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

共 35215 条记录，以下是291-300 订阅

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TCAD simulation of Radiation Effects on 180 nm Logic Inverters

TCAD Simulation of Radiation Effects on 180 nm Logic Inverte...

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Thermal, Mechanical and Multiphysics simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Eike Trumann Amen Allah Bahri Gia Bao Thieu Kirsten Weide-Zaage Dorian Von Wolff Andre Bausen Alexander Müller Guillermo Payá-Vayá Chair for Chip Design for Embedded Computing Technische Universität Braunschweig Braunschweig Germany RESRI Group Institute of Microelectronic Systems (IMS) Leibniz Universität Hannover Hannover Germany Bundeswehr Research Institute for Protective Technologies and CBRN Protection (WIS) Munster Germany

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

This work presents a physically based simulation evaluation of the effects of single ionizing particles and displacement damage on logic inverter gates in a complementary metal-oxide semiconductor (CMOS) with 180 nm structure size. A technology computer-aided-design (TCAD) model is combined with a radiation model to allow the simulation of charged particles with a linear energy transfer (LET) between 1 and 100 $\frac{M e V \cdot c m^simulation}{m g}$ . For displacement damage simulation, proton energies between 1 and 10 MeV and neutron energies between 1.6 and 14 MeV have been evaluated to estimate the resulting transistor threshold voltages.

关键词： Semiconductor device modeling Protons Micromechanical devices Neutrons Pulse inverters Threshold voltage Microelectronics Transistors Periodic structures MOS devices

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Neural Network Security Control of Markov Switching Systems: Finite-Time Performance Implementation 8th

Neural Network Security Control of Markov Switching Systems...

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8th international conference on Life System modeling and simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Cao, Zhiru Peng, Chen Wu, Jiancun Zhu, Kaiqun School of Mechatronic Engineering Shanghai University Shanghai200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China The State Key Laboratory of Internet of Things for Smart City University of Macau Macau999078 China

ISBN: (纸本)9789819602933

This paper is concerned with the security sliding mode control problem for Markov switching systems suffering from both physical and communication constraints, that is, the actuator saturation and injection attack. With the help of the learning capability of neural networks, the nonlinear upper bound of injection attacks is analyzed and estimated, and the security control strategy based on neural networks is proposed to improve the reliability of control. Considering the transient performance implementation, a time-dependent sliding mode controller is proposed to guarantee the reachability of the specified sliding surface within a given finite time, in which the adaptive rules of neural network parameters are designed to reduce the effects of the considered attack. The arrival and sliding stages are, respectively, analyzed and sufficient conditions are achieved to guarantee the random finite-time boundedness of the closed-loop system. Eventually, a simulation example is given, combined with a genetic algorithm, to illustrate the validity of the proposed adaptive security control method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Markov processes

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Dynamic Crowd modeling and Anomalous Behavior Prediction Using GMM and Time Series Analysis in Real-Time Smart City Environment

Dynamic Crowd Modeling and Anomalous Behavior Prediction Usi...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Kanchana R F. Mary Harin Fernandez Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The Research Concentrates on simulating and investigating dynamic crowd using Gaussian Mixture Models (GMM) and synthetic time-series data in real time smart city environments. A novel approach generates and designs various crowd behaviors, incorporating normal flow, high congestions, and erratic patterns, leveling to detect and predict anomalies that pose safety issues. Mock datasets, imitating real-world situations, are generated using spatial-temporal features such as crowd density, velocity, and movement direction. To cluster these behaviors into distinct operational states, the GMM is employed, while irregularities are recognized as outliers to the learned distributions. The simulation framework introduces temporal tendencies and real-time capabilities, aiding dynamic variance detection and proactive crowd organization. Investigational results prove the efficiency of the proposed approach in clustering behavioral states and detecting anomalies, providing a scalable and computationally efficient result for inner-city protection and resource optimization. The study implements a foundation for deploying real-time crowd monitoring systems in smart cities, addressing challenges in occasion management, transportation, and disaster awareness.

关键词： Analytical models Smart cities Organizations Predictive models Real-time systems Protection Gaussian mixture model Optimization Monitoring Context modeling

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Towards Trustworthy and Explainable Socially Assistive Robots: A Cognitive Architecture for Dietary Guidance

Towards Trustworthy and Explainable Socially Assistive Robot...

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IEEE international conference on simulation, modeling, and Programming for Autonomous Robots (SIMPAR)

作者： Luigi D’Arco Luca Raggioli Gianni Randazzo Giovanni De Gasperis Antonio Chella Stefania Costantini Silvia Rossi Department of Electrical Engineering and Information Technologies University of Naples Federico II Naples Italy Department of Engineering University of Palermo Palermo Italy Department of Information Engineering Computer Science and Mathematics Università Degli Studi Dell’Aquila L’Aquila Italy

ISBN: (数字)9798331516857

ISBN: (纸本)9798331516864

Socially Assistive Robots (SARs) are rising as promising tools for promoting healthy lifestyle habits. To achieve such a goal, it is necessary that they are able to perform trustworthy and legible behaviors. In this work, we propose a cognitive architecture that integrates multimodal perception, symbolic reasoning, memory-enhanced decision-making, and adaptive interaction strategies to create an explainable and engaging dietary assistant. The key idea is to provide the robot with the capability to iteratively interact with a user and adapt the dietary plan based on their current state, preferences, and food restrictions, while conveying explicitly the inner decision and thought process. To achieve this, we employ a graph-enhanced Large Language Model (LLM), which queries contextual, semantic, and episodic acquired knowledge to generate personalized meal recommendations. These must subsequently be refined through a verification process that enforces constraints such as caloric limits and ingredient intolerances, ensuring dietary adherence. To have a transparent decision-verification process, the robot has to progressively verbalize the reasoning process while providing justifications for the recommendations to also enhance the user's trust. Non-verbal context-relevant movements are also generated to allow the robot to express empathy. We expect our framework to increase user trust, engagement, and adherence to healthy behaviors, allowing SARs to function as credible and effective health assistants.

关键词： Visualization Decision making Semantics Refining Speech enhancement Assistive robots Cognition Time factors Reliability Usability

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New H∞ Fuzzy MPPT Control of Solar Systems using Descriptor Fuzzy Approach

New H∞ Fuzzy MPPT Control of Solar Systems using Descriptor...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Abdeljabar Aboulkassim Said Kririm Naoual Armi Bensalem Boukili El Hanafi Arjdal Materials Signals Systems and Physical Modeling Laboratory Faculty of Sciences Ibnou Zohr University Agadir Morocco Department of Physics Faculty of Sciences Dhar El Mahraz Computer Science Signal Automation and Cognitivism Laboratory Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This paper presents a novel approach to develop a fuzzy maximum power point tracking (MPPT) controller that performs well in variable weather photovoltaic (PV) systems. The photovoltaic output voltage can be adjusted using a DC-DC boost converter. The last step is controlled by the duty cycle, which is essentially bounded between 0 and 1 . Using the Lyapunov approach and Descriptor Takagi-Sugeno (DT-S) fuzzy modelling, we establish the stabilisation conditions with H∞ performance represented as linear matrix inequalities (LMIs). Finally, we apply the proposed control to a solar system and the results of our simulation validate the efficiency of the presented methods.

关键词： Maximum power point trackers Photovoltaic systems simulation Stability criteria Takagi-Sugeno model Solar system Linear matrix inequalities Meteorology Lyapunov methods Fuzzy systems

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AI-Enhanced Multi-Dimensional Stochastic Process modeling for QoS Analysis in 6G Networks Under Joint Fading and Dynamic Interference Constraints in $\alpha-\eta-\mu$ Environments

AI-Enhanced Multi-Dimensional Stochastic Process Modeling fo...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Deepak Upadhyay Alankrita Aggarwal Pardeep Singh Deepak Suresh Asudani Nookala Venu Aakash Singh Ranswal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering University Institute of Engineering Apex Institute of Technology UIE Chandigarh University Mohali India School of Computer Science and Engineering Galgotias University Noida India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

In this paper, we investigate AI-enhanced multidimensional stochastic process modelling for QoS analysis in 6G networks subjected to $\alpha-\eta-\mu$ fading and dynamic interference constraints. simulation results and heatmaps are utilized for the investigation of fading parameters such as shape factors and interference power, that impacts network performance. Our experimental findings show that AI-driven models provide markedly superior results in improving a number of QoS metrics, including throughput, latency, and packet loss compared to traditional methods. The research illustrates how AI improves connectivity, and operational stability of a network which adapts in times of fluctuating conditions. Our results demonstrate the ability of AI to preserve QoS while balancing performance and power, thereby providing a perspective for 6G network design and optimization.

关键词： Fading channels 6G mobile communication Adaptation models Analytical models Stochastic processes Packet loss Quality of service Throughput Stability analysis Optimization

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Multi-Objective Optimization Scheme for Energy Systems in Distribution Networks Based on Cloud-Edge-Device Collaborative Framework

Multi-Objective Optimization Scheme for Energy Systems in Di...

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Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Jingzhi Liu Jianming Zhang Hongyi Yang Zhenghong Bao Fei Zhan Zhidong Liu State Grid Qinghai Electric Power Research Institute Xining Qinghai China State Grid Qinghai Electric Power Company Xining Qinghai China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

As energy systems evolve, single-system economic optimization no longer meets the growing multi-energy demands of users. To address this problem, this paper proposes a multi-objective optimization method for distribution network energy systems based on cloud-edge-device collaborative (CEDC) architecture. First, a CEDC system for the energy system of the distribution network is constructed in response to energy scheduling requirements, and edge-device clusters are used to achieve distributed management of energy system units. Second, based on the energy scheduling requirements, corresponding energy scheduling commands are generated for the target energy systems. These energy scheduling commands are decomposed into at least one computational task, then the computational tasks are assigned to the edge cluster control units of the corresponding target energy systems. Then, a multi-objective optimization scheme is designed, which jointly minimizes the total power supply cost of a single energy system and the total delay time across multiple energy systems. Finally, the scheme is solved by the particle swarm optimization (PSO) algorithm introducing the simulated annealing. simulation results show that the proposed algorith can minimize the total energy cost and overall system delay.

关键词： Costs Processor scheduling Computational modeling Collaboration Distribution networks Simulated annealing computer architecture Delays Particle swarm optimization Distributed management

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Performance Enhancement and Secrecy Analysis of Hybrid RF/FSO Communication Links Under Combined κ-μ Fading and Exponential Turbulence Conditions

Performance Enhancement and Secrecy Analysis of Hybrid RF/FS...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Deepak Upadhyay Prakhar Prakhar Rashmy Moray Rashmi Sharma Nookala Venu Shipra Shukla Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE Roorkee Institute of Technology Pune India Symbiosis Institute of Management Studies (SIMS) A Constituent of Symbiosis International Deemed University Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of CSE (Data Science) G. L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In this study, the secrecy performance evaluation of hybrid RF/FSO communication systems over κ-μ fading and exponential turbulence channels are conducted. We analyse the secure information rate and evaluate the throughput and secrecy capacity of the system through simulations. The results indicate that the proposed hybrid system improves reliability and security by combining adaptive modulation, beamforming, and encryption to combat interference and secure data transmission. Geographical heat maps help to visualize the influence of environmental factors on system performance. Federated learning in hybrid RF/FSO systems have been investigated in detail through simulations and numerical results to show the considerable improvements in communication reliability and performance that the increased redundancy and robustness of the hybrid scheme provides. This helps gain valuable insights into a better design of wireless networks in the future by highlighting the benefits of hybrid communication techniques and simulation-driven design to enhance performance and security across diverse complex environments and conditions.

关键词： Fading channels Analytical models Adaptation models Array signal processing Atmospheric modeling Computational modeling Modulation Throughput Encryption Security

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Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks

Reinforcement Learning for Adaptive Traffic Signal Control U...

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Sustainable Energy Technologies and Computational Intelligence (SETCOM), international conference on

作者： Asha Rani Bohra Thamarai Selvi V Vishal Pawan Sai Sunkara Balaji Sunku Bhavsar Nitya Jignesh Dept. of Computer Science and Engineering New Horizon College of Engineering Bengaluru Karnataka India Dept. of Information Science and Engineering New Horizon College of Engineering Bengaluru Karnataka India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

Traffic congestion is a critical issue in urban areas, contributing to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal control methods, such as fixed-time systems, cannot adapt to real-time changes in traffic conditions. This paper presents a novel approach using Deep Q-Networks (DQN) to control traffic signals based on realtime traffic data dynamically. Our simulation environment models a four-way intersection, where vehicle densities and wait times are continuously monitored. The DQN agent optimizes green light durations by minimizing vehicle wait times and maximizing traffic throughput. Extensive simulation results demonstrate that our system significantly reduces congestion and improves traffic flow compared to traditional fixed-time systems. Furthermore, the approach shows potential for real-world applications in urban traffic control systems, particularly in smart cities equipped with Internet of Things (IoT) devices.

关键词： Adaptation models Adaptive systems Smart cities Biological system modeling Reinforcement learning Traffic control Throughput Real-time systems Internet of Things Vehicle dynamics

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8th international conference on Applied Cognitive Computing, ACC 2024, and 26th international conference on Artificial Intelligence, ICAI 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

8th International Conference on Applied Cognitive Computing,...

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8th international conference on Applied Cognitive Computing, ACC 2024, and 26th international conference on Artificial Intelligence, ICAI 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031856273

The proceedings contain 37 papers. The special focus in this conference is on Applied Cognitive Computing. The topics include: Assessing Information Influence for Node Attribute Prediction;in-Vehicle Sensing Platform for the Inference of Older Drivers’ Mild Cognitive Condition;infusing Human Feedback into Intermediate Prompting Steps of Large Language Models;experimental Analysis of Contemporary Trends, Performance, and Limitations in Graph Embeddings: A Concise Review;a Secure Self-Adaptive System in Applications of Cognitive Computing;revisiting the Nexus Between Handwriting and Personality: Graphology;from Static Graph Attention Generation to Dynamic Graph Attention Coefficient;An Integrated Approach for AI-Assisted Survey Systems Using Deterministic and Nondeterministic Models;a Voxel-Representation-Based Data Generator of Adversarial Objects for Robotic Manipulators;an Analysis of Different Information Granularity Distribution Protocols to Improve Consistency in Intuitionistic Reciprocal Preference Relations;enhancing Differential Evolution for Neural Network Optimization Through Boundary Individual Consideration;predictive modeling of Shading Effects on Photovoltaic Panels Using Regression Analysis;enhancing Cultural Algorithm Guided Policy Gradients with Parameter-Based Exploration Through Topographic Knowledge and Adaptive Weighting;Enhancing Clinical Documentation Through NLP-Driven Disease Categorization and Visualization: A Case Study Utilizing the Llama2 AI Model;automatic Modulation Recognition: A Hybrid Approach Using Deep Learning and K-Means Clustering;An ISR Asset Planning Application;causal Discovery with Interactive Human Inputs;implementation of a Lightweight Gradient Descent Trained Expert System Algorithm;Leveraging JSBSim and Gymnasium: A Reinforcement Learning Approach for Air Combat simulation;orthogonal Activation Functions in Neural Networks: Utilizing Chebyshev, Legendre, and Hermite Polynomials;XAI-Based Assessment of Software Vulnerabil

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