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

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An Investigation of the Bibliometric Landscape of the Many Facets of Machine Learning From the Years 2000 to 2022

An Investigation of the Bibliometric Landscape of the Many F...

引用

Computational Modelling, simulation and Optimization (ICCMSO), international conference on

作者： Neeru Sharma Raman Chadha UIE Chandigarh University Punjab

Machine Learning as an emerging and applicable learning plays a vital role in the world of computer Science. MachineLearning has attracted much attention in the last decade. With the development of Machine Learning, huge researchers and publications opted for this as the path. This paper is about the bibliometric analysis that is deployed from the year 2000 to 2022. The main three fundamental factors that guide the progress of machine learning are Data, computing, and algorithmic advances. Mainly there are four different types of machine learning which accomplish Supervised ML, Unsupervised ML, Semi-Supervised ML, and Reinforcement Machine Learning which is used in different areas and applications. This paper is mainly focused on what are the major areas where machine learning has used and what are the minimal area where there is still a requirement of using Machine learning is to be used.

关键词： computer science Machine learning algorithms Tree graphs Computational modeling Bibliometrics Closed box Machine learning

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A Critical Study on Hybrid Computing and Its Current Influences and Future Challenges

A Critical Study on Hybrid Computing and Its Current Influen...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Praveen K V Joe Prathap P. M L Sherin Beevi W Vinil Dani Department of Information Technology St. Peter’s College of Engg. and Tech. Chennai India Department of Computer Science and Engineering R.M.D. Engineering College India Department of EEE St. Peter’s College of Engg. and Tech. Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computing Program processors Scalability Computational modeling Biological system modeling Standards organizations Machine learning Organizations Hardware Optimization

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Research of electromagnetic interference in the power supply network of the access monitoring and control system

Research of electromagnetic interference in the power supply...

引用

2021 international conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2021

作者： Gizatullin, Zinnur Shkinderov, Maksim Department of Computer Aided Design Systems Kazan National Research Technical University named after A.N. Tupolev-KAI Kazan Russia

ISBN: (纸本)9781728145877

Access monitoring and control system are a set of software and hardware for restricting and registering the entry and exit of objects to a given territory through special transitions. Reliable operation of these systems is an important element of security. Electromagnetic interference may impair the function of access control systems. One of the causes of system malfunctions can be electromagnetic interference through the power supply network. The article analyzes unintentional and deliberate sources of electromagnetic interference through the power supply network. a simulation model is proposed and the results of modeling electromagnetic interference in an access monitoring and control system when exposed to electromagnetic pulses through a power supply network are presented. The results obtained are in good agreement with experimental data. © 2021 IEEE.

关键词： Signal interference

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CTS: Sim-to-Real Unsupervised Domain Adaptation on 3D Detection

CTS: Sim-to-Real Unsupervised Domain Adaptation on 3D Detect...

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Meiying Zhang Weiyuan Peng Guangyao Ding Chenyang Lei Chunlin Ji Qi Hao Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology (SUSTech) China Department of Computer Science and Engineering SUSTech Kuang-Chi Institute of Advanced Technology China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

simulation data can be accurately labeled and have been expected to improve the performance of data-driven algorithms, including object detection. However, due to the various domain inconsistencies from simulation to reality (sim-to-real), cross-domain object detection algorithms usually suffer from dramatic performance drops. While numerous unsupervised domain adaptation (UDA) methods have been developed to address cross-domain tasks between real-world datasets, progress in sim-to-real remains limited. This paper presents a novel Complex-to-Simple (CTS) framework to transfer models from labeled simulation (source) to unlabeled reality (target) domains. Based on a two-stage detector, the novelty of this work is threefold: 1) developing fixed-size anchor heads and RoI augmentation to address size bias and feature diversity between two domains, thereby improving the quality of pseudo-label; 2) developing a novel corner-format representation of aleatoric uncertainty (AU) for the bounding box, to uniformly quantify pseudo-label quality; 3) developing a noise-aware mean teacher domain adaptation method based on AU, as well as object-level and frame-level sampling strategies, to migrate the impact of noisy labels. Experimental results demonstrate that our proposed approach significantly enhances the sim-to-real domain adaptation capability of 3D object detection models, outperforming state-of-the-art cross-domain algorithms, which are usually developed for real-to-real UDA tasks.

关键词： Gold Adaptation models Solid modeling Three-dimensional displays Uncertainty Pedestrians Noise Object detection Noise measurement Intelligent robots

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Kinematics Analysis of a Fully Driven Single Pendulum Spherical Mobile Robot

Kinematics Analysis of a Fully Driven Single Pendulum Spheri...

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Automation, Robotics and computer Engineering (ICARCE), international conference on

作者： Minggang Li Hanxu Sun Long Ma Ping Sun School of Modern Post (School of Automation) Beijing University of Posts and Telecommunications Beijing P.R. China CCTEG China Coal Research Institute Beijing P.R. China Department of Physics Beijing Normal University Beijing P.R. China

ISBN: (纸本)9781665475495

In order to further improve the reliability of the spherical robot and enrich its motion forms, a fully driven single pendulum spherical robot is proposed. An innovative way of kinematic modeling from pendulum to spherical shell is proposed. The velocity Jacobian matrix and the inverse kinematics solution of velocity level in the slope of the spherical robot are given. The simulation results verify the controllability of the spherical robot and the correctness of the kinematic model.

关键词： Jacobian matrices Trajectory planning simulation Dynamics Kinematics Reliability theory Controllability

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modeling of Supercritical Once-Through Boiler-Turbine Units Based on Improved Sine and Cosine Algorithm 7th

Modeling of Supercritical Once-Through Boiler-Turbine Units ...

引用

7th international conference on Life System modeling and simulation, LSMS 2021, and 7th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Hou, Guolian Su, Huilin Gu, Hongqun Gong, Linjuan Huang, Ting Lv, Xuming School of Control and Computer Engineering North China Electric Power University Beijing102206 China State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang110004 China

ISBN: (纸本)9789811672095

Under the background of flourishing development of renewable energy, higher requirements are put forward for the operation of supercritical once-through boiler-turbine (OTB) units. It is necessary but challenging to establish a precise dynamic model of supercritical OTB units to ensure safe and efficient operation of units. In this paper, a dynamic mathematical model of supercritical OTB units is derived from mass and energy conservation. Then, an improved sine and cosine algorithm (ISCA) fusing chaotic initialization and cloud theory is proposed to identify the parameters combined with operating data of a 600 MW supercritical unit. The experimental results illustrate that the model outputs can approach the actual data over a wide operating range with satisfactory accuracy. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Parameter estimation

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A Novel Meta-Learning Ensemble Framework for Cancer Classification Using Convolution Neural Networks

A Novel Meta-Learning Ensemble Framework for Cancer Classifi...

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Electronics, Computing, Communication and Control Technology (ICECCC), international conference on

作者： Anjaneyulu Nangunuri Abhijit T. Somnathe B Prasanthi E Latha Mercy S V Ramanan Pundru Chandra Shaker Reddy Electronics and Communication Engineering Geetanjali College of Engineering and Technology Hyderabad Telangana India Electronics and Telecommunication Engineering Smt. Kashibai Navale College of Engineering Pune India Department of CSE (AI& ML) CVR College of Engineering Hyderabad Telangana India Electronics and Communication Engineering PPG Institute of Technology Coimbatore India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Electrical and Electronics Engineering Government College of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Detecting cancer at an early stage is vital because it is a complicated global health crisis that killed 10 million people in 2018. Creating more accurate technology that provides information about the patient's cancer through early diagnosis helps clinicians make better-informed treatment decisions. This research delves deeply into a number of malignancies. A solid overview of machine-learning(ML) and deep-learning(DL) strategies employed in cancer research is also presented in this work. The paper also suggested a multi-neural ensemble learning strategy that relies on stacking to improve the anticipation performance on 8datasets. These datasets include prostate, non-small cell lung cancer survival, cervical, mesothelioma, and Wisconsin breast cancer. The 3real-time cancer datasets from the Jammu and Kashmir region-lung, ovarian, and leukemia-are also examined in this work. Our study's methodology achieved the highest level of anticipation accuracy across all types of cancer datasets, according to the simulation findings. Also, statistical validation of the proposed approach has been done. The researchers set out to create and test a model that could integrate anthropometric, clinical, imaging, and genetic data to make predictions about cancer risk. When compared to homogenous model-multi-modal and uni-modal frameworks, the simulation results show that the suggested framework is more accurate.

关键词： Metalearning Accuracy Biological system modeling simulation Stacking Solids Breast cancer

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UnetRay: A Prediction Method of Indoor Radio Signal Strength Distribution

UnetRay: A Prediction Method of Indoor Radio Signal Strength...

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international conference on Advanced Infocomm Technology (ICAIT)

作者： Zhitao Wang Tiankui Zhang Mingze Liu Xuebing Zhang Yapeng Wang School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China International Division Second High School Beijing Normal University Beijing China Intelligent Cloud Network Dispatching and Operation Center China Telecom Beijing Branch Beijing China Faculty of Applied Sciences Macao Polytechnic University Macao SAR China

Efficient and accurate indoor radio signal strength prediction methods are essential for the design and operation of wireless communication systems. Recently, attempts have been made to combine radio propagation prediction with deep learning. Inspired by recent advances in computer vision, we propose a prediction model using a convolutional encoder-decoder structure fused with Swin Transformer module. Specifically, we embed the Swin Transformer into the U-Net structure to enhance the global modeling capability of the U-Net network, which can be trained to predict the strength of signals received in a given indoor environment. More importantly, once trained for a sufficient number of scenarios, the model can directly predict the signal strength in unknown indoor environments. The simulation results verify that the model is more effective than the traditional U-Net, with a reduction in validation error of about 40%.

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Using Data Analytics to Forecast Violent Crime

Using Data Analytics to Forecast Violent Crime

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international conference on Computational Science and Computational Intelligence (CSCI)

作者： Cotton, Herious Kwembe, Tor A. Jackson State Univ Computat Data Enabled Sci & Engn Jackson MS 39217 USA Jackson State Univ Dept Math & Stat Sci Jackson MS 39217 USA

ISBN: (纸本)9781665458412

In this paper, we used data analytics to analyze criminal data. Prophet model, LSTM recurrent neural network model, a linear regression model, and traditional neural network model were used to predict homicide and rape in the Southeastern Cities of Memphis Tennessee, Jackson Mississippi, and New Orleans Louisiana. LSTM recurrent neural network model and traditional neural network model have smaller RMSE. Thus, LSTM recurrent neural network model and traditional neural network model performed better than the prophet and linear regression models. These promising outcomes will be significant to scholars, policymakers, and law enforcement officers.

关键词： data analytics data mining data visualization simulation and modeling data science

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Study on Impedance Characteristics of Moisture Ingress in Cable Joint and Its Detection Method 11

Study on Impedance Characteristics of Moisture Ingress in Ca...

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11th international conference on Power and Energy Systems (ICPES)

作者： Yu, Ying Di, Yunpeng Lv, Qishen Zhang, Lin Xu, Penglei Gao, Yincheng China Southern Power Grid Corp Shenzhen Power Supply Corp Shenzhen Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9781665413251

Cable joint is one of the most vulnerable parts of power cables. Moisture ingress with joints is a common defect of medium-voltage cross-linked cables. If not repaired timely, it will develop into premature failure of cable. This paper studies the impedance characteristics after moisture ingress into cable joints and their detection method. Based on the theoretical knowledge of time domain reflectometry (TDR), the three-dimensional model of single-core 10kV cold-shrinkable cable joint is established in the computer simulation technology (CST) Studio Suite to investigate the impedance characteristics and reflected waveforms of the joint under different moisture degree. Secondly, the moisture defects are made on the joint of the middle part of the 10 kV XLPE cable in the laboratory, and the S parameter measurement of joint and time-domain pulse reflectometry experiment are carried out respectively. Finally, the simulation and experimental results of impedance characteristics of moisture ingress on cable joint are analyzed and compared, and the field experiment proves the feasibility and effectiveness of using TDR to diagnose defected joints due to moisture ingress.

关键词： cable joint moisture ingress impedance characteristics modeling simulation S parameter TDR

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