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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Chande, Ketan Kanekar, Rahul Nair, Kiran Amandykova, Dina Addanke, Supriya Zhaina, Tolegen Hospitality NarseeMonjee Institute of Management Studies Navi Mumbai-NMIMS University Maharashtra India Abu Dhabi School of Management Department of Management United Arab Emirates International Education Corporation Department of Architecture Kazakhstan 050042 India Sri Padmavati MahilaVisvavidyalayam School of Engineering & Technology Department of Computer Science & Engineering Tirupati517502 India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

At present, green communication technology is receiving a significant research attention. The increasing research interest on green communication can also undermine the environment. Measuring the green communication intensity of various products, companies and processes is being carried out globally by following the rule that only the related effects are manageable, which is expressed as a carbon footprint. Green detections are having a direct, large-scale impact on carbon productions. The green initiatives can effectively reduce carbon productions by improving the energy efficiency. In summary, green discovery directly affect carbon production. This research work has attempted to reduce the carbon footprint energy by using Enhanced Recurrent Neural Network (ERNN). From an investment perspective, carbon footprint analysis can assist in evaluating a company's overall and comparative performance. It can be used as a tool to manage and evaluate the performance of a company. Effective production management demonstrates the quality of operations and can provide a significant competitive advantage. © 2022 IEEE.

关键词： Carbon footprint

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction

Impurity explicit optimal Machine learning and statistical p...

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Networking and Communications (ICNWC), International Conference on

作者： Nareshkumar R R. Shalinikumari S. Sri Nandhini Kowsalya Vijay Anand P Ajmal Mohamed S. Gayathri Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Architecture Bharath Institute of Higher Education and Research Selaiyur Chennai Tamilnadu India Department of Information Technology Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy.

关键词： Measurement Machine learning algorithms Urban areas Sociology Machine learning Predictive models Air pollution

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Data-Driven Building Occupancy Profile Identification and Application for Ventilation Operating Schedule Optimization

SSRN

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SSRN 2023年

作者： Vassiljeva, Kristina Matson, Margarita Ferrantelli, Andrea Petlenkov, Eduard Thalfeldt, Martin Belikov, Juri Tallinn University of Technology Tallinn19086 Estonia Department of Computer Systems Tallinn University of Technology Tallinn12618 Estonia Department of Software Science Tallinn University of Technology Tallinn12618 Estonia Department of Civil Engineering and Architecture Tallinn University of Technology Tallinn19086 Estonia Department of Civil Engineering Aalto University Aalto00076 Finland

Facing the current sustainability challenges requires reduction of the building stock energy consumption towards the European Green Deal targets by 2050. This can be accomplished by adopting techniques such as fault detection and diagnosis, and efficiency optimization. An occupancy-based algorithm for scheduling ventilation operations in buildings is here developed, aimed at optimizing the system’s operation according to occupancy profiles while maintaining a comfortable indoor climate. By relying only on electricity meters without using carbon dioxide or occupancy sensors, we use historical data of an Estonian school to develop a DBSCAN-based clustering algorithm that generates consumption profiles. A novel occupancy estimation algorithm, based on threshold and time series methods, then creates 12 occupancy schedules that are either based on classical detection with on-off method or on occupancy estimation for demand-controlled ventilation (DCV). We find that, compared to standard on-off scheduling, DCV-based occupancy estimation induces systematic savings in terms of both energy and costs from 18.1% up to 62.6%, still complying with current national regulations of indoor air quality. Remarkably, as our method relies only on occupancy schedules, thus eliminating reliance on weather and other external factors, it can immediately be extended to other countries. © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning

arXiv

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arXiv 2024年

作者： Gamazo-Real, Jose-Carlos Fernández, Raúl Torres Armas, Adrián Murillo Universidad Politécnica de Madrid Information Systems Department Computer Architecture and Technology Area ETSI Sistemas Informáticos Madrid28031 Spain Universidad Politécnica de Madrid Madrid28040 Spain

The large increase in the number of Internet of Things (IoT) devices have revolutionised the way data is processed, which added to the current trend from cloud to edge computing has resulted in the need for efficient and reliable data processing near the data sources using energy-efficient devices. Two methods based on low-cost edge-IoT architectures are proposed to implement lightweight Machine Learning (ML) models that estimate indoor environmental quality (IEQ) parameters, such as Artificial Neural Networks of Multilayer Perceptron type. Their implementation is based on centralised and distributed parallel IoT architectures, connected via wireless, which share commercial off-the-self modules for data acquisition and sensing, such as sensors for temperature, humidity, illuminance, CO2, and other gases. The centralised method uses a Graphics Processing Unit and the Message Queuing Telemetry Transport protocol, but the distributed method utilises low-performance ARM-based devices and the Message Passing Interface protocol. Although multiple IEQ parameters are measured, the training and testing of ML models is accomplished with experiments focused on small temperature and illuminance datasets to reduce data processing load, obtained from sudden spikes, square profiles and sawteeth test cases. The results show a high estimation performance with F-score and Accuracy values close to 0.95, and an almost theorical Speedup with a reduction in power consumption close to 37% in the distributed parallel approach. In addition, similar or slightly better performance is achieved compared to equivalent IoT architectures from related research, but error reduction of 35–76% is accomplished with an adequate balance between performance and energy efficiency. © 2024, CC BY.

关键词： Energy efficiency

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Communication-Efficient Stochastic Distributed Learning

arXiv

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arXiv 2025年

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multipliers (ADMM) to address the challenges of high communication costs, and large datasets. Our design tackles these challenges i) by enabling the agents to perform multiple local training steps between each round of communications;and ii) by allowing the agents to employ stochastic gradients while carrying out local computations. We show that the proposed algorithm converges to a neighborhood of a stationary point, for nonconvex problems, and of an optimal point, for convex problems. We also propose a variant of the algorithm to incorporate variance reduction thus achieving exact convergence. We show that the resulting algorithm indeed converges to a stationary (or optimal) point, and moreover that local training accelerates convergence. We thoroughly compare the proposed algorithms with the state of the art, both theoretically and through numerical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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A Direction Vector-Guided Multi-Objective Evolutionary Algorithm for Variable Linkages Problems

SSRN

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SSRN 2023年

作者： Gu, Qinghua Zhang, Shaopeng Wang, Qian Xiong, Neal N. School of Resources Engineering Xi’an University of Architecture and Technology Shaanxi Xi’an710055 China Xi’an Key Laboratory for Intelligent Industrial Perception Calculation and Decision Xi'an University of Architecture and Technology Shaanxi Xi’an710055 China Department of Mathematics and Computer Science Northeastern State University TahlequahOK United States School of Management Xi’an University of Architecture and Technology Shaanxi Xi’an710055 China

Recombination is a key component for the evolutionary algorithm to provide promising offspring solutions. However, conventional recombination operators cannot generate high-quality solutions for variable linkages problems due to the particularity of Pareto sets(PS). To tackle this problem, a two-stage multi-objective evolutionary algorithm based on direction vector guidance(DSMOEA) is proposed in this paper. Firstly, a portion of the population is transformed by a covariance matrix to increase the probability of generating high-quality offspring. Then the representative solutions are selected in the transformed population to create the direction vectors. Under the guidance of the direction vectors, the population rapidly approaches PS and generates promising offspring solutions. Finally, DE is performed for searching globally to increase the diversity of the population. The proposed algorithm is tested on RM, IM, and UF test problems with 30, 50, and 100 dimensions to verify its performance. The experimental results show that the algorithm is promising for variable linkages problems. © 2023, The Authors. All rights reserved.

关键词： Covariance matrix

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An improved end-to-end micro-expression recognition system for real-world videos via dual-input CNN architecture

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Expert Systems with Applications 2025年 286卷

作者： Gan, Y.S. Liu, Kun-Hong Wu, Min-Huan Liong, Gen-Bing Liong, Sze-Teng School of Architecture Feng Chia University Taichung40724 Taiwan Department of Digital Media Technology School of Film Xiamen University Xiamen China Bachelor of Science in Senior Wellness and Sport Science Tunghai University Taiwan Faculty of Computer Science & Information Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan

Micro-expression (ME) recognition reveals nonverbal emotions through subtle, involuntary facial muscle movements. However, the development and commercialization of ME recognition systems have been hindered by the lack of databases that accurately reflect real-world conditions. This study addresses this challenge by proposing a robust end-to-end system designed to operate effectively in unconstrained environments. Existing methods typically rely on a single apex frame, which may be unreliable due to noise, occlusions, or lighting variations. To address these issues, a 3D facial reconstruction technique is applied as a pre-processing step to normalize pose and lighting. A novel dual-peak frame detection strategy is then introduced to extract two expressive optical flow frames, reducing the impact of noise from any single frame. Finally, a Shallow and Small-size Dual-input (SSD) CNN architecture is designed to jointly process the two frames for improved emotion classification. The proposed system achieves strong performance on the challenging in-the-wild MEVIEW dataset, with accuracy and F1-score of 75 % and 77.68 %, respectively. Comprehensive evaluations further validate the effectiveness of the pipeline, highlighting its potential for real-world ME recognition applications. © 2025 Elsevier Ltd

关键词： Optical character recognition

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Porous g-C_(3)N_(4)-encapsulated TiO_(2) hollow sphere as a high-performance Z-scheme hybrid for solar-induced photocatalytic abatement of environmentally toxic pharmaceuticals

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Journal of Materials Science & technology 2021年第23期82卷 21-32页

作者： Dong-Eun Lee Satyanarayana Moru Wan-Kuen Jo Surendar Tonda School of Architecture CivilEnvironmental and Energy EngineeringKyungpook National University80 Daehak-roBuk-guDaegu41566Republic of Korea Department of Electrical and Computer Engineering Iowa State UniversityCoover HallOsborn DriveAmesIowa 50011USA Department of Physical Sciences Kakatiya Institute of Technology and ScienceWarangal506015TelanganaIndia

Herein,we rationally constructed a hybrid heterostructure comprising porous g-C_(3)N_(4)(CN)-encapsulated anatase TiO_(2) hollow spheres(TOHS)via a synthesis method that involves hydrothermal and calcination *** fabricated hybrid,termed CN/TOHS,demonstrated extraordinary activity toward the degradation of environmentally toxic pharmaceutical substances(acetaminophen and ciprofloxacin)in aqueous solutions under simulated sunlight irradiation;the activity of CN/TOHS was superior to that attained for individual TOHS and CN *** particular,the CN/TOHS hybrid containing 13.3 wt.%of CN on TOHS displayed the optimum degradation activity among the tested catalysts used in this study,and it also possessed exceptional recyclability and stability during consecutive degradation *** remarkable photocatalytic activity and stability of the hybrid were predominantly ascribed to the large solid interfacial contact between constituents,TOHS and CN,induced by effective hybrid structure,which boosted the interfacial charge transfer and impeded with the direct recombination of photo-induced ***,the results of the liquid chromatography-mass spectrometry analysis corroborated the effective mineralization of model pharmaceutical pollutants in the presence of the CN/TOHS *** simple interfacial engineering strategy presented in this study offers a potential route for the rational design of novel catalysts for application in environmental remediation and solar energy conversion.

关键词： TiO_(2)hollow sphere g-C3N4 Hybrid heterostructure Z-scheme charge transfer Pollutant degradation

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Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

Safety Guaranteed Power-Delivered-to-Load Maximization for M...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

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Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application

Modern Manufacturing for Alloy Wheel Defect Detection using ...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Tuaniai Archevapanich Woranidtha Krungseanmuang Vasutorn Chaowalittawin Posathip Sathaporn Punyisa Chaowalittawin Boonchana Purahong Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Indusstrail Physics School of Science Bangkok Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.

关键词： Uniform resource locators Image processing Metals Wheels Systems architecture User interfaces Mobile applications

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