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Laser scribed graphene for supercapacitors

Opto-Electronic Advances 2021年第7期4卷 1-21页

作者： Zhengfen Wan Xi Chen Min Gu Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Supercapacitors,with the merits of both capacitors for safe and fast charge and batteries for high energy storage have drawn tremendous ***,laser scribed graphene has been increasingly studied for supercapacitor applications due to its unique properties,such as flexible fabrication,large surface area and high electrical *** the laser direct writing process,graphene can be directly fabricated and patterned as the supercapacitor *** this review,facile laser direct writing methods for graphene were firstly *** precursors,mainly graphene oxide and polyimide were employed for laser scribed graphene and the modifications of graphene properties were also *** laser scribed graphene was applied for electrochemical double-layer capacitors,pseudo-capacitors and hybrid *** strategies including doping,composite materials and pattern design were utilized to enhance the electrochemical performances of *** supercapacitors with excellent flexible,ultrafinestructured and integrated functions were also reviewed.

关键词： laser graphene laser scribed graphene supercapacitor

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Optimizing Shielding Techniques for High-Frequency EMC Applications in Aerospace Systems

Optimizing Shielding Techniques for High-Frequency EMC Appli...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Parikshit N. Mahalle S. Hemelatha Mukul Mishra G Kalanandhini Bhavuk Samrat T Ramachandran Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Mechanical Engineering JAIN(Deemed to be University) School of Engineering and Technology Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The defense of high-frequency electromagnetic compatibility (EMC) for aerospace systems is important to ensure the functionality and reliability of aerospace cars. Moreover, the performance of the system has to follow each plane’s requirements and electromagnetic regulations, such as Federal Aviation Administration (FAA) regulations, to hold protection and trustworthiness. While protecting excessive-frequency EMC application is only sometimes taken into consideration, the electrical gadgets, wiring, and numerous components may be electrically laid low with electrical fields, resulting in malfunction of the machine, mistakes in calculations, and faulty indicators. This paper evaluates the optimization of protective techniques for high-frequency EMC packages in aerospace systems through the usage of a mixture of simulation and size strategies. The objectives of the look are to expand an efficient shielding structure and optimize the design to reduce the reflection of electromagnetic wave radiation in the system. A two-layered shielding method with an internal shield of aluminum foil is used to reduce the reflection of electromagnetic wave radiation. Furthermore, the field studies and the simulations executed permit the optimization of the measurements to be greater than it should be predicted. The obtained results imply that the optimized defensive approach makes a good sized enhancement in the defensive overall performance of the device, thereby disposing of errors due to outside EMC resources.

关键词： Performance evaluation Wiring Electromagnetic scattering FAA Electromagnetic compatibility Regulation Reflection Protection Optimization Aerospace engineering

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Discrimination-Aware Domain Adversarial Neural Network

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Journal of computer science & Technology 2020年第2期35卷 259-267页

作者： Yun-Yun Wang Jian-Min Gu Chao Wang Song-Can Chen Hui Xue College of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210046China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 210023China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing 210023China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention *** DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are *** a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not *** this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain ***2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple *** results show that DA2NN can achieve better classification performance compared with the DANN methods.

关键词： adversarial learning inter-class separation deep neural network discrimination-aware domain adaptation

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RSAFormer: A method of polyp segmentation with region self-attention transformer

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computers in Biology and Medicine 2024年 172卷 108268-108268页

作者： Yin, Xuehui Zeng, Jun Hou, Tianxiao Tang, Chao Gan, Chenquan Jain, Deepak Kumar García, Salvador School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune412115 India Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain

Colonoscopy has attached great importance to early screening and clinical diagnosis of colon cancer. It remains a challenging task to achieve fine segmentation of polyps. However, existing State-of-the-art models still have limited segmentation ability due to the lack of clear and highly similar boundaries between normal tissue and polyps. To deal with this problem, we propose a region self-attention enhancement network (RSAFormer) with a transformer encoder to capture more robust features. Different from other excellent methods, RSAFormer uniquely employs a dual decoder structure to generate various feature maps. Contrasting with traditional methods that typically employ a single decoder, it offers more flexibility and detail in feature extraction. RSAFormer also introduces a region self-attention enhancement module (RSA) to acquire more accurate feature information and foster a stronger interplay between low-level and high-level features. This module enhances uncertain areas to extract more precise boundary information, these areas being signified by regional context. Extensive experiments were conducted on five prevalent polyp datasets to demonstrate RSAFormer's proficiency. It achieves 92.2% and 83.5% mean Dice on Kvasir and ETIS, respectively, which outperformed most of the state-of-the-art models. © 2024 Elsevier Ltd

关键词： Decoding

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Unboxing the black-box of deep learning based reconstruction of undersampled MRIs 4

Unboxing the black-box of deep learning based reconstruction...

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4th Italian Workshop on Explainable artificial intelligence, XAI.it 2023

作者： Chatterjee, Soumick Das, Arnab Khatun, Rupali Nürnberger, Andreas Genomics Research Centre Human Technopole Milan Italy Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany German Research Centre for Artificial Intelligence Kaiserslautern Germany Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany Comprehensive Cancer Centre Erlangen-EMN Erlangen Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Deep learning has emerged as a very important area of research and has shown immense potential in solving different kinds of problem, including in the medical field. For tasks like undersampled MRI reconstruction - the process of speeding up MRI acquisition with the help of undersampling, deep learning has shown its dominance over the years. But one of the major problems with deep learning is trust: Complex reasonings done by these models appear black-box to the users. Therefore, to build trust and better acceptability, it is important to open up this black-box nature of these models. For classification models, several approaches have been proposed. Nevertheless, for models dealing with inverse problems, like the reconstruction of the undersampled MRIs, it is more challenging as the output of the model has the same number of output pixels as the input, making the interpretability of such models more complex. This research explores different methods to understand the working mechanism of a deep learning model for the task of undersampled MRI reconstruction. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Inverse problems

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Can LVLMs Describe Videos like Humans? A Five-in-One Video Annotations Benchmark for Better Human-Machine Comparison

arXiv

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arXiv 2024年

作者： Hu, Shiyu Li, Xuchen Li, Xuzhao Zhang, Jing Wang, Yipei Zhao, Xin Cheong, Kang Hao School of Physical and Mathematical Sciences Nanyang Technological University Singapore Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Automation Beijing Institute of Technology China Institute of Automation Southeast University Bangladesh School of Computer and Communication Engineering University of Science and Technology Beijing China College of Computing and Data Science Nanyang Technological University Singapore

Large vision-language models (LVLMs) have made significant strides in addressing complex video tasks, sparking researchers’ interest in their human-like multimodal understanding capabilities. Video description serves as a fundamental task for evaluating video comprehension, necessitating a deep understanding of spatial and temporal dynamics, which presents challenges for both humans and machines. Thus, investigating whether LVLMs can describe videos as comprehensively as humans—through reasonable human-machine comparisons using video captioning as a proxy task—will enhance our understanding and application of these models. However, current benchmarks for video comprehension have notable limitations, including short video durations, brief annotations, and reliance on a single annotator’s perspective. These factors hinder a comprehensive assessment of LVLMs’ ability to understand complex, lengthy videos and prevent the establishment of a robust human baseline that accurately reflects human video comprehension capabilities. To address these issues, we propose a novel benchmark, FIOVA (Five In One Video Annotations), designed to evaluate the differences between LVLMs and human understanding more comprehensively. FIOVA includes 3,002 long video sequences (averaging 33.6 seconds) that cover diverse scenarios with complex spatiotemporal relationships. Each video is annotated by five distinct annotators, capturing a wide range of perspectives and resulting in captions that are 4 ∼ 15 times longer than existing benchmarks, thereby establishing a robust baseline that represents human understanding comprehensively for the first time in video description tasks. Using the FIOVA benchmark, we conducted an in-depth evaluation of six state-of-the-art LVLMs (VideoLLaMA2, LLaVA-NEXT-Video, Video-LLaVA, VideoChat2, Tarsier, and ShareGPT4Video), comparing their performance with humans. Results show that while current LVLMs demonstrate some perception and reasoning capabilities, they st

关键词： Visual languages

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Research on Security Methods of Wireless Sensor Networks Based on Internet of Things Technology 1

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5th International Conference on Smart Computing and Communication, SmartCom 2020

作者： Yang, Chengjun Tan, Minfu Huang, Weihong Embedded Technology and Artificial Intelligence Hechi College of Electronic Information Engineering Hechi University Hechi546300 China Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou528138 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9783030747176

ISBN: (纸本)9783030747169

Wireless sensor network operation is an important part of the perception layer of the Internet of Things. However, due to its own physical structure and some characteristics of the use environment, wireless sensor network nodes can easily become the main targets of physical attacks and network attacks. These attacks affect the correctness, reliability and real-time performance of the information transmitted by wireless sensor networks. In this article, we first introduce the characteristics of wireless sensor network technology. We further conducts a security analysis on the network intrusion vulnerability in the wireless sensor network environment. Subsequently, we analyze the main solutions to these attacks. Finally, summarize several issues of wireless sensor network security, and look forward to the key research directions in the future. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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Competitive Facility Location under Random Utilities and Routing Constraints

arXiv

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arXiv 2024年

作者： Pham, Hoang Giang Dam, Tien Thanh Duong, Ngan Ha Mai, Tien Hà, Minh Hoàng ORLab Faculty of Computer Science Phenikaa University Ha Dong Hanoi Viet Nam ORLab-SLSCM Faculty of Data Science and Artificial Intelligence National Economics University Hai Ba Trung Hanoi Viet Nam School of Computing and Information Systems Singapore Management University 80 Stamford Rd 178902 Singapore Department of Engineering Computer Science and Mathematics University of L'Aquila AQ L'Aquila67100 Italy

In this paper, we study a facility location problem within a competitive market context, where customer demand is predicted by a random utility choice model. Unlike prior research, which primarily focuses on simple constraints such as a cardinality constraint on the number of selected locations, we introduce routing constraints that necessitate the selection of locations in a manner that guarantees the existence of a tour visiting all chosen locations while adhering to a specified tour length upper bound. Such routing constraints find crucial applications in various real-world scenarios. The problem at hand features a non-linear objective function, resulting from the utilization of random utilities, together with complex routing constraints, making it computationally challenging. To tackle this problem, we explore three types of valid cuts, namely, outer-approximation and submodular cuts to handle the nonlinear objective function, as well as sub-tour elimination cuts to address the complex routing constraints. These lead to the development of two exact solution methods: a nested cutting plane and nested branch-and-cut algorithms, where these valid cuts are iteratively added to a master problem through two nested loops. We also prove that our nested cutting plane method always converges to optimality after a finite number of iterations. Furthermore, we develop a local search-based metaheuristic tailored for solving large-scale instances and show its pros and cons compared to exact methods. Extensive experiments are conducted on problem instances of varying sizes, demonstrating that our approach excels in terms of solution quality and computation time when compared to other baseline approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Location

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FedEEG: Federated EEG Decoding Via inter-Subject Structure Matching

FedEEG: Federated EEG Decoding Via inter-Subject Structure M...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wenlong Hang Jiaxing Li Shuang Liang Yuan Wu Baiying Lei Jing Qin Yu Zhang Kup-Sze Choi College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nanjing Tech University School of Geographic and Biologic Information Nanjing University of Posts and Telecommunications School of Biomedical Engineering Shenzhen University Centre for Smart Health Hong Kong Polytechnic University Department of Bioengineering Lehigh University

With sufficient centralized training data coming from multiple subjects, deep learning methods have achieved powerful EEG decoding performance. However, sending each individuals’ EEG data directly to a centralized server might cause privacy leakage. To overcome this issue, we present an inter-subject structure matching-based federated EEG decoding (FedEEG) framework. First, we introduce a center loss to each client (subject), which can learn multiple virtual class centers by averaging the corresponding class-specific EEG features. To mitigate the client drift issue, we then explicitly connect the learning across multiple clients by aligning their corresponding virtual class centers, thus helping to correct the local training for individual subject. The proposed FedEEG can promote the discriminative feature learning while preventing the privacy leakage issue. The experimental results on benchmark EEG datasets show that FedEEG outperforms state-of-the-art federated learning methods.

关键词： Training Representation learning Federated learning Training data Benchmark testing Signal processing Electroencephalography

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A Motion Flow Guided MicroNet Framework for Micro Expression Recognition

TechRxiv

引用

TechRxiv 2022年

作者： Verma, Monu Vipparthi, Santosh Kumar Singh, Girdhari The Electrical and Computer Engineering University of Miami FL33155 United States The Mehta Family School of Data Science and Artificial Intelligence IIT Guwahati 781039 India The Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur302017 India

The micro-expression recognition (MER) has gained high attention in real-world applications: human-computer interaction, depression estimation, virtual reality, etc. However, MER systems still face difficulty in capturing the subtle information with spatial appearance of micro expressions. In this paper, an efficient and robust motion flow guided MicroNet framework is proposed for MER. The proposed MicroNet consists: motion flow generator (MFGen) and avalanche feature (AFeat) block. The MFGen is introduced to extract the temporal changes at expressive regions by analyzing the motion intensity of pixels among the frames. Further, the AFeat block is designed to capture the spatiotemporal feature using a multi-lateral complementary feature (MCFeat) block. The MCFeat block is proposed to elicit the coarse and deep edge responses from multi-scale receptive fields. Thus, the proposed MicroNet estimates not only momentary variations but also learns affective appearance features of micro expressions. Furthermore, the efficacy of the proposed MicroNet is examined by conducting experiments on two experimental setups using six comprehensive datasets: CASME-I, CASME-II, CAS(ME)2, SAMM, SMIC and COMPOSITE, with two validation schemes: leave-one-subject-out and cross-domain to demonstrate the generalization capacity and robustness of the proposed framework. Also, eight ablation experiments have been executed to validate each module’s role in the MicroNet framework. © 2022, CC BY.

关键词： Human computer interaction

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