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Virtual reality learning environments for vocational education: a comparative study with conventional instructional media on two-stroke engine

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IOP Conference Series: Materials Science and engineering 2020年第1期979卷

作者： F Sholichin N M Suaib D A Irawati Sutiman M Solikin A Yudantoko A Yudianto I W Adiyasa A J Sihes H A Sulaiman School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. UTM Big Data Center Ibnu Sina Institute of Scientific and Industrial Research Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. Automotive Engineering of Education Faculty of Engineering Universitas Negeri Yogyakarta School of Education Faculty of Social Science and Humanities Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka

Virtual reality technology has been adopted as a modern learning media in vocational education. Nowadays, the practical learning needs complete equipment and tools to achieve the goal study. However, the conventional media requirement is a challenge for academician for learning process, specifically in automotive and machine areas. Virtual reality provides a solution for this problem, but still needs proper analysis on the impact of usability between conventional and VR media to which each learning objective is achieved. The case study used in this research is a two-stroke engine, which is currently a rare item in the real world. We replicated this two-stroke engine in the developed VR system using Unity game engine and Oculus quest. A comparison on the usability effectiveness on conventional and virtual reality methods was conducted using cognitive walkthrough, which follows the four steps of learning objective training. The result shows that three out of four learning objectives are similar with conventional objective training. One of the process on preparation for disassembly machine has slight difference with conventional training; faulty engine due from environment elements (liquid, rusty iron) was not included.

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Partial-order Algorithm of Model Checking in µ-Predicate Ambient Logic

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Journal of Computers (Taiwan) 2018年第5期29卷 142-159页

作者： Jiang, Hua Lin, Rong-De Zou, Fu-Min Li, Ling-Xiang Key Lab of Granular Computing Minnan Normal University ZhangZhou China School of Mathematical Science Huaqiao university QuanZhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou Fujian350008 China School of Electronics and Information Engineering Hunan University of Science and Engineering Yongzhou425199 China

This paper introduces a kind of partial-order algorithm of model-checking in finite-control mobile ambients against μ-predicate ambient logic(Ambient logic based on first-order μ-calculus). Based on Tarski's fixpoint theorem, nested predicate equations and Block Dependency Graph, a group of partial ordering relation between inter-mediate results in the process of model checking is given, and thus a kind of local model-checking algorithm is proposed. To our knowledge, this is the first algorithm which exponent of time complexity is d/2+2, the exponent of space complexity is d/2 (d is the alternate nesting depth of fixpoint operator in the formula) and this is the third model-checking algorithm for predicate ambient logic with recursion. This paper's contributions are: (1) getting a group of partial ordering relation between intermediate results in the computing process of model checking for µ-calculus first-order predicate ambient logic based on Tarski's fixpoint theorem;(2) using the partial ordering relation to design an algorithm for model checking in mobile ambients;(3) analyzing the complexity of the algorithm. © 2018 Computer Society of the Republic of China. All rights reserved.

关键词： Model checking

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Author Correction: Greengenes2 unifies microbial data in a single reference tree

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Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

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Whey reverse logistics network design: A stochastic hierarchical facility location model 22

Whey reverse logistics network design: A stochastic hierarch...

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22nd International Congress on Modelling and Simulation: Managing Cumulative Risks through Model-Based Processes, MODSIM 2017 - Held jointly with the 25th National Conference of the Australian Society for Operations Research and the DST Group led Defence Operations Research Symposium, DORS 2017

作者： Esmaeilbeigi, Rasul Eshragh, Ali Garcia-Flores, Rodolfo Heydar, Mojtaba School of Mathematical and Physical Sciences University of Newcastle Australia Optimization Applications and Platforms CSIRO Data-61 Docklands Australia School of Electrical Engineering and Computing University of Newcastle Australia

ISBN: (纸本)9780987214379

Whey reverse logistics has received a considerable attention due to the recent environmental legis-lation and competitive advantages. In this paper, we study a whey reverse logistics network design problem with stochastic demand, where demand is the amount of raw whey produced by a cheese maker that should be converted into a commercial product. We formulate this problem as a hierarchical facility location problem with two levels of facilities, namely collection centers and plants. Collection centers receive raw whey from cheese makers and convert it to concentrated whey. Then the concentrated whey is sent to plants for further processing. A plant can be established in a potential location if a collection center has already been established in that point. The objective is to determine which facilities to open and to allocate each demand point to the open facilities such that the total expected cost is minimized. The total cost is comprised of transportation cost and fixed cost of opening facilities. The problem with deterministic demand is formulated as a mixed integer linear program. We use this formulation to model the stochastic version of the problem as a two-stage stochastic program, where decisions are made at two sequential stages. At the first stage, we decide which facilities to open and at the second stage, after observing a realization of demands, we decide how to allocate each cheese maker to the open facilities. We use the sample average approximation method to estimate the expected value function. The resulting formulation can be solved using a standard linear solver. Results of a computational study on a set of randomly generated instances will be reported. © 2017 Proceedings - 22nd International Congress on Modelling and Simulation, MODSIM 2017. All rights reserved.

关键词： Stochastic programming

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Semi-supervised dictionary learning with label propagation for image classification

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Computational Visual Media 2017年第1期3卷 83-94页

作者： Lin Chen Meng Yang College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina Key Laboratory of Machine Intelligence and Advanced Computing(Sun Yat-sen University) Ministry of EducationChina

Sparse coding and supervised dictionary learning have rapidly developed in recent years,and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification,which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semisupervised dictionary learning method using label propagation(SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors *** experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.

关键词： semi-supervised learning dictionary learning label propagation image classification

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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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Fuzzy Logic Controller for Classroom Air Conditioner

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Journal of Physics: Conference Series 2020年第1期1484卷

作者： Safa Riyadh Waheed Myasar Mundher Adnan Norhaida Mohd Suaib Mohd Shafry Mohd Rahim Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Computer Techniques Engineering Department Faculity of information Technology Imam Jaafar Al-sadiq University Baghdad Iraq Islamic University Najaf Iraq UTM Big Data Center Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia UTM-IRDA Digital Media Institute of Human-Centred Engineering University Technology of Malaysia Johar Bahru Malaysia

In the modernized era, the air conditioners are an integrated part of comfort living especially in hot climates. They are used to control the interior spatial temperature, relative humidity, degree of cleanliness, and speed of air streaming. The automatic controllers are the key elements of the modern air conditioning systems that ensure the reliable operation, improved quality, low operation cost, and better security. Thus, the realization, design, and application of the controller systems require the exact specifications of the involved processes. In this regard, controllers based on the fuzzy logic (FL) are prospective for air conditioners due to the easy accessibility of different output levels. Furthermore, using the FL it is possible to scale and control the users' air processing demand depending on the temperature and relative humidity of the space. Based on these factors, this paper reports the design and performance evaluation of a FL based controller useful for air conditioners when implemented in the classroom setting. This FL based control system can reduce the complexity of programming thereby can be executed on general microcontrollers utilized in the control panels of classroom air conditioner. The results revealed the outperforming nature of the FL based controllers over other traditional controllers used to adjust the indoor temperature and relative humidity by air conditioners.

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PnP-AdaNet: Plug-and-play adversarial domain adaptation network with a benchmark at cross-modality cardiac segmentation

arXiv

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arXiv 2018年

作者： Dou, Qi Ouyang, Cheng Chen, Cheng Chen, Hao Glocker, Ben Zhuang, Xiahai Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Department of Computing Imperial College London Department of Electrical Engineering and Computer Science University of Michigan Department of Computing Imperial College London Department of Computer Science and Engineering Chinese University of Hong Kong Imsight Medical Technology Co Ltd. China Department of Computing Imperial College London School of Data Science Fudan University China

Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks. Leveraging images from different modalities for the same analysis task holds clinical benefits. However, the generalization capability of deep models on test data with different distributions remain as a major challenge. In this paper, we propose the PnP-AdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner. Specifically, a domain adaptation module flexibly replaces the early encoder layers of the source network, and the higher layers are shared between domains. With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks. We have validated our domain adaptation method on cardiac structure segmentation in unpaired MRI and CT. The experimental results with comprehensive ablation studies demonstrate the excellent efficacy of our proposed PnP-AdaNet. Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation. We will make our code and database publicly available, aiming to promote future studies on this challenging yet important research topic in medical imaging. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Balanced distribution adaptation for transfer learning

arXiv

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arXiv 2018年

作者： Wang, Jindong Chen, Yiqiang Hao, Shuji Feng, Wenjie Shenk, Zhiqi Beijing Key Laboratory of Mobile Computing and Pervasive Device CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Institute of High Performance Computing A*STAR School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution Adaptation (BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation (W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Learning algorithms

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Needs analysis for the development of interactive virtual reality-based educational media on combustion engine mechanical technology

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IOP Conference Series: Materials Science and engineering 2020年第1期979卷

作者： Sutiman A Yudantoko A Yudianto M Solikin I W Adiyasa N M Suaib F Sholichin D A Irawati Automotive Engineering Education Department Universitas Negeri Yogyakarta Colombo Street 1 Yogyakarta Indonesia. School of Computing Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. UTM Big Data Center Ibnu Sina Institute of Scientific and Industrial Research Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia.

Needs analysis is the first and necessary step for the researchers to design a newly developed learning media to find out a set of specific requirements to conduct the research activities. The study aims (1) to investigate the user's learning preference, (2) to select the suitable learning materials and to choose the appropriate type of engines, and (3) to identify the needs for the virtual reality development. A focus group discussion (FGD) comprising some representative users and experts in learning media has been involved in the activities with the use of the FGD guidance sheet as the supplementary tools. Additionally, the literature study has also been conducted to collect the necessary additional data to perform a 3D design of the engine. The data were analyzed qualitatively by examining the comment from the users, the experts, and the outcome of the literature review study. It was then concluded that (1) the users require the learning process, which does not need to alternate the learning objects. (2) The two-stroke single-cylinder engine was decided to choose by considering the complexity of the engine mechanical construction. Finally, (3) the needs for engine assembly drawing and virtual reality development have also been described.

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