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Utility Conference and Exposition (IUCE), IEEE/IET International

作者： Frimpong Kyeremeh Fang Zhi Yang Yi Eric Gyamfi Isaac Kofi Nti Nanjing Tech University Nanjing China Electrical/Electronic Engineering Department Sunyani Technical University Ghana College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Computer Science University College Dublin Dublin Ireland School of Information Technology University of Cincinnati OH USA

Smart grids aim at achieving unprecedented flexibility in energy management and a resilient quality of supply. However, the inclusion of variable renewable energy (RE) generation units makes the realization of a resilient supply a challenging one. One of the solutions to the achievement of the goal is the ability to predict or forecast power generation as accurately as possible from the various RE in the grid. Despite extensive research on the subject, RE generation forecasting still remains a challenge, and research is ongoing to achieve a near-perfect and efficient prediction. Deep Neural Network (DNN) algorithms have performed efficiently in areas like speech recognition, image classification, as well as forecasting tasks, such as economic time series, but have been sparsely applied in renewable energy power forecasting. This paper proposes a hybrid long short term memory (LSTM)-Adaboost ensemble method for solar power generation forecasting. It also does a comparative study of different LSTM configurations tested on solar PV data from Germany. In particular, the work in this paper looks at how well the LSTM-AdaBoost ensemble model predicts solar power compared to machine learning methods that do not use ensembles.

关键词： Deep learning Renewable energy sources Neural networks Time series analysis Speech recognition Hybrid power systems Ensemble learning

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TDNCIB: Target-Driven Navigation with Convolutional Information Branch Based on Deep Reinforcement Learning

TDNCIB: Target-Driven Navigation with Convolutional Informat...

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2021 China Automation Congress, CAC 2021

作者： Zhao, Xinzhou Wang, Tian Liu, Kexin Zhang, Mengyi Shi, Peng Snoussi, Hichem School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Mathematics and Computer Science Fujian Normal University Fujian China Institute Charles Delaunay-LM2S University of Technology of Troyes Troyes France

ISBN: (纸本)9781665426473

Target-driven navigation has been a significant application field of reinforcement learning. However, sparse reward and lack of useful environment information are still existing problems. Therefore, in this paper, we propose a novel design to the target-driven visual navigation based on deep reinforcement learning: Target-Driven Navigation with Convolutional Information Branch (TDNCIB), which enriches the high-dimensional information learned by the network from the environment. It is an improvement of A2C algorithm. CIB can help make full use of environmental information and serve as a complement to the baseline. Besides, a novel reward design using similarity is introduced to relieve sparse reward. The ablation study is implemented in 4 different scenes of AI2-THOR framework comparing the baseline and our TDNCIB. According to our evaluation, TDNCIB converges faster than the baseline and achieves better performance in 5 common metrics with shorter trajectory length, less collision and higher success rate. © 2021 IEEE

关键词： Navigation

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Wood surface defect detection based on improved YOLOv8

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Signal, Image and Video Processing 2025年第8期19卷

作者： Ge, Yilin Ji, Haowen Liu, Xingli College of Computer and Information Engineering Heilongjiang Science and Technology University Heilongjiang Puyuan Harbin150027 China College of Computer and Control Engineering Northeast Forestry University Heilongjiang Hexing Harbin150040 China

The detection of wood surface defects is crucial for ensuring the quality of wood products in industries such as furniture manufacturing and construction. This study proposes DMD-YOLO, an advanced object detection model based on YOLOv8, specifically designed for wood surface defect detection. The model incorporates three key enhancements: Depth Attention Convolution (DAConv), Multi-Scale Attention Feature Fusion (MAFF), and a Decoupled Weighted Head (DWHead). DAConv integrates depthwise separable convolution with channel and spatial attention mechanisms to improve feature extraction efficiency and defect localization accuracy. MAFF enhances the fusion of multi-scale features by leveraging channel and spatial attention, enabling the model to capture global and local defect details. DWHead decouples classification and localization tasks, optimizing the detection of diverse and complex defects. Experimental results demonstrate that DMD-YOLO achieves state-of-the-art performance, with a mean Average Precision (mAP50) of 93.8%, a Recall of 90.4%, and an F1 score of 94.7%, significantly outperforming existing models such as YOLOv5s, Faster R-CNN and RT-DETR. The model shows excellent robustness in detecting small and complex defects while reducing false positives and missed detections, making it suitable for industrial applications and meeting the need for accuracy and robustness in quality inspection tasks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Furniture manufacture

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Predictive Monitoring Algorithm Based on Global Feature Encoding

Predictive Monitoring Algorithm Based on Global Feature Enco...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Jin, Mengsi Ye, JianHong Luo, JiLiang Lin, Yan College of Computer Science and Technology Huaqiao University Xiamen361021 China College of Information Science and Engineering Huaqiao University Fujian Engineering Research Center of Motor Control and System Optimal Schedule Xiamen361021 China

ISBN: (纸本)9781728168531

Predictive monitoring is a branch of process mining to provide some valuable information that enables proactive corrective actions to mitigate risks. This paper proposes a new predictive monitoring algorithm, which divides the data processing into three parts: prefix extraction, prefix bucketing and prefix encoding. The presented encoding methods are based on the data structure of event log, and it will cause the loss of information in the raw data. Our main contribution is to define a new global feature encoding method, which keeps more information in raw data and has better scalability. Experiments are presented to demonstrate the proposed approach. © 2020 IEEE.

关键词： Signal encoding

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An Exoskeleton BCI System for Stroke Rehabilitation Using Multi-modality Training Mode

An Exoskeleton BCI System for Stroke Rehabilitation Using Mu...

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2023 International Automatic control Conference, CACS 2023

作者： Su, Kai-Hsiang Chen, Chiao-Hsin Chen, Chia-Hsin Zhang, Yong-Liang Ko, Li-Wei National Yang Ming Chiao Tung University Former National Chiao Tung University Institute of Biological Science and Technology Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Former National Chiao Tung University Institute of Molecular Medicine and Bioengineering Hsinchu30010 Taiwan Medical University Chung-Ho Memorial Hospital Rehabilitation Department of Kaohsiung Taiwan College of Biological Science and Technology Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Former National Chiao Tung University Institute of Electrical and Control Engineering Hsinchu30010 Taiwan Brain Research Center Department of Electrical and Computer Engineering Hsinchu30010 Taiwan

ISBN: (纸本)9798350306354

With the advent of the global aging era, the impact of stroke on humans is continuously increasing. Therefore, new therapeutic approaches to enhance patient recovery are receiving growing attention. Stroke is a severe neurological disorder that causes brain damage, requiring extended periods of rehabilitation for patients to return to normal life. Currently, common clinical methods largely rely on traditional passive limb rehabilitation, primarily targeting peripheral nerve training. Nowadays, an increasing number of people are recognizing the importance of post-stroke neuroplasticity. This study aims to activate patients' central nervous system and proposes a brain-machine interface (BCI) exoskeleton system based on human cognition. By designing three cognitive brainwave training models, including an attention-based standing-up model detecting θ-band power decrease, a relaxation-based sitting-down model detecting α-band power increase, and a walking model detecting MRCP, patients can actively control lower limb exoskeleton movement using *** study provides cross-validation accuracy through both normal subjects and stroke patients, with accuracies of 87.13±9.38 and 80.83±9.68, respectively. Envisioning a rehabilitation system guided by human cognition, our team has developed an intuitive BCI exoskeleton system. © 2023 IEEE.

关键词： Brain

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Real-time gesture control UAV with a low resource framework

Real-time gesture control UAV with a low resource framework

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2021 International Symposium of Asian control Association on Intelligent Robotics and Industrial Automation, IRIA 2021

作者： Bhat, Manohar Mahto, Gopikishan Kesaria, Smit Femandes, Vikrant Arya, Kavi BMS College of Engineering Dept. of Electrical and Electronics Bangalore560019 India UPU Government Polytechnic Dept. of Computer Science and Engg. Durg491001 India Indian Institute of Technology Dept. of Systems and Control Mumbai400076 India Indian Institute of Technology Dept. of Computer Science and Engg. Mumbai400076 India

ISBN: (纸本)9781665433235

This study showcases a low-resource framework that enables people with no technical know-how to interact with drones, it also explores the capabilities of 2D- computer vision and deep learning techniques for gesture based interface systems on a low-cost micro drone with an onboard RGB camera. This Human-Robot Interaction system processes the real-time human pose to allow a user to command the drone, i.e., by providing direction to move and execute actions. A linear PD controller and image processing techniques are implemented to track humans whilst maintaining a safe distance from the user by perceiving depth information through pose estimation. We incorporated the gesture recognition results into a drone using the Robot Operating System (ROS) and evaluated system performance indoor and outdoor. This low computation framework can be applied further to control robotic arms or mobile robots. © 2021 IEEE.

关键词： Drones

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Keliang Niyogisubizo, Jovial Xiao, Linxia Pan, Yi Wei, Dongqing Rosiyadi, Didi Wei, Yanjie Chinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shanghai Jiao Tong University State Key Laboratory of Microbial Metabolism School of Life Sciences and Biotechnology Shanghai200240 China National Research and Innovation Agency Research Center for Artificial Intelligence and Cybersecurity Bandung Indonesia

ISBN: (纸本)9798350337488

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs. © 2023 IEEE.

关键词： cell segmentation cell tracking deep learning Deeplabv3+ instance segmentation iPS cell reprogramming

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WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equivalent Permittivity

WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equi...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Pengming Hu Weidong Yang Xuyu Wang Shiwen Mao Erbo Shen Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou College of Information Science and Engineering Henan University of Technology Zhengzhou China Knight Foundation School of Computing and Information Sciences Florida International University FL USA Department of Electrical and Computer Engineering Auburn University Auburn AL

Moisture content is one of the important indexes of food storage security. The existing detection methods are time-consuming and high cost such that it is difficult to realize online moisture detection. In this paper, according to the dielectric properties of wheat, we propose a wheat moisture content detection system with commercial Wi-Fi devices, termed WiWm-EP. First, we introduce the relationship between the moisture content of wheat and its dielectric constant. Then, we establish an equivalent permittivity (EP) model to characterize wheat moisture content, where the EP can be calculated from channel state information (CSI) of the dual antenna model. Besides, we build the fitting function between the EP and the moisture content as the wheat moisture detection model. Finally, we evaluate the performance of the system through different experiments. The average relative error of the detection results of five wheat samples with different moisture contents is less than 3%.

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A novel multi-modal tactile sensor design using thermochromic material

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science China(Information sciences) 2019年第11期62卷 189-191页

作者： Fuchun SUN Bin FANG Hongxiang XUE Huaping LIU Haiming HUANG Department of Computer Science and Technology Tsinghua University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University College of Information Engineering Shenzhen University

Tactile sensors are important for ensuring robotic perception [1], and their performance directly affects a robot’s capability for performing dexterous operations. These sensors mimic the tactile sensing capability of human hands. This is because the skin of human hands is involved in considerably meticulous works and can provide multi-

关键词： A novel multi-modal tactile sensor design using thermochromic material

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Non-convex potential games for finding global solutions to sensor network localization

arXiv

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arXiv 2023年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensor networks

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