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Application of Computational simulation and modeling: Optimizing the Control System of High-pressure Fuel Pump 5

Application of Computational Simulation and Modeling: Optimi...

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5th international conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Nan, Jing Jin, Ruiying Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Beijing Jiaotong Univ Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9781665423144

With the development of computer technology and the improvement of information technology, computer-aided is widely used in many fields such as machinery manufacturing to improve the efficiency of skilled workers and the quality of machinery manufacturing. In this paper, the specific application of computational simulation in the design and optimization of fuel engine high-pressure fuel pump is used to simulate the workflow of high-pressure fuel pump by using programming technology to derive the optimal injection time under the condition of maintaining stable pressure. Through practical applications, this paper analyzes the advantages of computer-aided technology and explores new ways of integration with other traditional technologies to improve the informationization of traditional manufacturing industries.

关键词： computer-aided technology modeling pressure control

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Quantum-Enhanced Spiking Neural Networks for Closed-Loop Neuromodulation Systems: A Theoretically Advanced Framework

Quantum-Enhanced Spiking Neural Networks for Closed-Loop Neu...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), international conference on

作者： Vallari Ashar Sarthak Bansal Pandiyaraju V School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Over one billion people worldwide are affected with neurological disorders and their economic impact is approximately $800 billion annually, which constitutes major medical challenge. Using neuromodulation systems currently available suffers from sensitivity, reaction time as well as energy consumption. The proposal in this research is to address these major issues in closed loop neuromodulation by using a Quantum enhanced Spiking Neural Network (QESNN) architecture. This paper represents the interfacing of two major fields: quantum sensing and neuromorphic computing. The QESNN architecture comprises three core components: This is implemented as an array of quantum sensors, a quantum classical hybrid interface, and a spiking neural network (SNN). Taking advantage of quantum superposition and entanglement principles, the quantum sensor array noninvasively images neural activity at the level of single action potentials using NV centers. These sensors work at ambient temperatures, which is unlike superconducting devices. For processing with neuromorphic processing, quantum-classical hybrid converts quantum sensor data into classical signals with advanced signal process such as quantum state estimation and noise reduction. By modeling biological neurons with leaky integrate and fire neurons, the SNN serves as a low power, timed neural dynamics modulation component that emulates biological event driven behavior. A key innovation in our architecture is adaptive thresholding, which dynamically adjusts detection thresholds based on signal distributions, improving sensitivity and reducing false positives by 45.6%. The system also achieves 20-30% higher power efficiency through techniques like adaptive sensor frequency control and low-power processing. simulation results that show how the QESNN performs better than classical systems with less false positives and greater energy efficiency are presented. A new platform is demonstrated that integrates quantum sensing with neurom

关键词： Neurological diseases Temperature sensors Sensitivity Quantum computing Neuromorphic engineering Spiking neural networks computer architecture Neuromodulation Quantum sensing Sensor arrays

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FUSION: A Flexible Unified Simulator for Intelligent Optical Networking

FUSION: A Flexible Unified Simulator for Intelligent Optical...

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international Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Ryan McCann Arash Rezaee Vinod M. Vokkarane Electrical and Computer Engineering Department University of Massachusetts Lowell United States

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

The increasing demand for flexible and efficient optical networks has led to the development of Software-Defined Elastic Optical Networks (SD-EONs). These networks leverage the programmability of Software-Defined Networking (SDN) and the adaptability of Elastic Optical Networks (EONs) to optimize network performance under dynamic traffic conditions. However, existing simulation tools often fall short in terms of transparency, flexibility, and advanced functionality, limiting their utility in cutting-edge research. In this paper, we present a Flexible Unified Simulator for Intelligent Optical Networking (FUSION), a fully open-source simulator designed to address these limitations and provide a comprehensive platform for SD-EON research. FUSION integrates traditional routing and spectrum assignment algorithms with advanced machine learning and reinforcement learning techniques, including support for the Stable Baselines 3 library. The simulator also offers robust unit testing, a fully functional Graphical User Interface (GUI), and extensive documentation to ensure usability and reliability. Performance evaluations demonstrate the effectiveness of FUSION in modeling complex network scenarios, showcasing its potential as a powerful tool for advancing SD-EON research.

关键词： Performance evaluation Optical fibers Technological innovation Elastic optical networks Optical network units Telecommunications Telecommunication network reliability Usability Software defined networking Graphical user interfaces

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Method for the simulation of deformation processes by processing metallographs

Method for the simulation of deformation processes by proces...

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international Mexican international conference on computer Science (ENC)

作者： Vazquez-Esquivel, Alexis V. Aguilera-Navarrete, Israel Juarez-Campos, Beatriz TecNM IT Morelia Engn Sci Morelia Michoacan Mexico CONACYT TecNM IT Morelia Div Profess Studies & Res Morelia Michoacan Mexico

ISBN: (纸本)9781665426121

Los materiales metálicos se constituyen por diferentes estados cristalinos, estos pueden ser estudiados de manera microestructural. Para lo cual se usan imágenes que, de acuerdo con el tamano y carácter de los elementos fotografiados, se denominan metalografías o micrografías. Las metalografías se ven como una función del plano, el procesamiento de imagen es posible convertilo en mallas de nodos mediante un conjunto de procesos que involucran la adquisición, el mejoramiento, la caracterización v detección de elementos dentro de una imagen. En las técnicas de procesamiento de imagen se usan aquellas enfocadas a la localización de regiones de interés. El algoritmo de Harris considera el diferencial del valor de la esquina directamente con respecto a la dirección, este valor de la esquina es a menudo llamado autocorrelación, en trabajos en los que se describe este detector. Las mallas de nodos se usan en el método de elemento finito para la creación de elementos, en donde cada elemento se analiza mediante nodos, estos elementos se consideran en el plano, mediante la teoría de elemento finito, se representan soluciones bidimensionales en el sisteina de coordenadas definido y con esto podemos representar los cambios de los desplazamientos en las metalografías, simulando los procesos de deformación.

关键词： Deformable models computer science Computational modeling Detectors Strain

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Analyzing V2I Channel and Spatial Consistency through simulation 7

Analyzing V2I Channel and Spatial Consistency through Simula...

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7th international conference on computer and Communications, ICCC 2021

作者： Liu, Mingmin Wang, Yuyuan Li, Huafu Jing, Yanmei Zhang, Gongsheng He, Wenxue Yunnan Normal University School of Physics and Electronic Information Kunming China YunNan Institute of Measuring and Testing Technology Kunming China Harbin Institute of Technology School of Electronics and Information Engineering Harbin China Library Yunnan Normal University Kunming China Yunnan University School of Information Kunming China

ISBN: (纸本)9781665409506

With the development of the vehicle to infrastructure (V2I) communication technology, the traditional wireless channel model can not match the highly dynamic environment of the Internet of vehicles, especially the spatial consistency of the channel has attracted more and more attention. As a new feature of the 5th generation (5G) channel different from traditional channels, spatial consistency has an important impact on the channel capacity and reliability of the Internet of vehicles system. In this paper, the V2I propagation scenario is established to study the influence of spatial consistency on the delay and angle variation characteristics of the multipath cluster in the single link from the base station to a mobile station. The simulation results show that the angle variation of multipath clusters presents obvious continuity when considering the spatial consistency, and more realistic V2I wireless propagation environment parameters are obtained. In this paper, the spatial consistency of the V2I channel is simulated and analyzed to provide a reference for the modeling and statistical characteristics of the internet of vehicles channel. © 2021 IEEE.

关键词： 5G mobile communication systems

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A Hybrid Approach for Diagnosing Diabetic Retinopathy in Retinal Fundus Images using an Adaptive Canny Edge Detection approach

A Hybrid Approach for Diagnosing Diabetic Retinopathy in Ret...

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international conference on Computing and Networking Technology (ICCNT)

作者： R Manjula Devi M Kowsalya P M Dinesh P Ponmurugan A Berlin K Kavin Kumar M Sangeetha Gokul Chandrasekaran Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thuduppathi Department of Electronics and Communication Engineering Sona College of Technology Salem Meenakshi Academy of Higher Education & Research (Deemed to be Univeristy) Chennai India Department of Electronics and Communication Engineering Jansons Institute of Technology Coimbatore Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Erode Department of Information Technology Dr.N.G.P. Institute of Technology Coimbatore Tamilnadu India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Individuals diagnosed with diabetes are susceptible to developing diabetic retinopathy (DR), a degenerative eye condition that affects the retina. When blood vessels in the retina get damaged, it can cause permanent vision impairment or even blindness if the underlying condition is not addressed quickly enough. Early diagnosis and treatment of this disease can save a patient’s eyesight and prevent blindness. Successful medications for DR are available, but people with diabetes need to be identified and monitored early on. In order to diagnose diabetic retinopathy, many physical examinations have to be conducted; however, these types of exams require more time to complete. Adaptive Canny Edge Detection Training using Adaptive Skipping technique (ACEDIAS) is used in this research to speed up the detection process of diabetic retinopathy by detecting the edge and boost diagnostic accuracy. The study team chooses to use a 2063image dataset compressed from Kaggle’s library. The suggested ACEDIAS model is evaluated for its performance using the measures such as accuracy, recall, precision, and F1score. It has been demonstrated through simulation that the suggested ACEDIAS model outperforms the existing models.

关键词： Training Diabetic retinopathy Adaptation models Accuracy Image edge detection Computational modeling Blindness Retina Classification algorithms Diseases

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Secure Interaction-Based Feature Selection for Vertical Federated Learning

Secure Interaction-Based Feature Selection for Vertical Fede...

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IEEE international conference on Communications (ICC)

作者： Zhenyu Meng Wenmiao Zhang Shuaiqi Shen Chong Yu Kuan Zhang Department of Electrical and Computer Engineering University of Nebraska-Lincoln Lincoln USA Department of Electrical Engineering University of Wisconsin Milwaukee Milwaukee USA Department of Computer Science University of Cincinnati Cincinnati USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Federated learning enables decentralized data own-ers to collaborate and train models in a distributed manner. A special type is Vertical Federated Learning (VFL), where each of the participated data owners only has a portion of the data features. To maintain a high accuracy and reasonable computational cost, selecting a set of features among the entire dataset is essential. Although some existing work selects features by calculating their individual contributions to the learning outcomes, knowing the joint contribution from multiple features becomes necessary but challenging. Meanwhile, security concerns are raised when calculating the joint contribution of a set of features where the feature data are stored by different owners. Using homomorphic encryption or secure computing over en-crypted data is possible, but it may cost too much when complex calculations are involved and repeated. To this end, this paper proposes a privacy-preserving feature selection protocol that considers the interactions between features stored across different data owners. Specifically, we first propose an interaction-based feature selection algorithm for vertically distributed datasets. This algorithm estimates the features' joint contributions to the model training outcomes. Then, we propose a privacy-preservation protocol to prevent the semi-honest cloud server from obtaining or inferring the raw data when aggregating the knowledge and calculating the complex interaction measure for feature selection. We create a new approximation method for interaction measures to address the high computational cost when securely calculating the interaction measure while maintaining the training accuracy. The security discussions show that the proposed protocol preserves data owner's privacy. The extensive simulations validate the achieved training accuracy and efficiency.

关键词： Training Data privacy Protocols Accuracy Federated learning Computational modeling simulation

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Data and Reference Semantic-Based Simulator of DB-Nets with the Use of Renew Tool 9th

Data and Reference Semantic-Based Simulator of DB-Nets with ...

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9th international conference on Analysis of Images, Social Networks and Texts, AIST 2020

作者： Rigin, Anton Shershakov, Sergey Faculty of Computer Science National Research University Higher School of Economics 20 Myasnitskaya Street Moscow101000 Russia

ISBN: (纸本)9783030726096

Complexity of software systems is constantly growing, which is even more aggravated by concurrency of processes in systems, so modeling and validating such systems is necessary for detecting and eliminating failures. One of the most well-known formalisms for solving this problem is Petri nets and their extensions such as colored Petri nets, reference nets, and similar models. Many software systems use databases for storing persistent data. However, Petri nets and their mentioned extensions are not designed for modeling persistent data manipulation since these formalisms aim at modeling a control flow of the considered systems. DB-nets, as a novel formalism, aim to solve this problem by providing three following layers: (1) the control layer represented by a colored Petri net with extensions, (2) the data logic layer, which allows to retrieve and update the persistent data, and (3) the persistence layer representing a relational database for storing the persistent data. To date, there are no publicly available software tools that implement simulation of db-net models with reference semantics support. The paper presents a novel software tool for the db-nets simulation. The simulator is developed as a pure plugin for the Renew (Reference Net Workshop) software tool without modifying existing Renew source code. Such an approach for development of the simulator allows to reinforce existing Renew reference semantics. The SQLite embeddable relational DBMS is used as a base tool to implement the db-net persistence layer. In the paper, theoretical foundations and architecture of the developed simulator are described. The results of the work can be used in research projects that involve modeling complex software systems with persistent data for both academic and industry-oriented applications. © 2021, Springer Nature Switzerland AG.

关键词： Petri nets

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network

Mobility and Behavior based Trustable Routing in Mobile Wire...

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international conference on Computing Methodologies and Communication (ICCMC)

作者： B. Praveen Kumar J. Umamageswaran A.V. Kalpana R. Dhanalakshmi Department of Computer Science and Engineering KL University Vijayawada Andhra Pradesh India Department of Information Technology RMK Engineering College Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features; they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN.

关键词： Wireless communication Wireless sensor networks Computational modeling Routing Throughput Behavioral sciences Delays

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AIM-CP: AI-Based Multi-Visual Components Analysis for Crowd Dynamics Prediction

AIM-CP: AI-Based Multi-Visual Components Analysis for Crowd ...

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international conference on Image and Vision Computing New Zealand, IVCNZ

作者： Elham Basunduwah Muhammad Mohzary Sejun Song Baek-Young Choi School of Science and Engineering University of Missouri-Kansas City MO USA College of Engineering and Computing in Al-Qunfudhah Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Jazan University Jazan Saudi Arabia Engineering and Technology Research Center Jazan University Jazan Saudi Arabia School of Computer and Cyber Science Augusta University Augusta GA USA

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Accurate prediction of crowd mobility dynamics is essential for effective crowd safety management. Traditional models, such as the Random Waypoint Model (RWM), have been widely employed for mobility simulation. However, they suffer inherent limitations, including non-uniform node distribution and speed decay, which reduce their predictive accuracy. Although vision-based crowd mobility tracking methods show promise, movement prediction still faces significant challenges in achieving accuracy, scalability, generalization, and computational efficiency. This paper introduces a novel AI-based multi-visual components analysis approach for crowd dynamics prediction called AIM-CP. This approach combines the YOLOv8 object detection system with a rule-based expert system to enhance prediction accuracy. AIM-CP integrates multi-modal data from face, body, and pose movements across 12 distinct classes, improving real-time processing capabilities and leveraging more scalable and robust AI models. To support this approach, we developed high-resolution datasets, including mall datasets, the CrowdHuman dataset, selected video frames from YouTube, and images from Unsplash. Experimental results demonstrate that AIM-CP is highly effective in detecting mobility dynamics, significantly improving single-modal prediction models with an accuracy of 90.12% in real-world mobility scenarios. These findings suggest that AIM -CP offers a powerful tool for improving crowd safety and mobility management in dynamic environments.

关键词： Accuracy Video on demand Computational modeling Object detection Predictive models Real-time systems Web sites Expert systems Artificial intelligence Faces

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