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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Patel Badari Narayana Geetha Mahalakshmi Yadi Reddy M B. Suresh Kumar R B R Prakash D Suganthi Dept Of Mechanical Engg Mahatma Gandhi Institute Of Technology (Mgit) Hyderabad EEE Easwari Engineering College Chennai Department of EEE Chaitanya Bharathi Institute of Technology Hyderabad Telangana Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical And Technical Sciences Chennai

An integral part of the new smart grid, battery storage is becoming an increasingly common requirement. Batteries are considered as an attractive alternative for other distributed intelligent energy resources because of their speed in responding to events like changes in renewable power or grid interruptions. Various research studied exist to explain about making money out of this talent. Any solution must take into account both the fast-paced nature of electrical phenomena and the more gradual changes in the relevant power markets. An area of AI called reinforcement learning has demonstrated promising results in optimizing difficult tasks with high degrees of uncertainty. As a result, the need for first-of-its-kind smart buildings is growing in tandem with the complexity of decentralized power systems and the widespread use of renewable energy sources. The initial portion of the algorithm is based on deep learning using recurrent neural networks to deal with the unknowns of power pricing and load needs in the future. The other method uses reinforcement learning to determine the best time to charge or discharge a battery bank based on profit, load, and grid peak conditions. This research study presents a reinforcement learning approach for battery scheduling, which is considered as a crucial component to meet the consumer objectives. The framework relies on a customer making a multi-criteria choice in order to maximize local energy production from batteries during peak demand. This will save costs by reducing the amount of power used by the grid. The reinforcement learning system used to choose the best battery scheduling actions uses predictions of available wind power. Consumers' understanding of what to do to maximize battery life in a variety of time-dependent settings is expanded thanks to the built-in learning mechanism. The established framework enables smart consumers to acquire knowledge of the stochastic environment and apply it to the process of making de

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Smart Energy Conservation in Homes using Fully Residual Convolutional Networks for Solar Power Forecasting

Smart Energy Conservation in Homes using Fully Residual Conv...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Praveen Kumar Yadaw M. Amutha V. Sidharthan Gajanan Babu Kumbhar T. Prabakaran Dharmendra Kumar Department of Electrical Engineering Kalinga University Naya Raipur Chhattisgarh India Department of Physics Khadir Mohideen College Adirampattinam Tamil Nadu India Department of Electronics Sri Ramakrishna College of Arts & Science (Autonomous) Coimbatore Tamilnadu India Department of Chemical Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Joginpally B.R. Engineering College Hyderabad Telangana India School of Basic Sciences and Technology IIMT University Meerut Uttar Pradesh India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

DG is a power production strategy that enhances efficiency by reducing carbon demand peaks, emissions, and transmission losses through the deployment of multiple smaller on-site energy sources located within individual buildings. Although net metering aids in balancing energy supply and demand, scaling distributed generation across several houses to support intermittent renewable energy sources remains challenging. Furthermore, distributed generation is not economically attractive for widespread implementation given the existing energy price framework. To maximise Smart Energy Conservation and Solar Power Forecasting, meticulous data preparation is crucial for addressing these challenges. This paper reviews the methodologies and procedures employed in data preparation in prior studies. Our approach employs a FCN architecture, utilising ResNet101 as the backbone, with integrated upsampling skip links to enhance prediction accuracy. The proposed method achieves an accuracy of 96.56%, outperforming leading models and techniques in solar power forecasting. These findings underscore the importance of robust machine learning algorithms and meticulous data preparation for smart homes to optimise the use of renewable energy. Efficient distributed generation utilisation in residential applications facilitates smart energy conservation, hence enhancing energy sustainability and improving the accuracy of solar power forecasts.

关键词： Renewable energy sources Accuracy Reviews Smart homes Prediction algorithms Distributed power generation Convolutional neural networks Sustainable development Forecasting Meteorology

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Employee Churn Retention System Using Association Rule Mining Techniques

Employee Churn Retention System Using Association Rule Minin...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Palanikumar S M.P. Flower Queen Resmi H B A. Salinda Eveline Suniram Department of Computer Science and Business Systems Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamilnadu India Department of Electrical and Electronics Engineering SRM TRP Engineering College Trichy Department of Electronics and Communication LBS Institute of Technology Kerala India Department of Computer Science St. Alphonsa College of Arts and Science College Soosaipuram Tamilnadu India

Employees are one of the main assets of a company therefore prediction of employee churn rate is of high importance. Employee churn or attrition rate is the total number of employees leaving the company at a particular interval of time. Employee attrition can bring financial loss to the company as the company has to immediately fill the vacant position with an efficient and trained employee to prevent further lose in the business. Here we predict the employee churn based on several factors collected from the employee data and also finds the perfect employee who can fill the vacant position. For calculating employee attrition and finding the right employee to fill the vacancy we have used the FP growth calculation on the human resource data and the results are compared with that of Apriori algorithm. This prediction will be very helpful to the companies as it reduces the human resource cost.

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Graph-Tensor FISTA-Net: Edge Computing-Aided Deep Learning for Distributed Traffic Data Recovery

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IEEE Transactions on Network Science and engineering 2025年

作者： Deng, Lei Liu, Xiao-Yang Zheng, Haifeng Feng, Xinxin Zhu, Ming Tsang, Danny H. K. Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China Internet of Things Thrust Guangdong Guangzhou511400 China Columbia University Department of Electrical Engineering NY United States Chinese Academy of Sciences Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Hong Kong

In intelligent transportation systems, deep learning is a widely adopted technique for traffic data recovery. In city-wide traffic data recovery tasks, traditional centralized deep-learning-model training strategies become inapplicable because of the expensive storage costs for large-scale traffic datasets. In this scenario, edge computing emerges as a natural choice, allowing decentralized data storage and distributed training on edge nodes. However, there is still a challenge: distributed training on edge nodes suffers from high communication costs for parameter transmission. In this paper, we propose a communication-efficient Graph-Tensor Fast Iterative Shrinkage-Thresholding Algorithm-based neural Network (GT-FISTA-Net) for distributed traffic data recovery. Firstly, we model the recovery task as a graph-tensor completion problem to better capture the low-rankness of traffic data. A recovery guarantee is also provided to characterize the performance bounds of the proposed scheme in terms of recovery error. Secondly, we propose a distributed graph-tensor completion algorithm and unfold it into a deep neural network called GT-FISTA-Net. GT-FISTA-Net requires small communication costs for distributed model training on edge nodes and thus it is applicable for city-wide traffic data recovery. Extensive experiments on real-world datasets show that the proposed GT-FISTA-Net can also provide excellent recovery accuracy compared with state-of-the-art distributed recovery methods. © 2013 IEEE.

关键词： distributed learning Edge computing Graph-tensor tensor completion traffic data recovery

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Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation

arXiv

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arXiv 2022年

作者： Zheng, Chaofan Gao, Lianli Lyu, Xinyu Zeng, Pengpeng El Saddik, Abdulmotaleb Shen, Heng Tao Future Media Center and School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Multimedia Communications Research Laboratory and School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada

The current studies of Scene Graph Generation (SGG) focus on solving the long-tailed problem for generating unbiased scene graphs. However, most de-biasing methods overemphasize the tail predicates and underestimate head ones throughout training, thereby wrecking the representation ability of head predicate features. Furthermore, these impaired features from head predicates harm the learning of tail predicates. In fact, the inference of tail predicates heavily depends on the general patterns learned from head ones, e.g., "standing on" depends on "on". Thus, these de-biasing SGG methods can neither achieve excellent performance on tail predicates nor satisfying behaviors on head ones. To address this issue, we propose a Dual-branch Hybrid Learning network (DHL) to take care of both head predicates and tail ones for SGG, including a Coarse-grained Learning Branch (CLB) and a Fine-grained Learning Branch (FLB). Specifically, the CLB is responsible for learning expertise and robust features of head predicates, while the FLB is expected to predict informative tail predicates. Furthermore, DHL is equipped with a Branch Curriculum Schedule (BCS) to make the two branches work well together. Experiments show that our approach achieves a new state-of-the-art performance on VG and GQA datasets and makes a trade-off between the performance of tail predicates and head ones. Moreover, extensive experiments on two downstream tasks (i.e., Image Captioning and Sentence-to-Graph Retrieval) further verify the generalization and practicability of our method. Our code is available at https://***/aa200647963/SGG-DHL/. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning systems

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Gamification, sensory feedback, adaptive function on virtual reality rehabilitation: a brief review

Gamification, sensory feedback, adaptive function on virtual...

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2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Zuki, Fatin Shamimi Mohd Merienne, Frederic Sulaiman, Suziah Ricca, Aylen Rambli, Dayang Rohaya Awang Saad, Mohamad Naufal Mohamad Arts et Metiers Institute of Technology Lispen Hesam Université Chalon-Sur-Saône France Universiti Teknologi Petronas Computer and Information Sciences Department Seri Iskandar Malaysia Productique et Informatique Imt Atlantique LS2N-CNRS Département Automatique Nantes France Universiti Teknologi Petronas Electrical and Electronic Engineering Department Seri Iskandar Malaysia

ISBN: (纸本)9798350397000

Feedback is often linked to rewards element in gamification to motivate users. However, there are more to feedbacks than rewards elements. Feedback can provide user with useful information and at the same time feedback from user are useful for an adaptive system. There has been lack of empirical basis for feedback design to maintain motivation in virtual reality environment especially for the less cognitively abled population such as stroke patients. This paper presents a review study on gamification, sensory feedback, and adaptive function with regards to the use of virtual reality in stroke rehabilitation. A further analysis was conducted on those work that involves gamification and virtual reality design principles. One major finding is the dominant use of visual feedback in the design as compared to auditory and haptics despite their potentials to encourage motivation and engagement. The literature findings will be used to inform future empirical research on virtual reality design for stroke rehabilitation. The idea is to investigate the effect of sensory feedback and the added value on motivation and engagement. Some plans on how to conduct such a study will be illustrated. © 2022 IEEE.

关键词： Virtual reality

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Towards Seamless Connectivity: Implementing 6G Communication Technologies In Next-Generation Networks

Towards Seamless Connectivity: Implementing 6G Communication...

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Advancement in technology (ICONAT), International Conference for

作者： K. Kalaiselvi R. Sankar S Supriya G Kaushik Hemanth Swamy M Devika Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of Electrical and Electronics Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Senior Software Engineer Motorola Solutions Department of AI& DS Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can’t meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues.

关键词： 6G mobile communication 5G mobile communication Wireless networks Complex networks Market research Communications technology Internet of Things Optimization Next generation networking Edge computing

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Iot Based Condition Monitoring of electrical Equipment

Iot Based Condition Monitoring of Electrical Equipment

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： J. Raja N. Kalyani Mukul Kumar Singh Akriti Garg Atul Sarojwal S. Rukmani Devi Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India School of Computing SASTRA Deemed University Thirumalaisamudram Tamil Nadu India Department of Electrical Engineering Mahatma Jyotiba Phule Rohilkhand University Bareilly Uttar Pradesh India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

In recent years, the surveillance of the health of electrical appliances has been transformed by the Internet of Things (IoT). This investigation offers a comprehensive account of a system that employs the Internet of Things (IoT) to conduct predictive maintenance and analysis of electrical equipment. The network of interconnected sensors in the proposed system enables the real-time monitoring of a variety of operating characteristics, including temperature, current, voltage, and vibration. Upon the identification of equipment health indicators, the data collected by these sensors is transmitted to a centralised cloud platform. Sophisticated analytics and machine learning algorithms analyse the data on this platform to detect any patterns. The system's predictive warnings and real-time insights facilitate the implementation of preventive maintenance, reduce downtime, and enhance the reliability and efficacy of electrical equipment. The article explores the benefits of utilising Internet of Things (IoT) condition monitoring in industrial and utility settings. It also addresses pertinent topics, including system architecture, data processing methodologies, and other pertinent topics.

关键词： Temperature sensors Condition monitoring Vibrations Temperature Surveillance Systems architecture Voltage Sensor phenomena and characterization Real-time systems Internet of Things

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Fog Computing in IoT: Architecture, Applications, and Research Challenges

Fog Computing in IoT: Architecture, Applications, and Resear...

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Technological engineering and its Applications in Sustainable Development (ICTEASD), International Conference for

作者： Mohammed I. Habelalmateen Neha Khare Abhishek Joshi Ar Aravind B. Sapaev Arti Badhoutiya Dharmapuri Siri The Islamic University Najaf Iraq Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal Madhya Pradesh India Department of Liberal Arts School of Liberal Arts Uttaranchal University Dehradun India Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Pharmaceuticals and Chemistry Faculty of Medicine Alfraganus University Tashkent Uzbekistan Department of Electrical Engineering GLA University Mathura India GRIET Hyderabad Telangana India

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

The union of Internet of Things (IoT) and Mist Figuring envoys a change in perspective in processing foundation. This paper gives an extensive investigation of Mist Figuring with regards to IoT, zeroing in on its design, applications, and the examination challenges that underlie this extraordinary innovation. Starting with an inside and out assessment of Haze Processing's calculated structure and its distinctive highlights, we clarify its basic differentiations from conventional Distributed computing models. Through a thorough examination of true applications, going from brilliant urban communities to medical care and modern IoT, this paper grandstands how Haze Figuring expands the capacities of IoT gadgets, giving restricted handling power and decreasing idleness. The conversation stretches out to the diverse design of Haze Registering, clarifying its progressive construction and hidden correspondence conventions. Also, the paper digs into safety efforts, asset allotment methodologies, and adaptability concerns, tending to the complicated difficulties that go with Haze Figuring executions. Through illustrative contextual investigations, we epitomize the common sense and viability of Mist Figuring, clarifying its effect across assorted areas. Besides, this paper offers bits of knowledge into arising patterns like Edge artificial intelligence joining and investigates expected cooperative energies with Blockchain innovation. The paper finishes up by summing up key discoveries and imagining future headings for Haze Registering in the IoT scene, accentuating its urgent job in molding the advanced biological system of tomorrow.

关键词： Technological innovation Biological systems Medical services Aerodynamics Blockchains Safety Internet of Things

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