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Application of High Conductive Nanoparticles to Enhance the Thermal and Mechanical Properties of Wood Composite

Materials Today: Proceedings 2018年第1期5卷 3143-3149页

作者： Arun Gupta Anuj Kumar K.V. Sharma Ritu Gupta Centre for Biocomposite and Innovative Materials (CBIM) Faculty of Chemical and Natural Resources Engineering University Malaysia Pahang 26300 Kuantan Pahang Malaysia Department of Building Structures Faculty of Civil Engineering Czech Technical University in Parague Czech Republic Department of Mechanical Engineering University Teknologi Petronas 32610 Seri Iskandar Perak Darul Ridzuan Malaysia Department of Computer Systems and Software Engineering University Malaysia Pahang 26300 Kuantan Pahang Malaysia

In the present work three different types of Nanofillers such as multiwalled carbon Nanotubes (CNTSs), aluminum oxide Nanoparticles and Nanosize activated charcoal were mixed with urea formaldehyde (UF) resin and used in the preparation of medium density fiberboard (MDF). The fillers has improved heat transfer during hot pressing and achieved proper curing due to enhanced thermos-physical properties of wood fibers. To improve the dispersion of Nanofillers into UF matrix, high speed mechanical stirring and ultrasonic treatments were used. The CNTSs were oxidized with nitric acid and the functional groups formed on its surface improved the dispersion and interaction with UF matrix. The mixing of CNTSs and Aluminum oxide with UF resin have reduced the curing time due to enhanced thermal conductivity of MDF matrix. The heat transfer during hot pressing of MDF improved significantly with the addition of CNTSs and Al 2 O 3 Nanoparticle. The activated charcoal did not show much effect on heat transfer. The curing rate of UF resin improved with all the three Nanofillers, as the activation energy of UF curing decreases. The physical and mechanical properties of MDF have improved significantly with CNTSs and Al2O3 Nanoparticle. The activated charcoal has significantly decreased the formaldehyde emission of MDF.

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Nine-level asymmetrical single phase multilevel inverter topology with low switching frequency and reduce device counts

Nine-level asymmetrical single phase multilevel inverter top...

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2017 IEEE International Conference on Industrial Technology, ICIT 2017

作者： Bin Arif, M. Saad Ayob, Shahrine Md. Iqbal, Atif Williamson, Sheldon Salam, Zainal Power Engineering Department Faculty of Electrical Engineering Universiti Technologi Malaysia Malaysia Department of Electrical Engineering Qatar University Doha Qatar Department of Electrical Computer and Software Engineering OshawaON Canada Center of Electrical Energy Systems Universiti Technologi Malaysia Malaysia

ISBN: (纸本)9781509053209

This paper presents a new asymmetrical single-phase multilevel inverter topology capable of producing nine-level output voltage with reduce device counts. In order to obtain the desired output voltage, dc sources are connected in all the combination of addition and subtraction through different switches. Proposed topology results in reduction of dc source, switch counts, losses, cost and size of the inverter. Comparison between the existing topologies shows that the proposed topology yields less component counts. Proposed topology is modeled and simulated using Matlab-Simulink software in order to verify the performance and feasibility of the circuit. A low frequency switching strategy is also proposed in this work. The results show that the proposed topology is capable to produce a nine-level output voltage with less number of component counts and acceptable harmonic distortion content. © 2017 IEEE.

关键词： Topology

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Exploring representativeness and informativeness for active learning

arXiv

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arXiv 2019年

作者： Du, Bo Wang, Zengmao Zhang, Lefei Zhang, Liangpei Liu, Wei Shen, Jialie Tao, Dacheng State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Collaborative Innovation Center of Geospatial Technology State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States School of Information Systems Singapore Management University Singapore QCIS University of Technology SydneyNSW2007 Australia

How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified Best-versus-Second-Best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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Enhancement the video quality forwarding Using Receiver-Based Approach(URBA) in Vehicular Ad-Hoc Network

Enhancement the video quality forwarding Using Receiver-Base...

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2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, ICRAMET 2017

作者： Hammood, Omar A. Kahar, Mohd Nizam Mohmad Mohammed, Muamer N. Faculty of Computer Systems and Software Engineering University Malaysia Pahang Pahang Malaysia IBM Center of Excellence University Malaysia Pahang Pahang Malaysia Department of Computer Sciences Bayan University Erbil Iraq

ISBN: (纸本)9781538638491

In Vehicular Ad-hoc Networks (VANETs), data routing is a major problem because of its quick change in topology and vehicular high mobility. VANETs are ad hoc wireless mobile networks which play important role in commercial applications and safety of public vehicular communications. The routing protocols that base on vehicular position are attracting much interest because of the availability and advancement of devices equipped with GPS. A major problem with VANETs is the frequent disruptions in the path of vehicles due to the high mobility of the participating vehicles. This can lead to the breaking of established links and result in low throughput, delay in service delivery, less overhead as well as low rate of delivery. In this paper, Using Receiver-Based Approach (URBA) is proposed. The proposed approach (URBA) compared to VANET, incorporates a potential receiver-based strategy which is traditionally better in performance and might achieve less delay in service delivery with a higher rate of delivery. © 2017 IEEE.

关键词： Vehicular ad hoc networks

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Faculty Workshops for Teaching Information Assurance through Hands-On Exercises and Case Studies

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Journal of Information systems Education 2017年第1期28卷 11-19页

作者： Yuan, Xiaohong Williams, Kenneth Yu, Huiming Rorrer, Audrey Chu, Bei-Tseng Yang, Li Winters, Kathy Kizza, Joseph Department of Computer Science North Carolina A&T State University GreensboroNC27411 United States Charlotte NC28223 United States Software and Information Systems University of North Carolina – Charlotte CharlotteNC28223 United States Department of Computer Science and Engineering University of Tennessee – Chattanooga ChattanoogaTN37403 United States

Though many Information Assurance (IA) educators agree that hands-on exercises and case studies improve student learning, hands-on exercises and case studies are not widely adopted due to the time needed to develop them and integrate them into curricula. Under the support of the National Science Foundation (NSF) Scholarship for Service program, we organized two faculty development workshops to disseminate effective hands-on exercises and case studies developed through multiple previous and ongoing grants. To develop faculty expertise in IA, the workshop covered a wide range of IA topics. This paper describes the hands-on exercises and case studies we disseminated through the workshops and reports our experiences of holding the faculty summer workshops. The evaluation results show that workshop participants demonstrated high levels of satisfaction with knowledge and skills gained in both the 2012 and 2013 workshops. Workshop participants also reported use of hands-on lab and case study materials in our follow-up survey and interviews. The workshops provided a valuable opportunity for IA educators to communicate and form collaborations in teaching and research in IA. © 2017. All Rights Reserved.

关键词： Curricula

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Neural networks based on radial basis function in high dimension domains 3

Neural networks based on radial basis function in high dimen...

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3rd International Conference on Numerical and Symbolic Computation: Developments and Applications, SYMCOMP 2017

作者： Tallón-Ballesteros, Antonio J. Rodriguez-Romero, Maria Correia, Luis Department of Languages and Computer Systems Higher Technical School of Computer Science Engineering University of Seville Reina Mercedes Av. 41012 Seville Spain Higher Technical School of Computer Science Engineering University of Seville Reina Mercedes Av. Seville41012 Spain Department of Computer Science University of Lisbon Lisbon1749-016 Portugal

ISBN: (纸本)9789899941038

This paper focuses on radial basis function neural networks to cope with the matter of effectively predicting the right class of unknown samples in the context of multiclass supervised machine learning problems. One of the main drawbacks of this kind of networks is the poor performance that is achieved as the number of dimensions grows. Basically, the idea emerges from the situation that is produced by the fact that the samples are far from the centre of the cluster. As an eventual breakthrough, some guidelines are proposed to be applied in the scope of high dimension domains. The analysis is supported by an empirical study in problems with more features than samples. © ECCOMAS, Portugal.

关键词： Radial basis function networks

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Handling constraints in combinatorial interaction testing in the presence of multi objective particle swarm and multithreading

arXiv

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arXiv 2018年

作者： Ahmed, Bestoun S. Gambardella, Luca M. Afzal, Wasif Zamli, Kamal Z. Manno-LuganoCH-6928 Switzerland Department of Computer Science Faculty of Electrical Engineering Czech Technical University Karlovo nam. 13 Praha 2121 35 Czech Republic School of Innovation Design and Engineering Mlardalen University Sweden Faculty of Computer Systems and Software Engineering University Malaysia Pahang Gambang Malaysia

Context: Combinatorial testing strategies have lately received a lot of attention as a result of their diverse applications. In its simple form, a combinatorial strategy can reduce several input parameters (configurations) of a system into a small set based on their interaction (or combination). In practice, the input configurations of software systems are subjected to constraints, especially in case of highly configurable systems. To implement this feature within a strategy, many difficulties arise for construction. While there are many combinatorial interaction testing strategies nowadays, few of them support constraints. Objective: This paper presents a new strategy, to construct combinatorial interaction test suites in the presence of constraints. Method: The design and algorithms are provided in detail. To overcome the multi-judgement criteria for an optimal solution, the multi-objective particle swarm optimisation and multithreading are used. The strategy and its associated algorithms are evaluated extensively using different benchmarks and comparisons. Results: Our results are promising as the evaluation results showed the efficiency and performance of each algorithm in the strategy. The benchmarking results also showed that the strategy can generate constrained test suites efficiently as compared to state-of-the-art strategies. Conclusion:The proposed strategy can form a new way for constructing of constrained combinatorial interaction test suites. The strategy can form a new and effective base for future implementations. Copyright © 2018, The Authors. All rights reserved.

关键词： Multiobjective optimization

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A privacy-aware two-factor authentication protocol based on elliptic curve cryptography for wireless sensor networks

A privacy-aware two-factor authentication protocol based on ...

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作者： Jiang, Qi Kumar, Neeraj Ma, Jianfeng Shen, Jian He, Debiao Chilamkurti, Naveen School of Cyber Engineering Xidian University Xi'an China Co-innovation Center for Information Supply and Assurance Technology Anhui University Hefei China Department of Computer Science and Engineering Thapar University Patiala India School of Computer and Software Nanjing University of Information Science and Technology Nanjing China State Key Lab of Software Engineering Computer School Wuhan University Wuhan China Department of Computer Systems and Computer Science La Trobe University Melbourne Australia

As one of the enabling components of Internet of things (IoT), wireless sensor networks (WSNs) have found applications in a wide range of fields, in which outside users need to directly interact with sensors to obtain sensed data. However, WSNs are vulnerable to various attacks over wireless links, such as eavesdropping and tampering. How to ensure that sensitive or critical information is only available to legal users becomes a challenging issue. Two-factor authentication combining password and smart card perfectly matches this requirement because of its practicality. However, a dozen of two-factor authentication protocols have been suggested in recent years. Owing to the challenging mission of reconciling efficiency and security requirements, it is still challenging to propose a privacy-aware two-factor protocol that is capable of providing various security features while maintaining acceptable efficiency. In this paper, we put forward a privacy-aware two-factor authentication protocol based on elliptic curve cryptography for WSNs. Our new protocol accomplishes various security features necessary for real-life application environments while maintaining acceptable efficiency. We prove that the new protocol fulfills mutual authentication in the Burrows–Abadi–Needham logic. Additionally, by way of informal security analysis, we show that the new protocol can withstand a variety of attacks and provide desirable security features. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Wireless sensor networks

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Artificial Intelligence Powered MOOCs: A Brief Survey

Artificial Intelligence Powered MOOCs: A Brief Survey

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IEEE International Conference on Agents (ICA)

作者： Simon Fauvel Han Yu Chunyan Miao Lizhen Cui Hengjie Song Liang Zhang Xiaoming Li Cyril Leung Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Engineering Shandong University Jinan China School of Software Engineering South China University of Technology Guangzhou China Institute of Network Computing and Information Systems Peking University Beijing China Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada

Massive Open Online Courses (MOOCs) have gained tremendous popularity in the last few years. Thanks to MOOCs, millions of learners from all over the world have taken thousands of high-quality courses for free. Artificial intelligence (AI) has played an important role in making MOOCs what they are today. By exploiting the vast amount of data generated by learners engaging in MOOCs, AI techniques have been proposed to improve our understanding of MOOC participants and enable MOOC practitioners to deliver better courses. These approaches have also greatly improved student experience and learning outcomes through constructing intelligent and personalized learning trajectories. In this paper, we first review the state-of-the-art AI research making an impact on MOOCs education, emphasizing on works which aim to enhance our understanding of student learning behaviours, improve student engagement, and improve learning outcomes. We then offer an overview of important future research to carry out in sub-fields of AI to enable MOOCs to reach their full potential.

关键词： Videos Artificial intelligence Tools Navigation Computational modeling Trajectory Education

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CyberPDF: Smart and Secure Coordinate-based Automated Health PDF Data Batch Extraction

CyberPDF: Smart and Secure Coordinate-based Automated Health...

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IEEE International Conference on Connected Health: Applications, systems and engineering Technologies (CHASE)

作者： Reza M. Parizi Liang Guo Yao Bian Amin Azmoodeh Ali Dehghantanha Kim-Kwang Raymond Choo Department of Software Engineering and Game Development Kennesaw State University Marietta GA USA NYC New York Institute of Technology NY USA Department of Computer Science & Engineering Shiraz University Shiraz Iran Department of Computer Science & Engineering Shiraz University Shiraz Iran Shiraz University Shiraz Fars IR Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio Texas USA

Data extraction from files is a prevalent activity in today's electronic health record systems which can be laborious. When document analysis is repetitive (e.g., processing a series of files with the same layout and extraction requirements), relying on data-entry staff to manually perform such tasks is costly and highly insecure. Particularly analyzing a large list of PDF files (as a widely used format) to extract specific data and migrate them to other destinations for later use is both tedious and frustrating to do manually. This paper addresses a very practical requirement of batch extracting data from PDF files in health data document analysis. Specifically, we propose a Coordinate Based Information Extraction System (CBIES) to instrument a smart and automatic PDF batch data extraction tool, releasing health organizations from duplicate efforts and reducing labor costs. The proposed technique enables users to query a representative PDF document and extract the same data from a series of files in the batch analysis manner swiftly. Furthermore, since security and privacy considerations are essential part of any health record systems, it is included in our approach. Based on CBIES, we implement a prototype tool for PDF batch data extraction technique named, CyberPDF. The tool exhibits great efficiency, security and accuracy in multi-file data processing, supported through an empirical evaluation in this research.

关键词： Portable document format Tools Security Text analysis Information retrieval Feature extraction

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