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RETRACTED ARTICLE: Soft computing methodologies for estimation of energy consumption in buildings with different envelope parameters

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Energy Efficiency 2015年第2期9卷 435-453页

作者： Sareh Naji Shahaboddin Shamshirband Hamed Basser U. Johnson Alengaram Mohd Zamin Jumaat Mohsen Amirmojahedi Department of Civil Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Faculty of Engineering Seraj University Tabriz Iran

In this study, soft computing methods are designed and adapted to estimate energy consumption of the building according to main building envelope parameters such as material thicknesses and insulation K-value. In order to predict the building energy consumption, novel intelligent soft computing schemes, support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) are used. The polynomial, linear, and radial basis function (RBF) is applied as the kernel function of the SVR to estimate the optimal energy consumption of buildings. The performance of proposed optimizers is confirmed by simulation results. The SVR results are compared with the ANFIS, artificial neural network (ANN), and genetic programming (GP) results. The computational results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach in comparison to the SVR estimation. Based on the simulation results, the effectiveness of the proposed optimization strategies is verified. The data used in soft computing were obtained from 180 simulations in EnergyPlus for variations of building envelope parameters.

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Human behavior modeling for multimodal interaction with robot partner

Human behavior modeling for multimodal interaction with robo...

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International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015

作者： Obo, Takenori Kiong, Loo Chu Kubota, Naoyuki Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Graduate School of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo Japan

ISBN: (纸本)9781479966783

If a robot partner can provide a multimodal interaction with a person, it may offer more smooth and natural human-robot communication. This paper proposes a method for modeling relationship between behavioral and emotional expression through human-robot interaction. First, we applied a neuro-fuzzy system to the classification of human posture. Next, we used the facial expression recognition algorithms based on Constrained Local Model and two-stage fuzzy reasoning model. Furthermore, we utilized spiking neural network for the relation modeling. In this paper, we show an experimental example to examine the proposed method. © 2015 IEEE.

关键词： Human robot interaction

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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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Dual fuel injection piggyback controller system

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AIP Conference Proceedings 2017年第1期1883卷

作者： Siti Zarina Mohd. Muji Muhammad Amirul Hafeez Hassanal Chua King Lee Mas Fawzi Fathul Hakim Zulkifli 1Embedded Computing System (EmbCoS) Department of Computer Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia 2Faculty of Mechanical and Manufacturing Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia 3Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia

Dual-fuel injection is an effort to reduce the dependency on diesel and gasoline fuel. Generally, there are two approaches to implement the dual-fuel injection in car system. The first approach is changing the whole injector of the car engine, the consequence is excessive high cost. Alternatively, it also can be achieved by manipulating the system’s control signal especially the Electronic Control Unit (ECU) signal. Hence, the study focuses to develop a dual injection timing controller system that likely adopted to control injection time and quantity of compressed natural gas (CNG) and diesel fuel. In this system, Raspberry Pi 3 reacts as main controller unit to receive ECU signal, analyze it and then manipulate its duty cycle to be fed into the Electronic Driver Unit (EDU). The manipulation has changed the duty cycle to two pulses instead of single pulse. A particular pulse mainly used to control injection of diesel fuel and another pulse controls injection of Compressed Natural Gas (CNG). The test indicated promising results that the system can be implemented in the car as piggyback *** article, which was originally published online on 14 September 2017, contained an error in the acknowledgment section. The corrected acknowledgment appears in the Corrigendum attached to the pdf.

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Integrated Model for E-Learning Acceptance

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IOP Conference Series: Materials Science and Engineering 2016年第1期105卷

作者： Ramadiani A Rodziah S M Hasan A Rusli C Noraini Computer Science Natural Resources and Mathematic Faculty Mulawarman University East Kalimantan Indonesia Information System Computer Science and Information Technology Faculty Universiti Putra Malaysia Malaysia

E-learning is not going to work if the system is not used in accordance with user needs. User Interface is very important to encourage using the application. Many theories had discuss about user interface usability evaluation and technology acceptance separately, actually why we do not make it correlation between interface usability evaluation and user acceptance to enhance e-learning process. Therefore, the evaluation model for e-learning interface acceptance is considered important to investigate. The aim of this study is to propose the integrated e-learning user interface acceptance evaluation model. This model was combined some theories of e-learning interface measurement such as, user learning style, usability evaluation, and the user benefit. We formulated in constructive questionnaires which were shared at 125 English Language School (ELS) students. This research statistics used Structural Equation Model using LISREL v8.80 and MANOVA analysis.

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Bug Detection Using Particle Swarm Optimization with Search Space Reduction

Bug Detection Using Particle Swarm Optimization with Search ...

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International Conference on Intelligent systems, Modelling and Simulation (ISMS)

作者： Arun Reungsinkonkarn Paskorn Apirukvorapinit Department of Computer Information System Assumption University Faculty of Information Technology Thai-Nichi Institute of Technology

A bug detection tool is an important tool in software engineering development. Many research papers have proposed techniques for detecting software bug, but there are certain semantic bugs that are not easy to detect. In our views, a bug can occur from incorrect logics that when a program is executed with a particular input, the program will behave in unexpected ways. In this paper, we propose a method and tool for software bugs detection by finding such input that causes an unexpected output guided by the fitness function. The method uses a Hierarchical Similarity Measurement Model (HSM) to help create the fitness function to examine a program behavior. Its tool uses Particle Swarm Optimization (PSO) with Search Space Reduction (SSR) to manipulate input by contracting and eliminating unfavorable areas of input search space. The programs under experiment were selected from four different domains such as financial, decision support system, algorithms and machine learning. The experimental result shows a significant percentage of success rate up to 93% in bug detection, compared to an estimated success rate of 28% without SSR.

关键词： computer bugs Software Complexity theory Particle swarm optimization Computational modeling Atmospheric measurements Particle measurements

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Robust decentralized control of large-scale systems via H∞theory and using descriptor system representation

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International Journal of systems Science 2016年第12-13期34卷 705-715页

作者： Labibi, B. Lohmann, B. Sedigh, A. Khaki Maralani, P. Jabedar Control Department Electrical Engineering Faculty K. N. Toosi University of Technology Seyyed Khandan Bridge Shariati Street 16314 Iran Dept. of System Dynamics and Control University of Bremen Otto-Hahn-Allee/NW1 28359 Bremen Germany Electrical and Computer Eng. Dept. Faculty of Engineering University of Tehran North Karegar Street Building No. 2 Tehran Iran

In this paper, a method for design of linear decentralized robust controllers for a class of uncertain large-scale systems in general form is presented. For a given large-scale system, an equivalent descriptor system in input-output decentralized form is defined. Using this representation, closed-loop diagonal dominance sufficient conditions are derived. It is shown that by appropriately minimizing the weighted sensitivity function of each isolated subsystem, these conditions are achieved. Solving the appropriately defined H∞local problem for each isolated uncertain subsystem, the interactions between the subsystems are reduced, and the overall stability and robust performance are achieved in spite of uncertainties. The designs are illustrated by a practical example.

关键词： Control system synthesis

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RETRACTED ARTICLE: Temperature-based estimation of global solar radiation using soft computing methodologies

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Theoretical and Applied Climatology 2015年第1期125卷 101-112页

作者： Mohammadi, Kasra Shamshirband, Shahaboddin Danesh, Amir Seyed Abdullah, Mohd Shahidan Zamani, Mazdak Faculty of Mechanical Engineering University of Kashan Kashan Iran Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Advanced Informatics School Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures (T max, T min, and T avg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of T max, T min, and T avg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using T max–T min and T max as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.

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Block based approach for key frame extraction on large video sequences

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ICIC Express Letters, Part B: Applications 2016年第12期7卷 2713-2717页

作者： Tin, Mie Mie Myo, Nyein Nyein Khin, Mie Mie Zin, Thi Thi Myanmar Institute of Information Technology P.O. Box 05053 73rd Street Ngu Shwe War Street Chanmyathazi Township Mandalay05053 Myanmar Information Science Department University of Computer Studies Mandalay Pathangyi Mandalay Myanmar Electrical and System Engineering Faculty of Engineering University of Miyazaki Kibana Campus 1-1 Gakuen Kibanadai-nishi Miyazaki-shi889-2192 Japan

Video segmentation and key frame extraction are very important in real world video systems. Key frames are essential to analyze on large amount of video frame sequences. This paper emphasizes surveillance video and the aim is to extract some meaningful key frames from long video sequences. This purpose is to reduce weak transaction frames in a large video stream by using block base algorithm and cluster the transition video shots. Kullback-Leible divergence method is used in key frame extraction for strong transition video shot. For weak transition video shot, the system will find three candidate key frames and they are compared. Key frames are meaningful frames for video sequences. These frames which represent video streams can be analyzed. Duplicated key frames from the video stream are analyzed in order to be extracted from different shots. Finally, key frames have many assets such as stability, accuracy, and summarize information for a large video. © 2016 ISSN 2185-2766.

关键词： Extraction

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Factors Affecting Lecturers Motivation in Using Online Teaching Tools

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Procedia - Social and Behavioral Sciences 2015年 195卷 1778-1784页

作者： Siti Nurul Mahfuzah Mohamad Mohd Azran Mohd Salleh Sazilah Salam Computer System and Support Unit Kolej Komuniti Selandar (KKSL) Malaysia Computer System and Support Unit Kolej Komuniti Masjid Tanah (KKMT) Malaysia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Malaysia

Motivation is the drive to will and desire to succeed or achieve something. Motivation in this treatment refers to when the lecturers feel the motivation in using online teaching tools in the classroom. Self-motivation of lecturers to change their teaching styles or methods is one of the problems stated by few researchers. The objective of this paper is to identify the factors that motivate lecturers to use online teaching tools, such as Web 2.0 tools. There are a variety of online teaching tools available to the lecturers. The popular tools are online whiteboard, WordPress, YouTube, Facebook, Edmodo and others. There are not many lecturers who apply the Internet as an alternative to provide access to information that can help to enhance teaching and learning materials. An evaluation of the lecturer's motivation had been completed by 30 lecturers from different subject matter experts. The instruments used were interview sessions and classroom observation, which were used to measure four motivation variables: ease of use, enjoyment, usefulness and self-confidence, based on the ARCS Motivation model. Based on the findings, factors that can affect lecturers’ motivation in using online teaching tools are knowledge, perceptions and skills.

关键词： Motivation Online Teaching Tools Web 2.0

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