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2022 International Conference and Utility Exhibition on Energy, Environment and Climate Change, ICUE 2022

作者： Nakkorn, Nat Phdungsilp, Aumnad Vorarat, Suparatchai College of Innovative Technology and Engineering Graduate Program in Engineering Management Dhurakij Pundit University Bangkok Thailand

ISBN: (纸本)9798350333268

Local governments require new techniques and innovative approaches to assess initiatives to support the transition towards carbon neutrality. This paper presents a methodological approach for integrated building energy modeling and analysis of the impacts of energy conservation measures (ECMs) and energy-related CO2 emissions in urban areas. To accomplish this goal, the proposed methodology follows four steps: (1) developing a building stock model for urban databases using a geographic information system (GIS) and QGIS software;(2) creating a detailed archetype-based building stock model using OpenStudio and EnergyPlus;(3) carrying out a scenario analysis of possible ECMs and pathways to achieve carbon neutrality;and (4) analyzing energy savings and energy-related CO2 emissions. The developed methodology was applied to a tourism urban area, using Pattaya city as a case study. Based on the GIS building model, six archetypes were identified from all residential buildings, including hotels located in the study area. Prototypes of representative building energy models were simulated for a reference case with four ECMs. The results indicated that the building stock in the reference case used 497,669.05 TJ annually and emitted 80,470.32 ktC02eq. Of the four ECMs, a change to double glazing with low-E coating gave the highest energy savings and reductions in CO2 emissions. This measure led to energy savings of 9,342.82 TJ and emission reductions of 356.23 ktC02eq compared to the reference case. Local governments and stakeholders can benefit from the proposed methodological approach for decision making related to energy and carbon neutrality pathways. © 2022 IEEE.

关键词： Energy efficiency

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A Systematic Evaluation of Clustering Algorithms Against Expert-Derived Clustering

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Operations Research Forum 2025年第2期6卷 1-24页

作者： Mikrou, Ioannis Sapidis, Nickolas S. Department of Mechanical Engineering University of Western Macedonia ZEP Kozani 50100 Greece Postgraduate Masters Program “Quality Management and Technology” Hellenic Open University Par. Aristotelous 18 Patras 26335 Greece

Clustering algorithms have long sought to replicate human expertise in data clustering. This research utilizes six mechatronic products and a team of domain experts to assess how closely the results of six clustering algorithms align with expert-generated outcomes. This comparison of clustering results includes validation indices, the analysis of component migrations between the generated clusters, and optical inspection. The mechatronic products were selected for their complex nature, which provides a challenging context for the clustering analysis. The algorithms used in this research are Ward’s method (WARD), partitioning around medoids (PAM), K-means (K-MEANS), divisive analysis (DIANA), density-based spatial clustering of applications with noise (DBSCAN), and clustering by fast search and find of density peaks (DPC), and the validation indices employed to evaluate the clustering results are the silhouette coefficient (SC) and the composed density between and within clusters (CDbw). © The Author(s) 2025.

关键词： Clustering performance DSM models Human expert benchmarks Machine learning Mechatronics

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Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks

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Mobile Networks and Applications 2024年第3期29卷 886-904页

作者： Lin, Chun-Cheng Peng, Yi-Chun Chen, Zhen-Yin Annie Fan, Yu-Hong Chin, Hui-Hsin Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Technology Overseas Chinese University Taichung407 Taiwan

Flexible job shop scheduling (FJSP) has garnered enormous attention within the realm of smart manufacturing, where, beyond job sequencing, the selection of machines holds considerable importance. As smart factories progress with the Internet of things (IoT) and cyber-physical systems (CPS), scheduling methodologies are advancing towards intelligent decentralization. However, with the expansion of factories, conventional cloud computing struggles to manage the substantial influx of data. To tackle this issue, this work incorporates a fog computing and edge computing framework into the distributed FJSP workstations. In this framework, the workstations each of which consists of multiple machines are categorized based on the different nature of the accommodated machines, and operate independently to reduce unnecessary information transmission, in which each machine is equipped with edge computing capacity. The fusion of fog computing and edge computing allows for the offloading of computational tasks from cloud computing, effectively reducing latency. While previous solutions for FJSP have predominantly relied on linear programming or metaheuristic algorithms, this work proposed a novel distributed approach based on a dual deep Q networks (dual DQN) architecture, integrating deep learning (DL) with reinforcement learning (RL). Within the cloud center, the initial neural network determines the machine selection rules for fog computing, while the secondary neural network decides the job dispatching rules for edge computing devices. Edge computing devices execute the schedule and provide feedback to the cloud, which refines the results through an iterative training process, so that to minimize the makespan. The experimental findings indicate that employing dual DQNs outperforms the methods of utilizing only one single machine selection rule. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analytics of Attack Patterns

ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analyt...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Fiza, Inbasat Khurshid, Fizza Fatima, Manahil Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan College of Engineering Seoul National University Technology Management Economics and Policy Program 1 Gwanak-Ro Gwanak-Gu Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The ATT&CK MITRE framework serves as an expansive repository of adversary tactics, techniques, and procedures. Given the sheer volume of these intricate attack patterns, the conventional manual navigation method proves to be time-intensive and less efficient for security analysts. As a remedy, there arises a compelling necessity to present the rich content encapsulated within the ATT&CK MITRE repository through interactive visualizations. In this paper ATT&CKViz an interactive web-based cyber-Attack patterns visualization tool is presented that aims to empower security analysts with the ability to effortlessly explore the repository from diverse angles, which facilitates deep insights into cyber threats. The aim is to understand cyber threat efficiently with a timely response. The empirical evaluations depict that effectiveness and user-friendliness of ATT&CKViz. © 2024 IEEE.

关键词： Cybersecurity

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Predicting Factors Influencing the Actual Use of E-Learning Platform among Medical Students in the Philippines: Unified Theory of Acceptance and Use of technology Approach 24

Predicting Factors Influencing the Actual Use of E-Learning ...

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4th International Conference on Internet and E-Business, ICIEB 2024

作者： Benito, Omar Paolo Prasetyo, Yogi Tri Roque, Ralph Andre C. Flores, Fernan Patrick Cahigas, Maela Madel L. Nadlifatin, Reny Ayuwati, Irene Dyah International Bachelor Program in Engineering Yuan Ze University Taiwan Department of Industrial Engineering and Management Yuan Ze University Taiwan School of Industrial Engineering and Engineering Management Mapua University Philippines School of Graduate Studies Mapua University Philippines Department of Information Systems Sepuluh Nopember Institute of Technology Indonesia University of Surabaya Indonesia

ISBN: (纸本)9798400709739

eLearning has been utilized as a mode of instruction among medical students amidst the COVID-19 pandemic. The present study aimed to investigate the factors influencing the acceptance of eLearning platforms in medical education during the COVID-19 pandemic in the Philippines by employing the latent variables of UTAUT2, along with two additional latent variables gathered from various related literature. A sum of 360 medical students participated and answered an online survey that consists of 40 questions. A stepwise multiple linear regression was utilized and found that performance expectancy (PE), habit (HB), and instructor characteristics (IC), can successfully predict the actual use (AU) of medical students with a precision of 54.22%. A higher performance expectancy (PE), habit (HB), and instructor characteristics (IC) can enhance and promote the acceptance of medical eLearning platforms. This is the first study that has examined the acceptance of eLearning platforms among medical students in the Philippines during the COVID-19 pandemic. The outcomes of this research can serve as guidelines for the Commission on Higher Education of the Philippines for the enhancement of eLearning platforms. Although this study only utilized the latent variables of UTAUT2 along with two additional variables: Learning Value and Instructor Characteristics;the findings of this study would still hold significant value for promoting open innovation in eLearning platforms in medical fields worldwide. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： COVID-19

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Direct matching between music and image for contextual relationship analysis

Direct matching between music and image for contextual relat...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Wattanapornmongkol, Smart Phaisalrattananukul, Maethaphan Saksermkiat, Phatthapol Sanguansat, Parinya Kaewprachum, Phanomyong Suankularb Wittayalai School EPLUS+ Program Bangkok Thailand Suankularb Wittayalai School Gate Program Bangkok Thailand School Dpste Bangkok Thailand Panyapiwat Institute Of Management Faculty Of Engineering And Technology Nonthaburi Thailand The Institute For The Promotion Of Teaching Science And Technology Bangkok Thailand

ISBN: (纸本)9798350370096

We wanted to understand the relationship between emotions and objects or events. Thus, we have created a dataset of music-themed images and studied language context models to convert image, text, and audio data into vectors. We then developed two deep neural network models, Multi-Layer Perceptron (MLP) and CLIP-Based, to predict the most suitable song for any given image. It was found that MLP achieved an accuracy of 0.2057 when given both the image and its caption, compared to 0.2710 for CLIP-Based. However, when given only the caption, the accuracy of the CLIP-based neural network decreased to 0.2676, yet the accuracy slightly increase when given only the image to 0.2717. This suggests that direct image data could be enough to relate to songs, but providing descriptions of the images may not help the neural network to identify the relationships more accurately. © 2024 IEEE.

关键词： Music

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Energy Performance and Thermal Comfort in a Library Building: A Case Study of Dhurakij Pundit University, Thailand

Energy Performance and Thermal Comfort in a Library Building...

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2022 International Conference and Utility Exhibition on Energy, Environment and Climate Change, ICUE 2022

作者： Thongsean, Auntika Phdungsilp, Aumnad Vorarat, Suparatchai Sarachitti, Rangsit Graduate Program in Engineering Management College of Innovative Technology and Engineering Dhurakij Pundit University Bangkok10210 Thailand

ISBN: (纸本)9798350333268

The building sector represents high potential to address the energy use and occupants' comfort and well-being. This paper aims to proposed a methodological framework to assess energy and thermal comfort performance in buildings. The research framework was applied in an educational building using a library of Dhurakij Pundit University (DPU), Thailand as a case study. A representative building was modeled using SketchUp plugin with OpenStudio platform and EnergyPlus simulation engine for energy simulation. Thermal comfort parameters were measured in each floor of DPU's library and calculation of thermal comfort indices based on the PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) using the CBE Thermal Comfort Tool. It was found that the simulated energy is in line with the measured data with differences between -0.26% to 3.87%, depending on the floor. The energy intensity of DPU's library is 66.53 kWh/m2/year. Based on the measurements, the PMV and PPD of a reference building are -0.57 and 12%. Ten measures were simulated to assess the energy performance as well as thermal comfort conditions. It was found that seven measures can address both energy performance and thermal comfort in a case study building. © 2022 IEEE.

关键词： Thermal comfort

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Atmospheric Meteorological Effects on Forecasting Daily Lightning Occurrence at Cape Canaveral Space Force Station

Atmospheric Meteorological Effects on Forecasting Daily Ligh...

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2023 Congress in Computer Science, Computer engineering, and Applied Computing, CSCE 2023

作者： Saul, Jon Wagner, Torrey Mbonimpa, Eric Langhals, Brent Graduate School of Engineering and Management Air Force Institute of Technology Data Analytics Certificate Program Wright-Patterson AFBOH United States

ISBN: (纸本)9798350327595

As the Cape Canaveral Space Force Station and Kennedy Space Center increase their launch rate, any process that could assist in the automation of the currently-manual lightning forecast would be valuable. This work examines the possibility of machine-learning assistance with the daily lighting forecast which is produced by the 45th Weather Squadron. A dataset consisting of 34 lightning, pressure, temperature and windspeed measurements taken from 334 daily weather balloon (rawinsonde) launches in the timeframe 2012-2021 was examined. Models were created using recursive feature elimination on logistic regression and XGClassifier algorithms, as well as Bayesian and bandit optimization of neural network (NN) hyperparameters. The modeling process was repeated after eliminating 13 features related to windspeed. The best performing models on both datasets were the optimized NN models, with an F1 metric of 0.79 on the full dataset and 0.66 on the reduced dataset. For comparison, a model that predicted randomly achieved F1 = 0.47 on this dataset. The addition of 13 windspeed-related features more than doubled the complexity of the 21-feature no-wind model while increasing model performance by 13 percentage points. A notable inference from the statistical modeling is that the most important feature from both datasets was the Thompson convective index, which is related to temperature, dewpoint, relative humidity and lapse rate. © 2023 IEEE.

关键词： launch operations lightning forecast lightning indices neural network

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Success Factor Analysis of Poverty Alleviation with Sustainable Livelihood Potential Development in Case Thailand

SSRN

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SSRN 2024年

作者： Ngamwong, Nitjakaln Darakorn Na Ayuthaya, Smitti Kiattisin, Supaporn Information Technology Management Program Faculty of Engineering Mahidol University Thailand

The United Nations Sustainable Development Goals (SDGs) focus on reducing inequality, Promoting economic growth, environmental protection, and access to critical services. The latest Multidimensional Poverty Index report shows Thailand's Multidimensional Poverty Index decreased. This paper study analyzes factors that significantly affect the increase in sustainable livelihood potential development based on 37 indicators of the relevant questionnaires. The sample size was 17,536 households from 3,612 villages and 193 districts, covering 20 provinces of Thailand, a region with the lowest Human Achievement Index (HAI). Analyzes and processes the data using structural equation modeling (SEM) statistical methods to confirm the factor structure and indicate appropriateness with empirical data according to the required criteria. The conclusion is that sustainable living potential development includes five dimensions based on 37 indicators of Thailand, with natural capital being the most important, followed by human capital, financial capital, social capital, and physical capital. The research will help community leaders or local agencies prioritize projects or activities to improve the quality of life of people in each locality. Including evaluating policies and various interventions. To be able to explain phenomena and measure them statistically. © 2024, The Authors. All rights reserved.

关键词： Factor analysis

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Metaverse with Mobile Twin Edge Computing: Empowering Optimal Efficiency and Accuracy

Metaverse with Mobile Twin Edge Computing: Empowering Optima...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Bibi, Asma Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan Kohsar University Department of Computer Science Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of 'Mobile Twin' is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of 'Revolutionary Ultra-low latency (RULL)' in the metaverse. © 2024 IEEE.

关键词： Electric power utilization

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