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Management Model for Excellent World-Class Research Center 2nd

Management Model for Excellent World-Class Research Center

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Lekapat, Sukanjana Sethakul, Panarit Phattanasak, Matheepot King Mongkuts Univ Technol North Bangkok Welding Inst Thailand Thai French Innovat Inst Bangkok Thailand King Mongkuts Univ Technol North Bangkok Welding Inst Thailand Bangkok Thailand King Mongkuts Univ Technol North Bangkok Dept Teacher Training Elect Engn Bangkok Thailand

ISBN: (纸本)9783030402716;9783030402709

Management modeling for the Renewable Energy Research Center (RERC), the research center under an umbrella of the Thai-French Innovation Institute (TFII), King Mongkut's University of Technology North Bangkok (KMUTNB) has been developed to go forward into an excellent world-class international research center. The model was a new finding based on case-studies from the earlier RERC, including from two world-class laboratories in France such as the Groupe de Recherche en Electrotechnique et Energy de Nancy (GREEN) and the Laboratoire Plasma et Conversion d'Energie (LAPLACE) as well as an Institute for Tropical Technology (ITT) in Vietnam. Two sets of the questionnaire are used as instruments for performance evaluation of international collaborative research management model and for the evaluation of the international collaborative research center. Thanks to the agreement of the international expert reviewers, the proposed model has a short command line. Transversal research activities between expertise groups in the Research Department are proposed to give solutions to massive sophisticated projects.

关键词： Excellent research center Management model international collaborative research

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M-learning as Support Tool in the Diffusion of the Traditional Food: Case Study Ambato 2nd

M-learning as Support Tool in the Diffusion of the Tradition...

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作者： Paez-Quinde, Cristina Torres-Onate, Francisco Viteri, Maria-Fernanda Porras, Maria-Emilia Univ Tecn Ambato Fac Ciencias Humanas & Educ Ambato Ecuador

ISBN: (纸本)9783030402747;9783030402730

The present investigation has as main objective to evaluate the impact that mobile technologies have about the current diffusion of the Ambato's gastronomy, starting from the origins, changes and contributions through the history of the main ingredients that form part of the city's traditional menu. Also the contribution of identity to the place, until the verification of the acceptability level that the different traditional culinary options have within the locals, that are subject to a promotion coupled to a technological society that expects to have all its activities and interests available through the use of mobile devices. This research was based on the ADDLE methodology, for the application development, which was also validated through the UTAUT technology acceptability instrument. Considering that the application had two approaches that were both the technological, and educational, the statistic used for this research was Friedman, since when the mobile application was developed, it was applied to the population having a pretest and a posttest with a final result that allowed accepting the alternate hypothesis proposed in the investigation. The result of the project allowed to use an auxiliary technological tool to boost the interest and knowledge that the current generations have regarding the traditional gastronomy of Ambato city through the distribution of culinary information in reference to the preparation processes of the different dishes in the local traditional menu that can strength the cultural identity of Ambato's society in a practical and didactic manner.

关键词： Culture Traditional food learning Mobile technology Gastronomic diffusion Applications

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Measure for Measure: How Do We Assess Human Autonomy Teaming? 22nd

Measure for Measure: How Do We Assess Human Autonomy Teaming...

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22nd international conference on Human-Computer Interaction,HCII 2020

作者： Richards, Dale Nottingham Trent University NottinghamNG11 8NS United Kingdom

ISBN: (纸本)9783030601270

The increasing use of advanced automation and intelligent support systems provides an opportunity for increased efficiency and minimizing the requirement of presenting the human with laborious, dangerous and repetitive tasks. It is important to remember that although a large degree of this capability is technology-focused, the significance of the human must not be relegated or dismissed. The human component is tightly coupled to the whole system and should be viewed as working in harmony with the autonomous system. In many instances we are seeing systems that bring both human and autonomy closer together, working as teams. With both the human and autonomy working together it is important to be able to evaluate the effectiveness of this teaming arrangement. However, methods for measuring the effectiveness of human-autonomy teaming tends to focus solely on the task, with little consideration being given to how the human integrates within the system. This paper explores the factors associated with task-based measures and highlights key cognitive factors that should be considered. It is unlikely that a single metric can be used to provide a score, but rather a combination of metrics that pull in different components of the human-autonomy team. The ability to measure the effectiveness of human-autonomy teams will not only provide feedback as to current manifestations of teaming, but has implications into their future design. © 2020, Springer Nature Switzerland AG.

关键词： Human engineering

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Partial discharge diagnosis based on TMR magnetic field sensor

Partial discharge diagnosis based on TMR magnetic field sens...

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22nd international Symposium on High Voltage engineering (ISH 2021)

作者： S. Pan J. Hu R. Bi H. Ma J. He Z. Liu P. Li B. Tian Z. Wang Q. Lv B. Luo Department of electrical engineering Tsinghua University Beijing People's Republic of China Digital Grid Research Institute China Southern Power Grid Guangzhou People's Republic of China

The detection of partial discharge signals is an effective means to avoid major safety accidents caused by partial discharges. If the measuring device can be buried inside the equipment and the partial discharge pattern recognition can be performed, faults can be better located and repaired in time. This article intends to design a partial discharge measurement device based on a Tunneling Magneto Resistive magnetic field sensor. The main research content of the thesis includes the following four aspects: First, use Rogowski coil to analysis the electromagnetic signal characteristics of the partial discharge. Then the energy distribution is concentrated between 20MHz-40MHz. The current amplitude is milliamp. According to the current characteristics of the partial discharge, the partial discharge detection device of the TMR sensor is developed. Complete the performance test of the sensor. The sensor is used for waveform detection and data statistics of the discharge model. Discover the features in the data. Finally, the data will be classified and visualized by dimensionality reduction, and machine learning methods will be used to train the data, with a recognition success rate of up to 100%.

关键词： coils magnetic field measurement magnetic sensors magnetoresistive devices partial discharge measurement signal detection tunnelling magnetoresistance

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Work-in-Progress: Reducing Social Loafing in Information Technology Undergraduate Group Projects 2nd

Work-in-Progress: Reducing Social Loafing in Information Tec...

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作者： Samarakoon, S. M. Uthpala Prasadini Imbulpitiya, Asanthika Sri Lanka Inst Informat Technol Malabe Sri Lanka

ISBN: (纸本)9783030402747;9783030402730

Project-based learning plays a major role in undergraduate education. In Information Technology education, almost all the undergraduate degree programs include group projects starting from the first year onwards. The main objective of these projects is to give students hands-on experience of the theories they have learnt so far, as well as to improve their soft skills required for teamwork, meeting deadlines, leadership and many more. Moreover, projects may help students to improve their critical thinking abilities, face challenges confidently and come up with creative solutions when there is group involvement. Though there are many advantages in group learning, social loafing or simply free riding turns out to be the major problem in this type of group-based projects. Some students in group projects put in less effort into the work than when they work alone. They try to survive in the group and then take the credit for someone else's work. This scenario tends to de-motivate the hard working members and also create group conflicts. Ultimately, this problem of social loafing affects the group performance and the final outcome of the project may not be as successful as expected. Therefore, finding effective mechanisms for reducing social loafing in group projects is becoming a critical need. The existing research on this topic mainly covers marketing and other fields, but not the field of Information Technology. Hence, this research is focused on reducing social loafing among Information Technology undergraduates.

关键词： Free-riding Group projects Information Technology Project based learning Social loafing

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A Convolutional neural network and sequence-to- sequence model based energy disaggregation algorithm for non-intrusive load monitoring

A Convolutional neural network and sequence-to- sequence mod...

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22nd international Symposium on High Voltage engineering (ISH 2021)

作者： W. Lian T. Wu Y. He Z. Shan G. Si Smart Grid Xian Electric Power Supply Company Xi'an People's Republic of China Smart Grid Shanxi Electric Power Company Xi'an People's Republic of China School of Electrical Engineering of Xi'an Jiaotong University Xi'an People's Republic of China

With the achievement of the sensing technology and communication network, the smart monitoring and computing equipment becomes more and more common, which generates the massive data of different types and sampling frequency. The detailed information in the collected electricity usage data shows the load features and residents' electricity utilizing style, and the achievement of artificial intelligence and machine learning algorithms provide better ways for process and analysis. The appliance-level energy consumption information obtained by non-intrusive load monitoring is a great benefit for energy saving by scientific management. For this purpose, this paper adopted an energy disaggregation algorithm based on CNN and Seq2Seq model to analyse the appliances' energy consumption information. We use the local attention mechanism in decoding to process the information in the long source sequence, expected to reduce the information compression and computational complexity. The proposed method for non-intrusive load monitoring succeeds in disaggregation from the total power profiles to target appliance power profiles very close and provides more effective performance than the traditional CNN and LSTM method.

关键词： building management systems computational complexity domestic appliances energy consumption learning (artificial intelligence) power consumption convolutional neural nets load monitoring

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Compogram: Development and evaluation of its for organizing programming-knowledge by visualizing behavior 22nd

Compogram: Development and evaluation of its for organizing ...

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22nd international conference on Human Computer Interaction, HCII 2020

作者： Koike, Kento Mogi, Tomohiro Tomoto, Takahito Horiguchi, Tomoya Hirashima, Tsukasa Graduate School of Engineering Tokyo Polytechnic University Kanagawa Japan Faculty of Engineering Tokyo Polytechnic University Kanagawa Japan Graduate School of Maritime Sciences Kobe University Kobe Japan Graduate School of Engineering Hiroshima University Hiroshima Japan

ISBN: (纸本)9783030601515

Currently, computing education, especially programming education has become more important. Meanwhile, programming education has many difficulties such as should learn many concepts and skills. Researches of intelligent Tutoring System (ITSs) have been attempted to reduce these difficulties. ITSs are educational systems that able to adaptively pedagogical behavior and feedback and aim to supply adaptive tutoring to learner’s profiles by alternate to human tutors. Despite there are much supports to programming education by ITSs, there no attempts for organizing knowledge in programming. Organizing knowledge is acquiring the systematized knowledge and its scalability which enabling to existing knowledge reuse to the same or similar problems that solved once by scaling knowledge. We considered that organizing knowledge is fosters problem-solving skills, and it gains Computational Thinking eventually. Therefore, we have been focused on supporting the process of solving problems by combining a bit of program. Then we selected the knowledge of "function" and "source program that achieves the function" as knowledge to be organized. And, we defined a pair of knowledge as a component. In this paper, we proposed and developed Compogram: an ITS for organizing knowledge by visualizing behavior in programming. Furthermore, for identifying learning gains, we conducted an evaluation compared to our conventional systems. Results were suggested that Compogram was fostering knowledge organizing skills that can apply to out of learning ranges. © Springer Nature Switzerland AG 2020.

关键词： Computer aided instruction

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Alzheimer's disease distinction based on gait feature analysis 22

Alzheimer's disease distinction based on gait feature analys...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： You, Zhiyang You, Zeng Li, Yilong Zhao, Shipeng Ren, Huixia Hu, Xiping Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Wisconsin-Madison Computer Sciences Department Madison United States Department of Neurology Shenzhen People's Hospital The 2nd Clinical Medical College of Jinan University Shenzhen China Clinical Medicine Postdoctoral Research Station Jinan University Guangzhou China

ISBN: (纸本)9781728162676

Alzheimer's disease(AD) is a neurodegenerative disease that progresses slowly but worsens gradually, also, the most common kinds of dementia. Clinically, the diagnosis of AD is mainly based on rating scales and neuroimaging technology which is invasive, costly and time-consuming. Other than that, the clinical pathology has become irreversible when neuroimaging characteristics appear. It is imperative to develop new noninvasive methods for early diagnosis of AD. Several studies indicated the probable association of cognitive decline with gait changes might shed light on potential features for distinction of AD. This paper aims to exploit the feasibility of gait features for early diagnosis of mild cognitive impairment(MCI) and AD by using machine learning methods. A device-free AD detection system is built, with a natural undisturbed gait collecting system and a well-performed Long Short-Term Memory(LSTM) based model, in this article. Moreover, it can serve as a simplified, non-invasive, and highly accurate clinical auxiliary tool for early diagnosis and distinction of AD. Experimental results showed a 90.48%, 92.00%, and 88.24% in accuracy, sensitivity, and specificity respectively for distinguishing AD by using the method with LSTM based model. Furthermore, the gait cycle and stride length in MCI or AD were more variable than in healthy controls through redefining and calculating the gait features with skeleton data obtained by Kinect devices. © 2021 IEEE.

关键词： Neuroimaging

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A Reinforcement learning-Based Virtual Machine Placement Strategy in Cloud data Centers 22

A Reinforcement Learning-Based Virtual Machine Placement Str...

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22nd IEEE international conference on High Performance Computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Long, Saiqin Li, Zhetao Xing, Yun Tian, Shujuan Li, Dongsheng Yu, Rong School of Computer Science Xiangtan University Key Lab. of Hunan Prov. for Internet of Things and Info. Secty. Hunan Intl. Sci. and Technol. Coop. Base of Intelligent Netwk. Xiangtan China Xiangtan University School of Computer Science Xiangtan China National University of Defense Technology College of Computer Changsha China

ISBN: (纸本)9781728176499

With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms. © 2020 IEEE.

关键词： Energy conservation

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Machine learning Methods on COVID-19 Situation Prediction

Machine Learning Methods on COVID-19 Situation Prediction

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international conference on Artificial Intelligence and Computer engineering (ICAICE)

作者： Yang, Zhihao Chen, Kang'an Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Wuhan Univ Sch Math & Stat Wuhan 430072 Hubei Peoples R China

ISBN: (纸本)9781728191461

COVID-19 has already had a devastating impact on economic and social development and people's life all over the world. Up to September 22nd, 2020, more than 30 million people have been infected. Finding out how to predict and estimate the pandemic trend precisely is of huge necessity because COVID-19 has made the world economy in a recession and deprived over 700 thousand lives. This paper is dedicated to making a comparison between conventional machine learning regression models, including ridge regression and lasso regression, and multivariate polynomial regression. Besides, we attempt to use statewide data to fit nationwide data in the US, proposing a novel aspect to forecast pandemic trend. Under the current situation, insufficient data limit the use of machine learning. To address the issue of data deficiency, a classification model based on neural network with Twitter data is applied. This gives out an alternative approach to estimate daily increase of infected people. We discovered that in a different period, specific models would outweigh another models' performance. Also, our result showed that the data of Georgia and Massachusetts could represent the whole nation data with linear transformations. And this paper verified that using alternative data that relate to the COVID-19 situation to alleviate data deficiency is feasible.

关键词： COVID-19 Machine learning Trend prediction Regression model Neural network

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