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Topology optimization of an Electromagnet Manipulator for 3D Uniform Magnetic Field 20

Topology Optimization of an Electromagnet Manipulator for 3D...

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

作者： thabuis, Adrien Rivera, Maribel Caceres Ren, Xiaotao Perriard, Yves Ecole Polytech Fed Lausanne EPFL Integrated Actuator Lab LAI Neuchatel Switzerland Univ Bourgogne Franche Comte FEMTO ST Inst Besancon France

ISBN: (数字)9781665468336

ISBN: (纸本)9781665468336

Magnetic manipulation is a method used for biomedical applications to remotely control micro-robots in a contactless manner. the electromagnet manipulator should control a magnetic field with 3 degrees of freedom inside a region of interest. In various case studies, this field is the result of the superposition of three decoupled uniform magnetic fields. Each of them is individually generated by a different pair of Helmholtz coils. Such arrangement requires the pairs to be nested within each other, inevitably reducing the size of the working area, degrading the performance and the integration of the whole system. the proposed work aims to design a novel electromagnetic manipulator with the help of computational design, to explore beyond the Helmholtz coils arrangement. A topology optimization framework is developed for this purpose. It investigates the distribution of coils that achieves the largest working area, while keeping the different degrees of freedom balanced.

关键词： computational design actuator magnetostatics magnetic manipulation micro-robots

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Scaling principle analysis and layout optimization design of grading electrode for inner cooling system of MMC valves

Scaling principle analysis and layout optimization design of...

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

作者： Qiuling Hu Guan Liu Zhilei Si Xiaofeng Zhou Pengzhong Wang Shuaiqing Wang Junyang Wang Xuji Electric Co. Ltd. Henan Xuchang 461000 People's Republic of China

ISBN: (数字)9781837241552

this paper analyzes the relationship between the scaling principle of the grading electrode for inner cooling system and the potential, leakage current, and electric field. the research results show that the electric field intensity of the grading electrode is generally large. On the main water pipe, the scaling site is generally at the position with high potential, and the higher the potential, the thicker the scaling layer. that is scaling is related to the electrode potential and the direction of leakage current. Based on the analysis results, the electrical wiring method and the layout mode of grading electrode is proposed to reduce the scaling of the grading electrode and facilitate maintenance.

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An Algorithmic Data Pipeline Architecture for the Production of Personalized Telecom Product Offers 1

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

作者： Stylianakis, Charis Bouras, Dimitrios Alexakis, Konstantinos Oikonomopoulos, Spyros Spitadakis, Vassilis Panagiotakis, Yorgos NEUROCOM SA Athens Greece NEUROCOM SA Iraklion Greece

ISBN: (数字)9783031632150

ISBN: (纸本)9783031632174;9783031632150;9783031632143

Telecommunications sales management require a better understanding of the client's profile and customer needs before selecting and proposing the "right" product at the "best" price, choosing from an increasingly complex collection of offers and tariff packages. To this end, various methods are aiming to understand and estimate the user's behavior, predict traffic and willingness to pay. Based on such information, sales channels select and propose a product at an acceptable price, which will both be attractive to the customer and provide satisfactory and sustainable revenue to the company. Such methods require complex machine learning exploration and exploitation algorithms, applied to high volumes data of variant complexity, with the additional requirement to execute the whole process instantly so that sales team can access the proposed offers in front of the customer. Furthermore, interaction outcome is fed back to the system to enable continuous updating of data, events and finally offers. the AutoSPRice project goal of Neurocom is a fully automated and autonomous revenue and customer lifecycle value management system, which produces the most appropriate offers for each sales channel both in batch and real-time conditions. the system can predict, using the appropriate machine learning algorithms, how future telecom subscriber needs will evolve at individual and group level (company, family). It also assesses the user's financial capacity and willingness to buy from different offerings. Hence, it enables sales channels to identify what is the right product that best meets a customer's needs by using reinforcement learning. Combined with systems that can perform big data management and processing, it immediately provides responses out of large datasets. Using exploration-exploitation algorithms, it identifies the price to achieve the best chance to be accepted by the customer and the provider. the system has been developed and tested extensively under realistic usag

关键词： telecom usage forecasting price optimization machine learning price suggestion reinforcement learning

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Negative Samples Selection Can Improve Graph Contrastive Learning in Collaborative Filtering 20th

Negative Samples Selection Can Improve Graph Contrastive Lea...

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20th International conference on Intelligent Computing (ICIC)

作者： Shao, Yifan Cai, Xu Gu, Fangming Li, Ximing Jilin Univ Coll SoftWare Changchun Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun Peoples R China

ISBN: (纸本)9789819756179;9789819756186

Nowadays, graph collaborative filtering is widely used in recommender system as an important technique. In order to alleviate the common data sparsity problem in collaborative filtering, contrastive learning techniques have been utilized to assist the modeling of user-item interaction graphs. However, the existing graph contrastive learning methods in collaborative filtering mainly focus on the construction of the augmented view and the loss function, which ignores the negative samples selection. they treat all other nodes as negative samples in learning process, which may be unreasonable. In fact, if two users have extremely similar preferences, they should not be treated as negative samples of each other. To address the aforementioned issue, we investigate various techniques for negative samples selection and introduce a graph contrastive learning framework called NSSGCL (Negative Samples Selection Graph Contrast Learning), which consists of two parts. First, we measure the similarity of nodes in terms of both their structure and semantics, and consider nodes that are highly similar in both structure and semantics aspects to be potential positive samples of each other. Second, we propose a debiasing algorithm that utilizes the probability distribution of positive and negative samples to further estimate the actual contribution of the potential positive samples obtained above. A substantial number of experiments on real datasets demonstrate the effectiveness of our strategy, with our approach achieving a 12% and 14% improvement compared to baseline methods on the ML-1M and Yelp datasets, respectively.

关键词： Recommender system Collaborative Filtering Contrastive Learning Graph Neural Network

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20th International conference on Information Technologies and Mathematical Modelling, ITMM 2021

20th International Conference on Information Technologies an...

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20th International conference on Information Technologies and Mathematical Modelling, ITMM 2021

ISBN: (纸本)9783031093302

the proceedings contain 28 papers. the special focus in this conference is on Information Technologies and Mathematical Modelling. the topics include: Stability of Applied Probability systems, their Limit Behavior and optimization;Retrial Queue MMPP/M/N Under Heavy Load Condition;on the Application of Queuing theory in the Analysis of Transients in the Operation of a Freight Railway Station;application of Machine Learning Methods to Solving Problems of Queuing theory;Resource Requirements Distribution Evaluation for Traffic Offloading Strategies in NR-U Networks;stability Analysis for Retrial Queue with Collisions and r-Persistent Customers;asymptotic Analysis of Retrial Queueing system M/M/1 with Non-persistent Customers and Collisions;Asymptotic Analysis of MMPP/M/1 Retrial Queueing system with Unreliable Server;analysis of Open Queueing Networks with Batch Services;n-Policy for a Production Inventory system with Positive Service Time;generalised Self-induced Service Interruption and Consequent Reduced Priority in Discrete-Time Queues;evaluation of the New and Accepted Customers Blocking Probabilties in a Network of Resource Loss systems;markovian Models of Queuing systems with Positive and Negative Replenishment Policies;an Explicit Solution for an Inventory Model with Positive Lead Time and Server Interruptions;simulation of Finite-Source Retrial Queueing systems with Impatient Customers Using Different Failure Modes;optimization of the Bartlett Conflict Traffic Flows Control Process in the Class of Algorithms with a Pedestrian Mode;PASTA for Remaining Service Time in Stable and Unstable Queues;on the Existence of the Stationary Distribution in a Cyclic Polling system with Autoregressive Poisson Inputs;preface;application of k-out-of-n:G system and Machine Learning Techniques on Reliability Analysis of Tethered Unmanned Aerial Vehicle;analyzing the Effect of Catastrophic Breakdowns with Retrial Queues in a Two-Way Communication system;on Comparison of Waiting Ti

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AoTI Minimization for Multi-Type Data Sampling in Industrial Wireless Sensor Networks 20

AoTI Minimization for Multi-Type Data Sampling in Industrial...

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20th IEEE International conference on Embedded and Ubiquitous Computing (EUC)

作者： Ying, Chen Zhao, Zhen Yi, Changyan Shi, You Wang, Ran Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Univ Sydney Dept Elect & Informat Engn Sydney NSW 2006 Australia

ISBN: (纸本)9798350396355

For practical industrial wireless sensor networks (IWSNs), the system freshness of a specific task is usually related to multiple and multitype sensing data. However, most existing research on freshness metrics, such as Age of Information (AoI) or Age of Processing (AoP), only considers a single-package setting with a single type of data. To fill this gap, we propose the Age of Task-oriented Information (AoTI) for measuring the freshness of industrial tasks in IWSNs. It measures the time elapsed of the latest analyzed results before arriving at the receiver since the generation of any type of sampling data belonging to one certain task. Furthermore, we aim to minimize the long-term AoTI for IWSNs applications by jointly optimizing access modes and sampling frequencies for all sensors. By first formulating the problem as a Mixed Integer Nonlinear Programming problem, we then transform it to a constrained Markov Decision Process (CMDP) and relax it as an un-constrained MDP using Lagrangian method. Finally, we develop a Learning-based Access mode selection and Sampling frequency Control (LASC) algorithm and verify its superiority through simulations.

关键词： Age of Task oriented Information (AoTI), industrial wireless sensor networks (IWSNs), long term optimization, sampling frequency control, access mode selection

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Enhanced BL-Based Positioning system in Road Tunnels to Increase Rescue Intervention Efficiency 20th

Enhanced BL-Based Positioning System in Road Tunnels to Incr...

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20th Transport systems theory and Practice conference

作者： Broz, Jiri Tichy, Tomas Pirnik, Rastislav Czech Tech Univ Fac Transportat Sci Prague Czech Republic Univ Zilina Fac Elect Engn & Informat Technol Zilina Slovakia

ISBN: (纸本)9783031857508;9783031857515

the paper provides an overview on mobility challenges in the tunnel environment, it summarizes requirements on safety and emergency response that are negatively affected by limited availability or even lack of Global Navigation Satellite systems in tunnels. the paper provides an alternative way on how to enable positioning in tunnels by using additional Bluetooth Low Energy (BLE) infrastructure with a focus on emergency response. the BLE infrastructure supports advanced systems such as Cooperative-Intelligent Transport systems (C-ITS), and therefore it is possible to disseminate locally relevant information among the cars and to the tunnel operators. the paper considers various scenarios and provides use case definition supported by testing of C-ITS and functional validation from the Blanka tunnel in Prague. the proposed solution improves usage applications that require accurate positioning data to ensure proper functions, and therefore enables use of navigation in the tunnel environment. these could help to general use for wide range of drivers, however, the main goal is to efficiently assist during the rescue and recovery, or even smooth pass of emergency vehicle. the presented outcomes support preparation of strategic documents for Czech legislation and standards for the gradual installation of technologies in the tunnel, leading to better dissemination about emergency and other unexpected events to improve driver awareness through vehicular communication and reliable navigation.

关键词： C-ITS emergency positioning rescue safety tunnel V2X

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Active Power Curtailment and Reactive Power Control in PV-Rich Low-Voltage Distribution Network

Active Power Curtailment and Reactive Power Control in PV-Ri...

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International conference on European Electricity Market, EEM

作者： Marina Dubravac Danijel Topić Krešimir Fekete Zvonimir Šimić Rene Prenc Michele Rojnić Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia Faculty of Engineering University of Rijeka Rijeka Croatia

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

High PV systems integration faces distribution networks (DN) with technical issues. However, PVs are inverter- based sources and with active power curtailment (APC) and reactive power control (RPC) can provide ancillary services for distribution system operators (DSOs) and participate as problem/solution principle. this paper proposes an optimal power flow (OPF)-based model for improving voltages and reducing losses by using PV inverter APC and RPC strategies. the proposed optimization model is presented as nonlinear programming (NLP). For solving this optimization problem without approximations, a co-simulation of OpenDSS power system analysis software and PyGMO package with embedded computational intelligence (CI) optimization methods is used. Several case studies are carried out over the IEEE European low-voltage test feeder (LVTF). Regardless of which strategy is applied, the obtained results show that the proposed model improves technical conditions in PV-rich DN and illustrate PV potential in providing ancillary services to DSO.

关键词： Technical requirements Reactive power control Low voltage Computational modeling Europe Distribution networks system integration

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20th International conference on the Semantic Web, ESWC 2023

20th International Conference on The Semantic Web, ESWC 2023

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20th International conference on the Semantic Web, ESWC 2023

ISBN: (纸本)9783031334542

the proceedings contain 41 papers. the special focus in this conference is on the Semantic Web. the topics include: NASTyLinker: NIL-Aware Scalable Transformer-Based Entity Linker;iSummary: Workload-Based, Personalized Summaries for Knowledge Graphs;neural Class Expression Synthesis;evaluating Language Models for Knowledge Base Completion;subsumption Prediction for E-Commerce Taxonomies;two-View Graph Neural Networks for Knowledge Graph Completion;GETT-QA: Graph Embedding Based T2T Transformer for Knowledge Graph Question Answering;Repairing EL Ontologies Using Weakening and Completing;activity Recommendation for Business Process modeling with Pre-trained Language Models;a Comparative Study of Stream Reasoning Engines;RELD: A Knowledge Graph of Relation Extraction Datasets;IMKG: the Internet Meme Knowledge Graph;Describing and Organizing Semantic Web and Machine Learning systems in the SWeMLS-KG;a Concise Ontology to Support Research on Complex, Multimodal Clinical Reasoning;lauNuts: A Knowledge Graph to Identify and Compare Geographic Regions in the European Union;HHT: An Approach for Representing Temporally-Evolving Historical Territories;an Upper Ontology for Modern Science Branches and Related Entities;k-Hub: A Modular Ontology to Support Document Retrieval and Knowledge Extraction in Industry 5.0;pyRDF2Vec: A Python Implementation and Extension of RDF2Vec;Boosting Knowledge Graph Generation from Tabular Data with RML Views;Join Ordering of SPARQL Property Path Queries;a Knowledge Graph of Contentious Terminology for Inclusive Representation of Cultural Heritage;LegalHTML: A Representation Language for Legal Acts;whyis 2: An Open Source Framework for Knowledge Graph Development and Research;Prototyping an End-User User Interface for the Solid Application Interoperability Specification Under GDPR;semReasoner - A High-Performance Knowledge Graph Store and Rule-Based Reasoner;LIS: A Knowledge Graph-Based Line Information system;combining Semantic Web and Machine Le

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Workshops on ASOCA, AI-PA, FMCIoT, WESOACS 2022, held in Conjunction with the 20th International conference on Service-Oriented Computing, ICSOC 2022

Workshops on ASOCA, AI-PA, FMCIoT, WESOACS 2022, held in Con...

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Workshops on ASOCA, AI-PA, FMCIoT, WESOACS 2022, held in Conjunction with the 20th International conference on Service-Oriented Computing, ICSOC 2022

ISBN: (纸本)9783031265068

the proceedings contain 43 papers. the special focus in this conference is on ASOCA, AI-PA, FMCIoT, WESOACS. the topics include: SASH: Safe Autonomous Self-Healing;trusted Smart Irrigation system Based on Fuzzy IoT and Blockchain;smart Edge Service Update Scheduler: An Industrial Use Case;Energy-Aware Placement of Network Functions in Edge-Based Infrastructures with Open Source MANO and Kubernetes;people Counting in the Times of Covid-19;a Service-Oriented Middleware Enabling Decentralised Deployment in Mobile Multihop Networks;Towards Engineering AI Planning Functionalities as Services;data Product Metadata Management: An Industrial Perspective;image Data Augmentation and Convolutional Feature Map Visualizations in Computer Vision Applications;FUSPAQ: A Function Selection Platform to Adjust QoS in a FaaS Application;Specification-Driven Code Generation for Inter-parameter Dependencies in Web APIs;bizDevOps Support for Business Process Microservices-Based Applications;towards Real-Time Monitoring of Blockchain Networks through a Low-Code Tool;data-Aware Application Placement and Management in the Cloud-IoT Continuum;towards a Context-Aware Framework for Internet of things and Smart Everything;simulating IoT systems from High-Level Abstraction Models for Quality of Service Assessment;internet of things Semantic-Based Monitoring of Infrastructures Using a Microservices Architecture;on Balancing Flexibility and Compliance of Business Processes: Functional Constraints modeling and Verification;change Recommendation in Business Processes;object-Centric Predictive Process Monitoring;board Miner: A Tool to Analyze the Use of Board-Based Collaborative Work Management Tools;a Tool for Business Processes Diagnostics;Node4Chain: Extending Node-RED Low-Code Tool for Monitoring Blockchain Networks;service-Oriented Integration of SuperTuxKart;Using Open API for the Development of Hybrid Classical-Quantum Services;quokka: A Service Ecosystem for Workflow-Based Execution of Variati

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