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检索条件"任意字段=2nd International Conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2022"

共 201 条记录，以下是171-180 订阅

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Parallel Offloading of Terminal Multitasking in Edge Computing

Parallel Offloading of Terminal Multitasking in Edge Computi...

引用

Signal Processing and Intelligent Computing (SPIC), international conference on

作者： Lincong Zhang Weinan Zhao Yongjun Li School of Information Science and Engineering Shenyang Ligong University Shenyang China Anyang No.63 Junior Middle School Anyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

As emerging technologies like autonomous driving continue to develop, they bring a plethora of latency-sensitive tasks to mobile terminals. Traditional single-terminal, single-task offloading models result in considerable waiting latency for subsequent offloading tasks. This paper introduces MIMO (Multiple Input Multiple Output) technology and proposes a single-terminal multi-task parallel offloading algorithm. The algorithm enables the simultaneous transmission of multiple tasks within a single timeslot, achieving rapid offloading of computational tasks. It also considers the issue of transmission power allocation for offloaded tasks, thereby reducing the latency introduced by data transfer. simulation analysis indicates that the proposed method performs better than other algorithms in reducing average task latency.

关键词： Heuristic algorithms computational modeling Signal processing algorithms Signal processing Multitasking Dynamic scheduling data transfer Resource management MIMO communication Edge computing

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An Evolutionary EMD-FA-ELM approach for Short Term Wind Power Prediction using Wind Speed as Input

An Evolutionary EMD-FA-ELM approach for Short Term Wind Powe...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Prachitara Satapathy Sabita Chaine Shaktinarayana Mishra Lokanath Tripathy P.K Dash Sumant Kumar Dalai Department of EE College of Engg. & Technology Bhubaneswar India Department of ECE National Institute of Technology Meghalaya India Department of EE Siksha ‘O’ Anusandhan Bhubaneswar India Department of EE National Institute of Technology Meghalaya India

ISBN: (纸本)9781665478403

A hybrid method combining Efficient Firefly Algorithm (EFA), Empirical Mode Decomposition (EMD) and Special Extreme Learning approach (SELM) is presented in this paper for wind power prediction. The time series wind speed data is decomposed in different Intrinsic Mode Functions (IMFs) by the help of EMD. The optimal IMFs can be selected by the help of the IFA to be given as input to the SELM. The absence of randomness in SELM makes it different from the conventional ELM approach. The optimal IMFs are considered as the input to the SELM to predict the wind speed. Here the wind speed data is considered for 10 minute prediction interval. The predicted wind power results from the proposed optimized EMD-SELM are compared with EMD-SELM and EMD-ELM through various performance metrics. The performance of the IF A is also assessed against other evolutionary approach like differential evolution (DE) and Genetic Algorithm (GA).

关键词： Measurement Power engineering Wind speed computational modeling Time series analysis Wind power generation Predictive models

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Identifiability analysis of Inverse Problems in Biology

Identifiability Analysis of Inverse Problems in Biology

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2017 2nd international conference on computational modeling, simulation and Applied Mathematics（CMSAM 2017)

作者： Varvara LATYSHENKO Olga KRIVOROTKO Sergey KABANIKHIN Shuhua ZHANG Victoriya KASHTANOVA Dariya YERMOLENKO Novosibirsk State University Institute of Computational Mathematics and Mathematical Geophysics of SB RAS Tianjin University of Finance and Economics

The paper focuses on sensitivity-based identifiability analysis of parameters for mathematical models described by systems of nonlinear ordinary differential equations. This analysis is carried out using eigenvalue method and orthogonal method. Both methods allow one to globally evaluate and compare the influence of parameter values on measurement data. The sensitivity analysis and numerical experiments for mathematical model of the spread of TB and HIV co-infection are demonstrated. The numerical results show that 4 parameters(from 15 available) are identifiable uniquely by the given data only about 3 measured time-point functions during 5 years.

关键词： ODE Inverse problem Epidemiology Tuberculosis HIV Identifiability Sensitivity-based analysis Orthogonal method Eigenvalue method

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Development and Comparison of Scoring Functions in Curriculum Learning

Development and Comparison of Scoring Functions in Curriculu...

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Computing and Machine Intelligence (ICMI), international conference on

作者： Himmet Toprak Kesgin Mehmet Fatih Amasyali Department of Computer Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9781665474832

ISBN: (纸本)9781665474849

Curriculum Learning is the presentation of samples to the machine learning model in a meaningful order instead of a random order. The main challenge of Curriculum Learning is determining how to rank these samples. The ranking of the samples is expressed by the scoring function. In this study, scoring functions were compared using data set features, using the model to be trained, and using another model and their ensemble versions. Experiments were performed for 4 images and 4 text datasets. No significant differences were found between scoring functions for text datasets, but significant improvements were obtained in scoring functions created using transfer learning compared to classical model training and other scoring functions for image datasets. It shows that different new scoring functions are waiting to be found for text classification tasks.

关键词： Training computational modeling Transfer learning Text categorization data models Task analysis Machine intelligence

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Combining Computer simulation Visualization and Multi-Objective Optimization Algorithm for Urban Light Pollution Research

Combining Computer Simulation Visualization and Multi-Object...

引用

Image Processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Yuhang Zhang Beijing Technology and Business University Beijing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This study aims to solve the problem of urban light pollution by establishing a series of models to achieve the best balance between economy and efficiency. Firstly, we decompose the complex problem into a simple one and establish a light pollution risk level evaluation model, a light pollution threshold differential equation model, a lighting system optimisation strategy model and a Poisson process simulation model validation. In Model I, we use hierarchical analysis and Topsis algorithm, combined with multiple attribute data, to determine the evaluation indexes of light pollution risk level, and effectively classify the light pollution risk level of the area. In Model II, we used the differential equation model to find out the light intensity threshold, and the results showed the diversity of different regions. In Model III, a multi-objective optimisation algorithm is used to achieve the balance of time, light intensity threshold and light range through the lighting system optimisation strategy. Model IV combines the data from Beijing and Shanghai and calculates the number of times residents exceed the permitted use of light using a Poisson distribution.

关键词： Analytical models Biological system modeling computational modeling Diversity reception Differential equations Artificial light Mathematical models data models Optimization Light sources

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A study on the adoption of Wireless Communication in Big data Analytics Using Neural Networks and Deep Learning

A study on the adoption of Wireless Communication in Big Dat...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： P. Rohini Sachin Tripathi C M Preeti A. Renuka José Luis Arias Gonzales Durgaprasad Gangodkar Computer Science and Engineering Faculty of Science and Technology (ICFAI Foundation for Higher Education)HyderabadTelangana State Symbiosis International (Deemed) University Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India JNTUH Department of CSE Department of Business University of British Columbia Department of Computer Science &#x0026 Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The study explores about the Wireless communication which is one of the most rapidly evolving and active technology fields in the communication world. Wireless communication is a means of transmitting data from one point to another without the use of wires, cables, or another physical medium. As a result, commercial network operators have risen at an accelerating rate, bringing in the era of big data. Machine learning has been applied in a range of corporate and academic research contexts as one of the most promising artificial intelligences (AI) methods for deciphering this deluge of knowledge. This study presents a high-level introduction of big data handling and technological advances, as well as their potential applications in next-generation wireless networks (NG). Following that, we employ advanced analytics to estimate mobile users' demands and then use that knowledge to improve the efficacy of “community wireless communication channels.” In specifically, a unified, huge data-aided computer learning framework comprised of feature extraction, data modelling, and prediction/online refinement is provided. The primary benefits of the proposed framework are that we could create the logic, problem formulations, and method of powerful computational models inside the frame of wireless networks by depending on vast data that reflects the both spectrum and other hard demands of users. Via general, information is conveyed from sender to receiver across a certain distance in a communication system. The transmitter may be located anywhere within a few meters using Wireless Communication… We examined currently known approaches and explored their benefits and drawbacks in order to identify new research avenues for future advancements in underwater sensor networks.

关键词： Underwater cables Wireless sensor networks data analysis Transmitters Wireless networks computational modeling Wires

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Research on Vulnerability Mining Method Based on Artificial Intelligence in Internet of Things

Research on Vulnerability Mining Method Based on Artificial ...

引用

Image Processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Peiyue Ma Liehuang Zhu Chuan Zhang School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In recent years, with the continuous development of computer technology, Internet of Things (IoT) technology has been widely used in various fields and has played an important role in various industries. The Internet of Things is responsible for information transmission and storage, and once there are network security issues, it will bring huge disasters to various industries. Detecting vulnerabilities in the Internet of Things is an urgent problem that needs to be addressed. In order to improve the efficiency of IoT vulnerability detection, this paper designs an IoT vulnerability mining solution based on the LSTM algorithm. The designed artificial intelligence IoT vulnerability mining algorithm uses deep learning for identification, and this framework includes steps such as data collection, data learning, and data detection. The results show that after comparing three models: LSTM, SeqGAN, and WGAN, the LSTM algorithm exhibits the highest accuracy.

关键词： Deep learning Industries Performance evaluation Accuracy computational modeling simulation Stability analysis Internet of Things data mining Long short term memory

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Performance Evaluation of a Risk-based Three-tier Inspection System

Performance Evaluation of a Risk-based Three-tier Inspection...

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2017 2nd international conference on computational modeling, simulation and Applied Mathematics（CMSAM 2017)

作者： Chia-Hung WANG Jinman LAN College of Information Science and Engineering Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications

The simulation model of a risk-based three-tier inspection system is presented in the paper for the security management of screening passengers. In this study, passengers are classified to three risk classes(Trusted class, Regular class, and Risky class) based on their assessed risk level. A hybrid simulation model is developed to simulate the passengers' behavior from the moment they enter the inspection system until they leave the system. We study the performance evaluation of the proposed inspection system through an Arena simulation platform. The sensitivity analysis from a series of our numerical experiments shows the managerial effect of the model parameters on two performance measures in terms of passengers' average waiting time and the overall safety level.

关键词： Arena simulation Security management Passenger screening Queueing system Sensitivity analysis

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Edge Computing Offloading Strategy Based on Particle Swarm Algorithm for Power Internet of Things

Edge Computing Offloading Strategy Based on Particle Swarm A...

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Big data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), international conference on

作者： Wenjing Li Wei Deng Rui She Ningchi Zhang Yanru Wang Wenjie Ma State Grid Information & Telecommunication Co Ltd Beijing China Beijing Fibrlink Communications Co. Ltd Beijing China Communication Technology Center Beijing Fibrlink Communications Co. Ltd Beijing China

ISBN: (数字)9781665415408

ISBN: (纸本)9781665447065

With the development of smart grid, IOT devices in the field of power control are widely used. The diversity of types, features and functions of IOT devices not only integrates detailed big data, but also challenges the real-time, compatibility and big data processing capability of system control. In this paper, the MEC edge computing model for the power IoT is investigated so as to reduce the challenges of increased latency and limited system storage space caused by the access of a large number of power using devices. The industrial IoT scenario is modeled as a multiuser multi-MEC latency optimization problem to minimize the total cost of the whole system with the constraint of energy consumption. In order to achieve the optimal MEC unloading strategy, the computational offloading strategy based on the basic particle swarm algorithm is proposed. The simulation results show that the proposed scheme of the MEC computing task offload model for the power IoT has more obvious advantages in reducing system overall cost of the network. Moreover, the proposed algorithm of PSAO is convenient and fast converging at the same time.

关键词： computational modeling simulation Big data Particle swarm optimization Task analysis Optimization Edge computing

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Research on Fault Detection and Prediction Model in Information System Operation and Maintenance

Research on Fault Detection and Prediction Model in Informat...

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Electrical, Automation and Computer Engineering (ICEACE), international conference on

作者： Wang Lin Sun Yuchen Fu Haixiang Xu Tianming Liu Hao Wang Xiaogang Information System Integration Branch Company Nari Technology Co.Ltd Nanjing China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This study explores the effectiveness of applying big data-driven fault detection and prediction models in information system operation and maintenance. It mainly focuses on building accurate fault prediction models through machine learning algorithms to detect potential system failures in advance and improve operation and maintenance efficiency. The study first collects and processes a large amount of operation and maintenance data in a big data environment, and combines a variety of machine learning algorithms such as decision trees, support vector machines, and neural networks to design a comprehensive fault detection and prediction model. The model cannot only classify and identify existing faults, but also predict possible future fault points based on historical data. Through system simulation, this paper verifies the performance of the model in multiple scenarios. The simulation results show that the model has achieved significant improvements in key indicators such as accuracy, recall rate, and F1 value, especially in complex and changeable system environments. It shows strong robustness. Accurate data analysis further shows that the designed model can effectively reduce system downtime and improve overall operation and maintenance efficiency.

关键词： Support vector machines Accuracy Machine learning algorithms Fault detection computational modeling Predictive models data models Maintenance Systems simulation Information systems

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