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Trends in preparing cyber-physical systems engineers

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Cyber-Physical Systems 2019年第2期5卷 65-91页

作者： Lieu Tran, Thi Bich Törngren, Martin Nguyen, Huu Duc Paulen, Radoslav Gleason, Nancy Webster Duong, Trong Hai Department of Education Management VNU University of Education Hanoi Viet Nam KTH ICES School of Industrial Engineering and Management Department of Machine Design Stockholm Sweden Vietnam National University Hanoi Viet Nam Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Bratislava Slovakia Centre for Teaching and Learning & Senior Lecturer Global Affairs Division of Social Sciences Yale-NUS College Singapore Institute of Science and Technology of Industry 4.0 & Department of Information Technology Nguyen Tat Thanh University Hochiminh City Viet Nam

Demand for prepared Cyber-physical systems (CPSs) engineers is growing in the Industry 4.0 economy and universities should respond accordingly. This article identifies trends in preparing the CPSs workforce of the future by reviewing educational curricula and research on CPSs of universities around the world. The research finds three main approaches being deployed: 1) Offering universal CPSs foundational training for different industrial fields;2) becoming more focus on CPSs training at the doctoral level and for a specific industrial field;and 3) CPSs training as an embedded or introduction course in engineering curricula. The research identifies a need for ‘educational renewal’ in the era of CPSs training. Reorganizing university structures for collaboration and innovation in CPSs training and research is essential. Preparing interdisciplinary CPSs doctoral-level graduates and engineers for specific industrial fields should become a central focus. Equipping CPSs engineers with skills, especially creativity and entrepreneurial competencies, should be a priority. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cyber Physical System

Join the Fold: Video Games, Science Fiction, and the Refolding of Citizen Science

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design ISSUES 2023年第1期39卷 70-87页

作者： Milburn, Colin Buse, Katherine Dhaliwal, Ranjodh Singh Wills, Melissa Aldosari, Raida Camarador, Patrick Miller, Josh Aaron Siegel, Justin Colin Milburn is Gary Snyder Chair in Science and the Humanities and a professor of Science and Technology Studies English and Cinema and Digital Media at the University of California Davis. His research focuses on science literature and media technologies. His books include Nanovision: Engineering the Future (2008) Mondo Nano: Fun and Games in the World of Digital Matter (2015) and Respawn: Gamers Hackers and Technogenic Life (2015). Katherine Buse is an assistant professor of Media Studies at the University of Chicago. Her research focuses on digital media the environmental humanities and the cultures of science and technology. Her current book project Speculative Planetology: Science Culture and the Building of Model Worlds tells the story of how the sciences and the arts co-constructed a toolkit for thinking about planets as whole systems. Ranjodh Singh Dhaliwal is Ruth and Paul Idzik College Chair in Digital Scholarship and an assistant professor of English and Film Television and Theatre at the University of Notre Dame. His work spans media theory science and technology studies and literary criticism. His current book project Rendering: A Political Diagrammatology of Computation shows how our cultural politico-economic and epistemic formulations crystallize into hardware and software architectures. Melissa Wills is a visiting scholar in the Department of Science and Technology Studies at the University of California Davis. She completed her PhD in English at UC Davis in 2021. Her research addresses the relationship between the life sciences and science fiction. Her current book project Ruptures: Life without Germs in the Microbiome Era examines microbiology's debt to the imagination of germ-free worlds. Raida Aldosari is the lead artist on the Foldit: First Contact development team. She received her BS in Nutrition Science from the University of California Davis in 2018. She has experience as a freelance graphic designer and as a project manager for nutrition training and courses.

This article explores the value of science fiction narratives in games for citizen science. Focusing on the protein-folding game Foldit, it describes the process of modifying and redesigning the game to feature a framing narrative and other alterations to the main tutorial campaign. The campaign narrative, Foldit: First Contact, situates the practices of citizen science in an expanded context of meanings and ethical implications, promoting critical self-reflection on the relations of science and civic values. A study of player responses to Foldit: First Contact suggests the significance of science fiction and critical game design for attuning citizen scientists to the collective responsibilities of experimentation and innovation, drawing attention to the intersecting social, technical, and environmental domains in which gamers may contribute to scientific research.

关键词： citizen science science fiction video games narratives

Effective fusion of deep multitasking representations for robust visual tracking

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arXiv 2020年

作者： Marvasti-Zadeh, Seyed Mojtaba Ghanei-Yakhdan, Hossein Kasaei, Shohreh Nasrollahi, Kamal Moeslund, Thomas B. Department of Electrical Engineering Yazd University Yazd Iran Sharif University of Technology Tehran Iran Vision and Learning Lab. University of Alberta Edmonton Canada Department of Electrical Engineering Yazd University Yazd Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark

Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This paper aims to evaluate the performance of twelve state-of-the-art ResNet-based FENs in a DCF-based framework to determine the best for visual tracking purposes. First, it ranks their best feature maps and explores the generalized adoption of the best ResNet-based FEN into another DCF-based method. Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters. Finally, it introduces a new and efficient semantic weighting method (using semantic segmentation feature maps on each video frame) to reduce the drift problem. Extensive experimental results on the well-known OTB-2013, OTB-2015, TC-128, and VOT-2018 visual tracking datasets demonstrate that the proposed method effectively outperforms state-of-the-art methods in terms of precision and robustness of visual tracking. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Factorized multimodal transformer for multimodal sequential learning

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arXiv 2019年

作者： Zadeh, Amir Mao, Chengfeng Shi, Kelly Zhang, Yiwei Liang, Paul Poria, Soujanya Morency, Louis-Philippe Language Technologies Institute Machine Learning Department SCS CMU Singapore University of Technology and Design

The complex world around us is inherently multimodal and sequential (continuous). Information is scattered across different modalities and requires multiple continuous sensors to be captured. As machine learning leaps towards better generalization to real world, multimodal sequential learning becomes a fundamental research area. Arguably, modeling arbitrarily distributed spatio-temporal dynamics within and across modalities is the biggest challenge in this research area. In this paper, we present a new transformer model, called the Factorized Multimodal Transformer (FMT) for multimodal sequential learning. FMT inherently models the intramodal and intermodal (involving two or more modalities) dynamics within its multimodal input in a factorized manner. The proposed factorization allows for increasing the number of self-attentions to better model the multimodal phenomena at hand;without encountering difficulties during training (e.g. overfitting) even on relatively low-resource setups. All the attention mechanisms within FMT have a full time-domain receptive field which allows them to asynchronously capture long-range multimodal dynamics. In our experiments we focus on datasets that contain the three commonly studied modalities of language, vision and acoustic. We perform a wide range of experiments, spanning across 3 well-studied datasets and 21 distinct labels. FMT shows superior performance over previously proposed models, setting new state of the art in the studied datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Correction to: A systemic framework of energy efficiency in schools: experiences from six European countries

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Energy Efficiency 2023年第4期16卷 1-2页

作者： Brychkov, Dmitry Goggins, Gary Doherty, Edelle Romero, Natalia Roudil, Nadine Di Trani, Antonella Singh, Abhigyan Smit, Sander McLoughlin, Eilish de Castro Rodrigues Lima, Raquel Günbay, Suzan Marie Delmonte, Branca Arthur Hill, Achim Domegan, Christine Clifford, Eoghan J.E. Cairnes School of Business and Economics University of Galway Galway Ireland School of Geography Archaeology and Irish Studies University of Galway Galway Ireland School of Engineering University of Galway Galway Ireland Faculty of Industrial Design Engineering Delft University of Technology Delft The Netherlands Sociology UMR CNRS LAVUE National School of Architecture of Paris-Val de Seine Paris France Anthropology UMR CNRS LAVUE National School of Architecture of Paris-Val de Seine Paris France R2M Solution Woking UK School of Physical Sciences and Centre for the Advancement of STEM Teaching and Learning Dublin City University Dublin 9 Ireland School of Engineering College of Science and Engineering University of Galway Galway Ireland Department of Engineering Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg Fachbereich Bauen+LebenFachrichtung Bauingenieurwesen Trier University of Applied Sciences Trier Germany

The Nexus Between VR Affordability, Cognition, and VR Addiction: A Gaming Perspective

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The Nexus Between VR Affordability, Cognition, and VR Addict...

Immersive learning Research Network (iLRN), Conference of the

作者： Xuesong Zhai Minjuan Wang Rongting Zhou Muhammad Azfar Anwar Saba Saneinia Intikhab Ahmad Man Li Fahad Asmi College of Education Zhejiang University Hangzhou China Learning Design and Technology San Diego State University San Diego US Department of Science and Technology Communication and Policy University of Science and Technology of China Hefei China School of Public Affairs University of Science and Technology of China Hefei China Hefei Institute of Adolescent Psychology Hefei China

ISBN: (数字)9781734899504

ISBN: (纸本)9781728152264

Although some experimental research investigated the positive effect of VR games on learning, the problematic use of VR games has become increasingly severe among a large proportion of learners. One of these is learner addiction in VR games. However, there is still a lack of literature on VR game addiction in the learning context. This study examines the formation process of addiction with a particular focus on the abilities of VR technology to understand the mechanism of VR game addiction. Based on the cognitive-behavioral model, this study used empirical data to generate findings and implications. This study used multiple mediations and hierarchal regression for moderation evaluation with a multi-perspective approach, which is developing the cognition behavioral changes of users. Findings show that addictive behavior is determined by VR experiences (i.e., presence and flow experience) of VR games. Besides, these experiences are influenced by Mental Imagery (MI), VR Game Curiosity (VGC), and VR Affordance (VRF). We enhance the cognitive-behavioral model in the current context by identifying MI, VGC, and VRF as distal causes and perceived presence and flow experiences as proximal causes. The practical implications of preventing VR game addiction are also discussed in this research.

关键词： Games Education Virtual environments Psychology Media Internet

RFID Characteristics Test as Business Needs at Logistic Companies in Indonesia 3

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RFID Characteristics Test as Business Needs at Logistic Comp...

3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Irsan, Muhamad Fitria Murad, Dina Fernando, Erick Touriano, Derist Corradini, Andrea Tangerang15118 Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Information System Faculty of Information Technology University of Adi Wangsa Jambi Indonesia Copenhagen School of Design and Technology Kea Digital Copenhagen Denmark

In embedded RFID chips for storing records or data, such as Electronic Product Code one of them using barcode, in tag. Tags have functions to be able to transmit data for readable readers or stationary readers. using radio transmission technology to read data in tags. Each computer in the same system network can share and track data, regardless of where the item is searched for within the scope of the marked or restricted area. Available information may include item code, item date, delivery date, and item price. This study presents a framework based on RFID Proxied technology Characteristics Real Time Data Processing, Continuous Data Tracking and Discrete Data, and Reuse and explores its relationship with Business Needs. The observed populations are supervisors and managers working in logistic companies taken cross section obtained from a number of logistics companies incorporated in the Indonesian Logistics Association (ALI) in 2015. Sampling is done using convenience sampling method (convenience sampling method) because the population of respondents working in logistics companies in Indonesia is not known with certainty. This research can contribute to how users can select and measure RFID technology from their characteristics so that the application of RFID systems and technology becomes an added value for the company not just as a complementary technology only. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Radio frequency identification (RFID)

Hybrid neuro-evolutionary method for predicting wind turbine power output

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arXiv 2020年

作者： Neshat, Mehdi Abbasnejad, Ehsan Heydari, Azim Garcia, Davide Astiaso Nezhad, Meysam Majidi Groppi, Daniele Tjernberg, Lina Bertling Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Australian Institute for Machine Learning University of Adelaide Australia Sapienza University of Rome Italy Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production. In recent years Machine learning (ML) methods have been successfully applied in a wide range of domains, including renewable energy. However, due to the challenging nature of power prediction in wind farms, current models are far short of the accuracy required by industry. In this paper, we deploy a composite ML approach-namely a hybrid neuro-evolutionary algorithm-for accurate forecasting of the power output in wind-turbine farms. We use historical data in the supervisory control and data acquisition (SCADA) systems as input to estimate the power output from an onshore wind farm in Sweden. At the beginning stage, the k-means clustering method and an Autoencoder are employed, respectively, to detect and filter noise in the SCADA measurements. Next, with the prior knowledge that the underlying wind patterns are highly non-linear and diverse, we combine a self-adaptive differential evolution (SaDE) algorithm as a hyper-parameter optimizer, and a recurrent neural network (RNN) called Long Short-term memory (LSTM) to model the power curve of a wind turbine in a farm. Two short time forecasting horizons, including ten-minutes ahead and one-hour ahead, are considered in our experiments. We show that our approach outperforms its counterparts. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

An evolutionary deep learning method for short-term wind speed prediction: A case study of the lillgrund offshore wind farm

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arXiv 2020年

作者： Neshat, Mehdi Nezhad, Meysam Majidi Abbasnejad, Ehsan Tjernberg, Lina Bertling Garcia, Davide Astiaso Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Sapienza University of Rome Italy Australian Institute for Machine Learning The University of Adelaide Australia School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy

Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study uses a new hybrid evolutionary approach that uses a popular evolutionary search algorithm, CMA-ES, to tune the hyper-parameters of two long-term-short-term (LSTM) ANN models for wind prediction. The proposed hybrid approach is trained on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea. Two forecasting horizons including ten-minutes ahead (absolute short term) and one-hour ahead (short term) are considered in our experiments. Our experimental results indicate that the new approach is superior to five other applied machine learning models, i.e., polynomial neural network (PNN), feed-forward neural network (FNN), nonlinear autoregressive neural network (NAR) and adaptive neuro-fuzzy inference system (ANFIS), as measured by five performance criteria. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems