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检索条件"任意字段=2018 International Conference on Computer Modeling, Simulation and Algorithm（CMSA2018）"

共 200 条记录，以下是131-140 订阅

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The use of Neural Network modeling for the Selection of Thermophysical Coefficients 2

The use of Neural Network Modeling for the Selection of Ther...

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2nd international conference on Electrical, Communication and computer Engineering, ICECCE 2020

作者： Platov, Sergey Latypov, Oleg Dema, Roman Kalugina, Olga Institute of Metallurgy Mechanical Engineering and Material Processing Nosov Magnitogorsk StateTehnical University Magnitogorsk Russia National University of Science and Tehnology MISIS Magnitogorsk Russia Computer Sciense and Information Security Nosov Magnitogorsk StateTehnical University Magnitogorsk Russia

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171166

In this work, a simulation model of the thermal state of the work rolls at the hot rolling mill 2000 (m) of Public Joint-Stock Corporation Magnitogorsk iron and steel works (PJSC MMK) is built. A neural network was used as a tool for adjusting the thermophysical transfer coefficients of hot rolling mills. The constructed neural network is a multilayer perceptron, consisting of an input layer, two hidden layers, and an output layer. The neural network is implemented in PyTorch, a scientific computing package based on the interpreted Python programming language. To train the neural network, we used the basic data array obtained from the control system of the hot rolling mill. During industrial experiments, the temperature of the work rolls was measured using HotFind-DXT series thermal imagers developed by SDS. A neural network has been built that allows adapting the thermophysical heat transfer coefficients with an average relative error of 11.7%. A neural network will speed up and simplify the selection of thermophysical coefficients for models of the thermal state of work rolls and adequately assess the current thermal state of work rolls during the rolling process. Using a neural network, a model of the thermal state of work rolls was developed. © 2020 IEEE.

关键词： Hot rolling

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Research on Soft Fault Diagnosis of Wavelet Neural Network Based on UKF algorithm for Analog Circuit

Research on Soft Fault Diagnosis of Wavelet Neural Network B...

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2018 7th international conference on Advanced Materials and computer Science(ICAMCS 2018)

作者： Qian Wang Huida Zheng Shiyao Ren Xijing College Air Traffic Control and Navigation College Air Force EngineeringUniversity

In order to improve the capacity of soft fault diagnosis for analog circuit,a Wavelet Neural Network(WNN) diagnosis method based on Unscented Kalman Filter(UKF) algorithm is *** overcome the shortcoming of the traditional BP algorithm of WNN,UKF algorithm is introduced to optimize the parameters of WNN,completing the training of *** simulation shows that the proposed modeling algorithm has the better diagnosis *** also verifies the feasible and effectiveness of proposed method.

关键词： Wavelet Neural network Unscented Kalman filter Fault diagnosis Analog circuit

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Research on Adaptive Filtering algorithm for Maneuvering Target Tracking

Research on Adaptive Filtering Algorithm for Maneuvering Tar...

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2018 3rd international conference on Computational modeling, simulation and Applied Mathematics (cmsaM 2018)

作者： Yi WANG Qiang LI Air Force Engineering University

For the maneuvering target tracking problem, based on the truncation of the normal probability density model, the self-adaptive adjustment of the noise variance is achieved by the functional relationship between the target maneuvering condition and the change of the position estimator at the adjacent sampling time, and a new adaptive filtering algorithm which is based on truncated normal probability density model modified is proposed. The computer simulation results show that the algorithm has good tracking performance when tracking maneuvering targets, and greatly improves the ability to track non-maneuvering targets.

关键词： Target tracking Adaptive filtering Truncated normal probability density model

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Improving the Cloud Server Resource Management in Uncertain Load Conditions Using ACO and Linear Regression algorithms

Improving the Cloud Server Resource Management in Uncertain ...

引用

international conference on Advanced Computing, Networking and Informatics, ICANI 2018

作者： Kothari, Nikita Baheti Mahalkari, Ajitab Department of Computer Science and Engineering S.D. Bansal College of Technology IndoreMadhya Pradesh India

ISBN: (纸本)9789811326721

The cloud is new generation computing technology. The technology is a huge infrastructure that is developed using networks, computational resources and memory units. These infrastructures are not working alone they are always shared with the other cloud infrastructures. Therefore these services can deal with the fluctuating loads on servers. Additionally, scaled when more and more resource requirements appears. Therefore any of the servers can face the issue of resource availability any time due to lake of computational resources. This event in cloud infrastructure is also termed as the uncertain load appearance because the significant amount of load appeared and to execute these request less amount of resources are available by which the waiting time of the jobs increases and server running cost also increases. In order to deal with such kind of situation, the proposed work introduces a two-phase scheduling technique that helps to monitor the load appearance and based on the load patterns optimization of resource allocation is performed. In this context, two popular algorithm namely regression analysis and ACO (ant colony optimization) algorithms are applied. The simulation and modeling of the proposed approach is performed on CloudSim simulator. The experiments of the given technique demonstrate the efficient and enhance resource management. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Resource allocation

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Multiple Sequential Network Attacks Detection Based on DTW-HMM 7

Multiple Sequential Network Attacks Detection Based on DTW-H...

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7th IEEE international conference on Data Science in Cyberspace, DSC 2022

作者： Liu, Haitian Jiang, Rong Zhou, Bin Rong, Xing Li, Juan Li, Aiping College of Computer National University of Defense Technology Changsha China China Electronic Device Systems Engineering Corporation Beijing China China Information Technology Security Evaluation Center Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

With the increasing severity of network threats, network security has become a global concern. Especially the Sequential network attacks (SNAs), are becoming increasingly prominent. Hidden Markov Model (HMM) is often used to model such problems. By revealing the early life cycle of SNAs, it helps to understand attacker strategies. Based on this, this paper proposes a new intrusion detection mechanism for SNA detection to meet the challenges of modeling and detecting multiple sequential network attack scenarios. We developed an architecture capable of identifying these organized attack processes. The system goes through three main stages. The first stage processes the alerts output by IDS and feeds them into different sub-alert sequences belonging to different attackers. The second stage uses the DTW&1NN algorithm to classify sub-alert sequences belonging to the same SNA scenario. The third stage of the proposed system is attack decoding, which utilizes a Hidden Markov Model (HMM) to determine the most likely sequence of SNA phases for a given sequence of relevant alerts. Finally, simulation experiments are carried out based on the public dataset of CSE-CIC-IDS2018 to verify the effectiveness of the proposed architecture. © 2022 IEEE.

关键词： Hidden Markov models

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13th international conference on Flexible Query Answering Systems, FQAS 2019

13th International Conference on Flexible Query Answering Sy...

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13th international conference on Flexible Query Answering Systems, FQAS 2019

ISBN: (纸本)9783030276287

The proceedings contain 37 papers. The special focus in this conference is on Flexible Query Answering Systems. The topics include: WeLink: A Named Entity Disambiguation Approach for a QAS over Knowledge Bases;a Heuristic Pruning Technique for Dialectical Trees on Argumentation-Based Query-Answering Systems;a Method for Efficient Argument-Based Inquiry;DAQAP: Defeasible Argumentation Query Answering Platform;an Efficient algorithm for Computing the Set of Semi-stable Extensions;using Word Embeddings and Deep Learning for Supervised Topic Detection in Social Networks;generalized Association Rules for Sentiment Analysis in Twitter;Data Exploration in the HIFUN Language;reducing Skyline Query Results: An Approach Based on Fuzzy Satisfaction of Concepts;Personal Big Data, GDPR and Anonymization;quantify the Variability of Time Series of Imprecise Data;semantic Understanding of Natural Language Stories for Near Human Question Answering;deductive Querying of Natural Logic Bases;clustering of Intercriteria Analysis Data Using a Health-Related Quality of Life Data;a Flexible Query Answering System for Movie Analytics;can BlockChain Technology Provide Information Systems with Trusted Database? The Case of HyperLedger Fabric;anticipating Depression Based on Online Social Media Behaviour;method for modeling and simulation of Parallel Data Integration Processes in Wireless Sensor Networks;find the Right Peers: Building and Querying Multi-IoT Networks Based on Contexts;handling Veracity of Nominal Data in Big Data: A Multipolar Approach;tutorial: Supervised Learning for Prevalence Estimation;interCriteria Analysis with Interval-Valued Intuitionistic Fuzzy Evaluations;business Dynamism and Innovation Capability in the European Union Member States in 2018 Through the Prism of InterCriteria Analysis.

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Bayesian Estimation of A Periodically-Releasing Biochemical Source Using Sensor Networks

Bayesian Estimation of A Periodically-Releasing Biochemical ...

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UKACC international conference on Control (CONTROL)

作者： Liang Hu Jinya Su Michael Hutchinson Cunjia Liu Wen-Hua Chen School of Computer Science and Informatics De Montfort University Leicester UK Department of Aeronautical and Automotive Engineering Loughborough University Loughborough UK

This paper develops a Bayesian estimation method to estimate source parameters of a biochemical source using a network of sensors. Based on existing models of continuous and instantaneous releases, a model of discrete and periodic releases is proposed, which has extra parameters such as the time interval between two successive releases. Different from existing source term estimation methods, based on the sensor characteristic of chemical sensors, the zero readings of sensors are exploited in our algorithm where the zero readings may be caused by the concentration being below the threshold of the sensors. Two types of Bayesian inference algorithms for key parameters of the sources are developed and their particle filtering implementation is discussed. The efficiency of the proposed algorithms for periodic release is demonstrated and verified by simulation where the algorithm with the exploitation of the zero readings significantly outperforms that without.

关键词： Atmospheric modeling Atmospheric measurements Biological system modeling Pollution measurement Bayes methods Estimation Dispersion

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modeling and Identification of Electrically Stimulated Muscles for Wrist Movement

Modeling and Identification of Electrically Stimulated Muscl...

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international conference on Control, Automation, Robotics and Vision

作者： Zan Zhang Yanhong Liu Bing Chu School of Electrical Engineering Zhengzhou University Zhengzhou China Department of Electronics and Computer Science University of Southampton Southampton UK

The modeling of electrically stimulated muscles is of great importance for the tremor suppression via functional electrical stimulation (FES) approach. In this paper, with fully consideration of the characteristics of wrist muscles, a four input two output wrist muscle model with Hammerstein structure is proposed, by which the four-channel functional electrical stimulation signals can simultaneously stimulate the wrist muscles of flexor carpi radialis (FCR), extensor carpi radialis (ECR), flexor carpi ulnaris (FCU) and extensor carpi ulnaris (ECU) to realize 2 degrees of freedom (DOF) wrist movements. Then, we use the recursive least squares identification algorithm to identify the parameters of the pre-existing four input and one output system. simulation results show that the least recursive squares identification algorithm of two-step method is advantageous in convergence and identification accuracy.

关键词： Muscles Wrist Dynamics Iron Recruitment Electrical stimulation Neuromuscular stimulation

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The Research on M2M Load Prediction algorithm

The Research on M2M Load Prediction Algorithm

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international conference on computer and Communications (ICCC)

作者： Chaofeng Fu Ningbo Zhang Guixia Kang Yifan Zhang Wuxi BUPT Sensory Technology and Industry Institute CO.LTD.

ISBN: (纸本)9781538683408

With the development of Internet of things and mobile communication network, more and more devices have the abilities of communication and networking, which makes all terminal devices to connect the network changing into reality gradually. However, with the explosion of machine-type's communication equipment, the problem of preamble collision in random access process becomes more and more prominent. In this article, a hidden markov load based on feedback correction is proposed for M2M network random access. By observing the status of preambles at the base station terminal, real-time load prediction was made for M2M access terminals at different time points, and the predicted load value was fitted with historical data to reduce the error of load prediction. Compared with the existing load prediction algorithm through simulation, the proposed scheme can predict the load of access terminal in real time, significantly improve the system access success rate and reduce the total service time.

关键词： Prediction algorithms Hidden Markov models Load modeling Machine-to-machine communications Real-time systems Performance evaluation Predictive models

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Experimental battery monitoring system design for electric vehicle applications

Experimental battery monitoring system design for electric v...

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IEEE international conference on Industrial Electronics for Sustainable Energy Systems (IESES)

作者： Seyed Mahmoud Salamati Cong Sheng Huang Bharat Balagopal Mo-Yuen Chow North Carolina State University Raleigh NC US Electrical and Computer Engineering North Carolina State University Raleigh U.S.A

Li-ion batteries are considered as main energy sources for next generation of transportation systems. This paper presents a systematic way to design an efficient hardware testbed for Battery Monitoring System (BMS) applications in Electric Vehicle (EV) industry following the standard industrial communication protocol. The hardware testbed performs both the battery voltage/current data acquisition and the Co-Estimation algorithm. Co-Estimation is an electric circuit model based SOC estimation algorithm which takes model parameter variations into account. In this paper, the Co-Estimation algorithm is firstly discussed. A battery hardware testbed design is then elaborated, and reasons for selecting main components, including microcontroller and voltage/current sensors are explained. The performance of the hardware testbed is compared with MATLAB simulation result using the same Co-Estimation algorithm, showing similar performance between two different platforms: hardware testbed and software simulation.

关键词： State of charge Batteries Sensors Voltage measurement Hardware Integrated circuit modeling Mathematical model

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