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The Research of Applying Affective Computing Based on Deep Learning for eSports Training 1

9th International Conference on Frontier Computing, FC 2019

作者： Hung, Jason C. Lin, Zong-Qi Huang, Chun-Hong Lin, Kuan-Cheng Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan City Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan

ISBN: (数字)9789811532504

ISBN: (纸本)9789811532498

During a competition, eSports players will experience many emotions due to the situation in the game. The generation of emotions will affect the performance of the players in the game. By monitoring players’ facial emotions and the game process, we can understand the cause of these emotions. Therefore, we propose to construct a gaming facial emotion recognition model to identify the emotions of players. The model was trained by DenseNet through transfer learning. Open Data Videos were applied as the data source. Images of facial expressions were extracted from these videos and classified into a data set for training and testing. The training and testing result indicate a room for improvement in accuracy. The future goal will be to develop an eSports player training system that would be able to understand the emotional states of players by analyzing the facial expressions that occur in a gaming video. This research will help the coach to provide advice and assistance in the training of the players, and help players find their suitable role in a game to improve their collaboration with team mates;overall, it will provide a well formed training system for eSports players. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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A performance comparison of graph analytic methods for supporting enterprise architecture model maintenance

A performance comparison of graph analytic methods for suppo...

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2019 Practice of Enterprise Modelling Conference Forum, PoEM 2019 Forum

作者： Rajashekar, Nikhitha Hacks, Simon Silva, Nuno Research Group Software Construction RWTH Aachen University Germany Division of Network and Systems Engineering KTH Royal Institute of Technology Sweden Department of Computer Science and Engineering Technical University of Lisbon Lisbon Portugal

Enterprise Architecture (EA) models help enterprise architects to make business decisions and to support organisations to understand and analyse its structure. Creating and maintaining such an EA model is expensive and time-consuming in large organisations. In this paper, the authors provide a review of how graph analytic methods can be used to support EA model maintenance. To achieve the best results for the duplicate identification, it is necessary to evaluate and optimise different graph analytic and Machine Learning (ML) algorithms to support the maintenance process. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

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Wireless for control: Over-the-air controller

arXiv

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arXiv 2021年

作者： Park, Pangun Di Marco, Piergiuseppe Fischione, Carlo The Department of Radio and Information Communications Engineering Chungnam National University Korea Republic of The Department of Information Engineering Computer Science and Mathematics University of L'Aquila Italy The Department of Network and Systems Engineering School of Engineering KTH Royal Institute of Technology Sweden

In closed-loop wireless control systems, the state-of-the-art approach prescribes that a controller receives by wireless communications the individual sensor measurements, and then sends the computed control signal to the actuators. We propose an over-the-air controller scheme where all sensors attached to the plant simultaneously transmit scaled sensing signals directly to the actuator;then the feedback control signal is computed partially over the air and partially by a scaling operation at the actuator. Such over-the-air controller essentially adopts the over-the-air computation concept to compute the control signal for closed-loop wireless control systems. In contrast to the state-of-the-art sensor-to-controller and controller-to-actuator communication approach, the over-the-air controller exploits the superposition properties of multiple-access wireless channels to complete the communication and computation of a large number of sensing signals in a single communication resource unit. Therefore, the proposed scheme can obtain significant benefits in terms of low actuation delay and low wireless resource utilization by a simple network architecture that does not require a dedicated controller. Numerical results show that our proposed over-the-air controller achieves a huge widening of the stability region in terms of sampling time and delay, and a significant reduction of the computation error of the control signal. © 2021, CC BY.

关键词： Actuators

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Hypergraphx: a library for higher-order network analysis

arXiv

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arXiv 2023年

作者： Lotito, Quintino Francesco Contisciani, Martina De Bacco, Caterina Di Gaetano, Leonardo Gallo, Luca Montresor, Alberto Musciotto, Federico Ruggeri, Nicolò Battiston, Federico Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Trento38123 Italy Max Planck Institute for Intelligent Systems Cyber Valley Tübingen72076 Germany Department of Network and Data Science Central European University Vienna1100 Austria Dipartimento di Fisica e Chimica Emilio Segrè Università di Palermo Viale delle Scienze Ed. 18 PalermoI-90128 Italy Department of Computer Science ETH Zürich8004 Switzerland

From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where hyperedges encode interactions among an arbitrary number of units. Here, we present an open-source python library, hypergraphx (HGX), providing a comprehensive collection of algorithms and functions for the analysis of higher-order networks. These include different ways to convert data across distinct higher-order representations, a large variety of measures of higher-order organization at the local and the mesoscale, statistical filters to sparsify higher-order data, a wide array of static and dynamic generative models, and an implementation of different dynamical processes with higher-order interactions. Our computational framework is general, and allows to analyse hypergraphs with weighted, directed, signed, temporal and multiplex group interactions. We provide visual insights on higher-order data through a variety of different visualization tools. We accompany our code with an extended higher-order data repository, and demonstrate the ability of HGX to analyse real-world systems through a systematic analysis of a social network with higher-order interactions. The library is conceived as an evolving, community-based effort, which will further extend its functionalities over the years. Our software is available at https://***/HGX-Team/hypergraphx. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph theory

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Fault-tolerant control of semi-active suspension in case of oil leakage of magnetorheological damper

Fault-tolerant control of semi-active suspension in case of ...

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European Control Conference (ECC)

作者： Hakan Basargan Filip Jeniš András Mihály Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2 Budapest Hungary Faculty of Mechanical Engineering Institute of Machine and Industrial Designs Brno University of Technology Technicka 2 Brnos Czech Republic Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende u. 13-17 Budapest Hungary

The paper presents a reconfigurable fault-tolerant control strategy for a semi-active suspension using magnetorheological (MR) damper. The aim of the control reconfiguration is to handle the adverse behaviour of the MR damper due to oil leakage induced by the wear of the suspension component. The proposed method relies on the data driven model of the MR damper, using an estimation procedure to quantify the healthiness of the damper and to estimate the performance degradation due to the oil leakage. The reconfiguration control strategy is founded on the Linear Parameter Varying (LPV) framework, where a scheduling variable is defined to represent the healthiness level of the MR damper. By the scaling of the control action through the scheduling variable, the performance degradation of the MR damper can be compensated to match the behaviour of the healthy dampers. The proposed method is demonstrated through simulations, comparing the performance of the fault-tolerant LPV control to conventional semi-active control methods

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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Development of a VS ultrasound diagnostic system with image evaluation functions

Development of a VS ultrasound diagnostic system with image ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Jiayi Zhou Norihiro Koizumi Yu Nishiyama Ryosuke Tsumura Hiroyuki Tsukihara Naoki Matsumoto Department of Mechanical and Intelligent Systems The University of Electro-Communications Tokyo Japan Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Tokyo Japan Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Gastroenterology and Hepatology Nihon University Tokyo Japan

ISBN: (纸本)9781665492331

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross-section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Two different image processing methods (BiSeNet V2) were used to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50) in developed system.

关键词： Deep learning Visualization Ultrasonic imaging Image processing Medical services Probes Servomotors

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Interconnectedness of Circular Economy, Artificial Intelligence and Vertical Farming as a Technosignature Model for Sustainable Space Food Production

Interconnectedness of Circular Economy, Artificial Intellige...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Ronnie Concepcion II Carlo Cabaccan Jullian Dominic Ducut R-Jay Relano Jonnel Alejandrino Ronnel Agulto Argel Bandala Roslina Mohamad Elmer Dadios Alvin Culaba Department of Manufacturing Engineering and Management De La Salle University Manila Philippines Intelligent Systems Research Unit Center for Engineering and Sustainability Development Research De La Salle University Manila Philippines Bio-energy Environment and Renewables Laboratory Center for Engineering and Sustainability Development Research De La Salle University Manila Philippines Plant and Soil Health Research Unit Center for Natural Sciences and Environmental Research De La Salle University Manila Philippines School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines Wireless High Speed Network Research Group (WHiSNet) School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Malaysia Department of Electronics and Computer Engineering De La Salle University Manila Philippines Department of Mechanical Engineering De La Salle University Manila Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

The degradation of Earth's natural resources and environmental health due to improper anthropological activities, mainly through unoptimized food production, is manifested through climate change that negatively alters the productivity in the agricultural sector. Because of this challenge in open terrestrial agriculture, the concept of space farming is believed to be significant in delivering fresh and quality food for the people on top of providing for astronauts and making them healthy in space. However, another problem emerges in the view of circular economy (CE) which is a production and consumption idealism that avoids degradation of natural resources and environment in producing one food unit and despite the collaborative advances in space technology, particularly in astrobotany, the materialization of CE and the absolutism of artificial intelligence (AI) are still partly imaginary. This study employed a bibliometric, scientometric and systematic analysis to highlight the advantages, limitations, and connections of CE, AI, and vertical farming (VF) which led to establishing a technosignature model for sustainable space food production. Here, CE, AI and VF were singularized as a model for sustainable space food production and identified their potential interplay. Based on scientific correlations, it is undoubtedly true that CE, AI, and VF have existing mutualism in Earth-based systems, but the degree of its importance as a whole is not yet completely appreciated for space farming. The developed CE-AI-VF technosignature model is scalable and adaptable if the government and scientific community put it into action accompanied by science policies.

关键词： Natural resources Degradation Productivity Adaptation models Systematics Space technology Bibliometrics

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Spatial-Temporal Enhancement Based Tourism Demand Forecasting: A Deep Model for a Case in Urban Area

SSRN

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SSRN 2022年

作者： Dong, Yunxuan Zhou, Binggui Yang, Guanghua Hou, Fen Hu, Zheng Ma, Shaodan School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China State Key Laboratory of Internet of Things for Smart City the Department of Electrical and Computer Engineering University of Macau 999078 China State Key Laboratory of Network and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

Accurate tourism demand forecasting requires the learning of the spatial-temporal features of tourism demand, which is challenging because the features may constantly change owing to human behavior. In this study, we developed a spatial-temporal feature enhancement model to ensure the integrity of the features of tourism demand. By modeling the tourism system as an undirected graph, we employ the steady-state analysis method of the undirected graph to extract the spatial-temporal features of the tourism system, and the extracted features are employed to construct the enhancement feature vector. However, the spatial-temporal features in the enhancement feature vector are often sparse. We propose a deep model with convolutional filters to ensure the feature learning ability for sparse features;however, there are challenges to using the high-dimensional convolutional filters to learn the features of an one-dimensional time series. Therefore, we convert the feature series into an image series while maintaining the relationship of the spatial-temporal features. The digital footprints of tourists from the urban area of Zhuhai were used to verify the effectiveness of the method. Numerical experiments show that the proposed model can effectively capture the evolution process of the tourism demand system, and the forecasting performance is better than that of state-of-the-art tourism demand forecasting models. © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Federated Edge Learning via Integrated Sensing, Computation, and Communication

Federated Edge Learning via Integrated Sensing, Computation,...

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IEEE International Conference on Communications (ICC)

作者： Peixi Liu Guangxu Zhu Shuai Wang Miaowen Wen Wu Luo H. Vincent Poor Shuguang Cui State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Shenzhen Research Institute of Big Data Chinese Academy of Sciences Shenzhen Institute of Advanced Technology School of Electronic and Information Engineering South China University of Technology Department of Electrical and Computer Engineering Princeton University the Guangdong Provincial Key Laboratory of Future Networks of Intelligence School of Science and Engineering (SSE) Future Network of Intelligence Institute (FNii) the Chinese University of Hong Kong Shenzhen

Sensing, computation, and communication (SC 2 ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

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