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Smart automated highway lighting system using IoT: a survey

Energy Informatics 2024年第1期7卷 76页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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An Intelligent Analysis of Mobile Evidence Using Sentimental Analysis

An Intelligent Analysis of Mobile Evidence Using Sentimental...

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International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Jones, G. Maria Santhiya, P. Winster, S. Godfrey Sundar, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology and Panimalar Engineering College Chennai India

ISBN: (纸本)9789819950904

Smartphones are compatible and easily accessible compared to computers irrespective of place and time. Smartphones merge with our routine which acts as a medium of communication in several ways such as messaging, voice and video calling, sharing of audio and video contact, and many multimedia contents. We express our emotions through words and these communications can be analyzed by a technique called Natural Language Processing (NLP). Many researchers in the field of text mining and NLP have analyzed the polarity of online product reviews, blogs, social media comments, tweets, and many more. Mobile forensics investigation will be more effective by providing the evidences based on sentimental analysis of the text where the suspected messages contain a negative content of having threats, harassments, text against the community guidelines, and some keywords related to the cases which may be the turning point during the forensics investigation. In this paper, a novel framework called Integrated Mobile Device Forensic Investigation Process (IMDFIP) with inclusion of sentimental analysis techniques in the text which is used to emotionally analyze the texts during the communication process. This framework provides additional information to forensic analysts which will trap the suspect during investigation. The proposed framework of IMDFIP identifies the important terms, emotions, and polarity of the text during the crime. IMDFIP capitulates results compared with the previous models. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Railway track fault detection using optimised convolution neural network

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International Journal of Reliability and Safety 2024年第2期18卷 163-186页

作者： Chitra, R. Bamini, A.M. Anusha Brindha, D. Jegan, T.M. Chenthil Kirubakaran, S. Stewart Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Mechanical Engineering Sri Ranganathar Institute of Engineering and Technology Tamil Nadu Coimbatore India

Railway accidents are an under-scrutinised cause of death in India. Train accidents are caused by various consequences of collisions, derailments, signal errors and so on. Furthermore, when train derailments become disastrous, they can have tremendous repercussions. It is practically difficult to identify the cause of the derailment efficiently within a limited period. In recent years, we have been making progress in reducing derailments, but even if not deadly, identifying faulty tracks can waste a lot of time and money. And doing this error-free is a pressing matter, as tracks always experience wear and tear with more usage. Here is where neural networks can pitch in their solution. We can train a model to look at train tracks and identify any issues. This paper goes into the methodology of achieving this and optimising a neural network to predict problems in the track with the best possible accuracy that images can provide. The objective of this paper is to identify, develop and optimise neural networks to detect faulty tracks. In this work, a good Convolution Neural Network model is developed to identify the crack in the railway track. The developed model produced 95.54% accuracy in fault classification. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Derailments

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Manipify:An Automated Framework for Detecting Manipulators in Twitter Trends

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Journal of Social Computing 2023年第1期4卷 46-61页

作者： Soufia Kausar Bilal Tahir Muhammad Amir Mehmood Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan

The rapid adoption of online social media platforms has transformed the way of communication and *** these platforms,discussions in the form of trending topics provide a glimpse of events happening around the world in ***,these trends are used for political campaigns,public awareness,and brand ***,these trends are sensitive to manipulation by malicious users who aim to mislead the mass *** this article,we identify and study the characteristics of users involved in the manipulation of Twitter trends in *** propose“Manipify”-a framework for automatic detection and analysis of malicious users in Twitter *** framework consists of three distinct modules:(1)user classifier,(2)hashtag classifier,and(3)trend *** user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour ***,the module classifies human and bot ***,the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different ***,the trend analyzer module examines users,hashtags,and tweets for hashtag reach,linguistic features,and user *** user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots,*** further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million *** analysis of trends reveals that the trending panel is mostly dominated by political *** addition,our results show a higher contribution of human accounts in trend manipulation as compared to bots.

关键词： trend manipulation bot classification user analysis text classification manipulator detection

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In situ TEM investigation of electron irradiation and aging-induced high-density nanoprecipitates in an Mg-10Gd-3Y-1Zn-0.5Zr alloy

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Journal of Magnesium and Alloys 2024年第5期12卷 1841-1853页

作者： M.Lv H.L.Ge Q.Q.Jin X.H.Shao Y.T.Zhou B.Zhang X.L.Ma Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of Sciences72 Wenhua RoadShenyang 110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China Materials Science and Engineering Research Center Guangxi University of Science and TechnologyLiuzhou 545006China

In-situ electron irradiation and aging are applied to introduce high-density precipitates in an Mg-10Gd-3Y-1Zn-0.5Zr(GWZ1031K,wt.%)alloy to improve the *** results show that the hardness of the Mg alloy after irradiation for 10 h and aging for 9 h at 250℃ is 1.64 GPa,which is approximately 64% higher than that of the samples before being *** is mainly attributed to γ'precipitates on the basal plane after irradiation and the high-density nanoscale β'precipitates on the prismatic plane after aging,which should be closely related to the irradiation-induced homogenous *** latter plays a key role in precipitation *** result paves a way to improve the mechanical properties of metallic materials by tailoring the precipitation through irradiation and aging.

关键词： Mg alloy Electron beam irradiation Hardening Precipitates In-situ TEM

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Enhanced Malware Image Classification through Ensemble model 3

Enhanced Malware Image Classification through Ensemble model

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Kotha, Eshwar Palivela, Lakshmi Harika Begum, Afruza Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

Classification of malwares and viruses is a very important work in the cyber security field to protect the computers and systems from threats and attacks. In this paper, we proposed a novel approach for the classification of the malwares by combining 2 models EfficientNet B0 and EffiCBNet models. Here we have used Malimg dataset which is available in Kaggle and it contains around 9,339 malware images and these malwares are from 25 different families, for evaluating the models. EffcientNetB0 is a Convolution Neural Network (CNN) model, which showed great and high results on various image classification works. We used transfer learning to fine tune the EfficientNet model;whereas the proposed EffiCBNet model is a deep learning model which contains of convolution layers and batch normalization layers. The results have shown that the proposed model has given the highest accuracy of 99.70 on the dataset we have used, exceeding all other models. The proposed model is the combination of EfficientNet and EffiCBNet, which showed great results for the classification. The proposed model can also be used in real time applications for detecting malware attacks and threats © 2024 IEEE.

关键词： Image classification

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Lewis base multisite ligand engineering in efficient and stable perovskite solar cells

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Journal of Energy Chemistry 2024年第12期99卷 277-291页

作者： Danqing Ma Dongmei He Qing Zhu Xinxing Liu Yue Yu Xuxia Shai Zhengfu Zhang Sam Zhang Jing Feng Jianhong Yi Jiangzhao Chen Key Laboratory of Optoelectronic Technology&Systems(Ministry of Education) College of Optoelectronic EngineeringChongqing UniversityChongqing 400044China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093YunnanChina Institute of Physical and Engineering Science/Faculty of Science Kunming University of Science and TechnologyKunming 650500YunnanChina School of Aeronautics Harbin Institute of TechnologyHarbin 150001HeilongjiangChina HIT Zhengzhou Research Institute Zhengzhou 450000HenanChina

Perovskite solar cells(PSCs) have stood out from many photovoltaic technologies due to their flexibility,cost-effectiveness and high-power conversion efficiency(PCE). Nevertheless, the further development of PSCs is greatly hindered by the trap-induced non-radiative recombination losses and poor long-term work stability. In the past decade, the huge advancements have been obtained on suppressing nonradiative recombination and enhancing device durability. Among them, the multisite ligands(MSLs) engineering plays a crucial role in precise control and directional modification of functional layers and interfaces,which contributes to markedly increased PCE and lifetimes of PSCs. In view of this, this review summarizes the advances of MSLs in PSCs. From the perspective of functional groups and chemical interaction,the modulation mechanisms of properties of different functional layers and interfaces and device performance via various MSLs are deeply investigated and revealed. Finally, the prospects for the application and development direction of MSLs in PSCs are legitimately proposed.

关键词： Perovskite solar cells Multisite ligands Defect passivation Ion migration suppression Stability

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Joint View Selection, Multigroup Multicast Beamforming, and DIBR for RIS-Aided Multi-View Videos

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6376-6393页

作者： Lee, Chi-Han Yang, De-Nian Lee, Guang-Siang Wang, Chih-Hang Liao, Wanjiun National Taiwan University Department of Electrical Engineering Taiwan Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan National Chung Cheng University Department of Computer Science and Information Engineering Taiwan

The rapid development of multi-view videos (MVV) transmission is an irresistible trend. Concurrently, reconfigurable intelligent surface (RIS)-assisted wireless communication has drawn significant attention. We observe that the view selection based on the base station and the view synthesis based on depth-image-based rendering (DIBR) can effectively reduce power consumption. Therefore, this paper studies the view selection and synthesis for RIS-aided MVV in multigroup multicast beamforming. To deal with this complicated scenario, we investigate a problem, named the joint View selection, Multicast beamforming, and DIBR (JVMD), to minimize the total multicast beamforming power, the view transmission operation power, and view synthesis, subject to quality-of-service (QoS), RIS phase shifts, view selection, and DIBR constraints. Unfortunately, the mathematical model is a complicated mixed discrete-continuous optimization problem. To tackle this challenging problem, we designed an algorithm, named View selection, Beamforming, RIS phase, and DIBR (VBRD) algorithm. First, we deal with the discrete optimization problem of selecting the view. VBRD uses the dual-based approximation methodology to round back a primal's integer solution. Then, in the continuous optimization problem, we apply the alternating optimization (AO) method to determine beamforming, RIS phase, and DIBR. Finally, simulation results show the performance of exploiting view synthesis for RIS-assisted wireless communication. © 2002-2012 IEEE.

关键词： Beamforming

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Multiscale damage and failure behavior of SiCf/SiC composites with initial defects

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Ceramics International 2025年第14期51卷 19366-19381页

作者： Wang, Ben Wang, Jialin Lyu, Qihui Guo, Zaoyang Research Institute of Interdisciplinary Science & School of Materials Science and Engineering Dongguan University of Technology Guangdong Dongguan523808 China School of Science Harbin Institute of Technology Guangdong Shenzhen518055 China

A multiple damage mechanisms-based multiscale model is developed and implemented within a multiscale simulation framework, which incorporates the statistical distributions of SiC fiber strength and SiC matrix microcrack size to capture the mechanical response and defect-induced nonlinear behavior of SiCf/SiC composites. The micro- and mesoscale representative volume element (RVE) models accounting for the initial defects, are established to simulate the key deformation and damage evolution at the fiber bundle and material levels, and whose predictions are in good agreement with the experimental results for SiCf/SiC minicomposites and 2D SiCf/SiC composites. Based on the validated multiscale models, the effects of the multiscale components and defects on the failure envelopes of 2D SiCf/SiC composites under combined loadings are studied. Subsequently, a unified failure criterion with minimal non-physical parameters is formulated. This multiscale modeling strategy provides an effective way to predict the strength of SiCf/SiC composite structures related to the micro-mesoscopic characteristics. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Ceramic matrix composites

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A Novel Deep Reinforcement Learning Approach for Stability-Based Parallel Disassembly Sequence Planning Problem

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SN computer science 2025年第3期6卷 1-19页

作者： Kumar, M. R. Mahesh Ravi, Chandrasekar Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

Parallel Disassembly Sequence Planning (DSP) deals with obtaining the order of disassembling the product with multiple parts disassembling simultaneously. Existing studies derive the product’s disassembly order based on the requirement for liaison data. However, the absence of stability data does not ensure that all the product parts are disassembled without damage. Since the order of retrieval of the product can also be obtained from stability data, this reduces the requirement of an initial precedence graph based on liaison data. The complexity of the problem increases if we adopt different representations for precedence and stability conditions of the product for constraint checking throughout the disassembly process. This paper constructs a stability-based precedence graph based on only the product’s stability data. The developed graph represents the precedence of the stable parts for disassembly, and the same is utilized to formulate the problem as a model-free deep Reinforcement Learning (RL) problem. A novel Exploration–Exploitation (EE) strategy-based Deep Q-Learning (DQL) algorithm is proposed to parallel DSP minimizing direction changes. Our effective reward mechanism accelerates the learning process for all the conditions and objectives of the problem considered. Based on past performance, the proposed algorithm dynamically decides the exploration and exploitation strategy to avoid local optima. We applied the proposed algorithm on five different-scale products and compared the results with uniform-sampling-based model-free deep RL algorithms. It showed significant improvements, particularly in cumulative rewards and disassembling all the parts for large-scale products with early convergence. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep Q-learning Deep Q-Networks Disassembly sequence planning Parallel disassembly Precedence graph Stability graph

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