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2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics, RI2C 2021

作者： Duangsuphasin, Akksatcha Rungsaksangmanee, Preecha Kengpol, Athakorn Elfvengren, Kalle King Mongkut's University of Technology North Bangkok Department of Industrial Engineering Faculty of Engineering Bangkok Thailand Infirmary of Kasetsart University Bangkok Thailand Mongkut's University of Technology North Bangkok Advanced Industrial Engineering Management Systems Research Center Department of Industrial Engineering Faculty of Engineering King Bangkok Thailand Lut University School of Engineering Science Department of Industrial Engineering and Management Lappeenranta Finland

ISBN: (纸本)9781665403009

The objective of this research is to develop a decision support framework using deep learning methods to select passion fruit for the ageing society. Many substances present in passion fruit such as vitamin A, vitamin C, and vitamin E can contribute to beneficial effects in our body. Especially, it could help to reduce the risk of cardiovascular diseases suitable for the ageing society. It is expected that 4-age levels of passion fruit for three groups of ageing society can be classified and suggested by using multi-layer perceptron neural network (MLPNN) architectures in Python program. The implications of the study are that selecting age levels of passion fruit is appropriate for three ageing society groups. The deep neural network model can generate the accuracy of a trained dataset is 73.9 % and a tested dataset is 72.5%. This model is used to create the computer software which is convenient for the selection of passion fruit or other fruits for the fruit juice industry. © 2021 IEEE.

关键词： Neural network models

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Methane degassing in global river reservoirs and its impacts on carbon budgets and sustainable water management

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Science of the Total Environment 2024年 957卷 177623页

作者： Zhou, Yanlai Xu, Hanbing Xia, Tianyu Xiong, Lihua Chang, Li-Chiu Chang, Fi-John Xu, Chong-Yu State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan430072 China Department of Water Resources and Environmental Engineering Tamkang University New Taipei City251301 Taiwan Department of Bioenvironmental Systems Engineering National Taiwan University Taipei106216 Taiwan Agricultural Carbon Management Association Taipei104509 Taiwan Department of Geosciences University of Oslo P.O. Box 1047 Blindern OsloN-0316 Norway

Degassing methane (CH4) through reservoir water compromises hydroelectricity's presumed low-carbon status, which has emerged as a critical hotspot for global carbon dynamics. However, a comprehensive understanding of the involved pathways remains elusive, hindering the accurate estimation of global reservoirs' carbon budget (emission-to-burial ratio). This study presents a holistic upscaling approach to assess methane degassing in global river reservoirs and its impacts on carbon budgets. Firstly, a machine learning model is utilized to characterize the contributions of climate and human factors to annual water residence time. Secondly, the stepwise multiple linear regression method is used to calculate CH4 degassing emissions for each reservoir. Finally, to systematically tackle all sources of uncertainty, separate uncertainty analyses are implemented for the estimates of degassing emissions, areal emissions, and organic carbon burial. Analyzing 30-year data from 6695 reservoirs worldwide, our assessment considers water residence time, temperature, catchment area, and reservoir size. Findings indicate that water releases contribute significantly to global CO2 emissions from reservoirs, elevating the carbon budget by 20 % from 2.02 to 2.18 TgC/year, underscoring the previously underestimated significance of CH4 degassing in shaping the carbon cycle impact of river reservoirs. We propose a redefined threshold for low carbon credits, suggesting that reservoirs with power densities exceeding 6.1 MW/km2, instead of the conventional 4 MW/km2, should qualify. This study underscores the need for sustainable water management and reshaping the carbon dynamics associated with hydroelectricity. Future research can advocate Artificial Intelligence (AI) techniques to enhance water management and mitigate carbon emissions by multi-objectively optimizing reservoir operations. © 2024 Elsevier B.V.

关键词： Low emission

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Risk and Potential:An Asset Allocation Framework with Applications to Robo-Advising

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Journal of the Operations Research Society of China 2022年第3期10卷 529-558页

作者： Xiang-Yu Cui Duan Li Xiao Qiao Moris S.Strub School of Statistics and Management Shanghai University of Finance and EconomicsShanghai200437China School of Data Science City University of Hong KongHong KongChina School of Data Science and Hong Kong Institute for Data Science City University of Hong KongHong KongChina Department of Information Systems and Management Engineering Southern University of Science and TechnologyShenzhen518055GuangdongChina

We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance *** utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility ***,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth *** naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and *** proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal *** establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its ***,we discuss applications of this framework to robo-advisors.

关键词： Mean-risk optimization Mean-variance Expected utility maximization Portfolio choice Risk Potential Robo-advising FinTech

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Temporal Mode Conversion in a Waveguide by Fast Motion of its Plates

Temporal Mode Conversion in a Waveguide by Fast Motion of it...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Solís, Diego M. Yin, Shixiong Galiffi, Emanuele Alù, Andrea Engheta, Nader Departamento de Tecnología de los Computadores y de las Comunicaciones University of Extremadura Cáceres10003 Spain City University of New York Photonics Initiative Advanced Science Research Center New York10031 United States City University of New York Physics Program Graduate Center New York10031 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States

ISBN: (纸本)9781957171395

We show, semi-analytically and numerically, how the relativistic accelerated motion of a waveguide's parallel plates induces fast mode conversion including reflection, similar to the temporal interface by sudden variation of interplate distance through conductivity switching. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Waveguides

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Comparing Classification Algorithms to Analyze Twitter Sentiment on Public Opinion on Fuel Oil

Comparing Classification Algorithms to Analyze Twitter Senti...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Gita Widarma Rice Novita Mustakim Nesdi Evrilyan Rozanda Information Systems Department Universitas Islam Negeri Sultan Syarif Kasim Riau Graduate Program Pekanbaru Indonesia Information Systems Postgraduate Programs Universitas Negeri Padang Pekanbaru Indonesia Master of Informatics Engineering Postgraduate Programs Institut Pertanian Bogor Pekanbaru Indonesia Master of Information Systems Management Postgraduate Programs Universiti Teknologi Malaysia Pekanbaru Indonesia

Twitter is currently used as one of the applications that is often used to convey opinions or unrest by the public such as the current issue that is currently rife, namely regarding the increase in fuel oil prices (BBM) in Indonesia. With so much data scattered in a tweet that only consists of a few fragments of words, there must be valuable information or meaning that Twitter users want to convey. Text mining is a very appropriate method to do the analysis because in text mining there is a method that focuses on an opinion which will be concluded into positive and negative opinions. There are several algorithms that are most often used in data mining, namely K-Nearest Neighbor, Decision Tree, and Naive Bayes. The three algorithms used in this study were also compared with the use of feature selection Particle Swarm Optimization (PSO) which is well known for improving the performance of algorithms. There are 2165 negative opinion data and 2835 positive sentiment data. The data will be tried based on distinctive sums of data namely 1000 data, 3000 data, and 5000 data. The classic K-Nearest Neighbor algorithm obtained an accuracy result of 75.60% and made it the best when tested with 1000 data. The use of Particle Swarm Optimization (PSO) is proven to improve accuracy as when tested using 3000 data with an accuracy value of 78.21% on the K-Nearest Neighbor algorithm. In the mean time, in testing 5000 Data, the classic K-Nearest Neighbor once more gotten the most elevated exactness with a esteem of 74.02%.

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Cyberoception: Finding A Painlessly-Measurable New Sense In The Cyberworld Towards Emotion-awareness In Computing 25

Cyberoception: Finding A Painlessly-Measurable New Sense In ...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Okoshi, Tadashi Gao, Zexiong Tan, Yi Zhen Karasawa, Takumi Miki, Takeshi Sasaki, Wataru Balan, Rajesh Krishna Faculty of Environment and Information Studies Keio University Kanagawa Fujisawa Japan Graduate School of Interdisciplinary Information Studies The University of Tokyo Tokyo Tokyo Japan School of Computing and Information Systems Singapore Management University Singapore Singapore Singapore Graduate School of Media and Governance Keio University Kanagawa Pref Fujisawa city Japan Department of Engineering University of Cambridge Cambridge Cambridge United Kingdom Graduate School of Science and Technology Nara Institute of Science and Technology Nara Ikoma Japan

ISBN: (纸本)9798400713941

In Affective computing, recognizing users' emotions accurately is the basis of affective human-computer interaction. Understanding users' interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users' interoception challenging. This study aims to determine other forms of data that can explain users' interoceptive or similar states in their real-world lives and propose a novel hypothetical concept "cyberoception,"a new sense (1) which has properties similar to interoception in terms of the correlation with other emotion-related abilities, and (2) which can be measured only by the sensors embedded inside commodity smartphone devices in users' daily lives. Results from a 10-day-long in-lab/in-the-wild hybrid experiment reveal a specific cyberoception type "Turn On"(users' subjective sensory perception about the frequency of turning-on behavior on their smartphones) significantly related to participants' emotional valence. We anticipate that cyberoception to serve as a fundamental building block for developing more "emotion-aware", user-friendly applications and services. © 2025 Copyright held by the owner/author(s).

关键词： Cyberoception Emotion / Affective Computing Interoception Mobile Devices Personalization Sensing Wearable Devices

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New Level-Set-Based Shape Recovery Method and its application to sparse-view shape tomography 21

New Level-Set-Based Shape Recovery Method and its applicatio...

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2021 4th International Conference on Digital Medicine and Image Processing

作者： Haytham A. Ali Hiroyuki Kudo Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Japan and Department of MathematicsFaculty of Science Sohag University Egypt Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Japan

ISBN: (纸本)9781450386487

The recovery of shapes from a few numbers of their projections is very important in Computed tomography. In this paper, we propose a novel scheme based on a collocation set of Gaussian functions to represent any object by using a limited number of projections. This approach provides a continuous representation of both the implicit function and its zero level set. We show that the appropriate choice of a basis function to represent the parametric level-set leads to an optimization problem with a modest number of parameters, which exceeds many difficulties with traditional level set methods, such as regularization, re-initialization, and use of signed distance function. For the purposes of this paper, we used a dictionary of Gaussian function to provide flexibility in the representation of shapes with few terms as a basis function located at lattice points to parameterize the level set function. We propose a convex program to recover the dictionary coefficients successfully so it works stably with only four projections by overcoming the issue of local-minimum of the cost function. Finally, the performance of the proposed approach in three examples of inverse problems shows that our method compares favorably to Sparse Shape Composition (SSC), Total Variation, and Dual Problem.

关键词： Compressed sensing Computed tomography Convex optimization Gaussian function Level set Reconstruction method Shape identification X-ray tomography

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A Hybrid Machine Learning Approach to Wave Energy Forecasting

A Hybrid Machine Learning Approach to Wave Energy Forecastin...

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2021 North American Power Symposium, NAPS 2021

作者： Pani, Naveen Kumar Kodanda Jha, Vijeta Ashok Bai, Linquan Cheng, Long Zhao, Tiefu University of North Carolina at Charlotte Systems Engineering and Engineering Management CharlotteNC United States Avangrid Renewables Department of Investment Planning and Analysis BostonMA United States

ISBN: (纸本)9781665420815

Significant wave height and wave period are important parameters of statistical distribution when it comes to ocean engineering applications. The accuracy of wave energy forecasting is important for power production and grid integration. A Stacking Regressor-based hybrid machine learning model integrating Extreme Gradient Boosting model (XGBoost) and Decision Tree (DT) is proposed in this paper for wave energy forecasting. The prediction parameters are selected as input variables based on a correlation study for predicting the wave height and wave period using the hybrid learning model. The forecast wave height and wave period are further used to calculate the wave energy flux and wave power production. The results show that the hybrid model outperforms other ML models such as XGBoost, DT regressor, K-Nearest Neighbour (KNN), and linear regression, in forecasting wave energy using site data of a location in North Carolina coast. © 2021 IEEE.

关键词： Decision trees

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A Conceptual Study of High Spatial Resolution Neutron Imaging for Water Exploration on the Moon with a Full Satellite System Design 74

A Conceptual Study of High Spatial Resolution Neutron Imagin...

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74th International Astronautical Congress, IAC 2023

作者： Taniguchi, Kentaro Tsurumi, Miwa Komatsu, Ryusei Kudo, Raiki Izawa, Azumi Kaieda, So Aizawa, Naoto Gomi, Atsuhiro Nagai, Yuutarou Enoto, Teruaki Department of Applied Mechanics and Aerospace Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Department of science and engineering Aoyama Gakuin University 5-10-1 Fuchinobe Kanagawa Sagamihara252-5258 Japan 3-1-1 Yoshinodai Chuo-ku Kanagawa Sagamihara-shi252-0222 Japan Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Science and Engineering Aoyama Gakuin University 5-10-1 Fuchinobe Chuo-ku Kanagawa Sagamihara252-5258 Japan Graduate School of Engineering and Science Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Department of Aeronautics and Astronautics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8654 Japan Department of Physics Kyoto University Kitashirakawa-Oiwake-cho Sakyo-ku Kyoto606-8502 Japan

An increasing number of lunar exploration programs, including NASA's Artemis program, are recognizing the vital importance of water resources. In order to identify potential drilling locations on the lunar surface, a high-resolution lunar orbiter is anticipated to be utilized. This study proposes a mission that focuses on neutrons generated by galactic cosmic rays that leak into space and measure the concentration of hydrogen within water under the lunar subsurface. Neutron detectors used in previous lunar exploration mission probes have encountered challenges in achieving both high spatial resolution and enough neutron count rates for statistical reliability. To overcome these challenges, the proposed mission satellite is equipped with the world's first imager dedicated for thermal neutrons. The satellite aims to achieve the highest spatial resolution of 5 km and provide neutron mapping with sufficient statistical reliability using lightweight optics. Furthermore, the satellite uses neutron flux data for measurement of the neutron lifetime, and the results are compared with those obtained using two established methods on the ground. To accomplish these tasks, a 50-kg lunar orbiter is conceptually designed after several design iterations, including detailed simulations of each subsystem. The satellite map neutron fluxes and water distribution from a lunar polar frozen orbit over a one-year mission period. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Conceptual study Full satellite system design Lunar water Neutron Neutron Imaging

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Performance Analysis of Interleaved Bidirectional DC-DC Converters for Battery Control in Islanded Microgrid

Performance Analysis of Interleaved Bidirectional DC-DC Conv...

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IEEE General Meeting Power& Energy Society

作者： Lindemberg Roberto de Lima Pedro A. de Alcântara Luana C. S. Soares Camila M. V. Barros Luciano S. Barros Mathematics and Computational Modeling Graduate Program João Pessoa Brazil Electrical Engineering Graduate Program Federal University of Paraíba João Pessoa Brazil Computer Systems Department Federal University of Paraíba João Pessoa Brazil

In this paper, considering photovoltaic/battery sourced microgrids with grid-forming droop control in the load interface converter for power management, two alternative converter topologies for the battery are tested: dual boost interleaved converter and three-phase interleaved converter. These topologies are based on the conventional bidirectional buckboost converter and their employment is to allow a reduction in component sizing, improvement in power quality, and higher output voltage. Analysis of the operation of these converters in different operation modes of the microgrid is carried out, including battery normal charging, battery normal discharging, battery charging limit, and maximum state-of-charge, through simulations in MATLAB/Simulink environment. Results suggest advantages such as reduced stress on switches and battery charging current ripple for the two interleaved converters tested and the potential implementation of higher output voltage for the dual boost converter.

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