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computer Science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Paul Oke Sing Kiong Nguang Dept. of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand

ISBN: (数字)9781728163031

ISBN: (纸本)9781728163048

This paper studies the problem of designing robust model predictive control (RMPC) for four-wheel independently driven electric vehicles (FWIDEV) using differential speed steering. The nonlinear dynamic model of the electric vehicle is recast as a linear time-varying polytopic uncertain system. An important issue for this controller is the recursive solution for the lateral stabilisation of the uncertain system without the need for conventional steering. Consequently, based on the developed predictive model for the FWIDEV, sufficient conditions for the existence of an RMPC are given in terms of linear matrix inequalities (LMI) constraints, importantly, the stability conditions of the closed loop systems are also explained. Lastly, the proposed RMPC design shows strong disturbance attenuation and robustness for the uncertain system. The simulation results have also supported the proposed design as compared with a custom Matlab solver and other LMI designs.

关键词： Robustness Predictive models Electric vehicles Roads Predictive control Mathematical model Uncertain systems

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Improved Weighted Learning Support Vector Machines (SVM) for High Accuracy 2

Improved Weighted Learning Support Vector Machines (SVM) for...

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2nd International Conference on Computational Intelligence and Intelligent Systems, CIIS 2019

作者： Dzulkifli, Syahizul Amri Salleh, Mohd Najib Mohd Talpur, Kashif Hussain Software Engineering Dept. Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (纸本)9781450372596

Support Vector machine (SVM) is a linear model designed for classification problem and popular due to a number of their attractive features such as high generalization ability and promising performance. However, the high generalization ability of SVM is only achieved by depending on a small part of the data points to determine an optimal hyperplane. During the learning process, the noise still exists to deviate severely the corresponding decision boundary from the ideal hyperplane. Two different weighted SVM such as one-step WSVM (OWSVM) and iteratively WSVM (iWSVM) has been reviewed besides the standard SVM. This method assigns relative important weights to achieve optimal margin hyperplane. In this study, an improved WSVM using moving weighted average is introduced to generate useful weighted and unweighted support vector for the optimal margin hyperplane. The methods are compared based on correctly labeled, mislabeled data within margin and classification accuracy using three datasets in KEEL repository with 20% noise. The results show that the proposed method yields better classification accuracy compared to OWSVM and iWSVM. © 2019 ACM.

关键词： Support vector machines

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Kin Genomic Data Inference Attacks Through Factor Graph

Kin Genomic Data Inference Attacks Through Factor Graph

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Mobile Ad Hoc and Sensor Systems Workshops (MASSW), International Conference on

作者： Hui Shang Zaobo He Dept. of Computer Science and Software Engineering Miami University Oxford OH USA

ISBN: (数字)9781728141213

ISBN: (纸本)9781728141220

The high availability of genome sequencing data and advancement in data mining stimulate the progress of biomedical breakthroughs and the hope of personalized medicine. Meanwhile, the popularity of personalized and commercialized genome testing brings about increasing privacy concerns. Moreover, personal genomic data is a kind of Big Data, sensitive and highly correlated among relative by blood. Therefore, it is challenging to quantitatively evaluate the risks of kin genomic data privacy. To address this problem, we construct a genome data model to capture the characteristics of kin genomic data. Besides, we develop an inference attack method by operating belief propagation on a factor graph.

关键词： Bioinformatics Genomics Diseases Belief propagation DNA Inference algorithms

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Virtual Cluster Head Based PSO and Fuzzy Logic Approach for WSN to Extend Network Lifetime

Virtual Cluster Head Based PSO and Fuzzy Logic Approach for ...

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2020 International Conference on Computing and Information Technology, ICCIT 2020

作者： Manikandan, V. Mavaluru, DInesh Gobinathan, K. Rajanarayanan, S. Jenifer, M. Rohini, R. Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq Saudi Electronic University College of Computing and Informatics Department of Information Technology Saudi Arabia Gnanamani Collge of Technology Department of CSE Namakkal India Arba Minch University Dept. of Business Administration and Information System Ethiopia Arba Minch University Arba Minch Institute of Technology Faculty of Computing and Software Engineering Ethiopia Vivekanandha College of Engineering for Women Department of CSE Namakkal India

ISBN: (纸本)9781728126807

In recent times, Wireless Sensor Networks (WSNs) are broadly utilized in targeted monitoring applications. Traditional target coverage techniques usually consider that environment is known completely and every target is covered by single sensor. However those algorithms are inflexible when the sensor is about to die, that is, lack of energy and therefore target has to be covered with more than a sensor which is termed as n coverage crisis. In general, n coverage problem is energy and time consuming process, as well sensor organization is needed all the time. To resolve this crisis, this work anticipates a n coverage model with VCH-PSO (Virtual Cluster Head based Particle Swarm Optimization and EFLM (Energy management based Fuzzy logic Model) to enlarge the lifetime of WSN. During the search of optimal area coverage, diverse factors like node position, energy consumption, and coverage range of sensors are considered significantly. Simulation was carried out in MATLAB environment and compared with prevailing techniques. The proposed model shows better trade off in terms of network lifetime and time needed for switching between various coverage regions. © 2020 IEEE.

关键词： Fuzzy logic

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Detecting Sybil Attack In Wireless Sensor Networks Using Machine Learning Algorithms

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IOP Conference Series: Materials Science and engineering 2021年第1期1042卷

作者： Mandala Mounica R. Vijayasaraswathi R. Vasavi M.Tech Student of Software Engineering Dept. of Computer Science & Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Assistant Professor Dept. of Computer Science & Engineering VNR Vignana Jyothi Institute Of Engineering and Technology Hyderabad Telangana India

In sensitive areas such as battlefields, a Wireless Sensor Network (WSN) is especially in military and civilian applications and it is of utmost importance to develop security in these networks. In various respects, this can improve the quality of life. But to be used for protection reasons in multiple situations such as implementation. There is a high risk of being exposed to multiple viruses and hacking attacks. Unauthorized APs for information protection needs to be detected. Any malicious attacks against these networks, Like Sybil Attack leads to breach of security by enacting as a node that illegitimately declares several false identities at the same time. This misleads valid nodes, and they presume each of those identifiers as actual independent nodes by accident. Thus we proposed a machine learning model to detect Sybil attack in network where rawtraffic data has been collected and is used identify authorized and unauthorized APs in an integrated wired/wireless environment.

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Modeling boundary conditions in high-order, nodal, time-domain finite element methods 23

Modeling boundary conditions in high-order, nodal, time-doma...

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23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Pind, Finnur Jeong, Cheol-Ho Hesthaven, Jan S. Engsig-Karup, Allan P. Strømann-Andersen, Jakob Henning Larsen Denmark Acoustic Technology Dept. of Electrical Engineering Technical University of Denmark Denmark Chair of Computational Mathematics and Simulation Science École polytechnique fédérale de Lausanne Switzerland Scientific Computing Dept. of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (纸本)9783939296157

Accurate modeling of boundary conditions is an important aspect in room acoustic simulations. It has been shown that the acoustics of rooms is not only dependent on the frequency characteristic of the complex boundary impedance, but also on the angle dependent properties of the impedance ("extended reaction"). This paper presents a computationally efficient method for modeling local-reaction (LR) and extended-reaction (ER) boundary conditions in high-order, nodal, time-domain finite element methods, such as the spectral element method (SEM) or the discontinuous Galerkin finite element method (DGFEM). The frequency and angle dependent boundary impedance is mapped to a multipole model and formulated in differential form. The solution of the boundary differential equations comes with minimal computational cost. In the ER model, wave splitting is applied at the boundary to separate the incident and reflected parts of the sound field. The directional properties of the incident sound field are determined from the incident particle velocity and the boundary conditions are adjusted continuously according to the wave angle of incidence. The accuracy of the boundary condition model is assessed by comparing simulations against measurements, where a significantly improved match between simulations and measurements is found when the ER model is used. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： *fpin@ Extended-reaction High-order numerical methods Sound absorption Wave-based room acoustic simulations

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The Influence of Agile Methodology (Scrum) on software Project Management

The Influence of Agile Methodology (Scrum) on Software Proje...

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

作者： Faisal Hayat Ammar Ur Rehman Khawaja Sarmad Arif Kanwal Wahab Muhammad Abbas Dept. of Computer & Software Engineering National University of Sciences & Technology (NUST) Islamabad Pakistan

software project management has main role in the software industry. It includes different processes and knowledge areas. The triple constraint of the software project like time, cost and scope is directly dependent on the requirement of the project. Agile methodology is the iterative way for developing the software project for frequent changes, fast delivery and reduce risk. software project management also plays important role in agile based software project. Agile methodology influence software project management at 10 knowledge areas. In this study we carried out survey from different software companies and it shows that almost every software company uses agile development (Scrum) and has a positive impact on the software project management.

关键词： software Scrum (software development) Project management Companies Bibliographies software engineering

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A two-level stacking model for detecting abnormal users in Wechat activities

A two-level stacking model for detecting abnormal users in W...

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Information Technology and computer Application (ITCA), International Conference on

作者： Jiayuan Ling Gangmin Li Dept. of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9781728164946

ISBN: (纸本)9781728164953

Machine learning algorithms are widely employed in plenty of classification or regression problems. While in real business world, it is confronted with huge and disorder data pattern. To recognize different kinds of users on the internet accurately and fast becomes a challenge. In a Wechat online bargain activity, the staff found that some strange users are highly like robots or malicious users. Thus we tried a two-level stacking model to detect them. This design got a good result of 0.98 accuracy after the training phase and an accuracy of 0.90 in a new term of the testing set. Moreover, this model is adaptable to linear and nonlinear datasets because of its diverse stacking of first-level classifiers. Therefore, this paper indicates a potential of the stacking classification model in big data times.

关键词： Stacking Companies Support vector machines Robots Data models Training Classification algorithms

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Towards automated boundary value testing with program derivatives and search

arXiv

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arXiv 2019年

作者： Feldt, Robert Dobslaw, Felix Dept. of Computer Science and Engineering Div. of Software Engineering Chalmers University of Technology Gothenburg Sweden

A natural and often used strategy when testing software is to use input values at boundaries, i.e. where behavior is expected to change the most, an approach often called boundary value testing or analysis (BVA). Even though this has been a key testing idea for long it has been hard to clearly define and formalize. Consequently, it has also been hard to automate. In this research note we propose one such formalization of BVA by, in a similar way as to how the derivative of a function is defined in mathematics, considering (software) program derivatives. Critical to our definition is the notion of distance between inputs and outputs which we can formalize and then quantify based on ideas from Information theory. However, for our (black-box) approach to be practical one must search for test inputs with specific properties. Coupling it with search-based software engineering is thus required and we discuss how program derivatives can be used as and within fitness functions. This brief note does not allow a deeper, empirical investigation but we use a simple illustrative example throughout to introduce the main ideas. By combining program derivatives with search, we thus propose a practical as well as theoretically interesting technique for automated boundary value (analysis and) testing. Copyright © 2019, The Authors. All rights reserved.

关键词： Information theory

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Android malware detection using feature selections and random forest 4

Android malware detection using feature selections and rando...

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4th International Conference on software Security and Assurance, ICSSA 2018

作者： Eom, Taehoon Kim, Heesu An, SeongMo Park, Jong Sou Kim, Dong Seong Dept. of Computer Engineering Korea Aerospace University Seoul Korea Republic of Dept. of Computer Science and Software Engineering University of Queensland Australia

ISBN: (纸本)9781538692103

Malicious software (Malware) applications in Android ecosystem is one of the critical issues. Manual detection of malware is not cost-effective and cannot keep up with the fast evolution of malware development in Android. A machine learning based malware detection has attempted to automate the detection of malware in Android. In this paper, we present new Android malware detection methods. The main idea of our proposed approach is to use three different feature selection methods before malware detection model using a machine learning algorithm is constructed. We used both Malware Genome Project dataset and our own crawled dataset to show the effectiveness of the proposed methods. © 2018 IEEE.

关键词： Feature Selection

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