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2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Chien, Wei Tsai, shangte Lunghwa University of Science and Technology Department of School of Information Management and Master's Program Taiwan Business School Yulin Normal University China

ISBN: (纸本)9781665437554

The printed inverted-F antenna is used to design a multi-band antenna, in which the frequency includes LTE, WLAN, and GPs frequency. The printed inverted-F antenna is applied in the environment and architecture of the 14-inch notebook computer. The design frequency of the antenna is 0.7-5 GHz. The usable frequency band includes three different systems: LTE, WLAN, and GPs. They comply with the wireless network specifications in the industry. Based on the analysis data such as current distribution and antenna field type, the variability and difference of the antenna in the environment are analyzed and compared. © 2021 IEEE.

关键词： Microwave antennas

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Predicting Potential Fires in Indonesia by Analyzing VIIRs Night Data during 2018 – 2022

Predicting Potential Fires in Indonesia by Analyzing VIIRS N...

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International symposium on Grids and Clouds 2023 in conjunction with HEPiX spring Workshop, IsGC and HEPiX 2023

作者： suhardiman, Basuki Hermanto, Benni Rio Directorate of Information Technology Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Master’s Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRs (Visible Infrared Imaging Radiometer suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, sumatra, Kalimantan, sulawesi, and the general areas of Indonesia. The VIIRs night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax scaler, we choose variables Temperature, Radiant Heat Intensity, and source Footprint. simulations are made to show the ranges of these variables that predict the possibility of’flame’ status occurring. We conclude that the ANN method can give more accurate results than the existing classification. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Fires

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A Note on Even-sized spectral Clustering

A Note on Even-Sized Spectral Clustering

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International symposium on soft Computing and Intelligent systems (sCIs)

作者： Hiroki MIYAKAWA Yasunori ENDO Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master's Program in Risk and Resilience Engineering University of Tsukuba Tsukuba Ibaraki Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Clustering is a typical unsupervised learning method for classifying unsupervised data. One of the clustering meth-ods, even-sized clustering based on optimization (ECBO), is a clustering algorithm that imposes a constraint to equalize the size of each cluster. ECBO has been suggested to be effective in delivery and other problems. However, it is limited to Euclidean space. On the other hand, spectral clustering with a wide range of applicability for partitioning graph data has been proposed. In this paper, we propose even-sized spectral clustering, which imposes a size-equal constraint on spectral clustering, and show that it is an extension of the graph partitioning problem. We also verify the validity of the results through numerical examples.

关键词： Clustering algorithms Partitioning algorithms Classification algorithms Optimization Unsupervised learning

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Lower bounds for minimizing two types of unnecessary costs in the film and television industry

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Computers and Operations Research 2024年 170卷

作者： Lin, Lie-Fen Wang, Jen-Ya Master's Program in Statistics and Actuarial Science Feng Chia University Taiwan Taiwan Department of Information Management National Taichung University of Science and Technology Taiwan Taiwan

To optimize film and television production costs, we aim to minimize unnecessary expenditures such as the idle time of top-paid actors and the rental periods of expensive equipment. By scheduling scenes that require these resources on consecutive days, significant cost reductions are achieved. In this study, a branch-and-bound algorithm with two lower bounds has been developed to serve as a benchmark for evaluating other metaheuristic algorithms. The novelty of these lower bounds stems from their set-based operations, which avoid time-consuming computations involving large sparse matrixes. The computational results confirm that the proposed lower bounds effectively reduce these costs. Clearly, this approach can be extended to other industries with similarly complex job compositions, such as scenarios involving multiple small tasks in a single job. © 2024 Elsevier Ltd

关键词： Branch-and-bound algorithm Lower bound Multi-agent scheduling Overlap effect set operation Talent scheduling

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Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

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International Journal of Hydrogen Energy 2024年 68卷 388-397页

作者： Wang, Xianze Wang, Yifan Tseng, Yu-Yao Gao, Yidi Li, Keqing Wang, Ming-Hung Wu, Jinghui Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment and Green Development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production is influenced by various environmental factors that can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes. This study focuses on the application of ML in predicting hydrogen production rates (HPR) during anaerobic fermentation of biomass energy. Grey relation analysis was employed to investigate the correlation between operational parameters and HPR. Five ML algorithms, namely decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were utilized in conjunction with operating conditions and digestate characteristics as features, with HPR as the target variable. The evaluation of the models was conducted using mean squared error (MsE) and R squared (R2). Notably, butyrate, oxidation-reduction potential (ORP), and volatile suspended solids (Vss) were identified as key factors influencing hydrogen production during sucrose anaerobic fermentation. Among the ML algorithms tested, XGBoost achieved the highest accuracy, with an R2 value of 0.91 and an MsE of 0.0052. © 2024 Hydrogen Energy Publications LLC

关键词： Machine learning

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Review of “Numerical Methods in Environmental Data Analysis” by Moses Eterigho Emetere

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Journal of Agricultural, Biological and Environmental statistics 2023年第3期29卷 645-647页

作者： Mudarris, Mudarris Permatasari, Desy Master’s Program in Electrical Engineering Department of Electrical Engineering and Information Technology Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia

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semi-Automated Classification of Arabic User Requirements into Functional and Non-Functional Requirements using NLP Tools

Semi-Automated Classification of Arabic User Requirements in...

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2021 International Conference on information technology, ICIT 2021

作者： shehadeh, Karmel Arman, Nabil Khamayseh, Faisal Palestine Polytechnic University Deanship of Graduate Studies/Master's of Informatics Program Hebron Palestine Palestine Polytechnic University Department of Computer Science and Information Technology Hebron Palestine

ISBN: (纸本)9781665428705

Functional and non-functional requirements are equally important in software engineering. Both of them are mixed together within the same software requirement document. Usually, they are expressed in natural languages. so, a lot of human effort is required to classify them. software requirements classification is a challenging task. Requirements classification can help developers to deliver quality software that meets users' expectations completely. In this paper, we present a semi-Automated classification approach of Arabic functional and non-functional requirements using a natural language processing (NLP) tool. We propose a set of heuristics based on basic constructs of Arabic sentences in order to extract information from Arabic software requirements to classify the requirements into functional and non-functional requirements. This research aims to help software engineers by reducing the cost and time required in performing manual classification of software requirements. © 2021 IEEE.

关键词： Requirements engineering

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Overview of Bearing Fault Diagnosis Based on Deep Learning

Overview of Bearing Fault Diagnosis Based on Deep Learning

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Xue-Ling Zou Kai-Xu Han Wei Chien Xin-Yan Gan Liu-Yue shi School of Electric and Information Engineering Beibu Gulf University Qinzhou China School of Information Management and Master’s Program Lunghwa University of Science and Technology Taoyuan City Taiwan Guangxi University of Chinese Medicine Nanning China

Deep learning has excellent learning and feature classification abilities, extracting features in massive, multi-source, and high-dimensional measurement data. It also diagnoses and generalizes models without relying on manual intervention. Thus, it is widely used in equipment fault diagnosis. We introduce the common models of deep learning: deep confidence network, convolutional neural network, and automatic encoder. Using deep learning, the fault diagnosis of rolling bearings is executed, and the result provides the basis for future development.

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Variable-speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning

arXiv

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arXiv 2024年

作者： Masuya, Nozomu sato, Hiroshi Yamane, Koki Kusume, Takuya sakaino, sho Tsuji, Toshiaki Master's Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Japan Doctoral Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Japan Department of Intelligent Interaction Technologies Institute of Systems and Information Engineering University of Tsukuba Japan Graduate School of Science and Engineering Saitama University Saitama Japan

Because imitation learning relies on human demonstrations in hard-to-simulate settings, the inclusion of force control in this method has resulted in a shortage of training data, even with a simple change in speed. Although the field of data augmentation has addressed the lack of data, conventional methods of data augmentation for robot manipulation are limited to simulation-based methods or downsampling for position control. This paper proposes a novel method of data augmentation that is applicable to force control and preserves the advantages of real-world datasets. We applied teaching-playback at variable speeds as real-world data augmentation to increase both the quantity and quality of environmental reactions at variable speeds. An experiment was conducted on bilateral control-based imitation learning using a method of imitation learning equipped with position-force control. We evaluated the effect of real-world data augmentation on two tasks, pick-and-place and wiping, at variable speeds, each from two human demonstrations at fixed speed. The results showed a maximum 55% increase in success rate from a simple change in speed of real-world reactions and improved accuracy along the duration/frequency command by gathering environmental reactions at variable speeds. Copyright © 2024, The Authors. All rights reserved.

关键词： Demonstrations

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Non-contact digital signage allowing users to change contents with their face orientation 9

Non-contact digital signage allowing users to change content...

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9th IEEE Global Conference on Consumer Electronics, GCCE 2020

作者： Kurono, Haruka shibuya, Yu Kyoto Institute of Technology Master's Program of Information Science Graduate School of Science and Technology Kyoto Japan Kyoto Institute of Technology Faculty of Information and Human Sciences Kyoto Japan

ISBN: (纸本)9781728198026

In today's society, digital signage is widely used in various places such as commercial facilities and train stations. Most digital signages use a liquid crystal display or LED panel. One of the advantages of digital signage is the ability to dynamically change the content of the advertisement. However, the method of change is not due to the viewer's intention, but rather to elapsed time to show, and it is merely a sequential display of static advertisements. In this study, we propose and implement a non-contact interactive digital signage system using a web camera that allows users to change the content according to their face orientation. In the implementation, we use OpenCV for showing the content and image processing, and the machine learning library, named dlib, is used for detecting user's face and identifying its orientation. The proposed system changes the layout and enlarges the content in which the user is interested. The user does not need to wear any device and to do any special action. Furthermore, the proposed system offers an intuitive interaction manner to the user. In this paper, some experimental designs are also described. © 2020 IEEE.

关键词： Eye tracking

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