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检索条件"任意字段=Conference on Data Analysis and Modeling for Process Control"

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19th International Workshop on data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

19th International Workshop on Data Privacy Management, DPM ...

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19th International Workshop on data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

ISBN: (纸本)9783031823619

The proceedings contain 33 papers. The special focus in this conference is on data Privacy Management. The topics include: reteLLMe: Design Rules for Using Large Language Models to Protect the Privacy of Individuals in Their Textual Contributions;plausible Deniability of Redacted Text;exploring Distribution Learning of Synthetic data Generators for Manifolds;HEDAS: Secure and Efficient Distributed OLAP Using Fully Homomorphic Encryption;card-Based Cryptographic Protocols for Three-Input Functions with a Standard Deck of Cards Using Private Operations;grid-Based Decompositions for Spatial data Under Local Differential Privacy;balancing Privacy and Utility in Multivariate Time-Series Classification;dynamic k-Anonymity for Electronic Health Records: A Topological Framework;Using Static Code analysis for GDPR Compliance Checks;privacy-Preserving Tabular data Generation: Systematic Literature Review;A DPIA Repository for Interdisciplinary data Protection Research;secrecy and Sensitivity: Privacy-Performance Trade-Offs in Encrypted Traffic Classification;route Discovery in Private Payment Channel Networks;A Comparative Study of Rust Smart Contract SDKs for Application-Specific Blockchains;offchain Runtime Verification (for The Tezos Blockchain);quantifying Liveness and Safety of Avalanche’s Snowball;We Will DAG You;assessing the Impact of Sanctions in the Crypto Ecosystem: Effective Measures or Ineffective Deterrents?;Practical Implementation of Pairing-Based zkSNARK in Bitcoin Script;Homomorphic Encryption Based ECDSA Generation Over Five Party Protocol;benchmarking Post-quantum Cryptography in Ethereum-Based Blockchains;exploring Anomaly Detection for Marine Radar Systems;hunting Vulnerabilities in the Maritime Domain: A Domain Wide Cybersecurity Vulnerability analysis;threat modeling in Satellite Communications for Maritime Operations;cybersecurity Challenges in Industrial control Systems: An Interview Study with Asset Owners in Norway;usage of Cybersecurity Standards

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Towards Improving data Interoperability for the Reconstruction of Existing Buildings 20th

Towards Improving Data Interoperability for the Reconstructi...

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20th International conference on Computing in Civil and Building Engineering, ICCCBE 2024

作者： Eder, Friedrich Höng, Simon Schmailzl, Marc Linner, Thomas Obergrießer, Mathias Ostbayerische Technische Hochschule Regensburg Galgenbergstrasse 30 Regensburg93053 Germany

ISBN: (纸本)9783031842078

Digital representations of buildings are the supporting structures of various use-cases in the emerging field of data-driven decision making. From large scale applications in the context of city planning to the detailed evaluation of critical infrastructure they enable specialists to observe problems, interpret relationships, test solutions virtually and apply them in the real world. This is only feasible if the individual underlying digital model meets the requirements imposed by the analysis at hand. In practice, especially models of existing buildings are not easy to come by as the information describing the existing structure is often scattered across multiple different data sources in various formats. Previous research efforts have outlined methodologies which leverage machine learning, computer vision and subsequent semantic enrichment in order to achieve the (re)construction of such building models. However, these methods are generally not integrated with each other, nor do they consider being able to interface with a shared repository of building related data. In this paper we present a methodology which focuses on establishing a common context for all building related data by utilizing the Industry Foundation Classes (IFC) schema. In particular we focus on utilizing readily available geometric and semantic data originating from geographic information systems as a basis, subsequently referencing additional data sources in their corresponding context and finally outlining interfaces with downstream enrichment processes in both directions. Through incorporating contextualized (IFC) data into the early stages of the remodeling workflow, we outline an end-to-end process from the initial component-based data-acquisition to the as-built building information model. In establishing a standardized foundation for data exchange and collaboration it enables all stakeholders to work more seamlessly across different stages of the remodeling project. © The Author(s), under

关键词： Building Information modeling (BIM) Industry Foundation Classes (IFC) Interoperability Linked data Machine Learning Semantic enrichment

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Fault Diagnosis and Fault Tolerant control Algorithm based on Neural Network in Modular Multi-level Converter (MMC)

Fault Diagnosis and Fault Tolerant Control Algorithm based o...

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Electronics and Renewable Systems (ICEARS), International conference on

作者： Jun Xie Xiaodong Qian Qiong Huang Anhui Vocational and Technical College Hefei China

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The state-of-the-art modulation strategy of MMC is closely related to the quality of its output voltage waveform and system loss, and is related to whether the entire MMC system can maintain efficient and stable operation. In fact, all multi-level modulation technologies can be applied in MMC systems. To deal with the challenges, this study proposes a novel neural network-based MMC fault diagnosis (FD) and fault-tolerant efficient control (FTEC) algorithm. First, initial step for the modeling is to conduct the comprehensive analysis of the MMC architecture characteristics and prone faults. In particular, considering the schemes of open circuit (OC) and short circuit (SC) related faults, a model based on recurrent neural network (RNN) is established to extract and classify fault features, and the network structure of deep learning is improved. At the same time, an improved CNN model is applied to process the voltage signal of MMC sub-module. In improved part, efficient Softmax function is used to realize the probability distribution of multi-fault classification. The entire fault detection and analysis framework includes the following points: data collection, data pre-processing, network parameter initialization, fault feature training and classification decision. The experiment uses a self-built MMC simulation model to test the performance. At same time, in specific experiment, high voltage and low voltage are displayed independently to achieve a comprehensive analysis.

关键词： Fault diagnosis Training Multilevel converters Recurrent neural networks Modulation Fault tolerant control Feature extraction Classification algorithms Circuit faults Integrated circuit modeling

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Research on Graphic 3D modeling Technology Based on Neural Network in Intelligent Construction System

Research on Graphic 3D Modeling Technology Based on Neural N...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International conference on

作者： Jiwen Zhang Changchun University of Architecture and Civil Engineering Changchun China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper mainly discusses the innovative application of neural network in 3D graphic modeling and designs an efficient 3D modeling algorithm. The algorithm takes deep learning as the core and realizes high-precision modeling of building structures by extracting multi-level features of graphic data. CNN is used to process multi-dimensional images to generate 3D models with rich details, which greatly improves the modeling efficiency and accuracy. In addition, this paper introduces the simulation technology of the model in the intelligent construction system, and verifies the stability and robustness of the algorithm through experiments under different parameters. The experimental results show that the proposed modeling algorithm achieves higher accuracy than traditional algorithms in complex structure recognition and saves nearly $\mathbf{3 0 \%}$ in computing time. data analysis further confirms the adaptability and effectiveness of the neural network model in complex building environments, and provides data support and algorithm basis for in-depth research on future intelligent construction systems.

关键词： Graphics Solid modeling Three-dimensional displays Accuracy Computational modeling Buildings Neural networks Feature extraction data models Stability analysis

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5th International conference on Computing and Network Communications, CoCoNet 2023

5th International Conference on Computing and Network Commun...

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5th International conference on Computing and Network Communications, CoCoNet 2023

ISBN: (纸本)9789819747108

The proceedings contain 44 papers. The special focus in this conference is on Computing and Network Communications. The topics include: Maximizing Efficiency: Unveiling the Potential of Kubernetes Metrics;resource-Aware Learning Automata for Low-Latency Task Scheduling in Fog Computing;Design of High Gain Wideband Petal-Shaped Magneto-Electric Dipole Metal Antenna for 5G Sub-6 GHz and Wi-Fi Applications;A Novel Billhook-Shaped 2-Element MIMO Array for Sub-6 GHz Millimeter Wave Application;energy-Efficient Transmission in Wireless Sensor Network Using Compressive Sensing;smart City Solutions for Traffic Management;an IoT-Assisted Real-Time Urban Air Pollution data Collection System;real-Time Stock Price Prediction Using Apache Kafka;RSS-Based Localization Using GRU and BiGRU Deep Learning Models in LoRaWAN-IoT Networks;locating and Routing for IoT-Based Fire Evacuation System;an Energy-Efficient data Management Approach Using Sparse Compression in Future-Ready IoT Networks;a Framework for Developing IoT and Edge Computing-Based Smart Transportation Applications;a Mathematical Model for data Traffic Management in Next-Generation IoT Networks;crowdsourced Mobile Applications for Disaster Relief: A Case Study of Landslide Tracker and Amrita Kripa App Deployments in Sikkim Schools;design and modeling of Modular Wheelchair for Therapeutic Exercises;analyzing data Traffic in IoT Networks: A Novel Graph-Based Architecture;A Lightweight Privacy-Preserving and Authentication Model (LiPAM-FSG) for Fog-Based Smart Grids;time Series analysis and Rule Mining for Detecting Industrial control System data Injection Attacks;Radius-Based Authentication: UFrames’ Novel Approach to Prevent Account Sharing;deep Neural Network-Based Secure Energy-Efficient Power Allocation in an Interference Network;An Adaptive Time Quantum-Based Efficient Round-Robin Algorithm for CPU Scheduling;low-Cost Advanced Undetectable Hardware-Based Malicious Keyboard;constructing a Blind Digital Signature Scheme

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5th International conference on Computing and Network Communications, CoCoNet 2023

5th International Conference on Computing and Network Commun...

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5th International conference on Computing and Network Communications, CoCoNet 2023

ISBN: (纸本)9789819745395

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Research on Multi-Criteria Decision Making Based on Greedy Algorithm and Genetic Algorithm

Research on Multi-Criteria Decision Making Based on Greedy A...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International conference on

作者： Zhengrong Deng Yilin Wu Wenjuan Jiang Jiao Huang Xiangtan University Xiangtan China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The purpose of this paper is to explore how to construct a multi-criteria decision-making model using greedy algorithm and genetic algorithm to optimize the decision-making problem in the production process of enterprises. For the assembly process of parts in actual production, this paper proposes an optimization strategy based on multi-criteria decision-making, which combines the greedy algorithm and genetic algorithm to solve the decision-making problem at each stage. First, for the problem of multi-process production process, the complex multi-process problem is transformed into multiple single-process sub-problems for step-by-step solution. Then, in each stage, the maximum profit value of each process is calculated by greedy algorithm and the optimal solution is obtained by loop iteration. In addition, the results of the greedy algorithm are further optimized by combining the genetic algorithm, and the consistency and feasibility of the two algorithms in the decision-making results are verified. Finally, the paper optimizes the decision-making model and proposes the optimal strategy through multiple simulations and normality tests [1], which further improves the overall efficiency of the production system. The mathematical problem of multi-process quality inspection explored in this paper is inherently complex, involving multiple influences and correlations between processes, making it difficult to trace the root cause of quality. The modeling process is challenged by the variety and volume of data and the need to iteratively adjust the model and capture correlations between processes. The complexity and resource consumption of dealing with large amounts of data, as well as the uncertainty and risk factors involved in computational analysis, make it difficult to optimize and control, requiring a balance between quality, cost, and efficiency. The research employs a variety of technological tools and methods to cope with these difficulties, such as data mining, ma

关键词： Greedy algorithms Solid modeling Decision making Production Prediction algorithms Mathematical models data models MCDM Optimization Genetic algorithms

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analysis of the Effect of Flexible Deformation on the Aerodynamic Performance of Bionic Micro Aerial Vehicle 7th

Analysis of the Effect of Flexible Deformation on the Aerody...

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7th China Aeronautical Science and Technology conference, CASTC 2024

作者： Wang, Chen Peng, Liansong Chen, Xinning AVIC China Aero-Polytechnology Establishment Beijing100028 China China Academy of Information and Communications Technology Institute for the Integration of Informatization and Industrialization Beijing100191 China China Foreign Affairs University Beijing102206 China

ISBN: (纸本)9789819797677

In this paper, a finite element modeling method based on biological observation is proposed, which can construct a finite element model that conforms to the structural strength characteristics of insect wings. A fast and accurate simulation of large deformation motions in anisotropic complex models is realized by establishing a fluid–structure coupling model based on the Lattice-Boltzmann Method and the Central Difference Method. By analyzing the effect of flexible wing deformation on the aerodynamic performance during the flapping process of dragonfly forewing, a physical mechanism to improve the aerodynamic performance of flapping wings is generalized as the chordwise bending mechanism: flexible wing generates chordwise bending during mid-downstroke, which prevents the destruction of the leading-edge vortex structure by the high-pressure region of the flow field, allowing the leading-edge vortex attached to the wing surface, which is conducive to stable lift generation. This mechanism can provide theoretical guidance and data support for the design of wings for micro aerial vehicles. © Chinese Society of Aeronautics and Astronautics 2025.

关键词： Vortex flow

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Role of Artificial Intelligence in Facilitating Open Innovation in VAT Management 5th

Role of Artificial Intelligence in Facilitating Open Innovat...

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5th 5th Olympus - International conference on Supply Chain

作者： Yordanova, Zornitsa Univ Natl & World Econ Sofia Bulgaria

ISBN: (纸本)9783031693434;9783031693441

In the dynamic landscape of fiscal policy and tax administration, the integration of Artificial Intelligence (AI) within Value-Added Tax (VAT) management systems offers transformative opportunities to enhance efficiency, compliance, and transparency. Amidst a complex global economic environment characterized by intricate transactions and international trade, there is a critical need for innovative approaches to VAT collection and compliance. This study embarks on an exploratory analysis of AI's role in revolutionizing VAT management practices through the prism of open innovation-a paradigm that advocates leveraging external knowledge and technological advancements to drive internal innovation. Utilizing a qualitative research methodology, inclusive of a literature review and case studies, this paper examines the current and potential impacts of AI on VAT management practices. It delves into the application of AI in automating the processing of tax regulations, facilitating data modeling, and enhancing decision-making processes within VAT systems. Furthermore, this research evaluates the contributions of open innovation strategies, such as collaboration with external partners and engagement with user communities, to the development and continuous improvement of AI-enhanced VAT management tools. Drawing insights from interviews with development teams and tax professionals, alongside an examination of documentary evidence, this study presents a novel framework for understanding and advancing AI applications in VAT administration. It highlights the synergistic effects of AI and open innovation in creating more resilient, efficient, and transparent VAT systems. The results show that AI technologies and open innovation practices have a lot of potential to bring VAT management up to date, as long as problems like data privacy, engaging stakeholders, and the fact that tax laws are always changing are properly dealt with. This research contributes to the burgeoning discourse

关键词： Artificial Intelligence Open Innovation Value-added tax Digital Transformation process automation ERP data Analytics Business Intelligence

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Using Big data and Kafka to Track Resource Utilization in Real Time

Using Big Data and Kafka to Track Resource Utilization in Re...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Goswami, Anjali Shivaji, Ghorpade Bipin Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331527495

With the scale of data in modern systems growing rapidly, the need to perform analytics in a fast and efficient, real-time way is critical as different industries - from healthcare to logistics, even for cloud computing - have to utilise a resource efficiently. Real-time Resource Utilization analysis: Natural Language processing, Big data tools and Streaming Frameworks are used for monitoring, getting insight and dynamically optimizing resources. Link: Real-Time Analytics You cannot make it with Apache Kafka: A High-Throughput distributed Streaming Platform. Here in this paper we proposed strong basement for analysing on Big data including Kafka Streams for real-time resource utilisation analysis framework and show its high executing efficiency, faster implementations, enhancing reduce for improving executing systems efficiency. The suggested framework also addresses the challenges associated with real-time ingestion, processing, and analysis of streaming data, especially considering the use of Kafka Streams as a fundamental element for distributed stream processing. Learn More About Kafka Streams Builds a Unified processing PlatformKafka Streams can be used to build real-time business applications, or for creating real-time transformations, or processing streams as continuous business transactions. Measuring the health of the application is thus designed to be scalable, fault-tolerant and abstract over shoe data source sow the data it will act on will be different (i.e. an IoT device or a system log file or an application metrics etc.). Because of distributed architecture of Kafka ensures that the system are highly available and fault tolerant that effectively means even if the analysis fails/terminate the process it does not get interrupted. This framework harnesses advanced analytics models that provide intensive, granular insights, one of its strongest features is monitoring the diversity of resource utilization. These ML-driven models analyse patterns of resour

关键词： Predictive analytics

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