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检索条件"任意字段=International Conference on Computer Modeling and Simulation"

共 35246 条记录，以下是4171-4180 订阅

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Exhaustive In-silico simulation of Single Amino Acid Insertion and Deletion Mutations

Exhaustive In-silico Simulation of Single Amino Acid Inserti...

引用

IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Alistair Turcan Grant Chou Lilu Martin Theo Miller Dylan Thompson Filip Jagodzinski Dept. of Computer Science Western Washington University Bellingham WA USA Department of Computer Science Western Washington University Bellingham WA USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The effects of insertion and deletion mutations (InDels) on protein structures are understudied because performing such experiments in vitro is prohibitive. Consequently, little real world data exists for these types of mutations, despite many diseases being caused by InDels. Computational modeling can support researchers in their efforts to understand the impacts of InDels. In this work we present an in silico approach to generate all exhaustive InDel mutants for a protein. We analyze the effects of the InDels via heatmaps and other visualizations using a variety of metrics, including rigidity analysis and structural data about the mutants relative to the wild type.

关键词： Proteins Measurement Protein engineering Computational modeling Software algorithms Predictive models Rigidity

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Model Construction for Far Field Eddy Current Examination simulation on Ferromagnetic Tubes 3

Model Construction for Far Field Eddy Current Examination Si...

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2021 3rd international conference on computer modeling, simulation and Algorithm, CMSA 2021

作者： Du, Haoyang Yang, Zhou Cui, Lun Xiao, Huixu Electric Power Research Institute of Jilin Electric Power Corporation SGCC Changchun130021 China Jilin Jihua Construction and Repair Co. Ltd Jilin132021 China Jilin Electric Power Corporation SGCC Changchun130021 China

Ferromagnetic tubes are widely applied in fossil power plants such as waterfall tubes, overheat tubes or reheating tubes. Outer surface corrosion, wearing, depth reduction, cracks are common defects during manufacturing and long term operation. Eddy current detection is a traditional method for the examination of surface and near surface defects. However the efficiency is restricted by lift-off effect and skim effect. Far field eddy current overcomes the above disadvantages with high efficiency and fast examination speed and high sensitivity. In this paper, numerical simulation has been carried out to study the far field eddy current Detection for defects in small diameter ferromagnetic tubes in fossil power plants. © Published under licence by IOP Publishing Ltd.

关键词： Ferromagnetic materials

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An Enhanced Framework for 5G Network Slicing Optimization With Prioritized Resource Allocation Using 2-Edge-Connected Subgraphs

An Enhanced Framework for 5G Network Slicing Optimization Wi...

引用

international Colloquium on Smart Grid Metrology (SmaGriMet)

作者： Igor Begić Drago Bago Adrian Satja Kurdija Department of Telecommunications JP Elektroprivreda HZ HB d.d. Mostar Mostar Bosnia and Herzegovina Management Board JP Elektroprivreda HZ HB d.d. Mostar Mostar Bosnia and Herzegovina Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9798350357028

ISBN: (纸本)9798350357035

Emerging 5G network slicing technologies require robust optimization frameworks to ensure efficient resource allocation and path protection. This paper presents an improved slicing optimization model that enhances resource distribution while maintaining computational efficiency. The model builds upon our previously developed framework, which utilizes 2-edge-connected subgraphs for path protection. The proposed enhancement introduces prioritized resource allocation by integrating a weighting factor that optimally balances slice importance and resource distribution, without increasing algorithmic complexity. The model is evaluated against our previous 2-edge-connected subgraph framework. simulation results demonstrate that the improved model achieves better resource efficiency and fairness, while maintaining network resilience and ensuring optimal performance in dynamic network environments. The proposed framework provides valuable insights for network operators in optimizing 5G slicing strategies under constrained resource conditions.

关键词： 5G mobile communication Network slicing Heuristic algorithms Optimization models Computational modeling Dynamic scheduling Computational efficiency Resource management Protection Optimization

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Performance Evaluation of Two-Tier Hybrid Cryptographic Models for Secure Data Transactions in Cloud Computing

Performance Evaluation of Two-Tier Hybrid Cryptographic Mode...

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Contemporary Computing and Communications (InC4), IEEE international conference on

作者： Pankoj Debnath Tonoy Chandra Kar Din Md Ashraf Redwanul Islam Arif Md Lysuzzaman Department of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Cloud computing is a trending and enormous technology. With the improvements in the field of Cloud computing, the main challenge arises in the transaction of data securely from one platform to another. Various cryptographic algorithms are used to provide a secure transaction of data. However, conventional cryptographic algorithms might not be sufficient enough for modern threats. To face these potential vulnerabilities, hybrid cryptographic algorithms can be the solution. Hybrid cryptography integrates multiple cryptographic algorithms to enhance performance and overall safety. RSA(Rivest-Shamir-Adleman) is the most common public key algorithm for encryption and decryption. This paper analyzes a modified form of RSA known as Modified-RSA that enhances security in hybrid cryptographic algorithms by incorporating three prime numbers instead of two. The Modified-RSA(MRSA) hybrid models are compared with the original RSA hybrid models with some theoretical and practical aspects. In this research, a comparative study of two-tier hybrid cryptographic models applied to secure data on the cloud is presented. Practical implementations were conducted with hybrid models of RSA and Modified-RSA using Python simulation. Encryption time, decryption time, average throughput, and efficiency were found using relatively more extensive data. The main motive was to find out the performance of every cryptographic hybrid and see which hybrid model is more efficient.

关键词： Performance evaluation Cloud computing Computational modeling Public key Throughput Data models Encryption

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Machine Learning-Based Workload Prediction in Vehicular Platooning Systems

Machine Learning-Based Workload Prediction in Vehicular Plat...

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Collaborative Advances in Software and COmputiNg (CASCON), international conference on

作者： Pooja Bhardwaj Nitin Auluck Akramul Azim Computer Science and Engineering IIT Ropar Punjab India Engineering and Applied Science UOIT Oshawa Canada

ISBN: (数字)9798331504830

ISBN: (纸本)9798331515621

As the demand for self-driving vehicles increases, efficiently scheduling vehicular tasks to maximize battery life and ensure timely completion is crucial. Vehicle platooning, where multiple vehicles travel together as a unit, enhances fuel efficiency, reduces congestion, and increases safety by maintaining optimal distances between vehicles. In this work, we categorize each vehicle's tasks into the Delay-Sensitive, Bounded-Delay, and Delay-Tolerant categories. However, platooning introduces significant computational challenges, such as real-time data handling, vehicle communication, and adapting to dynamic environments. These challenges necessitate advanced computational methods for effective task management. Machine Learning (ML) techniques are employed to determine the most suitable server for offloading tasks. This decision is supported by a workload prediction algorithm that forecasts the next slot's workload. We develop a task offloading approach using Random Forest models to predict workload, considering current server attributes and historical data. This enables us to schedule tasks on the best server in a dynamic environment. Our simulation results demonstrate significant improvements (up to 90%) in task success rates. Results show that our work can contribute towards developing more reliable and efficient autonomous driving systems by optimizing resource utilization and ensuring timely task completion in dynamic environments.

关键词： Machine learning algorithms Heuristic algorithms Computational modeling Predictive models Prediction algorithms Dynamic scheduling Data models Resource management Servers Vehicle dynamics

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Developing a Digital Twin of the Subsea System

Developing a Digital Twin of the Subsea System

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： Urvi Desai Tanvi Kulkarni Joella Susan Jacob Nupur Patil Mangesh Bedekar Department of Computer Science and Technology Dr. Vishwanath Karad MIT World Peace University Pune Maharashtra India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

A digital twin of a physical system models its behaviour in its intended environment. It has found application in testing, monitoring and maintaining crucial systems large and small while avoiding the risk of any actual damage to the physical systems. Subsea laboratories offer a safe, reliable and easy way to study, visualise, monitor and test various components of an offshore oil rig without needing to be physically present or use real equipment. This paper proposes a digital twin for a double stage reciprocating air compressor at the subsea laboratory at MIT World Peace University, Pune - the Centre for Subsea Engineering Research (CSER). In this work, we propose a framework for end-to-end functioning of the digital twin, including the usage of Machine Learning tools for modules such as testing and predictive maintenance. The User Interface, 3D simulation and ML models used are all detailed in the paper, with several machine learning and deep learning models being implemented. The best accuracy was provided by Polynomial Regression for Outlet Temperature and Pressure with a Root Mean Square Error (RMSE) of $\mathbf{0 . 0 9}$ and $\mathbf{0 . 0 3 6}$ respectively, while AdaBoost gave the best accuracy for Air Flow with an RMSE of 0.019. This system will allow virtual access to the physical system for various purposes, with work set to continue on more components from the laboratory, making simple, safe and costefficient testing and monitoring in subsea environments a widely accessible reality.

关键词： Temperature sensors Solid modeling Visualization Three-dimensional displays Accuracy Laboratories User interfaces

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Motion Error Analysis of High-speed Parallel Robots

Motion Error Analysis of High-speed Parallel Robots

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Robotics, Control and Automation (ICRCA), international conference on

作者： Liangliang Zhu Sergey S. Gavriushin Jingzhong Zheng Guannan Chi Depart. Automated Production Computer Systems Bauman Moscow State Technical University Moscow Russian Depart. Instrumentation Technologies Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

To enhance the operational speed of robots and minimize energy expenditure, a majority of Delta parallel robots employed in the industry have a lightweight design. At high operating speeds, the lightweight construction of Delta robots often causes elastic deformation and vibration. Traditional analysis and control methods for rigid robots do not ensure positioning accuracy of the Delta robot. To address this problem, in this paper we have chosen the WSC-600DJ Delta robot as the subject of our research. Our focus is on analyzing the robot's rigid-flexible coupling dynamics. Firstly, the active and passive arms of the Delta robot are made flexible using Hypermesh software. And then, the rigid-flexible coupled dynamics model of the Delta robot is established in Adams program. Finally, the end effector trajectory is planned in MATLAB using the modified sinusoidal correcting keystone acceleration curve. The driving angle of each active arm is determined via inverse kinematics solutions, which then is imported into Adams for simulation. By comparing the results of modeling the rigid-flexible coupled dynamics of the Delta robot with the theoretical trajectories of the rigid model, we are able to estimate the positioning errors of the robot's actuators caused by the elastic deformation. The results of this study establish a foundation for designing a high-accuracy control system for this particular robot model.

关键词： Parallel robots Service robots Deformation Dynamics Mathematical models Software Trajectory

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Optimizing the Allocation of Secure Fog Resources based on QoS Requirements 8

Optimizing the Allocation of Secure Fog Resources based on Q...

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8th IEEE international conference on Cyber Security and Cloud Computing and 2021 7th IEEE international conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2021

作者： Massey, Keith Moazen, Nadia Halabi, Talal University of Winnipeg Department of Applied Computer Science MB Canada

ISBN: (纸本)9781665443777

Fog computing plays a critical role in the provisioning of computing tasks in the context of Internet of Things (IoT) services. However, the security of IoT services against breaches and attacks relies heavily on the security of fog resources, which must be properly implemented and managed. Increasing security investments and integrating the security aspect into the core processes and operations of fog computing including resource management will increase IoT service protection as well as the trustworthiness of fog service providers. However, this requires careful modeling of the security requirements of IoT services as well as theoretical and experimental evaluation of the tradeoff between security and performance in fog infrastructures. To this end, this paper explores a new model for fog resource allocation according to security and Quality of Service (QoS). The problem is modeled as a multi-objective linear optimization problem and solved using conventional, off-the-shelf optimizers by applying the preemptive method. Specifically, two objective functions were defined: one representing the satisfaction of the security design requirements of IoT services and another that models the communication delay among the different virtual machines belonging to the same service request, which might be deployed on different intermediary fog nodes. The simulation results show that the optimization is efficient and achieves the required level of scalability in fog computing. Moreover, a tradeoff needs to be pondered between the two criteria during the resource allocation process. © 2021 IEEE.

关键词： Quality of service

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Efficient Feature Extraction for Classification of Alzheimer's Abnormalities Using Deep Learning

Efficient Feature Extraction for Classification of Alzheimer...

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Emerging Technology (INCET), international conference for

作者： Minal A. Zope Rakesh K. Deshmukh Priya Pise Shrikant D. Bhopale Dept. of Computer Engineering AISSMS's Institute of Information Technology Kalinga University Raipur Pune India Kalinga University Raipur India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr. D. Y. Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Recent machine learning AD predictions employed feature selection and extraction methods. The literature research revealed several attempts to identify AD by using different Deep learning layer and micro-simulation techniques. However, identifying important features that can detect AD extremely early remains a difficult task. Automation, poor classification accuracy, over fitting, etc. are the other challenges while using machine learning algorithms. The recent progression of using deep learning methods has received the significant attention for the automatic AD classification. CNN's strategy, while successful, has certain drawbacks: (1) The CNN model uses local receptive fields, shares weights, and subsamples, (2) deep neural networks need a lot of labeled sample training models to avoid over fitting, which may cause the loss of crucial information like representative features at various frequencies and orientations; and (3) the higher computational overhead is another challenge. Therefore, these challenges motivate us the need of the proposed study. The scope of the proposed study is limited to the classification of AD from the input medical images.

关键词： Deep learning Training Machine learning algorithms Automation Computational modeling Fitting Artificial neural networks

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A Reinforcement Learning Based Approach to Solve Voltage Issues in Distribution Networks

A Reinforcement Learning Based Approach to Solve Voltage Iss...

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Compatibility in Power Electronics (CPE)

作者： Muhammed Turhan Çakır Hasan Nayir Alper Demir Hüseyin Kaya Oğuzhan Ceylan Electrical and Electronics Engineering Marmara University İstanbul Türkiye R&D Payten Technology İstanbul Türkiye Computer Engineering İzmir University of Economics İzmir Türkiye Department of Management Information Systems Kadir Has University İstanbul Türkiye

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

This paper proposes a Proximal Policy Optimization (PPO)-based reinforcement learning approach to solve overvoltage problem in power distribution networks. The approach aims to minimize the voltage deviations and to keep voltage magnitudes in the allowed ranges. The numerical simulations are performed on a modified unbalanced 123 node network. The modified test system includes a total number of 34 single phase Photovoltaics (200 kVA) connected to three phases. We modified the base case load profile based on real-world daily variations obtained from EPIAS. The PV generation profile was modeled according to a typical sunny day. Using OpenDSS and Python, we implemented PPO-based RL to optimize the setpoints of smart inverters and voltage regulators. The model was trained with load and solar profiles at 09:00, 12:00, and 16:00 to derive optimal voltage regulation strategies for these time points. From the simulation results, we observed that the proposed PPO-based RL approach significantly reduces voltage deviations across all phases, which may help efficient operation of the distribution networks.

关键词： Photovoltaic systems Power engineering Regulators simulation Reinforcement learning Distribution networks Power systems Voltage control Optimization Load modeling

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