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2023 International Conference on Power, Energy and Innovations, ICPEI 2023

作者： Santos, Ian Jerome Q. Tangjitjetsada, Maleeya Rakluea, Paitoon Wongsin, Norakamon Rakluea, Chawalit Mahatthanajatuphat, Chatree Chudpooti, Nonchanutt Thianthong, Tawan Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Thanyaburi Pathum Thani 12110 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Wongsawang Bangsue Bangkok10800 Thailand King Mongkut's University of Technology North Bnagkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Wongsawang Bangsue Bangkok10800 Thailand Siam Airport Ground Service Co. Ltd. Don Mueang Bangkok10210 Thailand

ISBN: (纸本)9798350344585

This article provides an explanation of the use of a Twin-Array Quasi Frequency Selective Surface (FSS) Reflector with Dual Band Cross-Dipole for antenna applications in 5G base stations. The base material employed in this study is FR4 sheet, which has a dielectric constant (ϵr) of 4.3. The antenna obtained has dimensions of 100x100 mm2 and functions throughout the frequency spectrum spanning from 730 to 860 MHz, 1700 to 2130 MHz. The primary benefits of this antenna design are its lightweight structural composition, compatibility with the frequency range of 730-860 MHz, and discernible performance attributes. The CST Studio Suite software was used to conduct a simulation of the antenna's performance. The antenna has an operating frequency range of 730-860 MHz and 1700-2130 MHz, enabling it to support a unidirectional wave propagation pattern that is suitable for the implementation of a 5G mobile communication base station. © 2023 IEEE.

关键词： Dipole antennas

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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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Numerical Feature Preprocessing Method for Daily Solar Radiation Pattern Classification 15th

Numerical Feature Preprocessing Method for Daily Solar Radia...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Li, You Wang, Yafei Zheng, Ying Qian, Hui Zhou, Weisheng Dual-Carbon Research Center Hangzhou City University Hangzhou310015 China Asia-Japan Research Institute Ritsumeikan University Ibaraki567-8570 Japan School of Civil Engineering and Architecture Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science and Information Technologies Frostburg State University FrostburgMD21532 United States College of Policy Science Ritsumeikan University Ibaraki567-8570 Japan

ISBN: (纸本)9789819724468

One challenging issue in daily solar radiation classification involves addressing the geometric differences in daily solar radiation patterns caused by geographic location and seasonal influences. A standardization method based on matrix transformation has been proposed to eliminate the daily geometric variations of solar radiation using classification for daily patterns of solar radiation. This method was tested using measurements taken at the Tokyo monitoring station from 2000 to 2021. Through the implementation of the proposed classification algorithm, this paper discerned twelve categories of daily clearness index distributions. The suggested classification framework has the potential to assist in the development of solar radiation transformation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Solar irradiance

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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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CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ALEXNET

CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Rajavel, A. Karkuzhali, S. Sharma, Sarvdaman Singh, Sitesh Kumar Senthilkumar, P. Ida, S. Jhansi Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar India Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Vaddeswaram Greenfield AP Guntur India Department of Computer Science and Business Systems R.M.K Engineering College India

ISBN: (纸本)9798350305708

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle. © 2023 IEEE.

关键词： Construction progress monitoring Deep AlexNet Deep learning Infrastructure projects Smart cities

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Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

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Environmental Science and Pollution Research 2024年第22期31卷 32449-32463页

作者： Rajak, Upendra Chaurasiya, Prem Kumar Verma, Tikendra Nath Dasore, Abhishek Ağbulut, Ümit Meshram, Kundan Saleel, CAhamed Saboor, Shaik Cuce, Erdem Mian, Zhibao Department of Mechanical Engineering RGM College of Engineering and Technology Nandyal518501 India Department of Mechanical Engineering National Institute of Technology Raipur492010 India Department of Mechanical Engineering Maulana Azad National Institute of Technology Vellore462003 India Department of Biological and Agricultural Engineering Faculty of Engineering Universiti Putra Malaysia Selangor UPM Serdang43400 Malaysia Department of Mechanical Engineering Faculty of Mechanical Engineering Yildiz Technical University Istanbul34349 Turkey Department of Civil Engineering Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Mechanical Engineering College of Engineering King Khalid University Abha61421 Saudi Arabia School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore632014 India Department of Mechanical Engineering Faculty of Engineering and Architecture Recep Tayyip Erdogan University Zihni Derin Campus Rize53100 Turkey School of Engineering and the Built Environment Birmingham City University BirminghamB4 7XG United Kingdom Faculty of Science and Engineering School of Computer Science University of Hull Cottingham Road HullHU6 7RX United Kingdom Center for Research Impact & amp Outcome Chitkara University Punjab Rajpura140401 India

This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and NOx emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide (CO2) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： Diesel engines

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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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Analysis and Design of Mobile Model for DTN

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Chinese Journal of Electronics 2023年第4期18卷 635-638页

作者： Zhiting Lin Yugui Qu Yusheng Ji Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China Province Key Laboratory of Software in Computing and Communication Hefei China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Delay tolerant networks may become unexpectedly partitioned due to node mobility or variation in signal strength. However, most widely used models in some relative works are generally very simplistic. In order to exploit intelligent forwarding algorithms, a novel nodal mobile model of delay tolerant networks is presented to map reality with more accuracy. And several approaches are introduced to analyze the network structure, such as n-cliques, n-clans, degree, closeness and betweenness. Our research results showed that the centralizations became smaller when the wireless connections were concerned. It meant that quite a number of nodes became potential relays because of the new structure of networks.

关键词： Wireless communication Analytical models Wireless sensor networks Protocols Social networking (online) Sensitivity analysis Social network theory

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COVID-19 Detection Method from Chest CT Scans via the Fusion of Slice Information and Lung Segmentation 1st

COVID-19 Detection Method from Chest CT Scans via the Fusion...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Toledano Pavón, Jesús Morales Vega, Juan Carlos Carrillo-Perez, Francisco Herrera, Luis Javier Rojas, Ignacio Department of Computer Architecture and Technology University of Granada. C.I.T.I.C. Periodista Rafael Gómez Montero 2 Granada18014 Spain

ISBN: (纸本)9783030881627

Due to the high spread of the COVID-19 virus, several diagnosis support systems are being developed in order to detect the disease in a faster and accurate way. In this paper, a stacking method for Computed Tomography (CT) scans has been implemented for the pre-processing step. The method combines both slice normalization and lung segmentation in a single output image using RGB color channels, providing more information from the input slices to the CNN models. The binary classification step starts with a slice-level prediction, which applies fine-tuning to the whole model and dense layers are changed by a custom scheme to improve the performance. Then, a patient-level prediction is performed by fixing a threshold percentage of COVID positive slices that allows to make the final prediction, classifying patients as COVID or NORMAL. The accuracy and metrics obtained show the robustness of the presented method in comparison to using the normalised slices or the masks independently. Given the results obtained, the proposed method can accurately detect the COVID-19 disease and the fusion of information improves the results obtained. © 2021, Springer Nature Switzerland AG.

关键词： COVID-19

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Comparison of Fusion Methodologies Using CNV and RNA-Seq for Cancer Classification: A Case Study on Non-Small-Cell Lung Cancer 1st

Comparison of Fusion Methodologies Using CNV and RNA-Seq for...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Carrillo-Perez, Francisco Morales, Juan Carlos Castillo-Secilla, Daniel Guillen, Alberto Rojas, Ignacio Herrera, Luis Javier Department of Computer Architecture and Technology University of Granada. C.I.T.I.C. Periodista Rafael Gómez Montero 2 Granada18014 Spain

ISBN: (纸本)9783030881627

Lung cancer is one of the most frequent cancer types, and one among those causing more deceases worldwide. Nowadays, in order to improve the diagnosis of cancer more screenings are performed to the same patient and various biological sources are being gathered. Fusing the information provided by these sources can lead to a more robust diagnosis, which can improve the prognosis of the patient. In this work, a comparison of fusion methodologies (early and intermediate) using RNA-Seq and Copy Number Variation data for Non-Small-Cell Lung Cancer classification is performed. We found that great results can be attained using both fusion methodologies, with an AUC of 0.984 for the early fusion and 0.989 for the intermediate fusion, improving those obtained by each source of information independently (0.978 RNA-Seq and 0.910 Copy Number Variation). This work shows that fusion methodologies can enhance the classification of non-small-cell lung cancer, and that these methodologies can be promising for the diagnosis of other cancer types. © 2021, Springer Nature Switzerland AG.

关键词： RNA

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