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Credibility Evaluation of Aircraft HWIL Simulation Based on the Time-Varying Area Metric

Credibility Evaluation of Aircraft HWIL Simulation Based on ...

引用

High Performance and Smart computing (HPSC), ieee international conference on

作者： Huapin Geng Ning Wang Jiahui Tong Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China Beijing Electro-mechanical Engineering Institute Beijing China

ISBN: (数字)9798350389463

ISBN: (纸本)9798350389470

Credibility evaluation of aircraft HWIL simulation is usually measured by the degree of consistency between simulation test data and the measured data. A new method for evaluating the credibility of aircraft HWIL simulation is established based on the idea of time-varying area measurement and uncertainty measurement Mainly for univariate, single simulation, multivariable and multi-simulation. The DTW distance measurement method is used to obtain the weight coefficients of correlated multiple response variables, and the effectiveness of uncertainty measurement method in obtaining the credibility of aircraft HWIL simulation at a certain level of confidence is verified through typical examples.

关键词： Weight measurement Uncertainty Correlation Area measurement Measurement uncertainty Distance measurement Indexes

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Dynamic Fusion: Integrating CNN and LSTM for Hyperspectral Unmixing

Dynamic Fusion: Integrating CNN and LSTM for Hyperspectral U...

引用

ieee international conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Ajay Kumar K Tushar M Khatokar Chandrashekhar A Gagandeep Y Vijayashekhar S S Dept of AIML Acharya Institute of Technology Bangalore India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

This manuscript presents a novel approach to hyperspectral unmixing by leveraging the synergies between Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network. The proposed model dynamically integrates CNN for accurate endmember extraction, capturing complex spectral patterns and utilizes LSTM to model temporal dependencies for precise abundance estimation. The proposed integral framework efficiently addresses the challenges of ill-posedness in hyperspectral unmixing, offering improved performance in both endmember extraction and abundance estimation. Experiments are conducted on synthetic and two real benchmark hyperspectral data HYDICE Urban and AVIRIS Jasper Ridge. The efficacy of the combined CNN-LSTM model is evaluated by comparing results with state-of-the-art.

关键词： Representation learning Adaptation models Computational modeling Neural networks Noise Estimation data models

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Research on Optimization Scheduling Algorithm of Engineering Project Resources Based on Cloud computing

Research on Optimization Scheduling Algorithm of Engineering...

引用

Sensors, Electronics and Computer Engineering (ICSECE), ieee international conference on

作者： Ping Dong Cangzhou Jiaotong College Huanghua China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

Aiming at the problem of resource optimization scheduling for large and complex engineering projects, this paper proposes an optimization scheduling algorithm system based on cloud computing, which deeply integrates cloud computing, convolutional neural network (CNN) and Beetle Antennae Search (BAS). The Cloud Convolution Antennae Search (CCAS) model is proposed to realize efficient, accurate and dynamic resource allocation and scheduling. With cloud computing as the underlying support, the CCAS model realizes efficient storage, processing and real-time update of large-scale engineering project data through the powerful computing power and elastic expansion characteristics of cloud computing, ensuring that the model can still maintain efficient operation in the face of massive and dynamic data, and providing a solid foundation for subsequent intelligent decision-making. Secondly, at the algorithmic level, CNN is applied to the prediction of resource demand of engineering projects. It uses its advantages in spatiotemporal series data modeling to carry out deep learning on multi-dimensional data such as project schedule and resource consumption to achieve accurate prediction of future resource demand. At the same time, a Longniu whisker search algorithm is introduced to make resource scheduling decision. BAS simulates the natural process of Longurus searching for food, explores the scheduling space effectively by iteratively updating the search path, and finds the global optimal or near-optimal scheduling scheme. The organic combination of the three not only provides strong computing power support and the advantages of data management and deep learning in complex data mining, but also utilizes the efficiency of optimization algorithms in large-scale problem solving. In terms of system architecture, the three-layer cloud service model based on IaaS, PaaS and SaaS is built to provide comprehensive support for the research and development, deployment, operation and appli

关键词： Cloud computing Job shop scheduling Scheduling algorithms Computational modeling Prediction algorithms Scheduling data models Convolutional neural networks Optimization Antennas

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Empowering Autonomous Decision-Making Through Quantum Reinforcement Learning and cognitive Neuromorphic Frameworks

Empowering Autonomous Decision-Making Through Quantum Reinfo...

引用

Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： Balasubramani R Vidyadevi G Biradar Department of Information Science and Engineering NITTE (Deemed to be University) NMAM Institute of Technology Udupi India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The concept of Agentic AI, which focuses on empowering autonomous systems with human-like decision-making capabilities, has gained substantial attention in recent years. By leveraging advanced reinforcement learning (RL) techniques and cognitive frameworks, Agentic AI aims to enable machines to make complex decisions in dynamic environments with minimal human intervention. This paper introduces Agentic AI, a novel approach that combines Quantum Reinforcement Learning (QRL) and cognitive Neuromorphic Frameworks to empower autonomous decision-making in complex environments. The proposed model leverages quantum computing principles to accelerate learning in reinforcement-based systems, while cognitive neuromorphic frameworks enhance adaptability and real-time decision-making by mimicking human brain functionality. The integration of QRL allows for faster convergence in learning algorithms, improving decision accuracy by 25% and reducing training time by 40%. cognitive neuromorphic architectures offer enhanced scalability and resilience in dynamic environments, resulting in a 35% improvement in system robustness during testing. Simulations in autonomous navigation and robotic control show that Agentic AI achieves 20% more efficient task execution compared to traditional AI models. These values underscore the potential of the proposed system to revolutionize autonomous systems, providing a robust foundation for decision-making across a range of applications.

关键词： Wireless communication Adaptation models Accuracy Neuromorphics Decision making Reinforcement learning Computer architecture Brain modeling Cognition Artificial intelligence

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Scalable complex Scene Understanding for Edge computing

Scalable Complex Scene Understanding for Edge Computing

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ieee international conference on Web Services (ICWS)

作者： Nanxi Chen Lili Liu Xu Wang Jiamao Li Xiaolin Zhang Shanghai Institute of Microsystems and Information Technology CAS Shanghai China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

This paper aims to develop flexible and scalable scene understanding capabilities usable on resource-constrained devices. We propose an edge computing framework to enable large-scale object detection within complex visual scenes. The framework attempts to emulate human intuition - leveraging commonsense reasoning and fast concept learning from small data. Structured knowledge about objects and their relationships guides efficient deduction of scene contents. Specifically, we develop an ontology-based scene deduction model to refine an initial scene parsing, which incorporates declarative constraints on objects, their attributes, and interactions. It represents such domain knowledge explicitly and reasons over ontological relationships. By representing this domain knowledge explicitly and reasoning over ontological relationships, the model refines an initial scene parsing to better reflect commonsense consistency. The evaluation compared the proposed method to other large-scale object detection methods. The results show that our ontology-driven deduction model scale-ups the data-driven scene parser’s capability while reducing the data and compute requirements.

关键词： Training Visualization Web services Computational modeling Soft sensors Image edge detection Human-machine systems Object detection Ontologies Edge computing

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Building and Managing Cloud Infrastructure with OpenStack

Building and Managing Cloud Infrastructure with OpenStack

引用

ieee international conference on Advanced Scientific computing (ICASC)

作者： CA Lupşe F Fărcaş J Nagy MRC Truşcă National Institute for Research and Development of Isotopic and Molecular Technology - INCDTIM Cluj-Napoca Romania

ISBN: (数字)9798350356526

ISBN: (纸本)9798350356533

The article “Building and Managing Cloud Infrastructure with OpenStack” explores the development of cloud infrastructure at INCDTIM Cluj-Napoca using OpenStack. OpenStack, an open-source cloud platform, offers customizable services such as computing power, storage, and networking, empowering the institute to efficiently handle complex simulations, data processing, and research collaborations. The article delves into the architecture and implementation of OpenStack's core services, including the controller and compute nodes, which manage various virtual machine operations and networking tasks. It highlights the benefits of adopting OpenStack, such as scalability, flexibility, and cost-efficiency, which have revolutionized scientific research. Additionally, the paper details the user interface (Horizon) and its role in simplifying cloud management, as well as future prospects for expanding INCDTIM's cloud capabilities.

关键词： Cloud computing Technological innovation Scientific computing Computational modeling Scalability Collaboration Process control User interfaces data models Resource management

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Coordinating Search with Foundation Models and Multi-Agent Reinforcement Learning in complex Environments

Coordinating Search with Foundation Models and Multi-Agent R...

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ieee international conference on Robotic computing (IRC)

作者： Christopher Allred Jacob Haight Chandler Justice Isaac Peterson Rosario Scalise Ted Hromadka Jason Pusey Mario Harper Department of Computer Science Utah State University Department of Computer Science University of Washington NVIDIA Autonomous Systerns Branch under AEOP Contract No. W9115R-15:-2-0001 DEVCOM Army Research Lab (AARL)

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

We present a multi-agent system simulation designed for efficient coordination and collaboration among multiple robots, particularly suited for search operations. This simulation reflects unstructured and complex outdoor scenarios where significant obstruction and occluded terrain surfaces cause difficulties in search. The software integrates well with reinforcement learning (RL) and a centralized Multi-Agent Transformer (MAT) to enable autonomous robots to collect, process, and integrate data. Search, coverage, and complex mobility planning can be tested in this simulation with dynamic and unstructured environments. The project code and videos can be found at https://***/DIRECTLab/Coordinating-MAT-Env

关键词： Robot kinematics Software algorithms Collaboration Reinforcement learning Software systems Transformers Real-time systems Robots Surface treatment Videos

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Optimization of data Structures and Trade-Offs with Concurrency Control in Multithread Software Structures Using Artificial Intelligence

Optimization of Data Structures and Trade-Offs with Concurre...

引用

computing, Power and Communication Technologies (IC2PCT), ieee international conference on

作者： Bangar Raju Cherukuri V. Arulkumar Senior .Net Developer Germantown MD USA School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India

The optimization of data structures and trade-offs is a complex problem. This is simplified and provides solutions through the aid of artificial intelligence. There are various synchronization problems occur due to the concurrent access to the data structures. This affects the overall performance of the system. To overcome various challenges, deep learning with optimization algorithms is implemented. This helps to achieve the potential areas for optimization in multithread software by evaluating the historical patterns and performance information. Trade-offs are defined as the choices that are initiated in the optimization process for data structures in concurrency. Deep learning helps to analyze the impacts of these trade-offs by developing training models on the historical data. A large amount of data is retrieved through the deep learning techniques. This includes extracting image data for CNN and time series data for RNN and LSTM. The reduction of using data compression techniques helps in obtaining optimal performance parameters. The various forms of threats are neglected through data loading and preprocessing stages. The concurrency is essential due to the usage of multiple threats working on the neural networks. This is further accessed through synchronizing access to shared data structures. They must be properly done because excess synchronization leads to the origin of bottlenecks. A balance between data structure optimization and concurrency control must be maintained properly. Excessive concurrency control may reduce the performance. The execution speed of the CNN is enhanced through the aid of hardware accelerators. Thus the fine-tuning of trade-offs and creation of software are done through training and testing to obtain increased use of multi-core processors.

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Large Language Models to Enhance Malware Detection in Edge computing

Large Language Models to Enhance Malware Detection in Edge C...

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ieee international conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Christian Rondanini Barbara Carminati Elena Ferrari Ashish Kundu Akshay Jajoo DiSTA University of Insubria Italy Cisco Research USA

ISBN: (数字)9798350386745

ISBN: (纸本)9798350386752

The increasing sophistication of malware in recent years has underscored the need for more advanced detection approaches, particularly in edge computing environments. Traditional methods and machine learning models, have shown promise but face significant limitations in handling evolving malware types. To address these challenges, we explore the application of Large Language Models (LLMs) for malware detection in edge computing. LLMs offer enhanced pattern recognition, contextual understanding, and the ability to analyze complex data, making them well-suited for dynamic, resource-constrained edge environments. However, their use introduces challenges such as model size and computational demands. In this paper, we propose a vision architecture that leverages LLMs’ strengths while addressing their limitations, providing a framework for continuous learning and adaptability. We discuss the architecture’s potential impact on improving malware detection as well as future research directions in the field.

关键词： data privacy Image edge detection Computational modeling Large language models Training data Computer architecture Pressing Malware Security Edge computing

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Smarter Way to Monitor and Detect Intrusions in Cloud Infrastructure using Sensor-Driven Edge computing

Smarter Way to Monitor and Detect Intrusions in Cloud Infras...

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computing, Power and Communication Technologies (IC2PCT), ieee international conference on

作者： B Venkateswara Reddy Khader Basha Sk Kusuma Polanki Sai Srinivas Vellela Lavanya Dalavai Lakshma Reddy Vuyyuru K Kiran Kumar Dept. of CSE-Cyber Security Chalapathi Institute of Technology Guntur AP India Dept. of CSE-Data Science Chalapathi Institute of Technology Guntur AP India Dept. of CSE Chalapathi Institute of Technology Guntur AP India Dept. of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

In today's linked world, essential infrastructure must be protected from unauthorized access. Traditional security measures often depend on closed-circuit television (CCTV) systems for perimeter monitoring. However, developing technologies provide substantial opportunity for improving security standards. This study presents a novel technique for achieving enhanced real-time, long-range object monitoring capabilities by combining modern edge computing and a varied spectrum of sensors, including LiDAR (Light Detection and Ranging) and ultrasonic sensors. LiDAR and ultrasonic sensors outperform traditional video cameras by offering real-time, long-range object monitoring capabilities, making them suitable for critical infrastructure security. However, these sensors create complex 3D point cloud data, which presents issues for data transmission and storage. To overcome this problem, we suggest implementing an enhanced edge computing architecture. The suggested architecture optimizes data transmission by compressing and centralizing point cloud data at the network's edge. This strategy reduces the demand on network resources by transferring computing activities to edge devices, allowing for real-time monitoring. Furthermore, the optimized data is smoothly transported to cost-effective Cloud-based servers provide effective resource utilization while protecting the security system. This research not only introduces a novel security paradigm for critical infrastructure but also contributes to the wider discourse on edge computing applications. By combining advanced sensors and edge computing technology, our approach not only enhances security but also ensures scalability, cost-effectiveness, and real-time responsiveness. The findings of this study have significant. Implications for the design and implementation of future security systems, emphasizing the importance of leveraging cutting-edge technologies to safeguard vital assets and infrastructure.

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