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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

2023 China Automation Congress, CAC 2023

作者： Li, Jiazhuo Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru The School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland The Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey The Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798350303759

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results. © 2023 IEEE.

关键词： Convolution

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

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arXiv 2024年

作者： Zhou, Yuqi Zhu, Hao Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Electrical & Computer Engineering The University of Texas at Austin AustinTX78712 United States

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric power plant loads

Bi-level Co-Optimization Architecture for Transmission and Distribution Voltage Control

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Bi-level Co-Optimization Architecture for Transmission and D...

IEEE General Meeting Power& Energy Society

作者： Mohammad Abujubbeh Kumarsinh Jhala Karthikeyan Balasubramaniam Department of Electrical Engineering and Computer Engineering graduate intern at Argonne National Laboratory Kansas State University Center for Energy Environmental and Economic Systems Analysis (CEEESA) Argonne National Laboratory

With the increased penetration levels of behind-the-meter distributed energy resources in the distribution system and large utility-scale resources in the transmission system, it is critical to model and control these resources in an efficient and scalable manner to maintain the grid voltage stability. Traditional methods of voltage control mechanisms address the issue of voltage control in either transmission or distribution systems in isolation, which is not optimal. Therefore, there is a need for a co-optimization approach for improved grid efficiency. This work presents a bi-level architecture for transmission and distribution system voltage co-optimization. The proposed architecture uses sequential updates between the transmission and distribution system for improved accuracy. The proposed approach has been verified on the IEEE 9-bus transmission system coupled with the IEEE 13 and IEEE 123 node test distribution systems. In addition to capturing the impact of systems on each other, results show that the proposed approach helps to eliminate voltage violations at both the transmission and distribution systems.

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NiO Junction Termination Extension for Ga2O3Devices: High Blocking Field, Low Capacitance, and Fast Switching Speed 35

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NiO Junction Termination Extension for Ga2O3Devices: High Bl...

35th International Symposium on Power Semiconductor Devices and ICs, ISPSD 2023

作者： Xiao, Ming Wang, Boyan Zhang, Ruizhe Song, Qihao Spencer, Joseph Du, Zhonghao Qin, Yuan Sasaki, Kohei Wang, Han Tadjer, Marko Zhang, Yuhao Center for Power Electronics Systems Virginia Tech Blacksburg United States U.S. Naval Research Laboratory WashingtonDC United States University of Southern California Department of Electrical and Computer Engineering Los Angeles United States Novel Crystal Technology Inc. Saitama Japan

ISBN: (纸本)9798350396829

This work investigates the blocking electric field, capacitance, and switching speed of the p-type NiO based junction termination extension (JTE) for vertical Ga2O3 devices. The JTE comprises multiple NiO layers sputtered on the surface of Ga2O3 drift region, the acceptor concentration and length of which are carefully optimized. This NiO JTE enabled a breakdown voltage over 3 kV in vertical Ga2O3 diodes with a parallel-plate junction field of 4.2 MV/cm. Large-area Ga2O3 p-n diodes with a current over 1 A were fabricated to evaluate the JTE's capacitance and switching characteristics. The JTE accounts for only, 11 % of the junction capacitance of this 1 A diode, and the percentage is expected to be even smaller for higher-current diodes. The turn-ON/OFF speed and reverse recovery time of the diode are comparable to commercial SiC Schottky barrier diodes. These results show the good promise of NiO JTE as an effective edge termination for Ga2O3 power devices. © 2023 IEEE.

关键词： Nickel oxide

Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

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Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

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Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

2023 China Automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

BihoT: A Large-Scale Dataset and Benchmark for Hyperspectral Camouflaged Object Tracking

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arXiv 2024年

作者： Wang, Hanzheng Li, Wei Xia, Xiang-Gen Du, Qian The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

Hyperspectral object tracking (HOT) has exhibited potential in various applications, particularly in scenes where objects are camouflaged. Existing trackers can effectively retrieve objects via band regrouping because of the bias in existing HOT datasets, where most objects tend to have distinguishing visual appearances rather than spectral characteristics. This bias allows the tracker to directly use the visual features obtained from the false-color images generated by hyperspectral images without the need to extract spectral features. To tackle this bias, we find that the tracker should focus on the spectral information when object appearance is unreliable. Thus, we provide a new task called hyperspectral camouflaged object tracking (HCOT) and meticulously construct a large-scale HCOT dataset, termed BihoT, which consists of 41,912 hyperspectral images covering 49 video sequences. The dataset covers various artificial camouflage scenes where objects have similar appearances, diverse spectrums, and frequent occlusion, making it a very challenging dataset for HCOT. Besides, a simple but effective baseline model, named spectral prompt-based distractor-aware network (SPDAN), is proposed, comprising a spectral embedding network (SEN), a spectral prompt-based backbone network (SPBN), and a distractor-aware module (DAM). Specifically, the SEN extracts spectral-spatial features via 3-D and 2-D convolutions to form a refined prompt representation. Then, the SPBN fine-tunes powerful RGB trackers with spectral prompts and alleviates the insufficiency of training samples. Moreover, the DAM utilizes a novel statistic to capture the distractor caused by occlusion from objects and background and corrects the deterioration of the tracking performance via a novel motion predictor. Extensive experiments demonstrate that our proposed SPDAN achieves state-of-the-art performance on the proposed BihoT and other HOT datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Network embeddings

Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin Poor, H. Vincent The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices. Our code is available at https://***/HongyangDu/DistributedDiffusion. © 2023, CC BY.

关键词： Wireless networks

SSF-Net: Spatial-Spectral Fusion Network with Spectral Angle Awareness for Hyperspectral Object Tracking

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arXiv 2024年

作者： Wang, Hanzheng Li, Wei Xia, Xiang-Gen Du, Qian Tian, Jing School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

Hyperspectral video (HSV) offers valuable spatial, spectral, and temporal information simultaneously, making it highly suitable for handling challenges such as background clutter and visual similarity in object tracking. However, existing methods primarily focus on band regrouping and rely on RGB trackers for feature extraction, resulting in limited exploration of spectral information and difficulties in achieving complementary representations of object features. In this paper, a spatialspectral fusion network with spectral angle awareness (SST-Net) is proposed for hyperspectral (HS) object tracking. Firstly, to address the issue of insufficient spectral feature extraction in existing networks, a spatial-spectral feature backbone (S2FB) is designed. With the spatial and spectral extraction branch, a joint representation of texture and spectrum is obtained. Secondly, a spectral attention fusion module (SAFM) is presented to capture the intra- and inter-modality correlation to obtain the fused features from the HS and RGB modalities. It can incorporate the visual information into the HS spectral context to form a robust representation. Thirdly, to ensure a more accurate response of the tracker to the object position, a spectral angle awareness module (SAAM) investigates the region-level spectral similarity between the template and search images during the prediction stage. Furthermore, we develop a novel spectral angle awareness loss (SAAL) to offer guidance for the SAAM based on similar regions. Finally, to obtain the robust tracking results, a weighted prediction method is considered to combine the HS and RGB predicted motions of objects to leverage the strengths of each modality. Extensive experiments on the HOTC dataset demonstrate the effectiveness of the proposed SSF-Net, compared with state-of-the-art trackers. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction