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检索条件"任意字段=20th Conference on System Modeling and Optimization"

共 3723 条记录，以下是311-320 订阅

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20th EAI International conference on Mobile and Ubiquitous systems: Computing, Networking and Services, MobiQuitous 2023

20th EAI International Conference on Mobile and Ubiquitous S...

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20th EAI International conference on Mobile and Ubiquitous systems: Computing, Networking and Services, MobiQuitous 2023

ISBN: (纸本)9783031639883

the proceedings contain 65 papers. the special focus in this conference is on Mobile and Ubiquitous systems: Computing, Networking and Services. the topics include: High-Performance Features in Generalizable Fingerprint-Based Indoor Positioning;SCORE: Scalable Contact Tracing over Uncertain Trajectories;faultBit: Generic and Efficient Wireless Fault Detection Using the Internet of things;deepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data;Federated Reinforcement Learning for Automated LoRaWAN Management in Industrial IoT;a Hybrid Approach to Monitor Context Parameters for Optimising Caching for Context-Aware IoT Applications;LOADHoC: Towards the Automatic Local Distribution of Computation Using Existing IoT Devices;e-Go Bicycle Intelligent Speed Adaptation system for Catching the Green Light;FedGCS: Addressing Class Imbalance in Long-Tail Federated Learning;FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated Learning;RADEAN: A Resource Allocation Model Based on Deep Reinforcement Learning and Generative Adversarial Networks in Edge Computing;a Stream Data Service Framework for Real-Time Vehicle Companion Discovery;KS-Autoformer: An Autoformer-Based SOC Prediction Framework for Electric Vehicles;deep Reinforcement Learning-Based Multi-node Collaborative Task Offloading optimization in 6G Space-Air-Ground Integrated Networks;securing Wireless Communication in Critical Infrastructure: Challenges and Opportunities;cross-User Activity Recognition via Temporal Relation Optimal Transport;selfAct: Personalized Activity Recognition Based on Self-Supervised and Active Learning;a Novel Method for Wearable Activity Recognition with Feature Evolvable Streams;let’s Vibrate with Vibration: Augmenting Structural Engineering with Low-Cost Vibration Sensing;research on Data Drift and Class Imbalance in Android Malware Detection;Reputation-Based Dissemination of Trustworthy Information in VANETs;reputation system

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system-Level Robust Design optimization of a Permanent Magnet Motor Under Design Parameter Uncertainties 20

System-Level Robust Design Optimization of a Permanent Magne...

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20th Biennial IEEE conference on Electromagnetic Field Computation (CEFC)

作者： Mun, Jaegyeong Choi, K. K. Kim, Dong-Hun Kyungpook Natl Univ Dept Elect & Elect Engn Daegu 41566 South Korea Univ Iowa Dept Mech & Ind Engn Iowa City IA 52242 USA

ISBN: (数字)9781665468336

ISBN: (纸本)9781665468336

In the presence of design parameter uncertainties, a system-level robust design optimization (RDO) method for a permanent magnet motor is proposed to enhance steady and dynamic performances of the whole motor drive system. To achieve the goal, the influence of individual motor parameters on transient system responses is investigated essentially. then material characteristic of each motor component due to the operating temperature fluctuation is examined as well. Finally, a conventionally customized motor drive system is fully simulated based on the finite element method, and the motor is optimized by the univariate dimension reduction method (uDRM).

关键词： electromagnetics optimization reliability theory robustness

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20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research

20th International Workshop on Advanced Computing and Analys...

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20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2021

the proceedings contain 156 papers. the topics discussed include: outlook on software framework in low and intermediate energy nuclear physics;anomaly detection of I/O behaviors in HEP computing cluster based on unsupervised machine learning;blaze: a high performance big data computing system for high energy physics;CernVM-FS ephemeral publishers on Kubernetes;configuration information system for online processing and data monitoring in the NICA experiments;data handling optimization in Russian data lake prototype;deployment of high energy physics software with a standard method;Detray: a compile time polymorphic tracking geometry description;development of the automatic procedures for spinor matrix element calculation with massive particles;distributed file systems performance tests on Kubernetes/docker clusters;evaluating awkward arrays, uproot, and coffea as a query platform for high energy physics data;and evaluation and implementation of various persistent storage options for CMSWEB services in Kubernetes infrastructure at CERN.

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20th EAI International conference on Mobile and Ubiquitous systems: Computing, Networking and Services, MobiQuitous 2023

20th EAI International Conference on Mobile and Ubiquitous S...

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20th EAI International conference on Mobile and Ubiquitous systems: Computing, Networking and Services, MobiQuitous 2023

ISBN: (纸本)9783031639913

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Scheduling Method of Manufacturing system Based on Colored Petri Nets and Graph Neural Network 20

Scheduling Method of Manufacturing System Based on Colored P...

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20th IEEE International conference on Networking, Sensing and Control, ICNSC 2023

作者： Liu, Wei Luo, Jiliang Yi, Sijia Lin, Zexuan Zhou, Jiazhong Huaqiao University College of Information Science and Engineering Xiamen China Jiangxi University of Science and Technology School of Mechanical and Electrical Engineering Ganzhou China

ISBN: (纸本)9798350369502

When using timed Petri nets for modeling, Petri net model often tends to be huge, and this makes it difficult to design neural networks of Petri nets. To address this issue, this paper proposes a resource scheduling system automated guided vehicle(AGV) path planning method based on colored Petri nets and graph neural networks. First, the AGV system is described as a path Petri net, and different task types are assigned to the Petri net in different colors based on different process requirements, so that the same place or transition in the Petri net can represent multiple meanings, greatly simplifying the structure of the Petri net. Secondly, using the topological structure of the Petri net, a graph neural network that can be used on colored Petri nets is designed. the topological structure of the Petri net is divided into four categories, and is applied to the graph neural network as a correlation matrix for feature aggregation, achieving higher accuracy with fewer parameters. thirdly, deep reinforcement learning is used to train the graph neural network, and the trained model is used as a heuristic to construct the A∗ algorithm to solve the scheduling problem of the system. Finally, several numerical experiments are conducted, and the results show that the scheduling method based on colored Petri nets and graph neural networks can simplify the structure of the Petri net while solving the joint problem of AGV task allocation and path planning in a limited time. © 2023 IEEE.

关键词： Reinforcement learning

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Retail Store Customer Behavior Analysis system: Design and Implementation 20th

Retail Store Customer Behavior Analysis System: Design and I...

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20th International conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Tuan Dinh Nguyen Hihara, Keisuke Tung Cao Hoang Utada, Yumeka Torii, Akihiko Izumi, Naoki Nguyen thanh thuy Du Tien Pham Long Quoc Tran VNU Univ Engn & Technol Hanoi Vietnam Dai Nippon Printing Co Ltd Tokyo Japan

ISBN: (纸本)9783031632259;9783031632235;9783031632228

Understanding customer behavior in retail stores plays a crucial role in improving customer satisfaction by adding personalized value to services. Behavior analysis reveals both general and detailed patterns in the interaction of customers with a store's items and other people, providing store managers with insight into customer preferences. Several solutions aim to utilize this data by recognizing specific behaviors through statistical visualization. However, current approaches are limited to the analysis of small customer behavior sets, utilizing conventional methods to detect behaviors. they do not use deep learning techniques such as deep neural networks, which are powerful methods in the field of computer vision. Furthermore, these methods provide limited figures when visualizing the behavioral data acquired by the system. In this study, we propose a framework that includes three primary parts: mathematical modeling of customer behaviors, behavior analysis using an efficient deep learning-based system, and individual and group behavior visualization. Each module and the entire system were validated using data from actual situations in a retail store.

关键词： Customer Behavior Analysis Behavior Analysis system Retailing Group behavior Analysis

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An optimization Framework for Mixed-Signal Accelerators Based on F-2T2R Compute Cells

An Optimization Framework for Mixed-Signal Accelerators Base...

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International conference on Synthesis, modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)

作者： Michele Caselli Andrea Boni Department of Engineering and Architecture University of Parma

ISBN: (数字)9798350351927

ISBN: (纸本)9798350351934

this paper proposes a Matlab framework for the optimized design of mixed-signal accelerator for Deep Neural Networks (DNNs), based on the Flipped (F)-2T2R RRAM compute cell. the manuscript describes an analytical model including the fundamental sources of accelerator non-ideality, developed for a simplified yet accurate system description. the framework allows to explore the design space and optimize the accelerator, targeting system level performance. A trade-off between the accelerator energy consumption and the maximum SNR emerges from simulations.

关键词： Analytical models Energy consumption Computational modeling Artificial neural networks Space exploration Circuit synthesis Integrated circuit modeling MATLAB optimization Signal to noise ratio

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IMPEDANCE modeling OF DRU BASED HVDC system WIth GRID-FOLLOWING AND GRID-FORMING CONVERTERS 20

IMPEDANCE MODELING OF DRU BASED HVDC SYSTEM WITH GRID-FOLLOW...

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20th International conference on AC and DC Power Transmission 2024, ACDC 2024

作者： Wang, Minzhi Qu, Xiaohui He, Hanwen Ren, Zijun Dong, Zheng School of Electrical Engineering Southeast University Nanjing China School of Electrical Engineering Shandong University Jinan China

ISBN: (纸本)9781837242672

the escalating capacity of offshore wind power introduces substantial challenges for offshore integration and transmission systems. Diode rectifier unit (DRU) based high voltage direct current (HVDC) system has been acknowledged as a potential method to achieve economical offshore wind power transmission. For the purpose of the uncontrollability of DRU, grid-forming (GFM) converters are necessary to support offshore AC voltage. Moreover, the clamping effect of DRU cause unconventional power characteristic of active power-voltage and reactive power-frequency, thus leading to untraditional control loop. Simultaneously, due to the immaturity of GFM converters, grid-following (GFL) converters could be applied for a long period. However, considering the effect of the phase-locked loops (PLL), the stability of entire system is decreased. In this regard, this article aims at studying the dq-frame impedance model of GFL converters and DRU based GFM converters, which including active power-voltage loop and reactive power-frequency loop. Finally, a simulation of the two converters are built in MATLAB/Simulink, results are shared to prove the correctness. © the Institution of Engineering & Technology 2024.

关键词： Phase locked loops

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HDR Video Coding Based on Perceptual optimization 20th

HDR Video Coding Based on Perceptual Optimization

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20th International conference on Intelligent Computing (ICIC)

作者： Sun, Jiamin Zhu, Zhongjie Cu, Weifeng Bai, Yongqiang Yu, Zhijing Zhejiang Wanli Univ Ningbo Key Lab DSP Ningbo Peoples R China Ocean Univ China Fac Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9789819755967;9789819755974

High dynamic range (HDR) video is a high-quality video format that improves contrast and colors. Encoding HDR directly faces issues like visual redundancy, uneven bitrates, and subpar subjective visual perception. As a solution, a perception-based optimization algorithm for HDR video encoding is proposed. Firstly, leveraging the human visual system's perceptual redundancy, a perceptual lossless preprocessing model using Karhunen-Loeve (KL) transform and a subjective perceptual lossless threshold is created to cut visual redundancy. Subsequently, a coding tree unit (CTU)-level perceptual quantization model, based on human visual attention, is developed. this model estimates the perceptual saliency in spatial and temporal areas, directingCTUbitrate allocation for optimized bitrate that aligns with human perception. Experimental results show the algorithm effectively reduces visual redundancy and enhances coding performance. Compared to VTM 17.0, it reduces bitrate by an average of 22.67% (up to 53.03%), reduces coding time by 26.91% (up to 51.79%) and increases average PSNR by 5.07% (up to 10.46%).

关键词： VVC Perceptual optimization HDR

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Troubleshooting and optimization of a Deep-Water Multiphase Pump and of its Upstream Production Network: a Case Study 20

Troubleshooting and Optimization of a Deep-Water Multiphase ...

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20th International conference on Multiphase Production Technology, MPT 2023

作者： Montini, M. Nuvolati, C. Polonini, E. Ponteggia, L. Berretta, P. Perrina, M. Particelli, L. Eni S.p.A. Italy

this paper presents the work done to address the severe instabilities of a subsea multiphase pump installed at the riser base to boost the production of a deep-water West Africa field. Moreover, the actions performed to further optimize its operation are discussed. the analysis and troubleshooting of the behaviour of the subsea multiphase pump and production network was facilitated by Eni digital oilfield system, which makes the data available to all the relevant specialist regardless of their geographical collocation. the real time data archive and the capability to seamlessly correlate different variables available within the digital system environment were leveraged to quickly perform specific engineering analyses. For example, it is possible to compare the operating conditions with the design data and to feed the thermo-fluid dynamic simulators. In particular, one of the root causes of the encountered instability issues was the ratio of the production coming from two different reservoirs and commingled at the inlet of the boosting system. In fact, the mix temperature was higher than expected and higher than the pump maximum operating temperature, thus leading to automatic pump shut-downs. Once identified, this phenomenon was prevented by optimizing the mix from different wells and changing the setpoint of the temperature alarm after a requalification of the pumps and manifold materials. A further cause of frequent pump trips was due to hydrodynamic slugging of the production network and its interaction with the pump recirculation system, which were identified and partially prevented with a fine tuning of the pump control system. Also, the presence of spurious erroneous readings from the multiphase flow meter installed at the pump outlet, due to the presence of scales, was the cause of pump trips. Lab analyses confirmed the risk of scales precipitation at specific water ratios between the two reservoirs. An acid job helped reinstating the meter reliability and th

关键词： Pumps

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