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Group maintenance strategy for stochastic selective maintenance

International Journal of Wireless and Mobile Computing 2018年第4期14卷 362-368页

作者： Zhao, Jing Zeng, Jianchao Division of Industrial and System Engineering Taiyuan University of Science of Technology Taiyuan030024 China College of Information Engineering Guangdong Mechanical and Electrical Polytechnic Guangzhou510515 China School of Computer Science and Control Engineering North University of China Taiyuan030051 China

Selective maintenance is often applied to many industrial environments when the maintenance actions are performed between sequence missions. When the length of maintenance or work mission time is stochastic and there are multiple maintenance workers with different capacity, previous works to determine maintenance and mission times are unsuitable for it. Assuming that the time of work mission is random and maintenance time is determined, to maximise system reliability of next work mission, this paper proposes a stochastic model for a two-state system comprising several series-parallel components with multiple maintenance workers under the maintenance time limit. The optimal maintenance plan is obtained by hybrid intelligent optimisation algorithm. The validity and feasibility of the model are verified by the simulation. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Stochastic models

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Phyllotaxis-inspired nanosieves with multiplexed orbital angular momentum

arXiv

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arXiv 2021年

作者： Jin, Zhongwei Janoschka, David Deng, Junhong Ge, Lin Dreher, Pascal Frank, Bettina Hu, Guangwei Ni, Jincheng Yang, Yuanjie Li, Jing Yu, Changyuan Lei, Dangyuan Li, Guixin Xiao, Shumin Mei, Shengtao Giessen, Harald zu Heringdorf, Frank Meyer Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore College of Optical and Electronic Technology China Jiliang University Hangzhou310018 China University of Duisburg–Essen Lotharstrasse 1-21 Duisburg47057 Germany Department of Materials Science and Engineering Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Beijing Qianjunyide Technology Co. Ltd. Beijing100031 China University of Stuttgart StuttgartD-70569 Germany School of Physics University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Optoelectronic Materials Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing100190 China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Hong Kong Department of Materials Science and Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong Hong Kong State Key Laboratory on Tunable Laser Technology Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Shenzhen Graduate School Harbin Institute of Technology Shenzhen518055 China

Nanophotonic platforms such as metasurfaces, achieving arbitrary phase profiles within ultrathin thickness, emerge as miniaturized, ultracompact and kaleidoscopic optical vortex generators. However, it is often required to segment or interleave independent sub-array metasurfaces to multiplex optical vortices in a single nano-device, which in turn affects the device’s compactness and channel capacity. Here, inspired by phyllotaxis patterns in pine cones and sunflowers, we theoretically prove and experimentally report that multiple optical vortices can be produced in a single compact phyllotaxis nanosieve, both in free space and on a chip, where one meta-atom may contribute to many vortices simultaneously. The time-resolved dynamics of on-chip interference wavefronts between multiple plasmonic vortices was revealed by ultrafast time-resolved photoemission electron microscopy. Our nature-inspired optical vortex generator would facilitate various vortex-related optical applications, including structured wavefront shaping, free-space and plasmonic vortices, and high-capacity information metaphotonics. Copyright © 2021, The Authors. All rights reserved.

关键词： Plasmonics

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RETRACTED ARTICLE: Neural networks-based adaptive control of uncertain nonlinear systems with unknown input constraints

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Journal of Ambient Intelligence and Humanized Computing 2021年第1期15卷 167-167页

作者： Guo, Jian-lan Chen, Yu-qiang Lai, Guan-yu Liu, Hong-ling Tian, Yuan Al-Nabhan, Najla Wang, Jingjing Wang, Zhenhai Department of Computer Engineering Dongguan Polytechnic Dongguan China College of Automation Guangdong University of Technology Guangzhou China Department of Information Engineering Guangzhou Nanyang Polytechnic Guangzhou China School of Computer Engineering Nanjing Institute of Technology Nanjing China Department of Computer Science King Saud University Riyadh Kingdom of Saudi Arabia Department of Finance and Economics Shandong University of Science and Technology Qingdao China Department of Inform LinYi University LinYi China

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Preface

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Journal of Physics: Conference Series 2021年第1期2082卷 011001-011001页

作者： Han, Guoqiang Ming, Zhong Wu, Jiaoyu Tang, Yong Wang, Xuan Li, Zhenkun Liu, Caixing Jiang, Lingmin Jiang, Shengyi Yu, Aimin Han, Guoqiang Huang, Jing Xu, Yong Liu, Shuangyin Luo, Wenquan South China University of Technology China Shenzhen University China Guangdong Polytechnic China South China Normal University China Harbin Institute of Technology China Guangdong University of Technology China South China Agricultural University China Guangzhou College of Commerce China Guangdong University of Foreign Studies China Guangdong Polytechnic of Science and Technology China Zhuahai Computer Federation China Zhongkai University of Agriculture and Engineering China

In order to accelerate the development of advanced manufacturing, promote in-depth integration of the Internet, big data, artificial intelligence and the real economy, and foster new growth areas and new drivers in such areas as high-end and medium consumption, innovation-driven development, green and low-carbon development, sharing economy, modern supply chain and human capital services. computer Academy of guangdong, Association of Computing Education in Chinese Universities, and Zhuhai Ton-Bridge Medical technology together with AEIC Academic Exchange Information Center, hosted the 2021 guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2021) in Zhuhai, China during September 24 to 26, *** conference focused on artificial intelligence, big data and medical and big health AI science, aimed to provide a platform for experts and scholars, engineers and researchers to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote cooperation in the industrialization of academic *** 300 participants from academic, high-education institutes and other organizations took part in the conference. The conference model was divided into two sessions, including oral presentations and keynote speeches. In the first part, some scholars, whose submissions were selected as the excellent papers, were given 15 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. We were very honored to have Prof. Guoqiang Han, from South China University of technology as our Conference *** the keynote presentation part, we invited 16 professors as our keynote speakers. The first keynote speaker, Prof. Junlong Chen, Distinguished Chair Professor, Doctoral Supervisor, Dean of School of computer science and Eng

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Roadmap for Sustainable Mixed Ionic-Electronic Conducting Membranes

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Advanced Functional Materials 2022年第6期32卷 2105702-2105702页

作者： Chen, Guoxing Feldhoff, Armin Weidenkaff, Anke Li, Claudia Liu, Shaomin Zhu, Xuefeng Sunarso, Jaka Huang, Kevin Wu, Xiao-Yu Ghoniem, Ahmed F. Yang, Weishen Xue, Jian Wang, Haihui Shao, Zongping Duffy, Jack H. Brinkman, Kyle S. Tan, Xiaoyao Zhang, Yan Jiang, Heqing Costa, Rémi Friedrich, Kaspar Andreas Kriegel, Ralf Fraunhofer Research Institution for Materials Recycling and Resource Strategies IWKS Brentanostraße 2a Alzenau63755 Germany Institute of Physical Chemistry and Electrochemistry Leibniz University Hannover Callinstr. 3A Hannover30167 Germany Department of Materials and Earth Sciences Technical University Darmstadt Alarich-Weiss-Str. 2 Darmstadt64287 Germany Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia State Key Laboratory of Organic-Inorganic Composites College of Chemical Engineering Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences 457 Zhongshan Road Dalian116023 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical Engineering University of South Carolina ColumbiaSC29208 United States Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada Department of Mechanical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States School of Chemistry and Chemical Engineering Guangdong Provincial Key Lab of Green Chemical Product Technology South China University of Technology Guangzhou510640 China Department of Chemical Engineering Beijing Key Laboratory of Membrane Materials and Engineering Tsinghua University Beijing100084 China Western Australia School of Mines: Minerals Energy and Chemical Engineering Curtin University PerthWA6102 Australia College of Chemical Engineering Nanjing Tech University Nanjing210009 China Department of Materials Science and Engineering Clemson University ClemsonSC29634 United States State Key Laboratory of Separation Membranes and Membrane Processes Department of Chemical Engineering Tianjin Polytechnic University Tianjin300

Mixed ionic-electronic conducting (MIEC) membranes have gained growing interest recently for various promising environmental and energy applications, such as H2 and O2 production, CO2 reduction, O2 and H2 separation, CO2 separation, membrane reactors for production of chemicals, cathode development for solid oxide fuel cells, solar-driven evaporation and energy-saving regeneration as well as electrolyzer cells for power-to-X technologies. The purpose of this roadmap, written by international specialists in their fields, is to present a snapshot of the state-of-the-art, and provide opinions on the future challenges and opportunities in this complex multidisciplinary research field. As the fundamentals of using MIEC membranes for various applications become increasingly challenging tasks, particularly in view of the growing interdisciplinary nature of this field, a better understanding of the underlying physical and chemical processes is also crucial to enable the career advancement of the next generation of researchers. As an integrated and combined article, it is hoped that this roadmap, covering all these aspects, will be informative to support further progress in academics as well as in the industry-oriented research toward commercialization of MIEC membranes for different applications. © 2021 The Authors. Advanced Functional Materials published by Wiley-VCH GmbH

关键词： Carbon dioxide

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Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation

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Knowledge-Based Systems 2025年 324卷

作者： Qiangguo Jin Hui Cui Junbo Wang Changming Sun Yimiao He Ping Xuan Linlin Wang Cong Cong Leyi Wei Ran Su School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China Department of Computer Science and Information Technology La Trobe University Melbourne Australia CSIRO Data61 Sydney Australia Department of Computer Science School of Engineering Shantou University Guangdong China Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Shandong China Australian Institute of Health Innovation (AIHI) Macquarie University Australia School of Software Shandong University Shandong China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University Macao Special Administrative Region of China School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

Semi-supervised learning (SSL) has attracted considerable attention in medical image processing. The latest SSL methods use a combination of consistency regularization and pseudo-labeling to achieve remarkable success. However, most existing SSL studies focus on segmenting large organs, neglecting the challenging scenarios where there are numerous tumors or tumors of small volume. Furthermore, the extensive capabilities of data augmentation strategies, particularly in the context of both labeled and unlabeled data, have yet to be thoroughly investigated. To tackle these challenges, we introduce a straightforward yet effective approach, termed iterative pseudo-labeling based adaptive copy-paste supervision (IPA-CP), for tumor segmentation in CT scans. IPA-CP incorporates a two-way uncertainty based adaptive augmentation mechanism, aiming to inject tumor uncertainties present in the mean teacher architecture into adaptive augmentation. Additionally, IPA-CP employs an iterative pseudo-label transition strategy to generate more robust and informative pseudo labels for the unlabeled samples. Extensive experiments on both in-house and public datasets show that our framework outperforms state-of-the-art SSL methods in medical image segmentation. Ablation study results demonstrate the effectiveness of our technical contributions.

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二维材料最新研究进展

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物理化学学报 2021年第12期37卷 1-151页

作者：常诚陈伟陈也陈永华陈雨丁峰樊春海范红金范战西龚成宫勇吉何其远洪勋胡晟胡伟达黄维黄元季威李德慧李连忠李强林立凌崇益刘鸣华刘楠刘庄 Kian Ping Loh 马建民缪峰彭海琳邵明飞宋礼苏邵孙硕谭超良唐智勇王定胜王欢王金兰王欣王欣然 Andrew T.S.Wee 魏钟鸣吴宇恩吴忠帅熊杰熊启华徐伟高尹鹏曾海波曾志远翟天佑张晗张辉张其春张铁锐张翔赵立东赵美廷赵伟杰赵运宣周凯歌周兴周喻朱宏伟张华刘忠范 Institute of Science and Technology Austria Am Campus 13400 KlosterneuburgAustria Department of Chemistry National University of Singapore3 Science Drive 3117543Singapore Department of Chemistry The Chinese University of Hong KongShatinNew TerritoriesHong KongChina Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech UniversityNanjing 211816China School of Life Sciences Shanghai UniversityShanghai 200444China Centre for Multidimensional Carbon Materials Institute for Basic ScienceUlsan 44919Korea School of Chemistry and Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Department of Chemistry City University of Hong KongKowloonHong KongChina Department of Electrical and Computer Engineering and Quantum Technology Center University of MarylandCollege ParkMaryland 20742USA School of Materials Science and Engineering Beihang UniversityBeijing 100191China Department of Materials Science and Engineering City University of Hong KongKowloonHong KongChina Center of Advanced Nanocatalysis(CAN) Hefei National Laboratory for Physical Sciences at the MicroscaleDepartment of Applied ChemistryUniversity of Science and Technology of ChinaHefei 230026China College of Chemistry and Chemical Engineering State Key Laboratory of Physical Chemistry of Solid SurfacesCollaborative Innovation Center of Chemistry for Energy Materials(iChEM)Xiamen UniversityXiamen361005Fujian ProvinceChina State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of PhysicsRenmin University of ChinaBeijing 100872China School of Optical and Electronic Information Wuhan National Laboratory

Research on two-dimensional(2D) materials has been explosively increasing in last seventeen years in varying subjects including condensed matter physics, electronic engineering, materials science, and chemistry since the mechanical exfoliation of graphene in 2004. Starting from graphene, 2D materials now have become a big family with numerous members and diverse categories. The unique structural features and physicochemical properties of 2D materials make them one class of the most appealing candidates for a wide range of potential applications. In particular, we have seen some major breakthroughs made in the field of 2D materials in last five years not only in developing novel synthetic methods and exploring new structures/properties but also in identifying innovative applications and pushing forward commercialisation. In this review, we provide a critical summary on the recent progress made in the field of 2D materials with a particular focus on last five years. After a brief backgroundintroduction, we first discuss the major synthetic methods for 2D materials, including the mechanical exfoliation, liquid exfoliation, vapor phase deposition, and wet-chemical synthesis as well as phase engineering of 2D materials belonging to the field of phase engineering of nanomaterials(PEN). We then introduce the superconducting/optical/magnetic properties and chirality of 2D materials along with newly emerging magic angle 2D superlattices. Following that, the promising applications of 2D materials in electronics, optoelectronics, catalysis, energy storage, solar cells, biomedicine, sensors, environments, etc. are described sequentially. Thereafter, we present the theoretic calculations and simulations of 2D materials. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future outlooks in this rapidly developing field.

关键词： Two-dimensional materials Transition metal dichalcogenides Phase engineering of nanomaterials Electronics Optoelectronics Catalysis Energy storage and conversion

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Adaptive Locality Preserving Regression

arXiv

引用

arXiv 2019年

作者： Wen, Jie Zhong, Zuofeng Zhang, Zheng Fei, Lunke Lai, Zhihui Chen, Runze School of Computer Science and Technology Guangdong University of Technology GuangzhouGuangdong510006 China Institute of Textiles and Clothing Hong Kong Polytechnic University Hong Kong School of Information Technology & Electrical Engineering University of Queensland BrisbaneQLD4072 Australia College of Computer Science and Software Engineering Shenzhen University ShenzhenGuangdong518055 China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional ‘Frobenius norm’ with the special l2,1 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owning to the row-sparsity property of the l2,1 norm. Extensive experiments conducted on the synthetic database with manifold structure and many real-world databases prove the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Regression analysis

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P-1.16: Frontend Amplifier with Unipolar Oxide TFTs for Heart Rate Measurement

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SID Symposium Digest of technical Papers 2022年第S1期53卷

作者： Yuming Xu Bin Li Sunbin Deng Wei Zhong Xuemei Yin Zhaohui Wu Hoi-Sing Kowk Rongsheng Chen School of Microelectronics South China University of Technology Guangzhou 510640 China State Key Laboratory on Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Purdue University West Lafayette IN U.S.A. School of integrated circuits Guangdong University of Technology Guangzhou China School of artificial intelligence Zhuhai City Polytechnic College Zhuhai China

This paper presents a frontend amplifier based on the n-type only indium-tin-oxide (ITO)-stabilized ZnO thin film transistors (TFTs) on glass substrate. The amplifier has an external-bias AC-coupled, open-loop capacitor bootstrap structure. The area and power consumption are 41 mm 2 and 0.1 mW (8V), respectively. Measurement results show a gain of 20 dB, a bandwidth of 3 Hz–6 kHz, a common mode rejection ratio (CMRR) up to 42 dB, an input impedance of 100 MΩ, and an input-referred noise of 106 μVrms (integrated from 1 Hz–200 Hz). The noise efficiency factor (NEF) and power efficiency factor (PEF) of the amplifier are respectively 248 and 4.9×10 5 . The amplifier is successfully used to measure the heart-rate signal.

关键词： Frontend amplifier metal-oxide thin film transistors heart-rate measurement

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Surface Reconstruction from Normals: A Robust DGP-based Discontinuity Preservation Approach

Surface Reconstruction from Normals: A Robust DGP-based Disc...

引用

IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Wuyuan Xie Miaohui Wang Mingqiang Wei Jianmin Jiang Jing Qin College of Computer and Software Engineering Shenzhen University (SZU) Guangdong Key Laboratory of Intelligent Information Processing College of Information Engineering School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Nursing Hong Kong Polytechnic University

ISBN: (纸本)9781728132945

In 3D surface reconstruction from normals, discontinuity preservation is an important but challenging task. However, existing studies fail to address the discontinuous normal maps by enforcing the surface integrability in the continuous domain. This paper introduces a robust approach to preserve the surface discontinuity in the discrete geometry way. Firstly, we design two representative normal incompatibility features and propose an efficient discontinuity detection scheme to determine the splitting pattern for a discrete mesh. Secondly, we model the discontinuity preservation problem as a light-weight energy optimization framework by jointly considering the discontinuity detection and the overall reconstruction error. Lastly, we further shrink the feasible solution space to reduce the complexity based on the prior knowledge. Experiments show that the proposed method achieves the best performance on an extensive 3D dataset compared with the state-of-the-arts in terms of mean angular error and computational complexity.

关键词： Surface reconstruction discontinuities angular misalignment Solution space CONSERVATION optimum performance Arts complexity classes

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