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arXiv 2019年

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Atsuta, S. Awai, K. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M.A. Cannon, K. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chu, H. Chu, Y.-K. Craig, K. Creus, W. Doi, K. Eda, K. Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujimoto, M.-K. Fukunaga, M. Fukushima, M. Furuhata, T. Ge, G. Hagiwara, A. Haino, S. Hasegawa, K. Hashino, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirobayashi, S. Hirose, E. Hong, Z. Hsieh, B.H. Huang, G.-Z. Huang, P. Huang, Y. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Itoh, Y. Izumi, K. Jung, K. Jung, P. Kaji, T. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kanemura, S. Kaneyama, M. Kang, G. Kasuya, J. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawamura, S. Kawasaki, T. Kim, C. Kim, J.C. Kim, W.S. Kim, Y.-M. Kimura, N. Kinugawa, T. Kirii, S. Kita, N. Kitaoka, Y. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A.K.H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H.-S. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H.K. Lee, H.M. Lee, H.W. Lee, R. Leonardi, M. Lin, C. Lin, C.-Y. Lin, F.-L. Liu, G.C. Liu, Y. Luo, L. Majorana, E. Mano, S. Marchio, M. Matsui, T. Matsushima, F. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyamoto, T. Miyazaki, Y. Miyo, K. Miyoki, S. Morii, W. Morisaki, S. Moriwaki, Y. Morozumi, T. Musha, M. Nagano, K. Nagano, S. Nakamura, K. Nakamura, T. Nakano, H. Nakano, M. Nakao, K. Nakashima, R. Narikawa, T. Naticchioni, L. Negishi, R. Nguyen Quynh, L. Ni, W.-T. Nishizawa, A. Obuchi, Y. Ochi, T. Ogaki, W. Oh, J.J. Oh, S.H. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Ooi, C.P. Oshino, S. Pan, K. Pang, H. Park, J. Peña Arellano, F.E. Pinto, I. Sago, N. Saijo, M. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakai, Y. Mitaka City Tokyo181-8588 Japan Japan Department of Physics University of Tokyo Bunkyo-ku Tokyo113-0033 Japan University of Tokyo Bunkyo-ku Tokyo113-0033 Japan KAGRA Observatory University of Tokyo Kashiwa City Chiba277-8582 Japan Tsukuba City Ibaraki305-0801 Japan Earthquake Research Institute University of Tokyo Bunkyo-ku Tokyo113-0032 Japan Department of Mathematics and Physics Hirosaki University Hirosaki City Aomori036-8561 Japan Kamioka-cho Hida City Gifu506-1205 Japan Mitaka City Tokyo181-8588 Japan Graduate School of Science and Technology Tokyo Institute of Technology Meguro-ku Tokyo152-8551 Japan KAGRA Observatory University of Tokyo Kamioka-cho Hida City Gifu506-1205 Japan Yuseong-gu Daejeon34141 Korea Republic of National Institute for Mathematical Sciences Daejeon34047 Korea Republic of Department of Earth and Space Science Graduate School of Science Osaka University Toyonaka City Osaka560-0043 Japan Tsukuba City Ibaraki305-0801 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka City Fukuoka814-0180 Japan Department of Physics Tamkang University Danshui Dist. New Taipei City25137 Taiwan Department of Physics and Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Center for High Energy and High Field Physics National Central University Zhongli District Taoyuan City32001 Taiwan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics University of Toyama Toyama City Toyama930-8555 Japan Université Savoie Mont Blanc CNRS/IN2P3 AnnecyF-74941 France Department of Astronomy University of Tokyo Mitaka City Tokyo181-8588 Japan Chinese Academy of Sciences Xiaohongshan Wuhan430071 China Tsukuba City Ibaraki305-0801 Japan College of Industrial Technology Nihon University Narashino City Chiba275-8575 Japan Graduate School of Science and Technology Niigata University Nishi-ku Niig

We apply independent component analysis (ICA) to the real data from a gravitational wave detector for the first time. ICA separates various sources of signals from multiple detection channels making use of non-Gaussian nature of the statistical distributions of the sources. Specifically we use the iKAGRA data taken in April 2016, and calculate the correlations between the gravitational wave strain channel and 35 physical environmental channels. Using a couple of seismic channels which are found to be strongly correlated with the strain, we perform ICA. Injecting a sinusoidal continuous signal in the strain channel, we find that ICA recovers correct parameters with enhanced signal-to-noise ratio, which demonstrates usefulness of this method. Copyright © 2019, The Authors. All rights reserved.

关键词： Independent component analysis

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Edge detection of flat electroencephalography image via classical and fuzzy approach 2nd

Edge detection of flat electroencephalography image via clas...

引用

2nd International Conference on Soft Computing in Data science, SCDS 2016

作者： Zenian, Suzelawati Ahmad, Tahir Idris, Amidora Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia UTM Johor Bahru81310 Malaysia Centre for Sustainable Nanomaterials Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM Johor Bahru81310 Malaysia Department of Mathematics with Computer Graphics Faculty of Science and Natural Resources Universiti Malaysia Sabah UMS Jalan Kota KinabaluSabah88400 Malaysia

ISBN: (纸本)9789811027765

Edge detection is a crucial step in image processing in order to mark the point where the light intensity changed significantly. It is widely used to detect gray-scale and colour images in various fields such as medical image processing, machine vision system and remote sensing. The classical edge detectors such as Prewitt, Robert, and Sobel are quite sensitive towards noise and sometimes inaccurate. In this paper, the boundary of the epileptic foci of Flat EEG (fEEG) is determined by implementing some of the methods ranging from classical to fuzzy approach. There are two methods being applied for the fuzzy edge detector technique which are Minimum Constructor and Maximum Constructor methods;and Fuzzy Mathematical Morphology approach. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Edge detection

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Space-charge effect of the time-varying electron injection in a diode: classical and relativistic regimes

arXiv

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arXiv 2017年

作者： Liu, Yangjié Tang, Qi Wu, Biyi Faculty of Physics and Electronic Science Hubei University Hubei Collaboration Innovation Centre for Advanced Organic Chemical Materials Hubei Key Laboratory of Ferro & Piezoelectric Materials and Devices WuhanHubei Province430062 China Antennas Group School of Electrical Engineering and Computer Science Queen Mary University of London E1 4NS Mile End East London United Kingdom Department of Mathematical Sciences Rensselaer Polytechnic Institute United States Center for Electromagnetic Simulation Beijing Institute of Technology Haidian District Beijing China

Conventionally, space-charge (SC) limited current density is defined as the maximal current density allowed to traverse a diode under a DC voltage when a time-invariant flow is injected from the cathode. In this work, we study the SC limited current density under a time-varying injection for both classical and relativistic regimes and determine the maximal amount of limited current density under certain conditions. Our simulations show that it is unlikely that a time-varying injection emitted from cathode exceeds the known SC limits, in either classical or relativistic regime. Copyright © 2017, The Authors. All rights reserved.

关键词： Current density

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Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs

arXiv

引用

arXiv 2017年

作者： Yu, Jun Shi, Shengjie Gao, Fei Tao, Dacheng Huang, Qingming Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China UBTech Sydney Artificial Intelligence Institute The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney J12 Cleveland St DarlingtonNSW2008 Australia School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100190 China

Face photo-sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It is of wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. While existing methods achieve compelling results, they mostly yield blurred effects and great deformation over various facial components, leading to the unrealistic feeling of synthesized images. To tackle this challenge, in this work, we propose to use the facial composition information to help the synthesis of face sketch/photo. Specially, we propose a novel composition-aided generative adversarial network (CA-GAN) for face photo-sketch synthesis. In CA-GAN, we utilize paired inputs including a face photo/sketch and the corresponding pixel-wise face labels for generating a sketch/photo. In addition, to focus training on hard-generated components and delicate facial structures, we propose a compositional reconstruction loss. Finally, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add compelling details. Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data. Our method achieves the state-of-the-art quality, reducing best previous Fréchet Inception distance (FID) by a large margin. Besides, we demonstrate that the proposed method is of considerable generalization ability. We have made our code and results publicly available: Https://***/ca-gan/. Copyright © 2017, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Open boundary simulations of proteins and their hydration shells by hamiltonian adaptive resolution scheme

arXiv

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arXiv 2017年

作者： Tarenzi, Thomas Calandrini, Vania Potestio, Raffaello Giorgetti, Alejandro Carloni, Paolo Computation-Based Science and Technology Research Center CaSToRC Cyprus Institute 20 Konstantinou Kavafi Street Aglantzia Nicosia2121 Cyprus Department of Physics Faculty of Mathematics Computer Science and Natural Sciences Aachen University Otto-Blumenthal-Straße Aachen52074 Germany Computational Biomedicine Institute for Advanced Simulation IAS-5 Institute of Neuroscience and Medicine INM-9 Forschungszentrum Jülich Jülich52425 Germany Max Planck Institute for Polymer Research Ackermannweg 10 Mainz55128 Germany Department of Biotechnology University of Verona Ca' Vignal 1 Strada Le Grazie 15 Verona37134 Italy

The recently proposed Hamiltonian Adaptive Resolution Scheme (H-AdResS) allows to perform molecular simulations in an open boundary framework. It allows to change on the fly the resolution of specific subset of molecules (usually the solvent), which are free to diffuse between the atomistic region and the coarse-grained reservoir. So far, the method has been successfully applied to pure liquids. Coupling the H-AdResS methodology to hybrid models of proteins, such as the Molecular Mechanics/Coarse-Grained (MM/CG) scheme, is a promising approach for rigorous calculations of ligand binding free energies in low-resolution protein models. Towards this goal, here we apply for the first time H-AdResS to two atomistic proteins in dual-resolution solvent, proving its ability to reproduce structural and dynamic properties of both the proteins and the solvent, as obtained from atomistic simulations. Copyright © 2017, The Authors. All rights reserved.

关键词： Hamiltonians

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A linear domain decomposition method for partially saturated flow in porous media

arXiv

引用

arXiv 2017年

作者： Seus, David Mitra, Koondanibha Pop, Iuliu Sorin Radu, Florin Adrian Rohde, Christian Institute of Applied Analysis and Numerical Simulation Chair of Applied Mathematics Pfaffenwaldring 57 Stuttgart70569 Germany Department of Mathematics and Computer Science Technische Universiteit Eindhoven PO Box 513 Eindhoven5600 MB Netherlands Faculty of Sciences Hasselt University Campus Diepenbeek Agoralaan Building D DiepenbeekBE3590 Belgium Department of Mathematics University of Bergen P. O. Box 7800 BergenN-5020 Norway

The Richards equation is a nonlinear parabolic equation that is commonly used for modelling saturated/unsaturated flow in porous media. We assume that the medium occupies a bounded Lipschitz domain partitioned into two disjoint subdomains separated by a fixed interface Γ. This leads to two problems defined on the subdomains which are coupled through conditions expressing flux and pressure continuity at Γ. After an Euler implicit discretisation of the resulting nonlinear subproblems a linear iterative (L-type) domain decomposition scheme is proposed. The convergence of the scheme is proved rigorously. In the last part we present numerical results that are in line with the theoretical finding, in particular the unconditional convergence of the scheme. We further compare the scheme to other approaches not making use of a domain decomposition. Namely, we compare to a Newton and a Picard scheme. We show that the proposed scheme is more stable than the Newton scheme while remaining comparable in computational time, even if no parallelisation is being adopted. Finally we present a parametric study that can be used to optimize the proposed scheme. Copyright © 2017, The Authors. All rights reserved.

关键词： Domain decomposition methods

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Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

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BMC bioinformatics 2018年第SUPPL 19期19卷 521页

作者： Yuanlin Ma Zuguo Yu Guosheng Han Jinyan Li Vo Anh Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. yuzuguo@***. School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. yuzuguo@***. Advanced Analytics Institute Faculty of Engineering & IT University of Technology Sydney P.O Box 123 Broadway NSW 2007 Australia. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia.

BACKGROUND:Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. RESULTS:We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. CONCLUSIONS:The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs iden

关键词： Hibert-Huang transform Network PSI-BLAST profiles Pre-microRNA SVM mRMR

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Including gap junctions into distributed neuronal network simulations 1

引用

2nd International Workshop on Brain-Inspired Computing, BrainComp 2015

作者： Hahne, Jan Helias, Moritz Kunkel, Susanne Igarashi, Jun Kitayama, Itaru Wylie, Brian Bolten, Matthias Frommer, Andreas Diesmann, Markus School of Mathematics and Natural Sciences University of Wuppertal Wuppertal Germany and JARA BRAIN Institute I Jülich Research Centre Jülich Germany RIKEN Advanced Institute for Computational Science Programming Environment Research Team Kobe Japan Department of Computational Science and Technology School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden Simulation Laboratory Neuroscience Bernstein Facility for Simulation and Database Technology Institute for Advanced Simulation Jülich Aachen Research Alliance Jülich Research Centre Jülich Germany Okinawa Institute of Science and Technology Neural Computation Unit Okinawa Japan Laboratory for Neural Circuit Theory RIKEN Brain Science Institute Wako Japan HPC Usability Research Team RIKEN Advanced Institute for Computational Science Kobe Japan Jülich Supercomputing Centre Jülich Research Centre Jülich Germany Institut für Mathematik Universität Kassel Kassel Germany Department of Psychiatry Psychotherapy and Psychosomatics Medical Faculty RWTH Aachen University Aachen Germany Department of Physics Faculty 1 RWTH Aachen University Aachen Germany

ISBN: (数字)9783319508627

ISBN: (纸本)9783319508610

Contemporary simulation technology for neuronal networks enables the simulation of brain-scale networks using neuron models with a single or a few compartments. However, distributed simulations at full cell density are still lacking the electrical coupling between cells via so called gap junctions. This is due to the absence of efficient algorithms to simulate gap junctions on large parallel computers. The difficulty is that gap junctions require an instantaneous interaction between the coupled neurons, whereas the efficiency of simulation codes for spiking neurons relies on delayed communication. In a recent paper [15] we describe a technology to overcome this obstacle. Here, we give an overview of the challenges to include gap junctions into a distributed simulation scheme for neuronal networks and present an implementation of the new technology available in the NEural simulation Tool (NEST 2.10.0). Subsequently we introduce the usage of gap junctions in model scripts as well as benchmarks assessing the performance and overhead of the technology on the supercomputers JUQUEEN and K computer. © Springer International Publishing AG 2016.

关键词： Supercomputers

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The status of KAGRA underground cryogenic gravitational wave telescope

引用

Journal of Physics: Conference Series 2019年第1期1342卷

作者： T. Akutsu M. Ando A. Araya N. Aritomi H. Asada Y. Aso S. Atsuta K. Awai M. A. Barton K. Cannon K. Craig W. Creus K. Doi K. Eda Y. Enomoto R. Flaminio Y. Fujii M.-K. Fujimoto T. Furuhata S. Haino K. Hasegawa K. Hashino K. Hayama S. Hirobayashi E. Hirose A. H. Hsieh Y. Inoue K. Ioka Y. Itoh T. Kaji T. Kajita M. Kakizaki M. Kamiizumi S. Kambara N. Kanda S. Kanemura M. Kaneyama G. Kang J. Kasuya Y. Kataoka N. Kawai S. Kawamura C. Kim H. Kim J. Kim Y. Kim N. Kimura T. Kinugawa S. Kirii Y. Kitaoka Y. Kojima K. Kokeyama K. Komori K. Kotake R. Kumar H. Lee Y. Liu N. Luca E. Majorana S. Mano M. Marchio T. Matsui F. Matsushima Y. Michimura O. Miyakawa T. Miyamoto A. Miyamoto K. Miyo S. Miyoki W. Morii S. Morisaki Y. Moriwaki T. Morozumi M. Musha S. Nagano K. Nagano K. Nakamura T. Nakamura H. Nakano M. Nakano K. Nakao T. Narikawa L.Nguyen Quynh W.-T. Ni T. Ochi J. Oh S. Oh M. Ohashi N. Ohishi M. Ohkawa K. Okutomi K. Oohara F. E.Peña Alleano I. Pinto N. Sago M. Saijo Y. Saito K. Sakai Y. Sakai Y. Sasaki M. Sasaki S. Sato T. Sato Y. Sekiguchi N. Seto M. Shibata T. Shimoda H. Shinkai A. Shoda K. Somiya E. Son A. Suemasa T. Suzuki H. Tagoshi H. Takahashi R. Takahashi A. Takamori H. Takeda H. Tanaka K. Tanaka T. Tanaka D. Tatsumi T. Tomaru T. Tomura F. Travasso K. Tsubono S. Tsuchida N. Uchikata T. Uchiyama T. Uehara S. Ueki K. Ueno T. Ushiba M. H. P. M. van Putten H. Vocca S. Wada T. Wakamatsu T. Yamada S. Yamamoto T. Yamamoto K. Yamamoto A. Yamamoto J. Yokoyama T. Yokozawa T. H. Yoon H. Yuzurihara S. Zeidler Z.-H. Zhu KAGRA Collaboration National Astronomical Observatory of Japan Mitaka Tokyo 181-8588 Japan Department of Physics University of Tokyo Bunkyo Tokyo 113-0033 Japan Earthquake Research Institute University of Tokyo Bunkyo Tokyo 113-0032 Japan Department of Advanced Physics Hirosaki University Hirosaki Aomori 036-8561 Japan National Astronomical Observatory of Japan Hida Gifu 506-1205 Japan Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Institute for Cosmic Ray Research University of Tokyo Hida Gifu 506-1205 Japan Research Center for the Early Universe University of Tokyo Bunkyo Tokyo 113-0033 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa Chiba 277-8582 Japan Institute of Physics Academia Sinica Nankang Taipei 11529 Taiwan Department of Physics University of Toyama Toyama Toyama 930-8555 Japan Faculty of Engineering University of Toyama Toyama Toyama 930-8555 Japan Yukawa Institute for Theoretical Physics Kyoto University Sakyo Kyoto 606-8502 Japan Department of Physics Osaka City University Sumiyosi Osaka 558-8585 Japan Department of Physics Osaka University Toyonaka Osaka 560-0043 Japan Korea Institute of Science and Technology Information Yuseong Daejeon 34141 Korea Korea Astronomy and Space Science Institute (KASI) Yuseong Daejeon 34055 Korea National Institute for Mathematical Sciences Daejeon 34047 Korea Department of Computer Simulation Inje University Gimhae Gyeongsangnam 50834 Korea Department of Physics and Astronomy Seoul National University Seoul 151-742 Korea High Energy Accelerator Research Organization Tsukuba Ibaraki 305-0801 Japan Department of Physical Science Hiroshima University Higashihiroshima Hiroshima 903-0213 Japan Department of Applied Physics Fukuoka University Jonan Fukuoka 814-0180 Japan Department of Advanced Materials Science University of Tokyo Kashiwa Chiba 277-8582 Japan Istituto Nazionale di Fisica Nucleare Sapienza University Roma 00185 Italy Department of Ma

KAGRA is a 3-km interferometric gravitational wave telescope located in the Kamioka mine in Japan. It is the first km-class gravitational wave telescope constructed underground to reduce seismic noise, and the first km-class telescope to use cryogenic cooling of test masses to reduce thermal noise. The construction of the infrastructure to house the interferometer in the tunnel, and the initial phase operation of the interferometer with a simple 3-km Michelson configuration have been completed. The first cryogenic operation is expected in 2018, and the observing runs with a full interferometer are expected in 2020s. The basic interferometer configuration and the current status of KAGRA are described.

关键词：

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Selection probe of EEG using dynamic graph of autocatalytic set (ACS) 2nd

Selection probe of EEG using dynamic graph of autocatalytic ...

引用

2nd International Conference on Soft Computing in Data science, SCDS 2016

作者： Ashaari, Azmirul Ahmad, Tahir Zenian, Suzelawati Shukor, Noorsufia Abdul Department of Mathematical Science Faculty of Science Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Centre for Sustainable Nanomaterials Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Mathematics with Computer Graphics Faculty of Science and Natural Resources Universiti Malaysia Sabah USM Jalan Kota KinabaluSabah88400 Malaysia Fakulti Sains Komputer dan Matematik Universiti Teknologi Mara Cawangan Negeri Sembilan Kampus Seremban 3 SerembanNegeri Sembilan70300 Malaysia

ISBN: (纸本)9789811027765

Electroencephalography (EEG) machine is a medical equipment which is used to diagnose seizure. EEG signal records data in the form of graph which consist of abnormal patterns such as spikes, sharp waves and also spikes and wave complexes. This pattern also come in multiple line series which then give some difficulties to analyze. This paper introduce the implementation of dynamic graph of Autocatalytic Set (ACS) for EEG signal during seizure. The result is then compared with other publish method namely Principal Component Analysis (PCA) of same EEG data. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Electroencephalography

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