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An Improved Causal Disentanglement Single-Source Domain Generalization Method for Bearing Fault Diagnosis 5

An Improved Causal Disentanglement Single-Source Domain Gene...

5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Wang, Hongqi Wang, Yujing Kang, Shouqiang Wang, Qingyan Harbin University of Science and Technology Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception Harbin China

ISBN: (纸本)9798331529192

In the field of domain generalization for fault diagnosis, the majority of approaches concentrate on extracting domain-invariant features from multi-source domains. However, collecting samples from multi-source domains is extremely difficult, and the data typically originate from a single-source domain. To tackle the issue of inadequate generalization capability in unknown target domains when trained on only a single source domain, an improved prototypeguided causal disentanglement domain generalization network (ICDDG) is proposed for mechanical fault diagnosis. This network combines feature mean, similarity, and triplet loss to construct an improved prototype-based triplet loss function, which reduces the influence of outlier samples and achieves more effective prototype learning. The improved triplet loss function effectively guides the causal disentanglement network to separate causal features from non-causal features better, enhancing the model's adaptability and robustness when encountering unseen domains. Diagnostic experiments performed using two bearing datasets substantiate the efficacy of the ICDDG method. © 2024 IEEE.

关键词： bearing causal disentanglement fault diagnosis single-source domain generalization triplet loss

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An Improved Causal Disentanglement Single-Source Domain Generalization Method for Bearing Fault Diagnosis

An Improved Causal Disentanglement Single-Source Domain Gene...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Hongqi Wang Yujing Wang Shouqiang Kang Qingyan Wang Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

关键词： Fault diagnosis Accuracy Data analysis Velocity control Prototypes Focusing Feature extraction Robustness Artificial intelligence Load modeling

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Be Based on MoS2@ZnO Coated UV and Temperature Dual Parameter Fiber Optic Sensor

Be Based on MoS2@ZnO Coated UV and Temperature Dual Paramete...

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Academic International Symposium on Optoelectronics and Microelectronics Technology (AISOMT)

作者： Yuquan Wang Tao Shen Yuxuan Wu Hanyuan Cui Heilongjiang Provincial Key Laboratory of Pattern Recognition and Information Perception Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Quantum Manipulation & Control Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

Based on the demand for ultraviolet detection in environmental monitoring, industrial control, aerospace and other fields, this paper proposes a method based on MoS2@ZnO UV and temperature dual parameter fiber optic sensor for composite materials. This sensor is coated with a layer on the surface of the optical fiber MoS2@ZnO. The composite film utilizes the unique physical and chemical properties of Mos2and ZnO to achieve simultaneous monitoring of ultraviolet light and temperature. This sensor has the advantages of simple structure, low cost, and easy integration, providing a new solution for dual parameter monitoring of ultraviolet and temperature.

关键词： Temperature sensors Optical fibers Optical fiber sensors Temperature Sensitivity Zinc oxide Sensors Temperature control Safety Surface treatment

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First Principles Calculations of Doping Modification on Different Crystal Faces of TiO2

First Principles Calculations of Doping Modification on Diff...

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Academic International Symposium on Optoelectronics and Microelectronics Technology (AISOMT)

作者： Yue Yuan Tao Shen Yuxuan Wu Hanyuan Cui Heilongjiang Provincial Key Laboratory of Pattern Recognition and Information Perception Harbin University of Science and Technology Harbin China Heilongjiang Provincial Key Laboratory of Quantum Manipulation & Control Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

In order to improve the wide bandgap defects of TiO 2 , the effects of Sc and V element doping on the electronic structure and electrical properties of TiO 2 were systematically studied. By constructing Sc and V-doped TiO 2 (001) and TiO 2 (100) supercell models, we conducted a thorough analysis of the effects of doping elements on the band structure, density of states, and electrical conductivity of TiO 2 . The calculation results indicate that Sc and V doping occupy the Fermi level of TiO 2 , giving the doped system metallic properties. This change makes the doped TiO 2 exhibit stronger absorption ability in the visible light region, which is expected to improve its application performance in fields such as photocatalysis and solar cells. In summary, this study provides a theoretical basis for a deeper understanding of the electronic structure and electrical properties of Sc and V-doped TiO 2 , and also provides valuable references for the development of high-performance TiO 2 based photocatalysts, solar cells, and other new materials.

关键词： Absorption Photonic band gap Photovoltaic cells Photocatalysis Doping Crystals Semiconductor process modeling Titanium dioxide Photocatalysts Magnetic properties

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Research on lead ion sensing based on micro-nano fiber coupler cascade tilted fiber grating

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Optik 2025年 322卷

作者： Feng, Yue Wu, Haodong Esmail, Mohammed Hazaea Liu, Chi Liu, Xin Wang, Chao Shen, Tao Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception Harbin University of Science and Technology Harbin150080 China Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin150080 China Key Laboratory of Engineering Dielectrics and Its Application Ministry of Education Harbin University of Science and Technology Harbin150080 China School of Engineering University of Kent Canterbury United Kingdom

As water pollution becomes increasingly severe, human health is facing threats from heavy metal lead ions in water. Therefore, detecting trace amounts of Pb2+ in water has become crucial. Fiber optic sensors have garnered significant attention in the sensor research field due to their small size, high sensitivity, and ease of integration. However, developing a fiber optic sensor capable of detecting Pb2+ at the pmol/L level poses a challenge. The study proposes a novel micro-nano fiber coupler cascaded tilted fiber grating (MFC-TFG) sensor, which is modified with glutathione complexed titanium carbide MXene (GSH@Ti2C) for the detection of Pb2+ concentrations in water environments. It exhibits a selective response to Pb2+ and achieves a limit of detection (LOD) of 0.624 pM (0.013 ng/dL). In the sensor structure, the cladding modes of the TFG can respond to changes in refractive index (RI) and temperature, while the core mode is only affected by temperature. Consequently, the MFC-TFG sensor can simultaneously detect environmental parameters of RI and temperature. By combining fiber optic designs and optimizing sensitive materials, the study addresses ultra-low Pb2+ detection, offering a robust fiber optic solution for heavy metal sensing. © 2025 Elsevier GmbH

关键词： Fiber optic sensors

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First-Principles Study of the Heavy Metals Adsorption on Sns2 And Janus Monolayers

SSRN

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SSRN 2025年

作者： Zhu, Xiaoyu Liu, Chi Shen, Tao Liu, Xin Sun, Feifei Feng, Yue Heilongjiang Provincial Key Laboratory of Pattern Recognition and Information Perception Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Quantum Manipulation &Control Harbin University of Science and Technology Harbin150080 China College of Science and Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin150080 China

Heavy metal contamination in water bodies caused serious threats to health and ecosystems, necessitating adsorbing materials for rapid decrease in heavy metals. Therefore, we have carried out a first-principles study on the rapid decrease of heavy metals (Hg, As, Pb) by SnXS (X= S, O, Se) monolayer. Among, compared with SnS2, Janus structure SnXS (X= O, Se) narrows the band gap, and increases the electronic interactions between SnXS (X= O, Se) and heavy metals (Hg, As, Pb), thus exhibiting excellent adsorption capacity. In detail, the minimum increase in adsorption capabilities for SnOS and SnSeS are 67.6 % and 27.2 %, respectively. Moreover, the optical properties indicate that at the wavelength of 1550 nm, the refractive indexes of SnSeS are reduced after adsorbing As and Pb. Similarly, at the wavelength of 1310 nm, the refractive index of SnOS is reduced after adsorbing Hg. Hence, SnXS (X= O, Se) shows a prominent refractive index change characteristic, which has the potential as a functional nanomaterial in the field of fiber optic heavy metal ion detection. © 2025, The Authors. All rights reserved.

关键词： Monolayers

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Infrared Traffic Object Detection Network for Edge Device Deployment

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Guangxue Xuebao/Acta Optica Sinica 2025年第9期45卷

作者： Han, Yulan Chen, Deao Wu, Tong Liu, Xianlu Lan, Chaofeng Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Heilongjiang Harbin150080 China

Objective The rapid advancement of intelligent transportation systems has increased the demand for precise and swift traffic object detection capabilities, particularly under challenging low-light conditions and complex backgrounds. Infrared (IR) imaging technology has emerged as a critical tool for such scenarios due to its ability to capture heat signatures, thus enabling reliable detection even in darkness or through obscurants. However, traditional IR detection methods are often hindered by high computational complexity, large parameter sizes, and dependency on high-performance computing resources, which makes them unsuitable for deployment on resource-limited mobile devices. We introduce the Edge-DETR network, specifically designed for the efficient and accurate detection of traffic objects in IR imagery on edge devices. Methods The Edge-DETR network is an innovative detection framework that builds upon the RT-DETR model with several enhancements. It incorporates a context anchor attention multistage efficient layer aggregation network (CAA-MELAN) to adaptively extract multi-scale features and understand global dependencies, thereby addressing the challenges posed by varying object sizes and environmental dynamics. Additionally, a global information supplement module (GISM) is employed for effective feature downsampling, which ensures the preservation of essential spatial information while reducing computational load. The cross-level feature fusion module (CFFM) facilitates interaction among features at different scales, enhancing the network’s capability to integrate high-level semantic information with low-level spatial details. The HiLo attention mechanism is introduced to optimize intra-scale feature interaction, with a focus on target contours while minimizing parameter and computational requirements. To address the detection of objects with complex shapes and sizes, a Shape-IoU loss function is utilized, which accounts for the shape and size of bounding b

关键词： Thermography (imaging)

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Sn-Cd Modified Ti 2Co 2 MXene for Enhanced Harmful Gas Adsorption: A DFT Study

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SSRN 2024年

作者： Yu, Wenzhuo Shen, Tao Yuan, Hang Feng, Yue Wu, Haodong Liu, Xin Liu, Chi Heilongjiang Provincial Key Laboratory of Pattern Recognition and Information Perception Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Quantum Manipulation and Control Harbin University of Science and Technology Harbin150080 China Key Laboratory of Engineering Dielectrics and its Application Ministry of Education Harbin University of Science and Technology Harbin150080 China College of Science Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin150080 China

In this study, we have investigated the adsorption characteristics of Ti2CO2 monolayer based on density functional theory for the harmful gases (NH3, H2S, CH4, NO2, SO2). The optimum adsorption structure of Ti2CO2 monolayer for adsorption of different gases were determined, and the adsorption mechanism of gases on the material was investigated. NH3 exhibits the highest absolute adsorption energy (0.429 eV) among the gases (NH3, H2S, CH4, NO2, SO2), when physisorbed on Ti2CO2 monolayer. Substitution doping modification of the Ti2CO2 monolayer with the metal element Sn and the transition metal element Cd resulted in alterations to the electronic properties of the materials. The adsorption capacity of the material for NH3, H2S, CH4, NO2, SO2 gases has been improved, which makes the Ti2CO2 monolayer have a broader application prospect in adsorption of toxic and hazardous gases. The substantial enhancement of the adsorption performance of Ti2CO2 monolayers for NO2 (adsorption energy increases to 10.35 times of the original) after doping provides a new idea for doping modification of Ti2CO2 monolayers in order to facilitate the adsorption of different gases. © 2024, The Authors. All rights reserved.

关键词： Monolayers

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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