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Lifespan Prediction of Electronic Card in Nuclear Power Plant Based on Few Samples

Journal of Shanghai Jiaotong University (Science) 2023年 1-7页

作者： Xu, Yong Cai, Yunze Song, Lin Department of Automation Shanghai Jiao Tong University Shanghai200240 China Fujian Fuqing Nuclear Power Co. Ltd. Fujian Fuqing350318 China Key Laboratory of System Control and Information Processing of the Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

A lifespan prediction model was developed based on a few samples to provide decision-making information for operation and maintenance, as well as improve the economy and safety of nuclear power plant (NPP) operations. This paper applies a Weibull model to forecast the lifespan of electronic cards with a few samples in NPPs. Relationship between the lifespan prediction of electronic cards and the ambient temperature is revealed using the Arrhenius equation. Censored samples are used to compensate for the lack of fault electronic card data. Scale parameter and shape parameter of the Weibull model are optimized by adjusting the weight ratio between the censored data and the fault data. Characteristic life is then obtained using the rank regression fitting equation. Parameters of the Arrhenius equation can be calculated by dividing the samples into groups according to the ambient temperature. A case study of the intermediate range high-voltage electric card of ex-core neutron detectors demonstrates that the lifespan prediction of electronic cards in NPPs can be successfully predicted with a few samples by combining the Weibull model and the Arrhenius model. This can help provide preventive maintenance recommendations for electronic cards. Finally, operation suggestions for the electronic card’s ambient temperature can be made by utilizing the temperature-life model. © 2023, Shanghai Jiao Tong University.

关键词： Nuclear power plants

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Multi-Channel Based on Attention Network for Infrared Small Target Detection

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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A comprehensive survey of visible infrared person re-identification from an application perspective

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Multimedia Tools and Applications 2024年第42期83卷 90243-90270页

作者： Chang, Hua Xu, Xin Liu, Wei Lu, Lingyi Li, Weigang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430065 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

Person re-identification (ReID) is a significant issue in computer vision, aiming to match the same pedestrian across various cameras. Recent research on this issue has successfully reached a satisfactory performance level in the daytime scenario. However, the performance of ReID in low illumination scenarios suffers a dramatic degradation due to the insufficient personal information captured by visible cameras in low or no light scenarios. To address this challenge, the adoption of novel camera technologies for more effective pedestrian data is imperative. Owing to the constraints of distance and usage scenarios in deploying depth cameras, practical infrared cameras are frequently utilized, spurring research in visible infrared person re-identification (VI-ReID). Significant progress has been made in the field of VI-ReID in recent years, but only a few reviews have categorized this problem based on methodology. This article offers a novel perspective by classifying VI-ReID according to application scenarios. Following our categorization scheme, the main advancements in deep learning for VI-ReID over the past five years have been grouped into three categories as follows: retrieval of infrared images using visible images (V→IR), retrieval of visible images using infrared images (IR→V), and joint retrieval of infrared and visible images (V→IR and IR→V). Next, this survey outlines the available datasets and evaluation metrics. After that, we categorize diverse methodologies under the proposed classification and assess their performance metrics across common datasets. Finally, we present two prospective avenues for future research in this domain, informed by the current state of advancement. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Open Grounded Planning: Challenges and Benchmark Construction 62

Open Grounded Planning: Challenges and Benchmark Constructio...

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62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Guo, Shiguang Deng, Ziliang Lin, Hongyu Lu, Yaojie Han, Xianpei Sun, Le Chinese Information Processing Laboratory China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of System Software Chinese Academy of Sciences China

ISBN: (纸本)9798891760943

The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on leveraging the inherent language generation capabilities of LLMs to produce free-style plans or employs reinforcement learning approaches to learn decision-making for a limited set of actions within restricted environments. However, both approaches exhibit significant discrepancies between the open and executable requirements in real-world planning. In this paper, we propose a new planning task-open grounded planning. The primary objective of open grounded planning is to ask the model to generate an executable plan based on a variable action set, thereby ensuring the executability of the produced plan. To this end, we establish a benchmark for open grounded planning spanning a wide range of domains. Then we test current state-of-the-art LLMs along with five planning approaches, revealing that existing LLMs and methods still struggle to address the challenges posed by grounded planning in open domains. The outcomes of this paper define and establish a foundational dataset for open grounded planning, and shed light on the potential challenges and future directions of LLM-based planning. Our code and datasets are at https://***/Shiguang-Guo/Open-Grounded-Planning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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Stability Analysis of Boolean Networks: Exploring the Influence of Multi-bit Function Perturbations 63

Stability Analysis of Boolean Networks: Exploring the Influe...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Aixin Wang, Lin Chen, Guanrong Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China City University of Hong Kong Department of Electrical Engineering Hong Kong

ISBN: (纸本)9798350316339

This study investigates the impact of multi-bit function perturbations (MFPs) on the steady-state distribution of Boolean networks (BNs), with a focus on robust stability. First, the algebraic formulation of BNs under MFPs is introduced. Then, a novel stability criterion is proposed, demonstrating that monotonically decreasing perturbations can effectively ensure the stability of BNs against MFPs. A significant advantage of this approach is that it enables robust stability assessment without remodeling the perturbed BNs. Applying this methodology to a simplified Boolean model of the lactose operon in Escherichia coli reveals that MFPs can serve as strategic tools to promote stability within BNs, rather than being merely disruptive. © 2024 IEEE.

关键词： Stability criteria

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Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN

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Chinese Journal of Electronics 2023年第5期32卷 972-981页

作者： CHEN Xiaohan HU Xiaoxi WEN Tao CAO Yuan The School of Electronic and Information Engineering Beijing Jiaotong University The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University The National Engineering Research Center of Rail Transportation Operation Control System Beijing Jiaotong University

In the railway transportation industry,fault diagnosis of railway point machines(RPMs) is *** operational vibration signals can reflect the condition of various faults in mechanical devices, vibration sensing and monitoring and more importantly, vibration signal-based fault diagnosis for RPMs have attracted the attention of scholars and engineers. Most vibration signalbased fault-diagnosis methods for RPMs rely on data collected using high-sampling-rate sensors and manual feature extraction, hence are costly and insufficiently *** overcome these shortcomings, we propose a doublescale wide first-layer kernel convolutional neural network(DS-WCNN) for RPMs fault diagnosis using inexpensive and low-sampling-rate vibration sensors. The proposed wide first-layer kernels, which extract features from vibration observations, are particularly suitable for lowsampling-rate signals. Meanwhile, the proposed doublescale structure improves accuracy and noise suppression by combining two types of timescale features. Sufficient experiments, including noise addition and comparison,were conducted to demonstrate the robustness and accuracy of the proposed algorithm.

关键词： Vibrations Fault diagnosis Transportation industry Feature extraction Rail transportation Robustness Convolutional neural networks

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A Cloud-Edge-Based Multi-Label Learning and Fast Clustering Method for Emerging New PV Faults 13

A Cloud-Edge-Based Multi-Label Learning and Fast Clustering ...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Liu, Qi Yang, Bo Liu, Yuxiang Ma, Kai Guan, Xinping Shanghai Jiao Tong University Ministry of Education Key Laboratory of System Control and Information Processing Department of Automation Shanghai200240 China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9798350315196

Aiming at accurate identification of emerging new faults in PV systems, a novel PV fault diagnosis framework based on cloud-edge collaboration is designed. Model updates are deployed on the cloud servers, while real-time diagnosis is implemented at the edge devices. A multi-label learning model is developed to extract fault features efficiently, and a threshold scheme is further designed for feature enhancement. The obtained features are processed by K-Means clustering and the One-Class Support Vector Machine (OCSVM) methods, which can accurately diagnose existing types and identify unknown faults. For analysis and demonstration, the Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) methods are used for dimensionality reduction and visualization. Numerical results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： K-means clustering

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Multi-stage Image Deraining based on Pre-trained Diffusion Model 6

Multi-stage Image Deraining based on Pre-trained Diffusion M...

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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Zeng, Xiong Jiang, Min Huang, RongHua School of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798400712739

Image deraining typically involves synthesizing low-quality degraded data for training using a predefined degraded model of a single weather condition. While in real world scenarios, varying rain intensities result in different sizes and densities of raindrops and rain streaks, increasing the complexity of image degradation. In this paper, we proposed a multi-stage deraining framework based on pre-trained diffusion model, it can efficiently perform the rain removal task under a variety of weather situations. We diffuse degraded images into a noisy state where various types of degradation are transformed into Gaussian noise. Then, during the denoising process, the low-frequency information of the image is replaced through iterative refinement, guiding the pre-trained diffusion model for image reconstruction. Our method effectively utilizes the generative priors in diffusion models and avoid the computational burden of retraining conditional diffusion models. Experimental results on four rainy degradation image datasets show its robustness to different types and severities of degradation (such as raindrops and rain streaks). Compared to recent deraining algorithms, our method achieves a maximum improvement of 0.96 dB (3.5%) in PSNR and 0.021 dB (2.7%) in SSIM for the restored images. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Rain

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