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2018 UTP-UMP-VIT Symposium on Energy systems, SES 2018

作者： Abdalla, Ahmed N. Ibrahim, Thamir K. Tao, Hai Faculty of Engineering Technology University Malaysia Pahang Gambang26300 Malaysia CARRIFF University Malaysia Pahang Gambang26300 Malaysia Faculty of Computer System and Software Engineering University Malaysia Pahang Gambang26300 Malaysia Faculty of Mechanical Engineering University Malaysia Pahang Pekan26600 Malaysia

Liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industrial chemical processing and spray coating. A typical situation is one that requires fluid to be supplied to a tank at a constant rate which has Two-Input Two-Output (TITO). The accuracy and stability of TITO is one of the key factors of process which have cross coupling between process input and output. Unlike traditional neural network weight adaptation using gradient descent method, Fuzzy Adaptive PID control technique was utilized for adaptive tuning and fine tuning the controller's parameters. Design approach for controlling liquid levels of Coupled Tank TITO system by using Fuzzy Adaptive PID controllers. Tuning method for parameters of improved Fuzzy Adaptive, Particle Swamp Optimization and Neural network controllers ware also discussed. Experimental results demonstrate that the performances of proposed method perform well compared with convention Fuzzy and Tuned Fuzzy controllers;it was shown that Fuzzy Adaptive controller exhibited better performance in terms of transient response analysis. © 2018 The Authors, published by EDP Sciences.

关键词： Effluent treatment

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Critical Imaginaries and Reflections on Artificial Intelligence and Robots in Postdigital K-12 Education

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Postdigital Science and Education 2019年第2期1卷 427-445页

作者： Hrastinski, Stefan Olofsson, Anders D. Arkenback, Charlotte Ekström, Sara Ericsson, Elin Fransson, Göran Jaldemark, Jimmy Ryberg, Thomas Öberg, Lena-Maria Fuentes, Ana Gustafsson, Ulrika Humble, Niklas Mozelius, Peter Sundgren, Marcus Utterberg, Marie Division of Digital Learning KTH Royal Institute of Technology Stockholm Sweden Department of Applied Educational Science Umeå University Umeå Sweden Department of Applied IT University of Gothenburg Gothenburg Sweden Department of Economics and IT University West Trollhättan Sweden Faculty of Education and Business Studies University of Gävle Gävle Sweden Department of Education Mid Sweden University Sundsvall Sweden Department of Communication and Psychology Aalborg University Aalborg Denmark Department of Computer and System Science Mid Sweden University Östersund Sweden

It is commonly suggested that emerging technologies will revolutionize education. In this paper, two such emerging technologies, artificial intelligence (AI) and educational robots (ER), are in focus. The aim of the paper is to explore how teachers, researchers and pedagogical developers critically imagine and reflect upon how AI and robots could be used in education. The empirical data were collected from discussion groups that were part of a symposium. For both AI and ERs, the need for more knowledge about these technologies, how they could preferably be used, and how the emergence of these technologies might affect the role of the teacher and the relationship between teachers and students, were outlined. Many participants saw more potential to use AI for individualization as compared with ERs. However, there were also more concerns, such as ethical issues and economic interests, when discussing AI. While the researchers/developers to a greater extent imagined ideal future technology-rich educational practices, the practitioners were more focused on imaginaries grounded in current practice. © 2019, The Author(s).

关键词： Artificial intelligence Automation Educational robots K-12 education Postdigital education Symposium

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Towards Translation of Semantics of Automotive Interface Description Models from Franca to AUTOSAR Frameworks : An Approach using Semantic Synergies

Towards Translation of Semantics of Automotive Interface Des...

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International Conference on Applied Electronics, AE

作者： Sangita De Michael Niklas Brian Rooney Juergen Mottok Premek Brada Corporate System and Technology SW Continental Automotive GmbH Regensburg Germany Dept. of Electrical Engineering and Information Technology OstBayerische Technical University (OTH) Regensburg Germany Dept. of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

The automotive industry is eventually evolving into a complex network of services. The heterogeneous and distributed nature of automotive software systems demands flexible software components which can operate in different environments. Because of heterogeneous automotive development environments, the domain experts, must cope with too many diversities, adaption layers, and incompatibilities to design applications for the current generation of autonomous driving vehicles. In this context, interface adaptation is a promising approach to achieve flexibility without directly changing the respective components. AUTOSAR, which is the de-facto standard for describing automotive system architecture and is a hugely comprehensive standard allowing designers full control from abstract system description to bare metal level deployment. However, the vehicle subsystems have still evolved to include multifarious high-level domains not covered by AUTOSAR e.g. Infotainment, Telematics etc. Therefore, it seems beneficial to bridge the semantic gaps between AUTOSAR applications and other standards of automotive application domains. The goal of this paper is to investigate interface semantic mapping and achieve transparent integration of domain-specific applications using the translation of semantics among the AUTOSAR platform software component models and other software components models of open source development platforms e.g. GENIVI. A key goal of such a modelling approach is the reuse of existing interface description languages and respective code generators. This will enhance future interoperability and decrease in incompatibility among these platforms.

关键词： Semantics Automotive engineering Adaptation models Standards Connectors Receivers Software

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Characterizing Project Evolution on a Social Coding Platform

Characterizing Project Evolution on a Social Coding Platform

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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Koji Toda Haruaki Tamada Masahide Nakamura Kenichi Matsumoto Department of Computer Science and Engineering Fukuoka Institute of Technology Fukuoka Japan Faculty of Information Science and Engineering Kyoto Sangyo University Kyoto Japan Graduate School of System Informatics Kobe University Hyogo Japan Riken AIP Tokyo Japan

Social coding platforms (SCPs) have realized spontaneous software evolution, where new source code and ideas are spontaneously proposed by altruistic developers. Although there are many projects operated by active communities performing spontaneous evolution, it is yet unclear that how such successful projects and communities have been formed and governed. In this paper, we propose a method that can investigate the history of every project in the SCP. Introducing the concept of project as a city, we consider every project in the SCP as a city, where a government and citizens develop a city through collaborative activities. We then identify essential attributes that characterize a state of a city. For each attribute, we develop metrics that quantity the state S(p, t) of a project p at time t. An experimental evaluation investigating GitHub projects of famous code editors shows that the proposed metrics well visualize the history of the projects from essential perspectives of a city.

关键词： Urban areas Software Measurement Encoding Sociology Statistics Government

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Designing a New Employee Acceptance Personality Test Applicat on Using Web-based Edward Personal Preference Schedule

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Journal of Physics: Conference Series 2019年第1期1230卷 012077-012077页

作者： Delima Sitanggang Evta Indra Banjarnahor, Jepri Windania Purba Turnip, Mardi Girsang, Inka Septianingsih Yonata Laia Asaziduhu gea Department of Information System Faculty of Technology and Computer Sciences Universitas Prima Indonesia Indonesia Department of Information System Faculty of Technology and Computer Sciences Universitas Methodist Indonesia Indonesia

The diversity of personality possessed by humans makes one hesitate to explore the work that interests him. The world of psychology has several test tools that can be used to analyze a person’s personality which is the end result of a test that is used to see a person’s personality tendency and what type of work is suitable for the interest, where current state and private companies still use manual tests by making questionnaires or a series of questions which will be given to the object which is employee candidate to study. Then the questionnaire is filled in by each object so that the value is collected and recalculated, by using a manual system an employee will wait a very long time to see the *** solve this problem, a system is designed to facilitate the analysis of the personality of prospective employees and to place them in the positions or professions. The system was designed using the Edward Personal Preference Schedule method is one of the personality test tools that is able to reveal 15 personalities. Based on the results of the processed data there are 3 types of personality, the first type is a personality with the highest score, namely autonomy with a score of 18, the second type is affiliation with a score of 17, and the third type is domination with a score of 17 so it can be concluded that respondents X were advised to work in military sections such as the TNI.

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Preface

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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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Expression of Concern: Hybrid auto-regressive neural network model for estimating global solar radiation in Bandar Abbas, Iran

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Environmental Earth Sciences 2020年第13期79卷 1-1页

作者： Shamshirband, Shahaboddin Mohammadi, Kasra Piri, Jamshid Petkovic, Dalibor Karim, Ahmad Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Mechanical and Industrial Engineering University of Massachusetts Amherst USA Department of Water Engineering Soil and Water College University of Zabol Zabol Iran Department for Mechatronics and Control Faculty of Mechanical Engineering University of Niš Niš Serbia

Correction to: Environ

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Uncovering why deep neural networks lack robustness: Representation metrics that link to adversarial attacks

arXiv

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arXiv 2019年

作者： Vargas, Danilo Vasconcellos Kotyan, Shashank Matsuki, Moe Faculty of Information Science and Electrical Engineering Kyushu University Japan Department of Computer Science and Engineering Dr. SPM IIIT-NR India Department of Applied Science for Integrated System Engineering Graduate School of Engineering Kyushu Institute of Technology University Japan

Neural networks have been shown vulnerable to adversarial samples. Slightly perturbed input images are able to change the classification of accurate models, showing that the representation learned is not as good as previously thought. To aid the development of better neural networks, it would be important to evaluate to what extent are current neural networks' representations capturing the existing features. Here we propose a test that can evaluate neural networks using a new type of zero-shot test, entitled Raw Zero-Shot. This test is based on the principle that some features are present on unknown classes and that unknown classes can be defined as a combination of previous learned features without learning bias. To evaluate the soft-labels of unknown classes, two metrics are proposed. One is based on clustering validation techniques (Davies-Bouldin Index) and the other is based on soft-label distance of a given correct soft-label. Experiments show that such metrics are in accordance with the robustness to adversarial attacks and might serve as a guidance to build better models as well as be used in loss functions to improve the models directly. Interestingly, the results suggests that dynamic routing networks such as CapsNet have better representation while some DNNs might be trading off representation quality for accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Learning a no-reference quality assessment model of enhanced images with big data

arXiv

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arXiv 2019年

作者： Gu, Ke Tao, Dacheng Qiao, Junfei Lin, Weisi Beijing Key Laboratory of Computational Intelligence and Intelligent System BJUT Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney J12/318 Cleveland St DarlingtonNSW2008 Australia School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

In this paper we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g. object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g. visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images which are generally thought to be of the best quality. In this work, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measre of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image datasets. Results of experiments on nine datasets validate the superiority and efficiency of our blind metric compared with typical state-of-theart full-, reduced- and no-reference IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images and dehazed images. The source code will be released at https://***/site/guke198701/publications. Copyright © 2019, The Authors. All rights reserved.

关键词： Image enhancement

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Development of Water Pipelines Energy Harvesting system

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Journal of Physics: Conference Series 2021年第1期1793卷

作者： H F Liew Abd R Rosemizi M I Fahmi Romli M. T. Nuraidah Faculty of Engineering Technology University Malaysia Perlis Sungai Chuchuh 02100 Padang Besar (U) Perlis Malaysia School of Computer and Communication Engineering. Universiti Malaysia Perlis Pauh Putra Campus 02600 Arau Perlis Malaysia School of Electrical System Engineering Universiti Malaysia Perlis Pauh Putra Campus 02600 Arau Perlis Malaysia

Previously, the threats of negative effects caused from burning process of the fossil fuels is somewhat alarming which it may lead to acid rain, increasing greenhouse emissions and global warming. Based on the highlighted issue, this study aims to develop a system where the input power came from water pipes energy harvesting system. Water pipes harvesting system will be designed using polyvinyl chloride pipes and water turbines, to transform kinetic energy of a water flow into electrical energy and the energy performance will be monitored in a web-server using Arduino and ESP8266 Wi-Fi module as its processing centre. This research studies the feasibility of harvesting some energy from pipeline vibrations, and it is also observed that harvestable energy varies with harvesting location across a pipeline network as well as the pipe flow rate in the water-air and kinetic energy in the water pressure fluctuation. The water pipelines energy harvesting systems are modelled and then analyzed by using simulated data and real data using Arduino. Turbine generator are mounted on the pipeline surface at different locations to simultaneously collect vibrations-induced energy data from water pumping. The water flow rate energy harvester system and the water pressure fluctuation introduced in this research may be able to generate up to 6.8 mW's power. The output produced from the energy harvesting system is expected to be enough to light up a 3.8 V direct current light emitting diode bulb.

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