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Retraction: Design of Decision Support System Determination of Inventory Inventory Using Single Exponential Smoothing Forecasting Method (J. Phys.: Conf. Ser. 1114 012082)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Muhammad Iqbal Dahlan Abdullah H Hartono Cut Ita Erliana S Sriadhi Nurmawati M Adhi Prasnowo Abdurrozzaq Hasibuan Akbar Iskandar Kundharu Saddhono Abdussakir Hastuti Musa Muhammad Ikhsan Setiawan Youmeni Sagala Rumiris Siahaan Eka Mayastika Sinaga Anggraini Syahputri Feby Milanie Henry Aspan Muammar Khaddafi Department of Computer System Universitas Pembangunan Panca Budi Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Department of Industrial Engineering Universitas Maarif Hasyim Latif Sidoarjo Indonesia Faculty of Engineering Universitas Islam Sumatera UtaraMedan-Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Mathematics Universitas Muhammadiyah Parepare Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia STIE Bina Karya Tebing Tinggi Indonesia Universitas Pembangunan Panca Budi Medan Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Retraction: LUC Algorithm in Visual Cryptography (J. Phys.: Conf. Ser. 1114 012090)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Ratnadewi Christian Dio Alpha G Darmawan Napitupulu Heri Nurdiyanto Nuning Kurniasih Abdurrozaq Hasibuan GS Achmad Daengs Rosida Tiurma Manurung Indrya Mulyaningsih Robbi Rahim Castaka Agus Sugianto Dahlan Abdullah Department of Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Banten Indonesia STMIK Dharma Wacana Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Universitas Islam Sumatera Utara Indonesia University of 45 Surabaya Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Bandung Indonesia Department of Education Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia Politeknik TEDC Bandung Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia

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LUC Algorithm in Visual Cryptography

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Journal of Physics: Conference Series 2018年第1期1114卷

Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients. All of this system can be realization by using cryptography. LUC algorithms are introduced by Smith and Lennon in 1993. LUC algorithm is an algorithm that based on the use of Lucas sequence (specific arithmetic operations derived from Lucas row) that rarely used to enhance the security of the messages. In this research, LUC algorithm is combined with visual cryptography to process the encryption and the description of a colored image. Four different images were used in the trial here. The performance of the system is assessed using Structural similarity (SSIM) which has an assessment similar to human eye. If the image quality is the same as the original image, then the SSIM value is one. Whereas if the quality of decryption image is very different from the original image then the value of SSIM is zero.

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Radio-frequency characterization of multi-walled carbon nanotube/poly-lactic acid composites

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Materials Today: Proceedings 2017年第5期4卷 6548-6552页

作者： Nuttaya Sukgorn Krisana Siraleartmukul Visittapong Yordsri Nonchanutt Chudpooti Sarawuth Chaimool Prayoot Akkaraekthalin Suramate Chalermwisutkul Chanchana Thanachayanont Winadda Wongwiriyapan Khattiya Chalapat College of Nanotechnology King Mongkut’s Institute of Technology Ladkrabang Bangkok 10520 Thailand Metallurgy and Materials Science Research Institute Chulalongkorn University Bangkok 10330 Thailand National Metal and Materials Technology Center Pathunthani12120 Thailand Department of Electrical and Computer Engineering King Mongkut's University of Technology North BangkokBangkok 10800 Thailand International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok 10800 Thailand Thailand Center of Excellence in Physics CHE Bangkok 10400 Thailand Nanotec-KMITL Center of Excellence on Nanoelectronics Device Bangkok 10520 Thailand

Nowadays radio and microwave frequencies are widely used in wireless broadcastings and communications. But these waves can cause electromagnetic interferences in some electronic devices, including the equipment used in hospitals. The problems of the electromagnetic interference can be solved by using the materials that can reflect and/or absorb radio-frequency (RF) and microwaves. The shielding effectiveness (SE) of a material depends on its conductivity and the electrical permittivity. Recently, carbon nanotubes (CNTs) have been proposed as promising materials for shielding applications owing to its flexibility, durability, lightweight and exceptional electrical conductivity compared to conventional metal. In this work, the RF properties of multi-walled carbon nanotube (MWCNT) composites were investigated. Poly-lactic acid (PLA), a natural bio-degradable material, is used as the polymer matrix. The composites with different MWCNT concentrations (0 to 0.4 wt%) were prepared and molded into thin rectangular samples (5.6 cm x 6.3cm). The surfaces of the composites were morphologically characterized by a scanning electron microscope. The electrical permittivity of the samples was measured by using a vector network analyzer and a microstrip resonator within a range of 1–11 GHz. The effects of MWCNT concentration on electrical permittivity will be discussed.

关键词： Carbon Nanotube electrical Permittivity Poly-Lactic Acid Radio-Frequency Characterization

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A study on fitness function with parameter-free genetic algorithm for non-destructive estimation of permittivity distribution based on capacitance and electric potential

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IEEJ Transactions on Fundamentals and Materials 2016年第4期136卷 164-173页

作者： Ohchi, Masashi Tomiyama, Kazuyoshi Furukawa, Tatsuya Itoh, Hideaki Fukumoto, Hisao Department of Electrical Electronics and Computer Engineering Faculty of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino275-0016 Japan Master's Program in Electrical Electronics and Computer Engineering Graduate School of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino275-0016 Japan Graduate School of Science and Engineering Saga University 1 Honjo-machi Saga840-8502 Japan

We have carried out experimental and numerical analyses for reconstruction of permittivity distribution using a pair of long distance small size electrodes and additional arc electrodes. A reconstruction method for permittivity distribution that uses a genetic algorithm and numerical analysis (finite element method) based on measured capacitance has been proposed and investigated. However, the conventional method had a problem in accuracy. In this paper, the reconstruction method has been improved by using a Parameter-free Genetic Algorithm(PfGA) based on measured capacitance and electric potential. In addition, we have evaluated the fitness function with PfGA. © 2016 The Institute of electrical Engineers of Japan.

关键词： Finite element method

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Erratum to: Search for supersymmetry in final states with two same-sign or three leptons and jets using 36 fb−1 of = 13 TeV pp collision data with the ATLAS detector

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Journal of High Energy Physics 2019年第8期2019卷 1-19页

作者： Aaboud, M. Aad, G. Abbott, B. Abdinov, O. Abeloos, B. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adachi, S. Adamczyk, L. Adelman, J. Adersberger, M. Adye, T. Affolder, A. A. Afik, Y. Agatonovic-Jovin, T. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akatsuka, S. Akerstedt, H. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Alberghi, G. L. Albert, J. Albicocco, P. Alconada Verzini, M. J. Alderweireldt, S. C. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Ali, B. Aliev, M. Alimonti, G. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A. A. Alstaty, M. I. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Angerami, A. Anisenkov, A. V. Anjos, N. Annovi, A. Antel, C. Antonelli, M. Antonov, A. Antrim, D. J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Araujo Ferraz, V. Arce, A. T. H. Ardell, R. E. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Armitage, L. J. Arnaez, O. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Ashkenazi, A. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baas, A. E. Baca, M. J. Bachacou, H. Bachas, K. Backes, M. Bagnaia, P. Bahmani, M. Bahrasemani, H. Baines, J. T. Bajic, M. Baker, O. K. Baldin, E. M. Balek, P. Balli, F. Balunas, W. K. Banas, E. Bandyopadhyay, A. Banerjee, Sw. Bannoura, A. A. E. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisits, M-S Barkeloo, J. T. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barnovska-Blenessy, Z Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman U.S.A. Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France Department of Physics University of Toronto Toronto Canada Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz U.S.A. Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene U.S.A. Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb U.S.A. Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física Experimental de Partículas – LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos Universidad de Granada Granada Portugal Department of Physics Boston University Boston U.S.A. Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN Sezione di Roma Tor Vergata Roma Italy Dip

One correction is made to the figure 4e of the paper.

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A Hybrid Deep Learning Forecasting Model Using GPU Disaggregated Function Evaluations Applied for Household Electricity Demand Forecasting

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Energy Procedia 2016年 103卷 280-285页

作者： Vitor N. Coelho Igor M. Coelho Eyder Rios Alexandre S.T. Filho Agnaldo J.R. Reis Bruno N. Coelho Alysson Alves Guilherme G. Netto Marcone J.F. Souza Frederico G. Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Instituto de Pesquisa e Desenvolvimento de Tecnologias Ouro Preto Brazil Department of Computer Science State University of Rio de Janeiro Rio de Janeiro Brazil Institute of Computer Science Fluminense Federal University Niteroi Brazil Department of Control Engineering and Automation Universidade Federal de Ouro Preto Ouro Preto Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto Brazil Department of Electrical Engineering Universidade Federal de Minas Gerais BeloHorizonte Brazil

As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the amount of information to be handled by learning algorithms has been increasing. The Graphics Processing Unit (GPU) architectures provide a greener alternative with low energy consumption for mining big-data, harnessing the power of thousands of processing cores in a single chip, opening a widely range of possible applications. Here, we design a novel evolutionary computing GPU parallel function evaluation mechanism, in which different parts of time series are evaluated by different processing threads. By applying a metaheuristics fuzzy model in a low-frequency data for household electricity demand forecasting, results suggested that the proposed GPU learning strategy is scalable as the number of training rounds increases.

关键词： Microgrid Household Electricity Demand Deep Learning Graphics Processing Unit Parallel forecasting model Big Time-series Data

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Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: bioinformatics

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BMC Bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

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Multilayer CPW-Fed Patch Antenna on New AMC Ground Plane for 60 GHz Millimeter-Wave Communications

Multilayer CPW-Fed Patch Antenna on New AMC Ground Plane for...

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IEEE Vehicular Technology Conference

作者： Imad Ali Ronald Y. Chang Jenny Yi-Chun Liu Taiwan International Graduate Program in Social Networks and Human-Centered Computing Institute of Information Science Academia Sinica Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering National Tsing Hua University

ISBN: (纸本)9781509016990

This paper presents a 60 GHz CPW-fed patch antenna on multilayer LTCC- and LCP-based materials with new and compact AMC. The proposed antenna structure consists of top three layers of antenna and bottom three layers of AMC ground plane. First, a new and compact three-layer AMC is designed on both LTCC- and LCP-based substrates. The designed AMC is more compact and provides 37.5% higher in-phase bandwidth as compared to a state-of-the-art design. Then, a three-layer CPW-fed patch antenna is designed and optimized on LTCC- and LCP-based substrates on top of the three-layer AMC ground plane of the same material. We perform a gain-bandwidth comparative study of antennas on multilayer LTCC- and LCP-based substrates with AMC/PEC ground planes for 60 GHz. We observe that antennas on LCP-based substrate outperform those on LTCC-based substrate in terms of input impedance, bandwidth, and gains on both AMC and PEC ground planes, while antennas on LTCC-based substrate have smaller antenna and AMC dimensions.

关键词： AMCN gene antecedent moisture condition (amc) Patch antennas ANTENNA Ground plane Planing antennae impedance Substrates plane

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Parallel Solution of Diagonally Dominant Banded Triangular Toeplitz Systems Using Taylor Polynomials

Parallel Solution of Diagonally Dominant Banded Triangular T...

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IEEE international Conference on High Performance Computing and Communications

作者： Michael Kapralos Aron Wolinetz Brian J. Murphy Department of Electrical and Computer Engineering University of Connecticut Storrs CT 06269 The Graduate Center of the City University of New York Ph.D. Program in Computer Science NY NY 10016 Lehman College of the City University of New York Bronx NY 10468

ISBN: (纸本)9781509042982

We present a new numerical and inherently parallel algorithm for solving banded (strictly) diagonally dominant triangular and banded Toeplitz (BDDTT) systems of linear equations. A Taylor polynomial, derived from the matrix analog of the Taylor Series for evaluating (1-x) ~(-1)where |χ| < 1, is applied, in conjunction with other techniques, to compute an approximate solution to a BDDTT-type system. This method requires O(n log_2n) arithmetic operations (ops) and O(log_2n) parallel steps (steps). Two FFTs and an IFFT of order 2n are the main contributors to the workload. This matches the FFT count for a triangular Toeplitz-matrix-vector multiplication. Clearly then, our method improves on the many algebraic FFT based processes that first determine the triangular Toeplitz matrix that is the inverse of a BDDTT matrix in order to then solve the associated BDDTT system.

关键词： Banded Triangular Toeplitz Diagonally Dominant Linear System FFT

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