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Using the COMET Method in the Sustainable City Transport Problem: an Empirical Study of the Electric Powered Cars

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Procedia computer Science 2018年 126卷 2248-2260页

作者： Wojciech Sałabun Artur Karczmarczyk Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zołnierska 49 71-210 Szczecin Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zołnierska 49 71-210 Szczecin Poland

This paper investigates the problem of sustainable city transport. The significance of this problem is increasing, considering the inevitable increase of air pollution levels in cities. Currently, cars with diesel engines are not admitted to many city centers, and this trend can be expected to be maintained. Moreover, this restriction will be expanded to all combustion cars in the foreseeable future. Therefore, combustion vehicles will eventually be replaced by electric ones. In this paper, the authors use the Characteristic Objects METhod (COMET) to identify a decision-making model for the selection of the best model of car for sustainable city transport. The COMET method is a new approach that is free of the rank reversal phenomenon. The identified model provides information about the significance level of each of the criteria. The presented approach can easily be expanded by using a greater number of criteria and alternatives, depending on the specification of the considered problem. Moreover, the interval numbers concept is proposed as the basic approach to dealing with input data uncertainty by using the COMET method. The presented technique is an efficient and highly accurate approach to make decisions based on experts’ experience.

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Author Correction: Light Intensity-dependent Variation in Defect Contributions to Charge Transport and Recombination in a Planar MAPbI3 Perovskite Solar Cell

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Scientific reports 2020年第1期10卷 4317页

作者： Shinyoung Ryu Duc Cuong Nguyen Na Young Ha Hui Joon Park Y H Ahn Ji-Yong Park Soonil Lee Department of Energy Systems Research Ajou University Suwon 16499 Korea. Faculty of Engineering Physics and Nanotechnology VNU University of Engineering and Technology Vietnam National University Hanoi Vietnam. Department of Physics Ajou University Suwon 16499 Korea. Department of Electrical and Computer Engineering Ajou University Suwon 16499 Korea. Department of Energy Systems Research Ajou University Suwon 16499 Korea. soonil@ajou.ac.kr. Department of Physics Ajou University Suwon 16499 Korea. soonil@ajou.ac.kr.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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ORSIm detector: A novel object detection framework in optical remote sensing imagery using spatial-frequency channel features

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arXiv 2019年

作者： Wu, Xin Hong, Danfeng Tian, Jiaojiao Chanussot, Jocelyn Li, Wei Tao, Ran School of Information and Electronics Beijing Institute of Technology 100081 China ChinBeijing Key Laboratory of Fractional Signals and Systems School of Information and Electronics Beijing Institute of Technology Beijinga 100081 Wessling 82234 Germany Munich 80333 Germany Wessling 82234 Germany Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-lab Grenoble F-38000 France Faculty of Electrical and Computer Engineering University of Iceland Reykjavik 101 Iceland

This is the pre-acceptance version, to read the finalversion please go to IEEE Transactions on Geoscience andRemote Sensing on IEEE Xplore. With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-frequency channel feature (SFCF) by jointly considering the rotation-invariant channel features constructed in frequency domain and the original spatial channel features (e.g., color channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

Hyperspectral image classification with context-aware dynamic graph convolutional network

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arXiv 2019年

作者： Wan, Sheng Gong, Chen Zhong, Ping Pan, Shirui Li, Guangyu Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Science and Technology on ATR National University of Defense Technology Changsha410073 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. However, conventional spatial context-based methods simply assume that spatially neighboring pixels should correspond to the same land-cover class, so they often fail to correctly discover the contextual relations among pixels in complex situations, and thus leading to imperfect classification results on some irregular or inhomogeneous regions such as class boundaries. To address this deficiency, we develop a new HSI classification method based on the recently proposed Graph Convolutional Network (GCN), as it can flexibly encode the relations among arbitrarily structured non-Euclidean data. Different from traditional GCN, there are two novel strategies adopted by our method to further exploit the contextual relations for accurate HSI classification. First, since the receptive field of traditional GCN is often limited to fairly small neighborhood, we proposed to capture long range contextual relations in HSI by performing successive graph convolutions on a learned region-induced graph which is transformed from the original 2D image grids. Second, we refine the graph edge weight and the connective relationships among image regions by learning the improved adjacency matrix and the 'edge filter', so that the graph can be gradually refined to adapt to the representations generated by each graph convolutional layer. Such updated graph will in turn result in accurate region representations, and vice versa. The experiments carried out on three real-world benchmark datasets demonstrate that the proposed method yields significant improvement in the classification performance when compared with some state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

Adding Two Dimensions to Heart Rate Variability Research

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Adding Two Dimensions to Heart Rate Variability Research

computers in Cardiology (CinC)

作者： Joachim A Behar Ori Shemla Ido Weiser-Bitoun Aviv A Rosenberg Yael Yaniv The Technion Bioelectric and Bio-energetic Systems Laboratory Haifa Israel Faculty of Biomedical Engineering Technion Israel Institute of Technology Haifa Israel Department of Computer Science Israel Institute of Technology Haifa Israel

ISBN: (纸本)9781728109244

Introduction: Heart rate variability (HRV) analysis tools have been mainly available for analysis of human electrocardiographic derived heart rate. We explore extending HRV analysis to two additional dimensions: (1) analysis across multiple mammalian species and (2) analysis across different levels of integration for example sinoatrial tissue. Methods: We analyzed the beating rate variability (BRV) across the two additional dimensions using the PhysioZoo computer program that we recently introduced. We used published databases of electrocardiograms from four mammal types: human (n=18), dog (n=17), rabbit (n=4) and mouse (n=8). We computed the BRV measures for each. We also show how the PhysioZoo program can be used for the analysis of sinoatrial node tissue BRV. Results: The study of typical mammalian heart and respiration rates (obtained from the dominant high frequency peak) revealed a linear relationship between these two quantities. Analysis of the rabbit sinoatrial node tissue BRV showed that it had reduced overall variability when compared to in vivo heart BRV.

关键词： Heart rate variability Rabbits Databases Mice High frequency

Node verification to join the cloud environment using third party verification server

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International Journal of Interactive Mobile Technologies

International Journal of Interactive Mobile Technologies 2017年第4期11卷 55-65页

作者： Aljammal, Ashraf H. Bani-Salameh, Hani Alsarhan, Ayoub Kharabsheh, Mohammad Obiedat, Mamoon Department of Computer Science and its Applications Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University Zarqa Jordan

Currently, cloud computing is facing different types of threats whether from inside or outside its environment. This may cause cloud to be crashed or at least unable to provide services to the requests made by clients. In this paper, a new technique is proposed to make sure that the new node which asks to join the cloud is not composing a threat on the cloud environment. Our new technique checks the node before it will be guaranteed to join the cloud whether it runs malwares or software that could be used to launch an attack. In this way the cloud will allow only the clean node to join it, eliminating the risk of some types of threats that could be caused by infected nodes.

关键词： Network security

An API for development of user-defined scheduling algorithms in aneka PaaS cloud software: user defined schedulers in aneka PaaS cloud software

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arXiv 2018年

作者： Sandhu, Rajinder Toosi, Adel Nadjaran Buyya, Rajkumar CLOUDS Laboratory School of Computing and Information Systems University of Melbourne Australia Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat India Faculty of Information Technology Monash University Australia

Cloud computing has been developed as one of the prominent paradigm for providing on demand resources to the end user based on signed service level agreement and pay as use model. Cloud computing provides resources using multitenant architecture where infrastructure is generated from multiple or single geographical distributed cloud datacenters. Scheduling of cloud application requests to cloud infrastructure is one of the main research area in cloud computing. Researchers have developed many scheduling applications for which they have used different simulators available in the market such as CloudSim. Performance of any scheduling algorithm will be different when applied to real time cloud environment as compared to simulation software. Aneka is one of the prominent PaaS software which allows users to develop cloud application using various programming models and underline infrastructure. In this chapter, a scheduling API is developed over the Aneka software platform which can be easily integrated with the Aneka software. Users can develop their own scheduling algorithms using this API and integrate it with Aneka software so that they can test their scheduling algorithm in real cloud environment. The proposed API provides all the required functionalities to integrate and schedule private, public or hybrid cloud with the Aneka software. Copyright © 2018, The Authors. All rights reserved.

关键词： Scheduling

Implicit higher-order moment matching technique for model reduction of quadratic-bilinear systems

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arXiv 2019年

作者： Asif, Mian Muhammad Arsalan Ahmad, Mian Ilyas Benner, Peter Feng, Lihong Stykel, Tatjana School of Electrical Engineering and Computer Sciences National University of Sciences and Technology Islamabad Pakistan Research Center of Modelling and Simulation National University of Sciences and Technology Islamabad Pakistan Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Faculty of Mathematics Otto-von-Guericke University Magdeburg Germany Institut für Mathematik Unversität Augsburg Universitätsstr. 14 Augsburg86159 Germany

We propose a projection based multi-moment matching method for model order reduction of quadratic-bilinear systems. The goal is to construct a reduced system that ensures higher-order moment matching for the multivariate transfer functions appearing in the input-output representation of the nonlinear system. An existing technique achieves this for the first two multivariate transfer functions, in what is called the symmetric form of the multivariate transfer functions. We extend this framework to an equivalent and simplified form, the regular form, which allows us to show moment matching for the first three multivariate transfer functions. Numerical results for three benchmark examples of quadratic-bilinear systems show that the proposed framework exhibits better performance with reduced computational cost in comparison to existing *** Codes 35G50 Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

Viroinformatics-based investigation of SARS-CoV-2 core proteins for potential therapeutic targets

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arXiv 2020年

作者： Agrawal, Lokesh Poullikkas, Thanasis Eisenhower, Scott Monsanto, Carlo Bakku, Ranjith Kumar Universidad Integral del Caribe y América Latina Kaminda Cas Grandi #79 Curaçao United States Graduate School of Comprehensive Human Sciences University of Tsukuba 1-1-1 Tennodai Tsukuba305-8577 Japan Molecular Neuroscience Unit Okinawa Institute of Science and Technology Graduate University Kunigami-gun Okinawa904-0412 Japan Human Biology School of Integrative and Global Majors University of Tsukuba 1-1-1 Tennodai Tsukuba305-0006 Japan Department of Experimental Pathology Faculty of Medicine University of Tsukuba 2-1-1 Tennodai Tsukuba305-8576 Japan Department of Infection Biology Faculty of Medicine University of Tsukuba 1-1-1 Tennodai Tsukuba305-8575 Japan Research Workgroup Ronin Institute 127 Haddon Place MontclairNJ07043-2314 United States Faculty of Engineering Information and Systems Dept. of Computer Science University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8572 Japan University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8572 Japan

Due to SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) being a novel virus, there are currently no known effective antiviral drugs capable of slowing its progress. To accelerate the discovery of potential drug candidates, bioinformatics based in silico drug discovery can be applied as a very robust tool. In the present study, more than 60 antiviral drugs already available on the market, were chosen after literature survey. These can be used in clinical trials for the treatment of COVID-19. In this study, these candidate drugs were ranked based on their potential to interact with the Spike protein and RdRp (RNA-dependent RNA polymerase) of SARS-CoV-2. Additionally, the mechanism of their action as well as how the virus infection can utilize Hemoglobin to decrease the oxygen level in blood is explained. Moreover, multiple sequence alignments of the Spike protein with 75 sequences of different viruses from the Orthocoronavirinae subfamily were performed. This gives insight into the evolutionarily conserved domains that can be targeted using drug or antibody treatment. This multidimensional study opens a new layer of understanding about the most effective drug-targetable sites on the Spike protein of SARS-CoV-2. Copyright © 2020, The Authors. All rights reserved.

关键词： Viruses