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检索条件"任意字段=3rd Euro-NF Conference on Network Control and Optimization"

共 1643 条记录，以下是521-530 订阅

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Identifying Biomarkers of HCV-induced Dysplasia and Hepatocellular Carcinoma Based on network Centrality 21

Identifying Biomarkers of HCV-induced Dysplasia and Hepatoce...

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3rd International conference on Intelligent Medicine and Image Processing (IMIP)

作者： Wang, Yijuan Liu, Zhi-Ping Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

ISBN: (纸本)9781450390057

Gene regulatory network (GRN) is a model that characterizes the complex relationship between genes. Analyzing topological characteristics in GRNs is an important avenue for detecting crucial genes serving as potential biomarkers. Here, we propose a framework for identifying disease biomarkers based on network centrality. We employ the path consistency algorithm based on conditional mutual information to reconstruct the specific disease GRN from gene expression data with a background GRN. And then five network centrality evaluation methods are applied to the disease GRN. To get a more reliable ranking order, we aggregate the ranks by a combinational optimization formulation. The objective function based on Spearman distance is solved by genetic algorithm. Finally, we apply XGBoost to verify the classification ability of top genes and evaluate their importance scores. For a proof-of-concept study, we apply the framework to HCV-induced dysplasia and hepatocellular carcinoma. The selected biomarkers achieve the accuracy of 0.89 in the five-stage multiclassification.

关键词： Hepatocellular Carcinoma Biomarkers network Centrality Rank Aggregation XGBoost

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Voice Over Internet Protocol: Custom SIP Server with QoS optimization for Real-Time Communication

Voice Over Internet Protocol: Custom SIP Server with QoS Opt...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： B. V. Dinesh Krishna P. Yogendrasai S. Rajeev Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

VoIP is a revolutionary technology for transmission of voice over internet protocol than from plain telephony systems. But current VoIP solutions have some important issues which are packet loss, latency, and jitter especially when the network is in motion. This work outlines the design, implementation and performance of a locally developed VoIP system which includes a SIP Server Node, Sender Node and a Receiver Node. The SIP Server Node was designed nearly from the ground up to handle session initiation, packet forwarding, and adaptive QoS control. Both Sender and Receiver Nodes had facilities to record, compress and playback audio. The system's performance was evaluated in a LAN environment, achieving significant improvements in Quality of Service (QoS) metrics: achieving low latency under 150 ms, 95 % packet recovery and achieving a good audio quality with the MOS of 4.2. Evaluating the obtained results, it can be concluded that the proposed system is efficient in handling the issues of the packet-switched network and providing high-quality real time voice communication.

关键词： Packet loss Quality of service Receivers Jitter Real-time systems Encryption Servers Security Internet telephony optimization

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Distributed Optimal Scheduling of AC/DC Hybrid Distribution network Based on ADMM 3

Distributed Optimal Scheduling of AC/DC Hybrid Distribution ...

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3rd International conference on Electrical Engineering and control Technologies, CEECT 2021

作者： Sun, Fengjie Han, Aoyang Zhang, Yong Zhou, Shengqi Liu, Hongbo Zhang, Zijin Electric Power Company State Grid Shandong Qingdao Qingdao China Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Shanghai China

ISBN: (纸本)9781665440417

With the increasing in the penetration rate of energy storage system and distributed generator such as wind power, photovoltaics, electric vehicles in the distribution network, the operation scheduling of the AC/DC hybrid distribution network (AC/DC HDN) becomes one of the current research focuses at the field of Electrical Engineering. However, the traditional centralized optimization scheduling has problems such as large transmission requirements and low computational efficiency. A distributed optimal scheduling model of AC/DC HDN is put forward on the basis of the alternating direction multiplier method (ADMM). The internal coupling relationship of AC/DC HDN adopting the voltage source converter (VSC) is analyzed, and it is decomposed into the AC subsystem, DC subsystem, and VSC subsystem, correspondingly. Aiming at these three subsystems, with the goal of the lowest economic operation cost, a distributed optimization operation model is established respectively, and the alternate iterative solution is performed based on ADMM. Finally, based on the topology structure of the IEEE 33-node example system, related examples are designed to simulate and verify the proposed distributed optimal scheduling of the AC/DC HDN. © 2021 IEEE.

关键词： Distributed power generation

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Exploring the Design of Distributed Task-Driven networks within the Framework of SDN network Architecture

Exploring the Design of Distributed Task-Driven Networks wit...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Xueli Zheng Xiaoxi Wang Jiakun Cai Zhihuai Shan Jing Yu Science and Technology on Avionics Integration Laboratory Aeronautical Radio Electronics Reseach Institute Shanghai China Wireless Communication Research Laboratory Gauss Intelligent Technology Co. Ltd. Chengdu China

This article elaborates upon an aeronautical tactical network architecture grounded in distributed task requirements. In response to the communication demands inherent in future aerial combat paradigms, we have conceived a data-link network framework tailored to meet the imperatives of mission-driven scenarios. Owing to the intricate demands of the operational environment, conventional airborne data link systems encounter challenges when endeavoring to achieve cross-platform interoperability. The introduction of the Software-Defined networking (SDN) architecture, however, furnishes a solution. Through centralized control and network management, it empowers constituents to promptly respond, engage in efficient decision-making, and facilitate collaborative combat operations. Furthermore, this article delves comprehensively into topics including task allocation and scheduling, as well as the southbound protocol overhead, offering viable resolutions. In contrast to traditional network optimization, the design philosophy of Software-Defined networking (SDN) has been innovatively harnessed to shape a network architecture founded upon the principles of distributed task requirements. By amalgamating the tenets of SDN and task-driven attributes, this approach intertwines mission planning with network management, fulfilling the communication requisites of distributed task administration, ultimately culminating in a communication architecture tailored for aeronautical networks.

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Design and Implementation of a High-Performance Solar-Based Wireless Sensor network 3rd

Design and Implementation of a High-Performance Solar-Based ...

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3rd International conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Dandime, Gopal M. Sawale, Manish D. Oriental University Indore India

ISBN: (纸本)9783031691140

In this study, the research aimed to address the growing global energy consumption and related environmental issues by exploring ways to improve the efficiency of solar PV cells, a crucial form of renewable energy. We focused on the nonlinear characteristics of solar PV cells, which result in low efficiency and highlighted the importance of using MPPT (Maximum Power Point Tracking) to extract maximum power. To enhance the traditional MPPT controller performance, we proposed incorporating a PID controller and using optimization techniques to predict optimal tuning parameters. Simulation results showed that our proposed solar energy harvesting system achieved an efficiency of 97.3% using the Mutated Fire-fly Algorithm (MFA) for tuning the PID controller, revealing superior performance over comparative techniques. Overall, this research highlights the potential for improving the efficiency of solar PV cells through the integration of advanced controllers and optimization techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Three term control systems

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PSO Based Optimal Phasor Measurement Unit Placement Considering Measurement Redundancy 3

PSO Based Optimal Phasor Measurement Unit Placement Consider...

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3rd IEEE International conference on Energy, Power and Environment - Towards Clean Energy Technologies (ICEPE)

作者： Subbaiyan, Thangavel Venkatachalam, Yuvaraju Natl Inst Technol Puducherry Dept Elect & Elect Engn Karaikal 609609 India

ISBN: (纸本)9781665425360

Phasor measurement unit (PMU) is a device that gives a fast and accurate measurement of both current and voltage along with time synchronization. This PMU data is useful for effective monitoring and control of power systems in smart grid. To minimize the system cost, the number of PMUs required should be reduced and must be placed optimally in a power system. To achieve this objective, particle swarm optimization (PSO) based technique is presented in this paper for maximum power system observability. Also, the zero injection bus (ZIB) case is considered for further reduction in the MU count which in turn reduces system cost. The selection of appropriate PMU location set is identified based on the system observability redundancy index (SORI). In this paper, the PSO algorithm is tested on various test systems like IEEE 9-bus, 14-bus, and 24-bus systems for normal and ZIB cases. The simulation results are compared with various MATLAB solver-based optimization techniques. The simulation results show that PSO based technique is more effective in terms of observability.

关键词： Bus Observability Index Optimal Placement of PMU Particle Swarm optimization Phasor Measurement unit System Observability Redundancy Index

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Intrusion Detection Method of Industrial control network Based on Lightgbm 3

Intrusion Detection Method of Industrial Control Network Bas...

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3rd IEEE International conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Tong, Weiming Liu, Bingbing Li, Zhongwei Lin, Jingbo Jin, Xianji Laboratory for Space Environment and Physical Sciences Harbin Institute of Technology Harbin China Service Center of Government and Enterprise Customer State Grid Harbin Electric Power Supply Company Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9780738112152

Aiming at the requirements of high accuracy and high real-time for industrial control network intrusion detection, combined with the characteristics of high accuracy of supervised learning machine learning algorithm under label data set, an intrusion detection method for industrial control network based on lightgbm is proposed. And bayesian optimization algorithm is used to optimize the parameters of lightgbm model to solve the difficult problem of parameter adjustment;finally, this algorithm is compared with other traditional methods in the standard industrial control network data set. The simulation structure shows that the proposed method has higher detection accuracy and efficiency than the traditional method. © 2021 IEEE.

关键词： Intrusion detection

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Prediction of Isobaric Vapor-Liquid Equilibrium Data for the Ternary and Quaternary Acetonitrile +Water Mixture Containing Ionic Liquid Using Artificial Neural network: Development of feedforward backpropagation artificial neural network as an alternative to conventional thermodynamic models for the estimation of vapor-liquid equilibrium data 22

Prediction of Isobaric Vapor-Liquid Equilibrium Data for the...

引用

3rd International conference on Industrial control network and System Engineering Research, ICNSER 2022

作者： Solee, Jaypee Abrogar Caparanga, Alvin Repatacodo School of Chemical Biological and Materials Engineering and Sciences Mapua University Philippines

ISBN: (纸本)9781450384964

Vapor-liquid equilibrium (VLE) data are crucial for separation processes, especially for optimal extractive distillation design. However, there are limited related reports on ionic liquids (ILs) as entrainer for acetonitrile-water azeotrope mixture separation, and it is impractical to gather data through experimentation. Consequently, using a more advanced and generalized computing system to supply reliable VLE data that best represents the acetonitrile-water-IL mixture is important. In this study, a feedforward backpropagation artificial neural network (ANN) models were developed capable of predicting isobaric ternary (acetonitrile + water + 1-methyl-3-imidazolium chloride [BMIM][Cl]) and quaternary (acetonitrile + water + 1-butyl-3-methylimidazolium chloride [BMIM][Cl] + 1-butyl-3-methylimidazolium tetrafluoroborate [BMIM][BF4]) systems containing ionic liquids. The best ANN architecture developed from both models' training and optimization process consists of a three-layer structure with two hidden layers. The qualitative and statistical assessment suggests an excellent predictive ability of the proposed ANN models. The performance of the proposed ANN models was compared with the non-random-two-liquid (NRTL), electrolyte non-random-two-liquid (eNRTL), and Wilson equation of state. The results show that the proposed ANN models have superior performance and can be reliably used in predicting the VLE data of ternary and quaternary acetonitrile-water-IL systems. © 2022 ACM.

关键词： Acetonitrile

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Enhanced EEGNet optimization Using Lightweight Deep Neural network for Detecting Human Stress Levels from Raw EEG Signals Dataset 3

Enhanced EEGNet Optimization Using Lightweight Deep Neural N...

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3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Hana, Silabdi Hassan, Raini Faizabadi, Ahmed Rimaz Gubbi, Abdullah Bellary, Mohammed Zakir Manjula, V. International Islamic University Malaysia Kulliyyah of Information & Communication Technology Department of Computer Science Kuala Lumpur Malaysia International Islamic University Malaysia Centre for Unmanned Technologies Kuala Lumpur Malaysia Department of Electronics and Communication Engineering Mangalore India Department of Artificial Intelligence and Machine learning Mangalore India

ISBN: (纸本)9798331508456

Accurate stress detection is essential in neuroscience and healthcare since human stress levels have a substantial impact on general well-being. Although EEG-based stress detection is a popular non-invasive method, real-time applications are limited by the high processing costs of classic feature extraction techniques. This work removes the requirement for intricate feature extraction by presenting an efficient EEGNet framework for real-time stress prediction utilizing raw EEG signals. The suggested approach improves accuracy and efficiency by utilizing Z-score normalization and adjusting batch size. Compared to traditional CNNs, the lightweight EEGNet architecture's depth-wise and separable convolutions ensure computational efficiency by reducing trainable parameters by a factor of ten. The proposed method outperformed current EEGNet-based techniques in stress identification by 5% to 18%. Furthermore, the improved system efficiently handles raw EEG signals, which is suitable for real-time, resource-constrained scenarios. The results shows the effectiveness of model-centric optimization in deep learning and open the door to the creation of more effective stress detection techniques. This work provides a workable solution for real-time non-invasive stress monitoring, highlighting the significance of lightweight frameworks customized for particular applications. © 2025 IEEE.

关键词： Deep neural networks

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Comprehensive Review of Various optimization Algorithms for Image Captioning 3

Comprehensive Review of Various Optimization Algorithms for ...

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3rd International conference on Advances in Computing, Communication control and networking, ICAC3N 2021

作者： Juzer Gabajiwala, Hamza Kamlesh Jethwa, Nishant Joshi, Parth Bimal Anurag Mishra, Arundhati Natu, Prachi MPSTME NMIMS Department of Computer Engineering Mumbai India

ISBN: (纸本)9781665438117

Automatic Image captioning is a prominent and challenging research problem in the field of computer vision and natural language processing with applications ranging in biology, automotive and several other domains. Human brain being a complex organ, is able to constantly identify and establish all the intricate details of a scene in real-time, which a machine fails to imitate. However, with neoteric advances in Artificial Intelligence, image captioning has witnessed significant progress. This paper provides a comprehensive review of the latest advancements in caption generation with an emphasis on encoder-decoder and generative adversarial network frameworks. A comparative analysis is performed on existing approaches and methodologies along with an overview of commonly used datasets and evaluation metrics. © 2021 IEEE.

关键词： Generative adversarial networks

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