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Human Dynamic Behavior: Reconstruction Trajectories Using CDRs 2

Human Dynamic Behavior: Reconstruction Trajectories Using CD...

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2nd international conference on Physics and Applied Sciences, ICPAS 2021

作者： Behadili, Suhad Faisal Mohammed Ali, Israa Abdulqasim University of Baghdad College of Science Computer Science Dep. Baghdad Iraq Middle Technical University Institute of Technology Baghdad Iraq

Investigating the human mobility patterns is a highly interesting field in the 21th century, and it takes vast attention from multi-disciplinary scientists in physics, economic, social, computer, engineering⋯etc. depending on the concept that relates between human mobility patterns and their communications. Hence, the necessity for a rich repository of data has emerged. Therefore, the most powerful solution is the usage of GSM network data, which gives millions of Call Details Records gained from urban regions. However, the available data still have shortcomings, because it gives only the indication of spatio-temporal data at only the moment of mobile communication activities. In this chapter, Gama platform development environment of constructing spatially multi agent simulation is used for modeling and simulating the individual reconstructed trajectories. As a result, the individual trajectories revealed the human daily life patterns. © Published under licence by IOP Publishing Ltd.

关键词： Trajectories

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A Novel Architecture for Testing and Evaluating Programmable Logic Controller (PLC) Based Control Systems Using Software-in-the-Loop (SIL): A Naval Hull, Mechanical, and Electrical (HM&E) Subsystem Case Study

A Novel Architecture for Testing and Evaluating Programmable...

引用

international Automatic Testing conference, AUTOTESTCON

作者： Jonathan Rogers Daniel Vanaman John Ripple Naval Surface Warfare Center Philadelphia PA USA TDI Technologies Inc. King of Prussia PA USA

ISBN: (数字)9798350349436

ISBN: (纸本)9798350349443

Despite rigorous and exhaustive testing in a ship representative test site prior to going underway for sea trials, programmable logic controller (PLC) based control systems frequently have undiscovered software bugs—such as incorrect input/output (IO) signal usage, mismatched signal names, erroneous setpoint values, and incorrect logic. These bugs require the vessel to return to port for software corrections and updates before additional testing can commence. The expense and inflexibility of a full ship hardware representative set of material, coupled with the time and monetary cost associated with the sea trial interruptions, is an obstacle to agile software deployment. Motivated by increasing efficiency and decreasing costs, in this paper, we present a novel architecture to verify and validate (V&V) PLC control systems before they are deployed and tested on a vessel. Our agile framework loads a real-world Siemens 1500 Series PLC project file into a virtual simulation environment that runs custom test scenarios and evaluates the control system. The environment consists of virtualized Siemens 1500 Series PLCs—using Siemens PLCSIM Advanced—and loaded plant models to achieve software-in-the-loop (SIL) simulation testing. A python based software suite orchestrates the simulation by interacting with and facilitating the data flow between the virtualized PLCs and plant models, logging data, running the scenario scripts, and generating test reports. These reports detail the controller performance throughout the various scenarios and identify the parts that failed. Utilizing the test reports, control software bugs and incorrect logic is detected and resolved before platform testing. The all-virtual architecture eliminates the need to have a full ship hardware representative set of material. The framework also allows the plant simulation models to be V&V’d separately from the simulation environment in a setting more conducive for the modelers. Furthermore, the software suit

关键词： Software architecture Object oriented modeling Programmable logic devices Virtual environments computer architecture Control systems Software Numerical models Marine vehicles Testing

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Deep KNN Based Text Classification for Cyberbullying Tweet Detection

Deep KNN Based Text Classification for Cyberbullying Tweet D...

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international conference on System modeling & Advancement in Research Trends (SMART)

作者： M. Nisha J. Jebathangam Department of Computer Science VISTAS Chennai Department of Information Technology VISTAS Chennai

ISBN: (纸本)9781665487351

Nowadays, cyberbullying is a serious issue that many businesses must deal with. Existing technology makes use of machine learning to automatically detect cyberbullying. Deep learning-based algorithms have been shown to achieve higher accuracy in text classification than existing methods. In this paper, we develop a cyberbullying tweets detection using machine learning algorithm. The proposed method reads the tweets and then classifies the texts relating to cyberbullying and blocks the users. The study uses a k-NN classifier integrated with Deep Learning and show how effective the model is over large text datasets than other methods. The results of simulation shows that the proposed method has higher rate of classification accuracy than the other existing methods.

关键词： Learning systems Deep learning Machine learning algorithms Text categorization Cyberbullying Market research Data models

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A Survey of Cost Optimization in Serverless Cloud Computing 7

A Survey of Cost Optimization in Serverless Cloud Computing

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2020 7th international conference on computer-Aided Design, Manufacturing, modeling and simulation, CDMMS 2020

作者： Li, Zhe Tan, Yusong Li, Bao Zhang, Jianfeng Wang, Xiaochuan College of Computer National University of Defense Technology Changsha China

Recently serverless cloud computing which was proposed by Amazon in 2015 is getting more favored by developers because of its charging model of pay-by-usage, applicability for fine-grained services and transparency of the servers to developers. Although a lot of study has proved that the performance and cost of serverless platform is more ideal than the traditional cloud computing service, the new metering model of serverless which depends on execution time, usage count and memory footprint is still unfamiliar to most developers and researchers. In this paper, we firstly make an introduction to the new metering model and the advantages of serverless over traditional cloud services. Then according to the cost affecting factors, we make a comprehensive survey of some constraints that mainly impact the cost of serverless and related methods to reduce cost in three levels - function level, container level and cloud platform level. Based on the analysis, we also propose some directions worthy of in-depth study in the future. © Published under licence by IOP Publishing Ltd.

关键词： Surveys

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Impact of Oscillator Phase Noise on Time-Domain SNN Performance

Impact of Oscillator Phase Noise on Time-Domain SNN Performa...

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international SoC Design conference, ISOCC

作者： Jia Park Woo-Seok Choi Department of Electrical and Computer Engineering Inter-University Semiconductor Research Center Seoul National University Seoul Korea

ISBN: (纸本)9781665459723

Spiking neural networks have gained interests for its event-driven nature and good energy efficiency. As voltagedomain neurons suffer from accuracy degradation by circuit nonlinearities, time-domain neurons have been recently proposed. However, the impact of oscillator phase noise on SNN performance has not been considered in prior studies. This paper investigates the amount of accuracy degradation due to the phase noise of the time-domain neuron. To this end, the phase noise of a time-domain integrator is simulated in a 28nm CMOS process, and the noise is modeled in the system-level simulation to evaluate the SNN performance.

关键词： Phase noise Semiconductor device modeling Degradation simulation Neurons CMOS process Energy efficiency

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Enhanced Cross-Layer Communication Through Modified XGBoost-DNN Algorithm for Cognitive Radio Networks: Performance Analysis and Evaluation

Enhanced Cross-Layer Communication Through Modified XGBoost-...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Sureka N Ahmed Shahebaaz T. Shanthi V K Viswanadha Raju Mukesh Soni Department of Electronics and Communication Engineering Agni college of Technology Chennai India Department of Computer Science and Engineering (DS) CVR College of Engineering Telangana India Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Mechanical Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram India Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Cognitive Radio Networks (CRNs) are very promising communication systems that urgently need effective spectrum-sharing techniques. The overall functional integration across layers is effective in managing CRNs, enhancing performance, hence stability, under changing conditions. This study presents a modified XGBoost-DNN algorithm that enhances cross-layer information exchange in CRNs by selecting features using both XGBoost and DNNs for prediction. A simulation test with 1,000 CR nodes that lasted 100 seconds showed that the suggested algorithm is 15% better than current methods in terms of accuracy, throughput, and latency, achieving 92.5 percent spectrum sensing accuracy. Furthermore, it improves throughput by 10% and reduces latency by 20%, which sets it apart from previous methods. We have compared the Spectrum Utilization Ratio (SUR) and Bit Error Rate (BER) results, demonstrating the effectiveness of the proposed algorithm in cross-layer communication, as well as in spectrum control and management. This research addresses the integration issues to enhance cognitive radio, paving the way for the creation of the new generation of wireless communications systems.

关键词： Cross layer design Adaptation models Accuracy Bit error rate Throughput Prediction algorithms Data models Cognitive radio Integrated circuit modeling Radio spectrum management

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Remote Patient Monitoring Systems Network Adaptation Scenarios

Remote Patient Monitoring Systems Network Adaptation Scenari...

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Current Development in Engineering and Technology (CCET), IEEE international conference on

作者： Halimjon Khujamatov Nurshod Akhmedov Amir Lazarev Sobiya Arsheen Dean of the faculty of Computer Engineering Tashkent University of Information Technologies Tashkent Uzbekistan Data transmission networks and systems department Tashkent University of Information Technologies Tashkent Uzbekistan Department of Computer Science and Information Technology Maulana Azad National Urdu University India

This article is devoted to the actual topic of studying scenarios for adapting a network infrastructure to a network of remote patient monitoring systems (PCMS). The paper presents an overview and analysis of scenarios for adapting the network infrastructure of monitoring systems. The stages of scenario execution have been studied and simulation models of the RPMS network infrastructure have been developed that implement various adaptation scenarios. A comparative analysis of adaptation scenarios was carried out according to several criteria, such as the speed of request delivery, delivery time, and network load. simulation models built in the Anylogic environment, a powerful simulator of mass service system, showed the effectiveness of the mesh topology in relation to the ad-hoc topology according to several criteria. The theoretical significance of the work lies in carrying out analytical work on scenarios for adapting the network infrastructure of the RPMS, as well as in studying the network architecture. The practical significance of the work lies in the development of a simulation model that allows you to explore scenarios for adapting the network architecture.

关键词： Phase change materials Adaptation models Patient monitoring Network architecture Ad hoc networks Topology Load modeling

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Real-time detection of formation head vehicles based on improved YOLOv5s network

Real-time detection of formation head vehicles based on impr...

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computer Graphics, Image and Virtualization (ICCGIV), international conference on

作者： Zhigang Ren Guoquan Ren Dinhai Wu Army Engineering University of PLA Shijiazhuang China

With the development of military modernization in various countries, mechanized troops are working towards unmanned combat. The control and command of formation operations is an important part of unmanned operations. How to achieve stable formation autonomously following people in a complex battlefield environment has great technical difficulties. In this paper, the monocular vision sensor is used for acquisition, and the YOLOv5s neural network structure is modified to realize the rapid identification of the target vehicle. The simulation experiment is used to simplify the model, and the local path planning is designed for the complex environment. Better experimental results were obtained. In this paper, the network structure is optimized based on the existing YOLOv5s, which reduces the computational difficulty, improves the network solution efficiency, and improves the network real-time detection.

关键词： Head Computational modeling Neural networks computer graphics Vision sensors Real-time systems Path planning

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Enhancing the Autonomy of Soft Robots in Minimally Invasive Surgeries Through computer Vision

Enhancing the Autonomy of Soft Robots in Minimally Invasive ...

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Advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Hams Alsirhani Salma M Elhag King Abdulaziz and His Companions Foundation for Giftedness and Creativity “Mawhiba” Riyadh Saudi Arabia Department of Information Systems King Abdul-Aziz University Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Researchers have recognized the potential of employing soft robots in minimally invasive surgeries (MIS), which could significantly reduce the side effects associated with traditional surgical methods and enhance patient outcomes. However, the extent to which this potential is realized depends on the level of autonomy achieved by the soft robots. Lower levels of autonomy necessitate increased hands-on involvement during MIS, potentially compromising the robots' ability to perform procedures with consistent accuracy. Consequently, achieving high levels of accuracy in the autonomy of soft robots remains a significant challenge. The autonomy of these robots is influenced by various factors, including their capacity to accurately classify their surroundings, particularly anatomical structures, which is crucial for effective decision-making. To address the challenge posed by our research question, How can the application of Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL) improve the autonomy of soft robots in performing complex tasks in MIS, particularly in the precise classification of anatomical structures and decision making?, we conducted an in-depth literature review and investigation into the integration of DNNs and DRL within this context. Our study used a modeling and simulation methodology to evaluate and quantify the benefits of incorporating these advanced AI techniques. Through this approach, we measured the effects of DNNs and DRL on enhancing the autonomy of soft robots, particularly in their ability to perform complex tasks in MIS with improved precision and decision-making capabilities. This work represents a step toward optimizing robotic autonomy in surgical environments, potentially leading to more efficient and accurate outcomes in minimally invasive procedures.

关键词： Technological innovation Accuracy Minimally invasive surgery Decision making Artificial neural networks Anatomical structure Transforms Soft robotics Deep reinforcement learning Real-time systems

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Optimizing Open Resonant Cavity Antenna Design with Machine Learning and Metaheuristic Techniques

Optimizing Open Resonant Cavity Antenna Design with Machine ...

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Next Generation Communication & Information Processing (INCIP), international conference on

作者： B. Santosh Kumar Yagateela Pandu Rangaiah Navdeep Singh Dinesh Kumar Yadav Mohammed Wael Mohammed Department of Computer Science Engineering New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The objectives are there are a lot of ORCAs have been designed, therefore the design and performance optimization of ORCAs are predominant for improving wireless communication systems. This study focuses on the use of ML-GO-RAD to determine the optimization and enhancement of the ORCAs” design and their respective parameters. Therefore., this study attempts to enhance the efficiency of the optimization process by employing PSO and GA coupled with machine learning models., given that the ORCA configuration raises a number of high-dimensional parameters. The form of the proposed ML-GO-RAD methodology employs machine learning for predicting rational parameter sets by effectively cutting down the computations and convergence time as compared to the usual approaches. Detailed disclosures about the ORCA's resonant frequency., bandwidth., and radiation efficiency of the ORCA are made., which prove significant enhancements anew. Based on the simulation results., it can be concluded that the proposed approach improves the gain of the transmitting and receiving antennas as well as decreases the signal interference level. Furthermore., some related issues., such as the performance differences due to the geometrical characteristics of the cavity and the properties of dielectric materials used in the fabrication of the microstrip patch antenna., are also investigated. It has been illustrated that the novel application of machine learning to antenna design optimization varies the framework for future development of high-performance wireless communication systems.

关键词： Wireless communication Machine learning algorithms Design methodology Computational modeling Transmitting antennas Resonant frequency Machine learning Bandwidth Radar antennas Optimization

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