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Vibration-based classification of road damages: gyroscope data and a simple neural network model

IOP Conference Series: Earth and Environmental Science 2018年第1期195卷

作者： Fergyanto E. Gunawan Herriyandi Benfano Soewito Tuga Mauritsius Nico Surantha Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480

A system for automatic detection of road damages is essential for logistics management. At the core of the system lies an algorithm for classification of damages. This work intends to establish such an algorithm. In the current proposal, the algorithm receives data of the probe vehicle rate of rotations, extracts statistics descriptive from the data to produce a feature vector, and finally, feeds the vector to a simple-three-layer neural network to classify the damages. The types of damages are limited to four cases: normal, pothole, speed bump, and expansion joint. For the development, a probe vehicle is used to produce 400 empirical data involving those four damage cases. By using Monte Carlo approach, the established neural network model is evaluated. The results show that the approach is able to classify the cases with 85% accuracy. The study also finds that the rates of pitch and roll to be the determining factors.

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Designing Lead-Lag PSS Employing Backtracking Search Algorithm to Improve Power System Damping

Designing Lead-Lag PSS Employing Backtracking Search Algorit...

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IEEE GCC Conference & Exhibition

作者： Md Shafiullah Md Juel Rana L. S. Coelho M. A. Abido Ahmad Al-Subhi Department of Electrical Engineering King Fahd University of Petroleum & Minerals Dhahran KSA Industrial and Systems Engineering Graduate Program Federal University of Parana Curitiba PR Brazil

ISBN: (纸本)9781538627570

The power system is a complex network having highly nonlinear dynamics. Being electrically interconnected power systems are vulnerable to instability due to the disturbances in any one portion of the system. Even low-frequency oscillations (LFOs) originated from small disturbances in any part of the network may cause the outage of the whole system. Power System Stabilizer (PSS) with optimal parameter settings is an efficient tool for the suppression of these oscillations. In this paper, a novel approach is proposed to tune lead-lag controller based PSS for a single machine infinite bus (SMIB) network by using backtracking search algorithm (BSA) to suppress LFOs through improvement of power system damping. The efficacy of BSA tuned PSS has been investigated by comparing the simulation results with fixed gain conventional PSS. In addition, the damping ratio and eigenvalues of the same network have been compared with genetic algorithm (GA) based tuned PSS.

关键词： Power system stability Sociology Statistics Damping Oscillators Lead Eigenvalues and eigenfunctions

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Modeling diurnal gross primary production in East Asia using Himawari-8/9 geostationary satellite data

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Remote Sensing of Environment 2025年 328卷

作者： Yuhei Yamamoto Kazuhito Ichii Wei Yang Yui Shikakura Youngryel Ryu Minseok Kang Shohei Murayama Su-Jin Kim Yuta Takao Masahito Ueyama Tomoko Kawaguchi Akitsu Hiroki Iwata Hojin Lee Junghwa Chun Atsushi Higuchi Takashi Hirano AReum Kim Hyun Seok Kim Kenzo Kitamura Yuji Kominami Yukio Yasuda Institute for Advanced Academic Research (IAAR) Chiba University 1-33 Yayoi-cho Inage-ku Chiba 263-8522 Japan Center for Environmental Remote Sensing (CEReS) Chiba University 1-33 Yayoi-cho Inage-ku Chiba 263-8522 Japan Graduate School of Science and Engineering Chiba University 1-33 Yayoi-cho Inage-ku Chiba 263-8522 Japan Department of Landscape Architecture and Rural Systems Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul 08826 Republic of Korea Department of Atmospheric and Environmental Sciences Gangneung-Wonju National University 7 Jukheon-gil Gangneung-si Gangwon-do 25457 Republic of Korea National Institute of Advanced Industrial Science and Technology (AIST) AIST Tsukuba West 16-1 Onogawa Tsukuba Ibaraki 305-8569 Japan Dokkyo University 1-1 Gakuen-cho Soka Saitama 340-0042 Japan National Institute of Forest Science 57 Hoegi-ro Dongdaemun-gu Seoul 02455 Republic of Korea Graduate School of Agriculture Department of Environmental Sciences and Technology Osaka Metropolitan University 1-1 Gakuencho Naka-ku Sakai Osaka 599-8531 Japan Earth Observation Research Center Japan Aerospace Exploration Agency (JAXA) 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan Department of Environmental Science Shinshu University 3-1-1 Asahi Matsumoto Nagano 390-8621 Japan Chair of Ecosystem Physiology Faculty of Environment and Natural Resources Albert-Ludwigs-University Freiburg 79110 Freiburg im Breisgau Germany Interdisciplinary Program in Agricultural and Forest Meteorology Seoul National University Seoul 08826 Republic of Korea Research Institute of Disaster Medicine Chiba University 1-8-1 Inohana Chuo-ku Chiba 260-0856 Japan Typhoon Science and Technology Research Center (TRC) Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama 240-8501 Japan Research Faculty of Agriculture Hokkaido University Kita 9 Nishi 9 Kita-ku Sapporo Hokkaido 060-8589 Japan Department of Agriculture Forestry and Bioresources College of

Gross primary production (GPP) is a key indicator of plant growth and ecosystem health, and accurately capturing its diurnal variation is crucial for understanding vegetation responses to extreme heat and drought. However, the applicability of satellite-based semi-empirical models to diurnal GPP estimation remains limited. This study refined diurnal GPP estimation in humid temperate climates by leveraging Himawari-8/9 geostationary satellite data to incorporate direct/diffuse radiation and the nonlinear GPP response to diurnal variations in absorbed photosynthetically active radiation (APAR). The eddy covariance-light use efficiency (EC-LUE) model was employed by adopting three approaches: the direct/diffuse (DD) setting to consider the direct/diffuse components of APAR, DD with nonlinear relationship (DD-NL) setting to additionally consider the nonlinear GPP-APAR relationship, and the baseline setting. The model was calibrated and validated using the eddy-covariance tower observations from 18 sites across Japan and South Korea. The DD-NL setting improved accuracy by correcting the baseline's overestimation of GPP under high APAR and underestimation under low APAR. Particularly for forest sites, the DD-NL setting reduced midday overestimations by 12–30 % on clear-sky days and morning/afternoon underestimations by 25–40 % on cloudy days. In the baseline setting, low-APAR biases progressively accumulated across daily to annual timescales, whereas the DD-NL setting reduced them to −0.10 g C m −2 day −1 and -51 g C m −2 year −1 (−2.7 % of the site's average GPP). The DD setting had minimal impact in densely vegetated sites. Our findings show that the DD-NL setting in LUE models enhances geostationary satellite-based GPP estimates across diurnal to annual timescales, supporting ecosystem monitoring during extreme events and long-term carbon assessments.

关键词： Photosynthesis Gross primary production (GPP) Light use efficiency (LUE) model Diurnal variations Geostationary satellite data Himawari 8/9 Advanced Himawari imager (AHI)

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Resolution of one-dimensional bin packing problems using augmented neural networks and minimum bin slack

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International Journal of Innovative Computing and Applications 2016年第4期7卷 214-224页

作者： de Almeida, Ricardo Steiner, Maria Teresinha Arns Industrial and Systems Engineering Post-Graduate Program Pontifical Catholic University of Paraná - PUCPR Curitiba Brazil

The objective of this work is to compare the augmented neural network (AugNN) metaheuristic to minimum bin slack (MBS) heuristic to solve combinatorial optimisation problems, specifically, in this case, the one-dimensional bin packing problem (1D-BPP), a class of cutting and packing problems (CPP). CPP are easily found among various industry sectors and its proper treatment can improve the use of stocks in cutting problems or optimise physical space in packing problems. In order to optimise AugNN parameters, a design of experiment (DOE) was applied in order to guide a statistical analysis of different configurations of AugNN. The tests, developed in many benchmark problems found in the literature, showed that MBS heuristic was, in general superior, both in terms of the solution quality, which is about 70% better, and computational time, which is about 90% less. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Design of experiments

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Author Correction: Effect of de novo transcriptome assembly on transcript quantification

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Scientific reports 2019年第1期9卷 18041页

作者： Ping-Han Hsieh Yen-Jen Oyang Chien-Yu Chen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan. Department of Bio-Industrial Mechatronics Engineering National Taiwan University Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw. Genome and Systems Biology Program National Taiwan University and Academia sinica Taipei 10617 Taiwan. chienyuchen@ntu.edu.tw.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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An effect of tsunami to hotel occupancy: A case of Phuket, Thailand

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IOP Conference Series: Earth and Environmental Science 2019年第1期273卷

作者： J Tang N Leelawat A Suppasri F Imamura School of Management Technoogy Sirindhorn International Institute of Technology Thammasat University 131 Moo 5 Tiwanont Road Bangkadi Pathum Thani 12000 Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Phayathai Road Pathumwan Bangkok 10330 Thailand Disaster and Risk Management Information Systems Research Group Chulalongkorn University Phayathai Road Pathumwan Bangkok 10330 Thailand Risk and Disaster Management Program Graduate School Chulalongkorn University Phayathai Road Pathumwan Bangkok 10330 Thailand International Research Institute of Disaster Science Tohoku University 468-1 Aramaki-Aza Aoba Aoba-ku Sendai 980-0845 Japan

As a popular city for global tourists, more than one third of the Phuket GPP depends on the hotel and restaurant sector. The 2004 Indian Ocean Tsunami caused a serious physical damage to hotels and a high cancellation of bookings in the post disaster period, which, in turn, led to most THB 3 billion drop of the hotel and restaurant sector income in 2005 in Phuket. In addition, many tourism facilities in Phuket suffered bankruptcy or a slump, because they started their business again immediately after the reconstruction phase, while tourists did not really come back. This paper is an initiative study of our research project to evaluate the effects of the 2004 Indian Ocean Tsunami on Hotel Occupancy, with a focus on the speed for the physical reconstruction and tourism market recovery. We collected the physical reconstruction period and the hotel occupancy recovery data of six hotels that located in the Patong Beach of Phuket from 2003 to 2017. Through calculation, the average of damage rate (i.e., unavailability of the rooms in this study) is 55.89%, with an average speed for the physical reconstruction to rebuild the rooms about 6.07% of the overall rooms per month. The quotient of these two values indicates that the reconstruction period is around 9.2 months. However, the average period for reconstruction and tourism recovery is about 20 months. There is an about 11 months' delay between that hotel and resorts have finished the reconstruction and the tourists start to visit Phuket as usual. Therefore, the local government, local tourist authority, and hotel entrepreneurs can consider these findings in order to prepare the recovery plan for tourism industry in the future tsunami.

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Multi-objective symbiotic search algorithm approaches for electromagnetic optimization

Multi-objective symbiotic search algorithm approaches for el...

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IEEE Conference on Electromagnetic Field Computation

作者： Helon Vicente Hultmann Ayala Carlos Eduardo Klein Viviana Cocco Mariani Leandro dos Santos Coelho Pontificia Universidade Catolica do Parana Curitiba PR BR Pontifical Catholic University of Parana (PUCPR) Industrial and Systems Engineering Graduate Program (PPGEPS) Curitiba PR Brazil Pontifical Catholic University of Parana (PUCPR) Mechanical Engineering Graduate Program (PPGEM) Curitiba PR Brazil Department of Electrical Engineering Federal University of Parana Curitiba PR Brazil

ISBN: (纸本)9781509010332

Optimization metaheuristics are a powerful way to deal with many electromagnetic optimization problems. Recently, the symbiotic organisms search (SOS) algorithm was proposed to solve single-objective optimization problems. SOS mimics the symbiotic relationship among the living beings. In order to extend the classical mono-objective SOS algorithm approach, this paper proposes a new multi-objective SOS (MOSOS) and an improved IMOSOS. Results on a multi-objective constrained brushless direct current (DC) motor design show that the MOSOS and IMOSOS present promising performance.

关键词： Optimization Brushless DC motors Symbiosis Algorithm design and analysis Organisms

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Multiobjective cross entropy for electromagnetic optimization

Multiobjective cross entropy for electromagnetic optimizatio...

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IEEE Conference on Electromagnetic Field Computation

作者： Teodora Cardoso Bora Viviana Cocco Mariani Leandro dos Santos Coelho Piergiorgio Alotto Department of Electrical Engineering Federal University of Parana Curitiba PR Brazil Pontifical Catholic University of Parna (PUCPR) Mechanical Engineering Graduate-Program (PPGEM) Curitiba PR Brazil Pontifical Catholic University of Parana (PUCPR) Industrial and Systems Engineering Graduate Program (PPGEPS) Curitiba PR Brazil Dipartimento di Ingegneria Industriale Università di Padova Padua Italy

ISBN: (纸本)9781509010332

The Cross-Entropy (CE) method has been designed to solve rare event simulation problems and later extended to single-objective optimization problems. In this paper, we propose a novel CE approach for multi-objective optimization and applied it to the multiobjective version of TEAM (Testing Electromagnetic Analysis Method) workshop problem 22, demonstrating promising results in terms of performance.

关键词： Optimization Conferences Monte Carlo methods Entropy Testing Electromagnetic analysis Histograms

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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