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Renewable Energy and Power Quality Journal 2022年 20卷 116-120页

作者： Vilà, J. Luo, N. Pacheco, L. Pujol, T. Gonzalez, J.R. Ferrer, I. Massaguer, A. Massaguer, E. Department of Electrical Engineering Electronics and Automation Polytechnic School University of Girona Girona17003 Spain Department of Mechanical Engineering and Industrial Construction Polytechnic School University of Girona Girona17003 Spain Department of Computer Architecture and Technology Polytechnic School University of Girona Girona17003 Spain AULA ECOGranja Vilà sustainable engineering Camí Vell de Figueres a Olot S/N Veïnat de Pols Girona17772 Spain

The popularity of small wind turbines would rise in future, because of electricity prices are still growing. Next generation of small wind-turbines can have higher efficiency values. Under wind gusts and turbulences, the system should be very sensitive to wind conditions. In this work, it is presented a control system that can improve the performance of commercial small-scale wind turbines by including active pitch and load control. We show the electrical design of a smart active pitch control to increase the energy generation of micro-wind turbines ( © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Horizontal axis wind turbine

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Identifying Challenges and Opportunities for Intelligent Data-Driven Health Interfaces to Support Ongoing Care

Identifying Challenges and Opportunities for Intelligent Dat...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Knoche, Hendrik Abdul-Rahman, Alfie Clark, Leigh Curcin, Vasa Huo, Zhiqiang Iwaya, Leonardo Horn Lemon, Oliver Mikulík, Robert Neate, Timothy Roper, Abi Skovfoged, Milo Marsfeldt Verdezoto, Nervo Wilson, Stephanie Ziadeh, Hamzah Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark Department of Informatics King's College London London United Kingdom Bold Insight London United Kingdom School of Population Health and Environmental Sciences King's College London London United Kingdom Department of Population Health Science King's College London London United Kingdom Research Group Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Department of Computer Science Heriot-Watt University Edinburgh United Kingdom Neurology Department The St. Anne's University Hospital Brno Brno Czech Republic School of Health and Psychology City University of London London United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science City University London Londond United Kingdom

ISBN: (纸本)9781450394222

This workshop will explore future work in the area of intelligent, conversational, data-driven health interfaces both from patients' and health care professionals' perspectives. We aim to bring together a diverse set of experts and stakeholders to jointly discuss the opportunities and challenges at the intersection of public health care provisioning, patient and caretaker empowerment, monitoring provisioning of health care and its quality. This will require AI-supported, conversational decision-making interfaces that adhere to ethical and privacy standards and address issues around agency, control, engagement, motivation, and accessibility. The goal of the workshop is to create a community around intelligent data-driven interfaces and create a road map for their future research. © 2023 Owner/Author.

关键词： User interfaces

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Enhanced Recurrent Neural Network for Reducing Carbon Foot Printing in Industry 6

Enhanced Recurrent Neural Network for Reducing Carbon Foot P...

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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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Comparative Assessment Between Statistical and Soft Computing Methods for Accident Severity Classification

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Journal of The Institution of Engineers (India): Series A 2020年第1期101卷 27-40页

作者： Sarkar, Amrita Sarkar, Satyaki Department of Computer Science and Engineering Sarala Birla University Ranchi835103 India Department of Architecture Birla Institute of Technology Mesra Ranchi835215 India

Globally traffic scenario has experienced a vast change over the years as volumes of traffic on urban roads have increased by leaps and bounds. This increase has also marked an increase in the number of road accidents, both fatal and non-fatal. Accident prediction has a significant role in the improvement in traffic safety, and urban traffic management. Different statistical methods and various soft computing techniques are being used for developing accident prediction models. In this study, ensemble ranking method, comprising three filter-based ranking methods such as Information Gain, Gain Ratio, and Symmetric Uncertainty, has been employed to select significant features for prediction of accident severity. Through analysis, eight significant features have been selected for accident severity prediction. These are Annual Average Daily Traffic, Road Width, Percentage of Left-Turn Vehicles, Driver’s Age, Percentage of Right-Turn Vehicles, Types of Vehicles Involved, Spot Speed, and Cause of Accidents. Multiple logistic regression (MLGR) and artificial neural network (ANN) have been applied for accident severity prediction in urban roads. This analysis concludes that ANN model gives more accuracy and minimum errors compared with the MLGR method at the time of severity classification of a vehicular accident. © 2019, The Institution of Engineers (India).

关键词： Forecasting

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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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Performance Evaluation of Metaheuristic Optimization Techniques in Insulation Problem 8

Performance Evaluation of Metaheuristic Optimization Techniq...

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8th International Conference on Information technology Trends, ITT 2022

作者： Gunal, Ozen Akpinar, Mustafa Akpinar, Kevser Ovaz Turgutlu Vocational School Manisa Celal Bayar University Department of Computer Programming Manisa Turkey Sakarya University Department of Software Engineering Sakarya Turkey School of Engineering Architecture and Interior Design Amity University Dubai Dubai United Arab Emirates Rochester Institute of Technology Department of Computing Security Dubai United Arab Emirates

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

Selection of insulation material and determination of its thickness are the two most important parameters that prevent heat loss. Too much thickness complicates the price and use. The low coefficient of thermal conductivity of the insulation material reduces heat loss and increases the cost. One of the most important factors when determining the material thickness and conductivity coefficient is the price. In this study, two cases were examined under certain conditions, independent of the price. In the first case, the insulation material is determined, whereas the material thickness is determined in the second case. Six different current metaheuristic algorithms were used to calculate the two cases. It has been observed that harris hawks optimization (HHO), salp swarm optimization (SSO), and whale optimization (WO) algorithms determine the global optimum point without any error in determining the targets of the problem while the processing times were short. Overall, it has been seen that all six algorithms can create solutions to heat transfer problems with very low errors. © 2022 IEEE.

关键词： Optimization

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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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