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4th International Conference on Radiation Effects of Electronic Devices, ICREED 2021

作者： Lv, He Zhang, Hongwei Mei, Bo Yu, Qingkui Sun, Yi Li, Pengwei China Aerospace Components Engineering Center Beijing China National Innovation Center of Radiation Application Beijing China School of Computer Science and Technology Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781665441681

In this paper, Co-60 is used as the irradiation source, and the radiation evolution of the deep level defects and the electrical properties of 3DG2484 transistor are studied by using semiconductor parameter analyzer and deep level defect transient spectrometer. According to the radiation degradation and the transistor manufacturing technology, the radiation hardening methods such as optimizing the oxide layer process, increasing the passivation layer thickness, improving the base surface state, adjusting the base surface concentration and heavy metal doping (Platinum) are proposed. And the experimental verification is carried out. © 2021 IEEE

关键词： Heavy ions

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Autonomous Voltage Stabilization in Hybrid Energy Grids with Reinforcement Learning

Autonomous Voltage Stabilization in Hybrid Energy Grids with...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Saranya R K.R. Jansi M. Gowthami Priya N T. R. GaneshBabu S Murugan Department of Computer Science and Engineering Annapoorana Engineering College (Autonomous) Salem Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Application Vinayaka Mission's Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The growing incorporation of renewable energy sources into hybrid energy networks requires creative ways to maintain voltage stability. This work investigates using reinforcement learning (RL) algorithms to autonomously regulate voltage levels in hybrid energy systems that include solar, wind, and traditional power sources. It presents an innovative RL-based controller that dynamically modulates the output of distributed energy resources (DERs) in response to variable load demands and generation fluctuations. This research addresses voltage fluctuations in hybrid energy networks using RL. The proposed RL-based system improves voltage stability, flexibility, and efficiency, outperforming traditional techniques in both simulations and practical applications. The RL agent uses real-time data to learn optimum control techniques to reduce voltage variances successfully. The efficacy of the proposed system is assessed by simulations on a benchmark microgrid model, revealing substantial improvements in voltage regulation and system resilience relative to traditional control approaches. The results demonstrate that the RL-based method improves voltage stability and optimizes overall energy utilization efficiency within the grid. It emphasizes the possibility of incorporating AI-driven solutions in managing hybrid energy systems, facilitating the development of more sustainable and dependable energy infrastructures.

关键词： Adaptation models Wind Fluctuations Computational modeling Reinforcement learning Microgrids Power system stability Stability analysis Real-time systems Voltage control

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Low Rank Tucker Decomposition for 2D+3D Facial Expression Recognition 12

Low Rank Tucker Decomposition for 2D+3D Facial Expression Re...

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12th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2021 / 11th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2021

作者： Fu, Yunfang Deng, Yujuan Zhang, Yuekui Yang, Zhengyan Qi, Ruili School of Computer Science and Engineering Shijiazhuang University Shijiazhuang050035 China Hebei Prov. Internet of Things Intelligent Perception and Application Technology Innovation Center Shijiazhuang050035 China

In this paper, a novel low rank Tucker decomposition for 3D facial expression recognition (LRTDFER) approach is proposed, in which the core tensor is utilized to characterize the low-rank attribute among the samples combined with the factor matrices with the graph regularization embedding. At first, a model based on a 4D tensor is constructed from the facial expression data. By Tucker decomposing the constructed 4D tensor, a resulting core tensor and factor matrices in different tensor modes are utilized to characterize the low-rankness among samples. Because of the loss of information during modelling the 4D tensor, the missing data from partly observed facial expression data are recovered by embedding the tensor completion. Finally, the proposed model is handled and solved by adopting the alternating direction method of multipliers (ADMM). Meanwhile, the classification prediction of facial expressions are implemented by Multi-class-SVM. Numerical experiments are conducted on BU-3DFE database. The experiment results have been verified that our proposed approach is more competitive. © 2021 Elsevier B.V.. All rights reserved.

关键词： Tensors

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UWB Positioning Analysis and Algorithm Research 12

UWB Positioning Analysis and Algorithm Research

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作者： Zhang, Yuekui Chu, Yunxia Fu, Yunfang Li, Zhixiang Song, Yufei School of Computer Science and Engineering Shijiazhuang University Shijiazhuang050035 China Hebei Prov. Internet of Things Intelligent Perception and Application Technology Innovation Center Shijiazhuang050035 China

In order to improve the accuracy of indoor positioning, this paper studied the three-dimensional positioning method based on TDOA and designed the ranging experiment with or without occlusion. The error data were analyzed by many experiments, and the advantages of strong penetration and good anti-interference ability of UWB signal were verified. At the same time, the UWB positioning direct matrix algorithm is optimized, and the least square matrix method is used to process the positioning data to obtain more accurate position coordinates. The experimental results show that the accuracy of the positioning data can be improved by about 25% by using the least square matrix method. © 2021 Elsevier B.V.. All rights reserved.

关键词： Time difference of arrival

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A weakly supervised pavement crack segmentation based on adversarial learning and transformers

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Multimedia Systems 2025年第3期31卷 1-21页

作者： Apedo, Yvon Tao, Huanjie School of Computer Science Northwestern Polytechnical University Xi’an People’s Republic of China Engineering and Research Center of Embedded Systems Integration (Northwestern Polytechnical University) Ministry of Education Xi’an People’s Republic of China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an People’s Republic of China

While pixel-level crack segmentation has demonstrated significant potential in infrastructure inspections, its reliance on detailed annotations poses challenges for widespread adoption due to the time-consuming and expensive nature of such labeling. In response, weakly supervised crack segmentation has garnered attention as it eliminates the need for pixel-level annotations. However, existing methods, primarily based on class activation maps (CAM), involve complex training processes and suffer from poor performance due to misalignment between CAM-generated labels and the true target in the image. To address these challenges, we propose a weakly supervised approach for crack segmentation that simultaneously generates synthetic crack images and performs segmentation through adversarial learning. Unlike traditional methods that rely on coarse labels, our approach leverages synthetic crack images with corresponding labels, effectively eliminating issues of misalignment and noisy pseudo-labels. Our method introduces an encoder-decoder architecture for the segmentation model, incorporating a Transformer-based Feature Enhancement module (TFE) in the encoder. This module is specifically designed to capture long-range dependencies and efficiently integrate both high- and low-level features. Additionally, the model includes a Hilo block to extract both high- and low-frequency information from the images, along with a Progressive Shrinking Decoder (PSD) to aggregate and refine adjacent feature maps. Extensive experiments were conducted, and our model achieved an ODS of 67.12% on the CrackForest dataset, 60.11% on the Crack500 dataset, and 44.80% on the AEL dataset. These results demonstrate that our model outperforms several existing weakly supervised pavement segmentation methods across these datasets.

关键词： Supervised learning

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Deploying Reinforcement Learning Approaches for Smart Home Automation

Deploying Reinforcement Learning Approaches for Smart Home A...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Amit Prakash Sen Manish Kumar Goyal Shalini Department of Electrical & Electronics Engineering ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Management School of Mangement - PG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Reinforcement mastering is an area of synthetic intelligence that enables machines to adopt actions that optimize their environment or maximize a pre-defined reward. This paper investigates the potential makes use of reinforcement studying for clever domestic automation. Some of distinctive algorithms had been advanced that leverage reinforcement getting to know strategies for clever houses. The paper discusses the advantages of using reinforcement learning for optimizing electricity consumption, increasing comfort, and presenting protection as well as the potential challenges of such deployment. The paper also evaluations several present reinforcement studying-based totally methods applicable for smart domestic automation and highlights ability studies possibilities to address present methodological gaps in an effort to enable the reliable deployment of reinforcement getting to know for clever domestic automation.

关键词： Electric potential Automation Green products Smart homes Reinforcement learning Turning User experience Security Reliability Protection

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Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising

arXiv

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arXiv 2024年

作者： Li, Tong Wang, Lizhi Xu, Zhiyuan Zhu, Lin Lu, Wanxuan Huang, Hua School of Computer Science and Technology Beijing Institute of Technology China School of Artificial Intelligence Beijing Normal University China Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education China Aerospace Information Research Institute Chinese Academy of Sciences China

Image denoising enhances image quality, serving as a foundational technique across various computational photography applications. The obstacle to clean image acquisition in real scenarios necessitates the development of self-supervised image denoising methods only depending on noisy images, especially a single noisy image. Existing self-supervised image denoising paradigms (Noise2Noise and Noise2Void) rely heavily on information-lossy operations, such as downsampling and masking, culminating in low-quality denoising performance. In this paper, we propose a novel self-supervised single image denoising paradigm, Positive2Negative, to break the information-lossy barrier. Our paradigm involves two key steps: Renoised Data Construction (RDC) and Denoised Consistency Supervision (DCS). RDC renoises the predicted denoised image by the predicted noise to construct multiple noisy images, preserving all the information of the original image. DCS ensures consistency across the multiple denoised images, supervising the network to learn robust denoising. Our Positive2Negative paradigm achieves state-of-the-art performance in self-supervised single image denoising with significant speed improvements. The code is released to the public at https://***/Li-Tong-621/P2N. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

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Monthly streamflow forecasting with temporal-periodic transformer

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Journal of Hydrology 2025年 660卷

作者： Yin, Hanlin Zheng, Qirui Wei, Chenxu Liang, Congcong Fan, Minhao Zhang, Xiuwei Zhang, Yanning Shenzhen Research Institute of Northwestern Polytechnical University Sanhang Science & Technology Building No. 45th Gaoxin South 9th Road Nanshan District Shenzhen City518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an710072 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China Hydrology Bureau of the Yellow River Conservancy Commission Zhengzhou450004 China

Monthly streamflow forecasting is important for water resources planning and management in hydrology. In recent years, deep learning based data-driven approaches have received significant attention, especially the Long Short-Term Memory (LSTM) and the Transformer. Among the above two sorts of models for such a task, hardly any model considers the periodic information from the same month of different years directly. This periodic information is important for monthly streamflow forecasting and we propose a periodic attention mechanism to explore it in this paper. Specifically, we propose a novel Temporal-Periodic Transformer (TPT) model, which has temporal-periodic attention modules exploring the temporal information and the periodic information. As a comparison, the original Transformer-based streamflow forecasting model does not consider such periodic information explicitly. To show the performance of our TPT model, two datasets including the Catchment Attributes and Meteorology for Large-sample Studies in Australia (CAMELS-AUS) and a dataset from the Tangnaihai Hydrological Station located in Qinghai Province of China are employed in this paper. Our TPT model outperforms the benchmark Transformer model significantly, e.g., for Nash–Sutcliffe efficiency, the TPT model improves over the original Transformer-based model in 45.9% and furthermore its NSE achieves 0.9108 in Tangnaihai by pretraining in 20 selected basins in CAMELS-AUS. For monthly streamflow forecasting, the TPT model is a good choice. © 2025 Elsevier B.V.

关键词： Water management

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Investigation of Using Large-Scale Swarm Optimizers to Optimize Sub-Problems in Cooperative Co-Evolution

Investigation of Using Large-Scale Swarm Optimizers to Optim...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lu, Ming-Yuan Yang, Qiang Liu, Dong Ma, Yuan-Yuan Li, Tao Zhang, Jun College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Jinhua China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9798350337020

Cooperative co-evolutionary algorithms (CCEAs) have witnessed giant success in solving large-scale optimization problems (LSOPs). However, most existing CCEAs use low-dimensional EAs to optimize the decomposed sub-problems. Such utilization of low-dimensional EAs may limit the effectiveness of CCEAs because some of the decomposed sub-problems may still be high-dimensional. Since there exist many non-decomposition based large-scale EAs, it is interesting to investigate the optimization effectiveness of CCEAs by using these non-decomposition based large-scale EAs to solve the decomposed sub-problems. To this end, this paper incorporates two state-of-the-art large-scale swarm optimizers into CCEAs with five state-of-the-art decomposition strategies to solve LSOPs. Experiments conducted on the CEC'2010 and CEC'2013 LSOP benchmark sets have shown that the two large-scale swarm optimizers help CCEAs with the five decomposition strategies achieve much better performance than the most widely used low-dimensional EA. © 2023 IEEE.

关键词： Cooperative Co-evolution Evolutionary Algorithms High-Dimensional Optimization Large-Scale Optimization Problems Large-Scale Swarm Optimizers

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SFDE-net: A Spatial-Frequency Domain Feature Enhancement Network for Cloud Detection

SFDE-net: A Spatial-Frequency Domain Feature Enhancement Net...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Baotong Su Siyan Li Wenguang Zheng Yao Chen The School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences and Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Cloud detection is a crucial step in the preprocessing of satellite remote sensing images. Existing methods tend to have misjudgments when dealing with specific scenarios, such as challenges in distinguishing thin clouds from the background and addressing missing cloud boundaries. To solve this problem, we designed a novel Spatial–Frequency Domain Feature Enhancement Block (SFDE) embedded in a U-shaped network called SFDE-net. SFDE consists of three units: the Dual Frequency Feature Unit (DFF), the Spatial Domain Feature Unit (SDF), and the Cross-Domain Feature Fusion Unit (CDF). DFF globally learns the boundaries and overall structure of clouds in the frequency domain, SDF captures fine-grained information in the spatial domain, and CDF adaptively fuses features from both DFF and SDF. Our method’s effectiveness was evaluated on two public datasets, GF-1 WFV and LandSat8. Extensive experiments demonstrated that the proposed SFDE-net achieved accurate detection accuracy and outperformed several state-of-the-art methods.

关键词： Earth Accuracy Satellites Artificial satellites Fuses Frequency-domain analysis Merging

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