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2023 International Russian Automation Conference, RusAutoCon 2023

作者： Martinov, Georgi Salamatin, Evgeny Moscow State University of Technology "STANKIN" Department of Computer-Architecture Control Systems Moscow Russia

ISBN: (纸本)9798350345551

The problem of synchronizing parallel tasks in control systems at the level of the part program is reviewed. A general solution, based on a high-level part programming language extension, is proposed for synchronizing parallel tasks within one control system as well as tasks performed by several different control systems. A scheme for synchronizing the CNC with external autonomous devices through the Common PLC Memory, to which the part program, the PLC program and the inputs and outputs (I/Os) of electric automation have access, has been developed. A set of functions in a high-level part programming language, providing zeroing a certain number of bytes, writing bit, byte, long and float data types to the Common PLC Memory and reading them from it, has been implemented. As a part of practical research, a test stand on the basis of the AxiOMA Control CNC system, that controls actuators via the EtherCAT protocol, was assembled;master and slave part programs were implemented;tests, confirming the operability of the mechanism for synchronizing CNC system with external devices by using a part program, were carried out. © 2023 IEEE.

关键词： Control systems

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Hybrid framework for neuro-dynamic programming application to water supply networks

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6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001

作者： Damas, M. Salmerón, M. Ortega, J. Olivares, G. Department of Computer Architecture and Computer Technology Facultad de Ciencias University of Granada Campus Fuentenueva S/n E-18071 Granada Spain

ISBN: (纸本)9783540422372

A hybrid method, based on evolutionary computation, Monte Carlo simulation, and neural networks for functional approximation and time series prediction, is proposed to reduce the high computational cost usually required by dynamic programming problems, that appear in complex real applications. As an example of application a scheduling problem related with the control of a water supply network is considered. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Monte Carlo methods

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Scaling HMMER performance on multicore architectures

Scaling HMMER performance on multicore architectures

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5th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011

作者： Isaza, Sebastian Houtgast, Ernst Sanchez, Friman Ramirez, Alex Gaydadjiev, Gergi Computer Engineering Laboratory Delft University of Technology Netherlands Computer Architecture Department Technical University of Catalonia Spain Barcelona Supercomputing Center Spain

ISBN: (纸本)9780769543734

In bioinformatics, protein sequence alignment is one of the fundamental tasks that scientists perform. Since the growth of biological data is exponential, there is an ever-increasing demand for computational power. While current processor technology is shifting towards the use of multicores, the mapping and parallelization of applications has become a critical issue. In order to keep up with the processing demands, applications' bottlenecks to performance need to be found and properly addressed. In this paper we study the parallelism and performance scalability of HMMER, a bioinformatics application to perform sequence alignment. After our study of the bottlenecks in a HMMER version ported to the Cell processor, we present two optimized versions to improve scalability in a larger multicore architecture. We use a simulator that allows us to model a system with up to 512 processors and study the performance of the three parallel versions of HMMER. Results show that removing the I/O bottleneck improves performance by 3X and 2.4X for a short and a long HMM query respectively. Additionally, by offloading the sequence pre-formatting to the worker cores, larger speedups of up to 27X and 7X are achieved. Compared to using a single worker processor, up to 156X speedup is obtained when using 256 cores. © 2011 IEEE.

关键词： Bioinformatics

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Parametric and non-parametric feature selection for kidney transplants

Parametric and non-parametric feature selection for kidney t...

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12th International Work-Conference on Artificial Neural Networks, IWANN 2013

作者： Garcia-Del-Moral, Raimundo Guillén, Alberto Herrera, Luis Javier Cañas, Antonio Rojas, Ignacio Intensive Care Unit Santa Ana Hospital Motril Granada Spain Department of Computer Architecture and Computer Technology Universidad de Granada Spain

ISBN: (纸本)9783642386817

This paper presents a comparison of several methods of measuring the quality of a subset of features that characterise kidney's graft so they can be evaluated to be transplanted. First, two non-parametric methods, Delta Test and Mutual Information, are used isolated and in a multiobjective manner using a genetic algorithm and comparing the solutions will all the possible solutions obtained by brute force. Afterwards, LSSVM are used to approximate the score of the graft so, for smaller approximation errors, the subset of features is considered better. The results obtained are confirmed from the clinical perspective by an expert. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Genetic algorithms

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CHMasters: A scalable and speed-efficient metadata service in distributed file system

CHMasters: A scalable and speed-efficient metadata service i...

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2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2011

作者： Xu, Min Zhou, Junrui Zhou, Wei An, Hong Department of Computer Science and Technology University of Science and Technology Hefei 230026 China Key Laboratory of Computer System and Architecture Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9780769545646

Distributed file system (DFS) is playing important roles of supporting large distributed data-intensive applications to meet storage needs. Typically, the design of DFS, such as GFS in Google, DMS in Cisco and TFS in Alibaba, is driven by observations of specific application workloads, internal demands and technological environment. In such systems, the metadata service is a critical factor that can affect the file system performance and availability to a great degree. Five requirements have been summarized for the metadata service: location transparent file service, smart director, efficient speed, strong scalability and friendly collaborator. In this paper, we present metadata service module called CHMasters in our DFS. Consistent hashing protocol is used to relieve potential hot spots on name servers. Files'metadata and master nodes are mapped into the same hash space by consistent hash function. And then files'metadata are scattered to master nodes by clockwise "closest" principle. Chunkserver acts as a client when report its chunks info. Only a small proportion of files'metadata will be rehashed when master nodes state change. A new scalable file mapping strategy is also proposed to map file sizes from few MB to several GB efficiently. After intensive experiments, it shows CHMasters is satisfying the above five requirements. © 2011 IEEE.

关键词： Metadata

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Exploring Spatial Hybrid User Interface for Visual Sensemaking

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Tong, Wai Li, Haobo Xia, Meng Kam-Kwai, Wong Pong, Ting-Chuen Qu, Huamin Yang, Yalong Nanchang University School of Mathematics and Computer Sciences Nanchang 330031 China Shenzhen University School of Architecture & Urban Planning Shenzhen 518060 China University of Calgary Department of Geomatics Engineering Calgary T2N 1N4 Canada Wuhan University School of Geodesy and Geomatics Wuhan 430079 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan 430079 China Finnish Geospatial Research Institute Department of Remote Sensing and Photogrammetry Masala 02430 Finland Wuhan University of Technology School of Safety Science and Emergency Management Wuhan 430079 China Nanjing University of Information Science & Technology School of Remote Sensing & Geomatics Engineering Nanjing 210000 China Nanjing Forestry University College of Civil Engineering Nanjing 210037 China Guangzhou Urban Planning & Design Survey Research Institute Guangzhou 51100 China

We built a spatial hybrid system that combines a personal computer (PC) and virtual reality (VR) for visual sensemaking, addressing limitations in both environments. Although VR offers immense potential for interactive data visualization (e.g., large display space and spatial navigation), it can also present challenges such as imprecise interactions and user fatigue. At the same time, a PC offers precise and familiar interactions but has limited display space and interaction modality. Therefore, we iteratively designed a spatial hybrid system (PC+VR) to complement these two environments by enabling seamless switching between PC and VR environments. To evaluate the system's effectiveness and user experience, we compared it to using a single computing environment (i.e., PC-only and VR-only). Our study results (N=18) showed that spatial PC+VR could combine the benefits of both devices to outperform user preference for VR-only without a negative impact on performance from device switching overhead. Finally, we discussed future design implications. © 1995-2012 IEEE.

关键词： Data visualization

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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

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5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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Opposites attract: Complementary phenotype selection for crossover in genetic programming 7th

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7th International Conference on Parallel Problem Solving from Nature, PPSN 2002

作者： Dolin, Brad Arenas, M.G. Merelo, J.J. Computer Science Department Stanford University StanfordCA94305 United States Department of Architecture and Computer Technology University of Granada GranadaCP 18071 Spain

ISBN: (纸本)3540441395

Standard crossover in genetic programming (GP) selects two parents independently, based on fitness, and swaps randomly chosen portions of genetic material (subtrees). The mechanism by which the crossover operator achieves success in GP, and even whether crossover does in fact exhibit relative success compared to other operators such as mutation, is anything but clear [14]. An intuitive explanation for successful crossover would be that the operator produces fit offspring by combining the "strengths" of each parent. However, standard selection schemes choose each parent independently of the other, and with regard to overall fitness rather than more specific phenotypic traits. We present an algorithm for choosing parents which have complementary performance on a set of fitness cases, with an eye toward enabling the crossover operator to produce offspring which combine the distinct strengths of each parent. We test Complementary Phenotype Selection in three genetic programming domains: Boolean 6-Multiplexer, Intertwined Spirals Classification, and Sunspot Prediction. We demonstrate significant performance gains over the control methods in all of them and present a preliminary analysis of these results. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Genetic programming

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Behavior analysis and dynamic crowd management in video surveillance system

Behavior analysis and dynamic crowd management in video surv...

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2011 22nd International Workshop on Database and Expert Systems Applications, DEXA 2011

作者： Szczodrak, Maciej Kotus, Jozef Kopaczewski, Krzysztof Lopatka, Kuba Czyzewski, Andrzej Krawczyk, Henryk Multimedia Systems Department Gdansk University of Technology Gdansk Poland Department of Computer Architecture Gdansk University of Technology Gdansk Poland

ISBN: (纸本)9780769544861

A concept and practical implementation of a crowd management system which acquires input data by the set of monitoring cameras is presented. Two leading threads are considered. First concerns the crowd behavior analysis. Second thread focuses on detection of a hold-ups in the doorway. The optical flow combined with soft computing methods (neural network) is employed to evaluate the type of crowd behavior, and fuzzy logic aids detection of the hold-ups. The experiments with the behavior classification algorithm were performed employing prepared repository of typical and untypical behavior recordings. The effectiveness of the analysis was assessed by comparing algorithmic processing results to a set of prepared reference data, which provides a description of behavior type occurring in each video frame. Application of parallel image processing and influence of parallelization on achieved performance is explained. Apart from the crowd management the behavior analysis may be used in automatic surveillance system deployed in a city area. © 2011 IEEE.

关键词： Fuzzy logic

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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