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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Ivan S. Nekrasov Vadim S. Tynchenko Vladimir B. Bukhtoyarov Vera A. Kachaeva Kirill A. Bashmur Aleksandra E. Sinitskaya Laboratory of Biofuel Composition Siberian Federal Univercity Krasnoyarsk Russian Federation dept. of Technological Machines and Equipment of Oil and Gas Complex Siberian Federal Univercity Krasnoyarsk Russian Federation Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russian Federation

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

Intelligent automation is a term that can be applied to the more complex field of workflow automation, consisting of robotic workplace automation, robotic process automation, machine learning, and artificial intelligence. Depending on the type of business, companies often use one or more types of automation to improve efficiency or effectiveness. As you move from process-driven automation to more flexible data-driven automation, additional costs arise in the form of training datasets, technical development, infrastructure, and expertise. But the potential benefits in terms of new ideas and financial development can increase significantly. The development of mechanized oil production in recent years has been accompanied by significant achievements in the field of digitalization. Machine learning, as an important element of digitalization, can successfully solve many production problems. The paper describes the application of some machine learning algorithms for solving the problem of classifying and predicting failures of hydrocracking process equipment that occur during oil refining and diesel fuel production. The application of random forest, principal component analysis and hyperparameter tuning methods is considered. The effectiveness of their application is compared.

关键词： Radio frequency Intelligent automation Machine learning algorithms Biological system modeling Oils Refining Production

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Speech recognition using stochastic phonemic segment model based on phoneme segmentation

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systems and Computers in Japan 2000年第10期31卷 89-98页

作者： Furuichi, Chieko Aizawa, Katsura Inoue, Kazuhiko Faculty of Engineering Toin University of Yokohama 1614 Kurogane Midori Yokohama 225-8502 Japan Department of Physics Faculty of Science Ochanomizu Women's University Res. Lab. of Precision Engineering Tokyo Institute of Technology Dept. of Control System Engineering Faculty of Engineering Toin Yokohama University Acoustic Society of Japan Department of Control Systems Faculty of Engineering Toin Yokohama University

This paper discusses speech recognition based on a new statistical phoneme segment model which is trained by phoneme parameters derived from automatically extracted phoneme segments. The proposed system operates as follows. In preprocessing before recognition, the phoneme boundaries are detected by segmentation. The phonemes are discriminated using a stochastic phoneme segment model, and a phoneme segment lattice with scores is constructed. Next the speech recognition is performed by matching of symbol sequences to dictionary items. The segmentation system that is employed can infer phoneme boundaries with high accuracy. This helps to eliminate unnecessary parameters, leaving the feature parameters which are effective in separating phonemes. In other words, the phoneme recognition problem in continuous speech can be reduced to a discrimination problem and thus a speaker-independent model can be constructed from a relatively small number of training data. The stochastic phoneme segment model is trained with training samples extracted from a phoneme-balanced word set of 4920 words uttered by 10 speakers. In a recognition experiment with 6709 words uttered by 63 nontraining speakers, a recognition rate of 92.6% was obtained as the average for all speakers, using a word dictionary of 212 words.

关键词： Speech recognition

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Fault Detection of Large Parameter Changes in a Wastewater Plant

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IFAC Proceedings Volumes 1994年第5期27卷 153-158页

作者： M.J. Fuente P. Vega M.B. Zarrop Dept. Ingeniería de Sistemas y Automatica. Facultad de Ciencias Universidad de Valladolid C/Prado de la Magdalena S/N 47011 Valladolid SPAIN Control System Centre Department of Electrical Engineering and Electronics University of Manchester Institute of Science and Technology. Manchester M60 1QD U.K.

This report deals with the detection and diagnosis of faults (FDD) when they develop in different parts of a wastewater treatment plant, situated in Manresa, Spain. When a fault occurs estimates of parameters in a non-linear mathematical model of the plant change. Some methods for detection and tracking the values of different parameters are proposed, where only large parameter changes are considered in this work. The methods have a common structure with three basic elements: a basic identification algorithm, a detector of the parameter changes and an adaptive procedure for improving the tracking capability of the identifier. A set of simulation experiments are presented in order to compare them.

关键词： Fault Detection and Diagnosis biotechnology wastewater plant estimation algorithms RLS

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Speed-Sensorless control Using Elman Neural Network

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Journal of systems Engineering and Electronics 2001年第4期000卷

作者： XIE Qingguo Wan Shuyun Yi Yanchun Zhao Jin Shen Yi Dept. of Control Science & Technology， Huazhong University of Science and Technology， Wuhan 430074，P. R. China Dept. of System， Changtong Corp.， Wuhan 430072， P. R. China Dept. of Control Science & Technology， Huazhong University of Science and Technology， Wuhan 430074， P. R. China

This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed,with IM parameters employed as associated elements. The BP algorithm is used to provide an adaptive estimation of the motor speed. The effectiveness of the proposed method is verified by simulation results. The implementation on TMS320F240 fixed DSP is provided.

关键词： Induction motot Speed-sensorless Field orientated control Elman neural network Digital signal processor

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Improved Positioning of Delivery Quad copter Using GPS - Accelerometer Multimode controller and Unscented Kalman Filter

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IOP Conference Series: Materials science and Engineering 2020年第1期745卷

作者： Ahmed Abdulmahdi Abdulkareem Alawsi Basil H Jasim Safanah Mudheher Raafat College of Science Physics Dept. University of Wasit Wasit Iraq Electrical Engineering Dept. University of Basra Basra Iraq Control and System Engineering Dept. University of Technology Baghdad Iraq

In last area of technology, the quadrotor helicopter became essential equipment of everyday man use. Delivery by quadcopter has increased significantly in the last few years in different private and military fields because of its indispensable features such as autonomous flight, vertical take-off and landing. Most quadcopters used for delivery service utilize GPS signal for its autonomous flight to control itself and follow the desired path to the destination. Despite the prevalence of autonomous flight, it produces many challenges. GPS signal denial is one of the most critical challenges that may occur. GPS signal denial problem leads to affect the quadcopter control and potentially get lost. Many studies have tried to provide a solution toward this issue, yet, most solutions focused on specific applications and terms rather than achieving a general solution. In this paper, a multimode control algorithm is proposed as a solution for GPS signal denied through the autonomous flight. The algorithm consists of two control modes: GPS and accelerometer modes with Unscented Kalman Filter (UKF). The results show that the proposed algorithm is reliable and suitable for various applications of the quadcopter in different environments. The proposed algorithm can keep the quadcopter under control during GPS signal denial period and continue moving toward the desired path.

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Development of Firewall Optimization Model Using by Packet Filter

Development of Firewall Optimization Model Using by Packet F...

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International Conference on Computer Modeling and Simulation, UKSIM

作者： Myo Thant Kyaw Zaw Ye Kyaw Myat Thu Si Thu Thant Sin Mandalay Division Defence Services Science and Technology Research Center PyiOoLwin Myanmar Department of Informatics and Computer Software System National Research University of Electronic Technology Zelenograd Moscow Russia Department of Automatic Control Systems Bauman Moscow State Technical University Moscow Russia Dept. of Comput. Sci. Nat. Res. Univ. of Electron. Technol. Zelenograd Russia

ISBN: (纸本)9781509008896

This paper studies one particular aspect of providing communication security: firewall technology. This article provides a security framework in the form of packet filtering model within the firewall technology system and its components can be designed and evaluated. This paper introduces a reference model based on packet filtering firewall technology. All components are governed by a centralized security policy and they can be deployed in a distributed fashion to achieve scaling. This packet filtering firewall design is proven technology that provides confidentiality, integrity and availability in own information and network resources. The required design implement to set-up packet filtering firewall for network to protect and unauthorized access to our network. We describe the filtering process design depends on its underlying network technologies. The resulting security mechanism can be used as a bastion-host in the construction of firewall system.

关键词： Computational modeling Filtering Transport protocols Computers Informatics Software

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Maintaining Diversity in an SVM integrated Case Based GA for Solar Flare Prediction

Maintaining Diversity in an SVM integrated Case Based GA for...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Yoshio Taniguchi Yoshihiko Kubota Setsuo Tsuruta Yoshiyuki Mizuno Takayuki Muranushi Yuko Hada Muranushi Yoshitaka Sakurai Rainer Knauf Andrea Kutics Hitachi Industry & Control Solutions Ltd. Yokohama Japan School of System Design and Technology Tokyo Denki University Tokyo Japan Faculty for the Study of Contemporary Society Kyoto Women’s University Kyoto Japan RIKEN Advanced Institute of Computational Science Kobe Japan Kwasan and Hida Observatories Kyoto University Kyoto Japan School of Interdisciplinary Sciences Meiji University Tokyo Japan Dept. of Computer Science and Automation Technische Universität Ilmenau Ilmenau Germany Dept. of Natural Sciences International Christian University Tokyo Japan

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Unusually intense solar flares may cause serious calamities such as damages of electric/nuclear power plants. It is thereupon highly demanded, but is quite difficult, to predict intense solar flares due to the imbalanced character of the available data. To cope with this problem, we have heretofore developed and applied a Case Based Genetic Algorithm (CBGALO) that contains a Local Optimizer, which is a Support Vector Machine (SVM). However, the prediction performance significantly depends on input data for learning. Hereupon, CBGALO is further extended by a Case Based automatically restartable Good combination searching GA for both learning features and input data (CBRsGcmbGA). Even the powerful but computationally expensive Deep Learning cannot automatically (evolutionarily, in our approach) search the learning data. Our approach solved this problem a little better by the case-based approach. However, it became obvious that even this work suffers from the typical GA effect in falling into local optima. To improve the results, we hence developed newly a diversity maintenance approach that inserts good individuals with large Hamming distance into the case base as elite individuals in GA's population. In 2 out of 3 classes of solar flares, the performance of our new approach became as high as the best ones among the conventional world top records. Namely, even in those ≥ C class solar flares, our approach applying the Hamming distance to increase diversity had as high a performance 0.662 as compared with the conventional world top record 0.650.

关键词： Genetic algorithms Support vector machines Evolutionary computation Deep learning

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A Solution to Shape Optimization Problems for the Rigidity Design of Plate and Shell Structures

A Solution to Shape Optimization Problems for the Rigidity D...

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The Sixth World Congress on Computational Mechanics in Conjunction with the Second Asian-Pacific Congress on Computational MechanicsⅠ

作者： Masatoshi Shimoda Jiro Tsuji Yasuhiro Kanda Hisayuki Ishizu Hideyuki Azegami Department of Mechanical Design Engineering Shonan Institute of Technology 1-1-25 Tsujido-nishikaigan Fujisawa 251-8511 Japan Development Planning Department Mitsubishi Automotive Engineering Co. Ltd. 1 Nakashinkiri Hashime-cho Okazaki -444-8501 Japan Okazakilst System Department MMC Computer Research Ltd. 1 Nakashinkiri Hashime-cho Okazaki 444-8501 Japan Digital Technology Dept. Mitsubishi Motors Corp. Hashime-cho Okazaki 444-8501 Japan Dept. of Complex Systems Science Nagoya University Chigusa-ku Nagoya 464-8601 Japan

Plate and shell structures are widelyused in industrial products and structures such as automotives, vessels and architectures for its good formability and lightweight property. This paper presents a numerical shape optimization method for the rigidity design of plate and shell structures. A solution to a compliance minimization problem subject to a volume constraint is proposed, and its reciprocal problem is also solved. The boundaries are assumed to be movable in the in -plane directions so as to keep the initial curvatures in the design domain. For simplicity, the structures to be optimized are discretized by plane elements based on the Mindlin-Reissner plate theory. The problems are formulated as non-parametric, or distributed shape optimization problems involving the design of free boundaries. The shape gradient functions are theoretically derived using the material derivative method and the Lagrange multiplier method. With the shape gradient function, the smooth domain variation that minimizes the objective functional is de termined by the traction method developed by the authors. The validity and practical utility of this numerical solution for the rigidity design of plate and shell structures are verified through simple examples and applications to automotive components.

关键词： Shell A Solution to Shape Optimization Problems for the Rigidity Design of Plate and Shell Structures Shape

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A Snapshot about the Mobile Colistin Resistance (mcr) in The Middle East and North Africa Region

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Journal of Infection and Public Health 2019年第1期12卷 149-150页

作者： A. Redhwan M. Choudhury B. Al Harbi A. Kutbi M. Alfaresi R. AlJindan H. Balkhy S. Al Johani E. Ibrahim A. Deshmukh M. Ahmed A. AlJardani S. Al-Abri J. AlSalman A. Dashti S. Abdelrahman M. Shabban A. Aqel H. AlZoubi H. Sidjabat H. Zowawi College of Health and Rehabilitian Sciences Princess Nourah University UQ Centre for Clinical Research The University of Queensland Faculty of Pharmacy Taibah University Faculty of Medicine Taif University Pathology and Laboratory Medicine Department Sheikh Khalifa General Hospital College of Medicine Imam Abdulrahman Bin Faisal University King Saud bin Abdulaziz University for Health Sciences King Abdullah International Medical Research Centre WHO Collaborating Centre for Infection Prevention and Control and GCC Center for Infection Control Microbiology Department of Pathology and Lab Medicine King Abdulaziz Medical City Department of Neurosurgery Hamad Medical Corporation Weill Cornell Medical College The Life Science Centre Biology School of Science and Technology Örebro University Public Health Laboratory Medical Microbiology Department Department of Medicine The Royal Hospital Samlaniya Medical Complex Infectious Diseases Unit Medical Laboratory Department Faculty of Allied Health Sciences Health Science Center Kuwait University Faculty of Medicine Medical Microbiology and Immunology dept. Ain Shams University Faculty of Medicine Mutah University

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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