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2023 IEEE International Conference on electrical, computer and Energy Technologies, ICECET 2023

作者： Adams, Adrian Abu-Mahfouz, Adnan M. Hancke, Gerhard P. University of Pretoria Department: Electrical Electronics and Computer Engineering Pretoria South Africa Pretoria South Africa City University of HongKong Department of Computer Science Kowloon Tong Hong Kong

ISBN: (纸本)9798350327816

Transport systems are fundamental to supporting economic growth, and the need for smarter, safer, more efficient and climate neutral transport systems continues to grow. Maintenance and operation of transport infrastructure is expensive and has many difficulties. In recent years, the application of machine learning to solve problems has been pursued with varying success rates. Many open problems still remain at this stage. This paper provides a review of deep learning applications in transport systems. Multiple deep learning applications are discussed e.g. railway safety, self-driving cars, pedestrian crossing and traffic light detection. Reviewed papers are evaluated in terms of challenges, contribution, weakness, research gaps. Key research questions for future study are proposed: performance optimization, data set improvement and the need for research on real-time performance on edge devices. © 2023 IEEE.

关键词： deep learning edge-devices neural network object detection railway safety self-driving transport systems

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High-performance graphics processing unit-based strategy for tuning a unmanned aerial vehicle controller subject to time-delay constraints

High-performance graphics processing unit-based strategy for...

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作者： Fagundes-Junior, Leonardo Canesche, Michael Ferreira, Ricardo Brandão, Alexandre Department of Computer Science Federal University of Viçosa Viçosa Brazil Department of Computer Science Federal University of Minas Gerais Belo Horizonte Brazil Department of Electrical Engineering Federal University of Viçosa Viçosa Brazil

Recently, high-performance computing strategies have been implemented to improve performance analysis and reduce the development time of new solutions in robotic applications, such as path planning, machine learning, and vision, which require massive matrix computations. In this sense, this work aims to study the aerial robots' behavior during their mission execution. Due to the large search space in the set of parameter combinations and the high computational cost required to perform such an analysis after sequentially executing thousands of simulations, this work proposes an open-source graphics processing unit (GPU)-based implementation to simulate the robot behavior. A GPU-accelerated flight route analysis for multi-unmanned aerial vehicle (UAV) systems is proposed for the tuning control problem in the parameters‘ space considering the problem of delay in sending information to a ground control station. Considering our implementation, the experimental results show a speedup up to 325 (Figure presented.), 629 (Figure presented.), and 5959 (Figure presented.) in comparison to the parallel version with 16 threads, C coder converter, and native Matlab code, respectively. The implementation is available in the Colab Google platform and it can easily be expanded for analyses involving larger amounts of different parameters, robot models, strategies, and controllers. © 2023 John Wiley & Sons, Ltd.

关键词： MATLAB

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Exploring the concept of multi-modality using grounding dino with evaluation of its performance in the context of different types and complexity of input text prompts

Exploring the concept of multi-modality using grounding dino...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Rahim, Abdul Singh, Amanjot Sharma, Ankur Garg, Rachit Ravisankar, Ch Yamsani, Nagendar Malla Reddy Engineering College for Women Department of ECE Hyderabad India Lovely Professional University School of Electronics and Electrical Engineering Punjab Phagwara India Institute of Technology and Science Mohan Nagar Ghaziabad India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India SR University School of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350394412

We have known artificial intelligence, deep learning models can be trained with a certain type of input format to perform a task e.g., OCR models takes input in the form of image to read the text characters from the image, a weather prediction model can take weather data like wind speed, moisture, etc. as an input which is in text format to predict future weather data. But the multi-modality concept which is comparatively newer in the market paves the way for models to be built which can take more than one type of inputs e.g., text and audio, text and image, etc. In the same concept one of the popular architectures is specially designed for detection of any hindrance, model grounding dino about which we will be discussing in this setting. It is a zero-shot object detection model which can perform inference just by taking a text prompt input from the user describing what they want the model to detect in the provided image. In this study, we will be discussing about its capabilities, architecture and also its performance. We will be testing and quantifying its performance using standard self-created input text prompts with variations including description of the object, complexity of the object, etc. on a standard image. © 2024 IEEE.

关键词： Zero-shot learning

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Reinforcement Learning for Online Dispatching Policy in Real-Time Train Timetable Rescheduling

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 478-490页

作者： Yue, Peng Jin, Yaochu Dai, Xuewu Feng, Zhenhua Cui, Dongliang Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Bielefeld Nature Inspired Computing and Engineering Group Bielefeld33619 Germany University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Surrey School of Computer Science and Electronic Engineering GuildfordGU2 7XH United Kingdom

Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the presence of unexpected disturbances. However, it is challenging to promptly create a rescheduled timetable in real time. In this study, we propose a reinforcement-learning-based method for real-time rescheduling of high-speed trains. The key innovation of the proposed method is to learn a well-generalized dispatching policy from a large amount of samples, which can be applied to the TTR task directly. At first, the problem is transformed into a multi-stage decision process, and the decision agent is designed to predict dispatching rules. To enhance the training efficiency, we generate a small yet good-quality action set to reduce invalid explorations. Besides, we propose an action sampling strategy for action selection, which implements forward planning with consideration of evaluation uncertainty, thus improving search efficiency. Extensive experimental results demonstrate the effectiveness and competitiveness of the proposed method. It has been proven that the local policies trained by the proposed method can be applied to numerous problem instances directly, rendering it unnecessary to use human-designed rules. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Innovative Deep Learning Techniques for Solving Complex computer Vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jayachitra, T. Sathya, S. Ramesh, P.S. Jothikumar, R. Gouri, M.S. Hussain, Mohammed Waheeduddin Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruttani India Vellore Institute of Technology Department of Smart Computing Vellore India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Baselios Mathews - Ii College of Engineering Department of Computer Science and Engineering Sasthamcotta Kollam India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India

ISBN: (纸本)9798350375442

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications. © 2024 IEEE.

关键词： Stochastic systems

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Cyber-Physical GNN-Based Intrusion Detection in Smart Power Grids 14

Cyber-Physical GNN-Based Intrusion Detection in Smart Power ...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Sweeten, Jacob Takiddin, Abdulrahman Ismail, Muhammad Refaat, Shady S. Atat, Rachad Tennessee Technological University Department of Computer Science CookevilleTN United States Florida State University Department of Electrical and Computer Engineering TallahasseeFL United States University of Hertfordshire Department of Engineering and Technology Hertfordshire United Kingdom Texas A&m University at Qatar Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9781665455541

The smart power grid is a critical infrastructure that has been targeted recently by several cyber-attacks. Hence, it is important that advancements are made in intrusion detection systems (IDSs). Recently, promising results have been reported using deep machine learning techniques to develop effective IDSs. However, the existing studies suffer from the following limitations: (a) The adoption of either only physical features (power system measurements) or only cyber features (network logs) in the development of IDSs;(b) The adoption of deep learning techniques that operate on 2D data, while power system measurements are graph-structure data. In this paper, we address these limitations and propose an effective IDS against false data injection and ransomware attacks. Our proposed IDS improves the attack detection performance by (a) fusing cyber-physical features collected from a practical testbed and (b) adopting a topology-aware model based on a graph neural network (GNN) to exploit the spatial and temporal correlation within the data. Our experimental results demonstrate the superior performance of our IDS compared with benchmarks that are based on topology-unaware models and use solely cyber or physical data. © 2023 IEEE.

关键词： Deep learning

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Recent Advances in Parity-Time Symmetry-Enabled Electromagnetic Sensors

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Progress In Electromagnetics Research B 2024年 105卷 107-121页

作者： Yang, Minye Ye, Zhilu Chen, Pai-Yen Erricolo, Danilo Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States State Key Laboratory for Manufacturing Systems Engineering Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education School of Electronic Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China State Key Laboratory for Manufacturing Systems Engineering The Key Laboratory of Biomedical Information Engineering of Ministry of Education Center for Mitochondrial Biology and Medicine School of Life Science and Technology International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology Xi’an Key Laboratory for Biomedical Testing and High-end Equipment Xi’an Jiaotong University Shaanxi Xi’an710049 China

Parity-time (PT) reversal symmetry, as a representative example in the field of non-Hermitian physics, has attracted widespread research interest in the past few years due to its extraordinary wave dynamics. PT symmetry enables unique spectral singularities including the exceptional point (EP) degeneracy where two or more eigenvalues and eigenvectors coalesce, as well as the coherent perfect absorber-laser (CPAL) point where laser and its time-reversal counterpart (i.e., coherent perfect absorber) can coexist at the same frequency. These singular points not only give rise to new physical phenomena but also provide new plausibility for building the next-generation sensors and detectors with unprecedented sensitivity. To date, investigations into EPs and CPAL points have unveiled their great potential in various sensing scenarios across a broad spectral range, spanning optics, photonics, electronics, and acoustics. In this review article, we will discuss ongoing developments of EP- and CPAL-based sensors composed of PT-synthetic structures and offer a glimpse into the future research directions in this emerging field. © (2024), (Electromagnetics Academy). All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Deciphering Storytelling Events: A Study of Neural and Prompt-Driven Event Detection in Short Stories 27

Deciphering Storytelling Events: A Study of Neural and Promp...

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27th International Conference on Asian Language Processing, IALP 2023

作者： Kirti, Chaitanya Chattopadhyay, Ayon Anand, Ashish Guha, Prithwijit Centre for Linguistic Science and Technology Assam Guwahati India Department of Computer Science and Engineering Assam Guwahati India Indian Institute of Technology Department of Electronics and Electrical Engineering Assam Guwahati India

ISBN: (纸本)9798350330786

Detecting events in literary narratives is relatively challenging due to the presence of both realis and non-realis events. Realis events are those that actually take place. Unlike other domains, such as biomedical and news articles, events in literary narratives are not always factual. This paper introduces "Gatha-200", a manually annotated dataset of 200 short stories for realis events. We employed several neural models for realis event detection. The best-performing neural model reached a macro Fl score of 92.2 %. Furthermore, we also evaluated the capability of the prompt-based learning approach to detect the realis events in zero-shot and few-shot settings and got promising results. © 2023 IEEE.

关键词： Zero-shot learning

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Assessing the Feasibility of Integrated Communications Software Design for Networks

Assessing the Feasibility of Integrated Communications Softw...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Doda, Davendra Kumar Kumar, Prashant Nidhya, M.S. Vivekananda Global University Department of Electrical Engineering Jaipur India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This paper evaluates the feasibility of integrated communications software design for networks. The incorporated communications software program is designed to permit users to ship and acquire data, facts, multimedia, and other types of messages thru a ramification of devices and media types, which include electronic mail, instantaneous messaging, VoIP, and mobile structures. This paper will examine the abilities of such software programs, the benefits and downsides of their use, their possible programs in businesses and different companies, and their relevance to the modern technological panorama. The studies will also examine potential conflicts and compatibility issues that can arise when employing integrated communications software. The overall intention of the evaluation is to provide a comprehensive evaluation, from a technical and enterprise-oriented angle, of the ability benefits and disadvantages of incorporating this generation into existing network designs. Eventually, the look will define potential approaches to cope with any demanding situations that might be encountered, in addition to the most straightforward strategies for implementation. Incorporated Communications software program design (ICSD) is an idea designed to allow disparate communication structures to perform together inside a community environment. This idea is supposed to facilitate ease and reliable communication among users inside the network and permit them to collaborate efficaciously. This paper explores the feasibility of implementing ICSD for networks, perceives key technical troubles related to its adoption., and discusses feasible solutions. The paper evaluates the contemporary country of the artwork associated with ICSD, current protocols, frameworks that may be used to assist ICSD, infrastructure issues that must be considered, and security measures that need to be implemented. In conclusion, enforcing ICSD for networks is feasible, given the proper infrastructure and protocol

关键词： panorama potential programs protection structures

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