signal, Control and Communication, SCC 2021

作者： Belaid, Mohamed Ali Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Umm Al-Qura University College of Engineering at Al Leith Makkah Saudi Arabia

ISBN: (纸本)9781665402705

This Study presents a bench is dedicated to high RF power device lifetime tests under RF pulses for radar application. This bench combined the stress tests (RF, electrical and thermal,) applied to devices and have a direct impact on their lifetime. A complete device electric characterization (I-V, C-V and RF) has been conducted before/after different temperatures (3000 hours at 150° C and 10° C), and material clarifications for the phenomenon of failure, the reliability of different tests was compared. The prevailing physical acoustics concerned were studied and validated using the Silvaco-Atlas simulator. The main objective is to find a relationship between the electrical parameters degradation to failed physical phenomena caused by impact ionization. The degradation of N-LDMOS performance is tied to the hot carriers (traps) and trapped electrons, which leads to the accumulation of negative charge in the Si/SiO2 interface. More interface states (i.e. important buildup of negative charge) are created at low temperature due to impact ionization phenomenon. This is the reason why the electrical degradations are so high at 10°C. © 2021 IEEE.

关键词： Reliability

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Experimental Design of Lower-limb Movement Recognition Based on Support Vector Machine 41

Experimental Design of Lower-limb Movement Recognition Based...

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第41届中国控制会议

作者： Wangyang Ge Juan Zhao Feng Wang Chi Xu Zhaohui Yang Jinhua She 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 Department of Rehabilitation Union HospitalTongji Medical CollegeHuazhong Univ.Science & Technology School of Engineering Tokyo University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

As a rehabilitation aid,the lower extremity rehabilitation robot usually works together with the patient during the rehabilitation training process,and achieves therapeutic effects by assisting the patient to complete movements within a certain trajectory for *** realize the active training of the rehabilitation robot,the rehabilitation robot needs to be able to recognize the patient's movement intention and cooperate with *** signals contain rich neuromuscular movement information and are often used to extract patients' movement *** this paper,using the principle of human lower extremity force generation,we designed an experiment for lower extremity action recognition of sEMG *** extracting the composition of feature vectors through time-domain feature values,we designed an SVM classifier to achieve the classification of five common lower extremity actions and obtained an average accuracy rate of 90.66% through the experiment,which verified the effectiveness of the experimental design.

关键词： sEMG signal SVM movement recognition lower-limb

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Vision Transformers for Road Accident Detection from Dashboard Cameras

Vision Transformers for Road Accident Detection from Dashboa...

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IEEE Conference on advanced Video and Signal Based Surveillance (AVSS)

作者： Feten Hajri Hajer Fradi LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Institut Supérieur des Sciences Appliquées et de Technologie de Sousse Sousse Tunisie

ISBN: (数字)9781665463829

ISBN: (纸本)9781665463836

Road accidents are increasing at a worrying rate and have raised one of the major concerns in traffic road monitoring. Their detection is becoming a very important aspect for intelligent traffic management systems. Unlike most of the existing anomaly detection systems that mainly monitor traffic status from static cameras, we focus in this paper on more challenging scenario using dashboard cameras. To handle this problem, we propose to adopt vision transformers with positional embeddings and based on multi-head attention mechanism for traffic monitoring following the increasing development of such models in natural language processing and computer vision communities. Precisely, to accomplish accident identification while exploiting the spatio-temporal aspect of videos, we employ a mix architecture. This architecture has the advantage of incorporating convolutional layers to capture local correlations of different patterns within the same image and vision transformer to learn the sequential correlations between the extracted features. Extensive experiments on two popular datasets DAD and CCD have been conducted to demonstrate the effectiveness of the proposed approach in terms of detection accuracy. The obtained results are compared to some recurrent neural networks commonly used to process sequential input data such as CNN-RNN, Conv-LSTM, and LCRN.

关键词： Road accidents Correlation Convolution Roads Computer architecture Feature extraction Transformers

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Machine learning application to the identification of sheet metal constitutive model parameters

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Machine Learning for Computational Science and Engineering 2025年第1期1卷 1-17页

作者： Marques, Armando E. Parreira, Tomás G. Pereira, André F. G. Ribeiro, Bernardete M. Prates, Pedro A. CEMMPRE Department of Mechanical Engineering University of Coimbra Coimbra Portugal TEMA Centre for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Aveiro Portugal ARISE Advanced Production and Intelligent Systems Associated Laboratory Porto Portugal CISUC Department of Informatics Engineering University of Coimbra Coimbra Portugal LASI Intelligent Systems Associate Laboratory Guimarães Portugal

In this work, the application of machine learning algorithms to the identification of constitutive model parameters is explored. Multiple machine learning algorithms are tested, considering two different datasets, one including uniaxial tensile test results, and the other including biaxial tensile test results. Of the algorithms tested, Gaussian Processes achieved the best predictive performances. Afterwards, the influence of dataset size is explored, considering both the number of materials included in the dataset, and the number of input parameters. For the latter, a feature importance methodology, namely SHAP (Shapley Additive Explanations) analysis, is applied to identify the most relevant input parameters in the datasets. The performance of the models trained with the inputs identified by the SHAP analysis is competitive for one of the datasets, which shows the potential of this type of analysis to create more efficient parameter identification methodologies.

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Fast CU Partition for VVC Using Texture Complexity Classification Convolutional Neural Network

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Computers, Materials & Continua 2022年第11期73卷 3545-3556页

作者： Yue Zhang Pengyu Liu Xiaowei Jia Shanji Chen Tianyu Liu Chang Liu The Information Department Beijing University of TechnologyBeijing100124China School of Physics and Electronic Information Engineering Qinghai Minzu UniversityXining810000China Advanced Information Network Beijing Laboratory Beijing100124China Computational Intelligence and Intelligent Systems Beijing Key Laboratory Beijing100124China Department of Computer Science University of PittsburghPittsburgh15260USA

Versatile video coding(H.266/VVC),which was newly released by the Joint Video Exploration Team(JVET),introduces quad-tree plus multitype tree(QTMT)partition structure on the basis of quad-tree(QT)partition structure in High Efficiency Video Coding(H.265/HEVC).More complicated coding unit(CU)partitioning processes in H.266/VVC significantly improve video compression efficiency,but greatly increase the computational complexity *** ultra-high encoding complexity has obstructed its real-time *** order to solve this problem,a CU partition algorithm using convolutional neural network(CNN)is proposed in this paper to speed up the H.266/VVC CU partition ***,64×64 CU is divided into smooth texture CU,mildly complex texture CU and complex texture CU according to the CU texture ***,CU texture complexity classification convolutional neural network(CUTCC-CNN)is proposed to classify ***,according to the classification results,the encoder is guided to skip different RDO search *** optimal CU partition results will be *** results show that the proposed method reduces the average coding time by 32.2%with only 0.55%BD-BR loss compared with VTM 10.2.

关键词： Versatile video coding(VVC) coding unit partition convolutional neural network(CNN)

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