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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

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Virtual and Augmented Reality in manufacturing Companies in Slovakia

Virtual and Augmented Reality in Manufacturing Companies in ...

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13th international conference on Ambient systems, Networks and Technologies, ANT 2022 / 5th international conference on Emerging data and Industry 4.0, EDI40 2022

作者： Balco, Peter Bajzík, Peter Škovierová, Klára Faculty of Management Comenius University in Bratislava Odbojárov 10 P.O.BOX 95 Bratislava25 82005 Slovakia

Virtual and augmented reality technologies are gaining a stable presence in many industries, while supporting the efficiency and productivity of individual business processes. Education, virtual prototyping, production system design, remote maintenance, digital twin scenario simulations, digital tutorials are areas in which these technologies have found their place and at the same time record visible development. These are solutions that shorten product development time, reduce prototyping costs, streamline training and assembly processes, and can be used as a decision support tool through simulations of various production scenarios. Slovakia, as a pre-developed country, has the ambition to implement modern technologies in industry and education. Reflecting on this strategy, the aim of our paper was to quantify and analyze the level of knowledge and interest in these technologies in Slovak manufacturing companies. © 2022 Elsevier B.V.. All rights reserved.

关键词： Augmented reality manufacturing Virtual reality

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Image Processing Algorithm Design for Low-Light EBCMOS Devices Based on FPGA 3

Image Processing Algorithm Design for Low-Light EBCMOS Devic...

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3rd international conference on Electronic Information engineering and Computer Communication, EIECC 2023

作者： Cao, Yi Wei, Na Zhu, Xiangping Ma, Jun Institute of Optics and Precision Mechanics University of Chinese Academy of Sciences State Key Laboratory of Transient Optics and Photonics Xi'an China Xi'An Institute of Atomic Precision Manufacturing Technology Co. Ltd. Xi'an China

ISBN: (纸本)9798350359961

In this study, a hardware platform based on the Xilinx 7-series Field-Programmable Gate Array (FPGA) is employed. The design of a Static Storage Control Module and a Median Filtering Module is accomplished through programming in the Verilog hardware description language using Vivado software. The functionality of these modules is rigorously verified through logical validation using Modelsim software. The algorithm is applied to process low-light images captured by a custom EBCMOS image sensor. Comparative analysis with software testing platforms reveals a substantial improvement in image processing speed when utilizing the FPGA-based hardware resources. This improvement allows the processing of 1 pixel per clock cycle. As a result, this work lays the foundation for the design of lightweight, integrated data acquisition systems for the nextgeneration night vision helmet systems. © 2023 IEEE.

关键词： Algorithm Architecture EBCMOS Field-Programmable Gate Array (FPGA) Hardware Acceleration Hardware Description Language Image Processing

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A Multi-Material and Multi-Scenario dataset for Additive and Subtractive manufacturing Operations

A Multi-Material and Multi-Scenario Dataset for Additive and...

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international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Mohammad Uddin Sebastiano Gaiardelli Michele Lora Dong Seon Cheng Franco Fummi Department of Engineering for Innovation Medicine Industrial Computer Engineering Laboratory University of Verona Italy

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Smart manufacturing systems often use data to optimize production processes. Artificial intelligence algorithms can be used to tune the parameters of production recipes to achieve the desired results. Indeed, such algorithms will require to be trained by using data collected while executing manufacturing operations. However, the required sets of data are rarely available to most production companies. This work-in-progress paper introduces the Print+Mill dataset: a collection of data related to additive and subtractive manufac-turing operations. The data are collected during the execution of various production recipes, utilizing different materials and process parameters. For each recipe, the dataset includes data on the materials and parameters used, sensor readings from the machinery, such as power consumption and temperature, and information on the quality of the resulting product. The data are collected in a complex research facility; in the future, we plan to extend the dataset by considering other manufacturing operations, materials, and types of field data.

关键词： Temperature sensors Temperature distribution Additives Power demand Production Artificial intelligence Machinery Tuning manufacturing automation Smart manufacturing

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2022 4th international conference on System Reliability and Safety engineering, SRSE 2022

2022 4th International Conference on System Reliability and ...

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4th international conference on System Reliability and Safety engineering, SRSE 2022

ISBN: (纸本)9781665473880

The proceedings contain 93 papers. The topics discussed include: research on the classification method of reliability critical parts and important parts;condition-based maintenance for two-component series systems considering component reallocation;resilience modeling of interdependent supply chain networks with company collaboration against ripple effects;research on the construction method of system off-nominal states model based on ontology model;an improved approach based on linear programming model for assessing rail transit system resilience under disruptions;put theory into practice knowledge graph based aviation quality reliability knowledge system;uncertainty analysis technology of complex system based on evidence theory;leveraging ground test data for aero-engine thrust estimation;reliability assessment of man-machine systems subject to probabilistic common cause errors;application of software reliability in civil aircraft airborne software development;reliability modeling and analysis for space phased-mission system;on modeling of repairable systems with multi-output Gaussian convolution process;and reliability optimization of the filling and discharging system for lead-based fast reactor.

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international conference on Recent Evolutions in Energy, Drives and E-Vehicles, REED-EV 2022

International Conference on Recent Evolutions in Energy, Dri...

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international conference on Recent Evolutions in Energy, Drives and E-Vehicles, REED-EV 2022

ISBN: (纸本)9789819707621

The proceedings contain 56 papers. The special focus in this conference is on Recent Evolutions in Energy, Drives and E-Vehicles. The topics include: Comparative Analysis of Mobile Substation;solar Photovoltaic Technology and Future Trends;Crop Leaf Disease Detection Using DCCN;Extraction of Seven Parameters of Double Diode Model Using Analytical and Optimization Method with RTC FRANCE PV Module manufacturing data;ioT in Agriculture: A Review;Development and Characterization of Electrode Using PAN/TiO2 Nanofibers to Improve the Battery Capacity;Enhancement of Power Quality of PV-Interfaced Distribution System Using Adaptive Backpropagation;Power Quality Improvement in a Transmission System Using (Series-Active Filter or DVR);breast Cancer Detection Using Deep Learning and Thermal Images;a Harmonic Current Compensation on a Grid-Connected Electric Vehicle;application of Electro-Mechanical Kinetic Energy Regenerative System and E-Lock Differential for an Electric Vehicle: A Review;challenges and Future Scope in Fake News Detection: A Survey;a Novel Analysis of Urban and Rural Thermal Comfort Levels as a Futuristic Human Dwelling Strategy;a Survey on Performance of Broadcasting Protocol for Ad hoc and Sensor Network;wheelchair Control Using IoT and Fall Detection Mechanism;modeling and Simulation of a Distributed Generation System for Power Quality Assessment;A Comprehensive PVsyst Design for a College Campus in Baghdad;Control of VSI in Microgrid Using Improved Particle Swarm Optimization;Emmet Territory Optimization Algorithm for Reduction of Pilot Contamination in Massive MIMO systems;Assessment of FSPV-Grid System for Electrification of Industrial Sections with Shrinking Derating Factor;Modeling and Speed Control of DC Motor;PV-STATCOM in Distribution System.

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A Blockchain-Based Decentralized Privacy-Preserving Mobile Payment Scheme Using Anonymous Credentials 24

A Blockchain-Based Decentralized Privacy-Preserving Mobile P...

引用

24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on data Science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Yu, Linsheng He, Mingxing Xiong, Ling Luo, Qian Niu, Xianhua School of Computer and Software Engineering Xihua University Chengdu China The Second Research Institute Civil Aviation Administration of China Chengdu China

ISBN: (纸本)9798350319934

Mobile communication technology has facilitated the rapid spread of convenient mobile payments. Leading third-party payment platforms (TPPs) such as Alipay, Paypal, and Wexpay, have launched popular mobile payment services. However, TPPs-led centralized mobile payments increase the risk of single-point failure and the leakage of user privacy. In this paper, we propose a blockchain-based decentralized privacy-preserving mobile payment scheme by utilizing threshold anonymous credentials. The integration of the blockchain and threshold cryptography technology enables mobile payment to possess fault tolerance for malicious attacks. Additionally, we leverage Pedersen commitments and zero-knowledge proofs to prevent users' sensitive transaction information from being leaked to curious entities on the transparent blockchain. Through performance analysis, we make clear that users in the proposed scheme have low computation overhead and storage costs in the payment process, and can adopt flexible payment strategies to execute micro-payments and large payments. © 2022 IEEE.

关键词： Blockchain

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An optimized Faster R-CNN model for Cassava Brown Streak Disease Classification 3

An optimized Faster R-CNN model for Cassava Brown Streak Dis...

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3rd international conference on Advances in Computing, Communication, Embedded and Secure systems, ACCESS 2023

作者： Rajasree, R. Latha, C. Beulah Christalin Paul, Sujni Appu, M. Aswathy, N. Karunya Institute of Technology and Sciences Department of Digital Sciences Coimbatore India Higher College of Technology Department of Computer Science Dubai United Arab Emirates Sree Narayana Guru College Department of Computer Science Coimbatore India Adi Sankara Institute of Engineering and Technology Department of Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350346169

The scientific community has shown considerable interest in plant disease detection and classification based on deep learning. In order to address these research gaps, this study proposes an optimized, fine-tuned model for the detection of Cassava Brown Streak Diseases. Casѕava is a vital Thai manufacturing harvest. Thailand is a pioneer in cassava production;therefore, a lot of cassava has been produced and exported. But, caѕsava infection could be the key to cut back caѕsava creation and immediately has an effect on growers' earnings. This research is to develop a model using an effective deep learning algorithm for cassava leaf disease detection. We split the classification into two phases, with Model1 and Model2. First model is used to do the cassava disease classification and second model for identifying the Cassava Brown Streak Virus Disease using VGGNet, AlexNet and Faster R-CNN algorithm. Furthermore, data augmentation techniques are employed during network training to improve the performance of the proposed network. The proposed model has been evaluated its performance using accuracy and confusion matrix. The experimental results demonstrates that our approach can accurately classify Cassava Brown Streak Diseases with an accuracy of 96% using Faster R-CNN. © 2023 IEEE.

关键词： Computer vision

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Ontological knowledge-Based Representation System to Prevent Web Attacks in iMANET's

Ontological Knowledge-Based Representation System to Prevent...

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Communication, Computer Sciences and engineering (IC3SE), international conference on

作者： Priyanka Chandani Anand Kumar Gupta Manivasagam Department of Data Science Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

This paper implies securing Internet-based Mobile Ad-hoc Networks (iMANETs) with semantic web techniques. To illustrate safety issues, ontologies will be utilised rather than taxonomies. These ontologies can be added to an intrusion detection system's (IDS) information repository. By employing inference based on semantic relations, intrusion detection systems can identify and incorporate upcoming attacks into their current knowledge base.

关键词： Semantic Web Computer hacking knowledge based systems Taxonomy Semantics Intrusion detection Ontologies

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4b/4b/8b Precision Charge-Domain 8T-SRAM Based CiM for CNN Processing 5

4b/4b/8b Precision Charge-Domain 8T-SRAM Based CiM for CNN P...

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5th IEEE international conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Zang, Qibang Goh, Wang Ling Chong, Yi Sheng Do, Anh Tuan School of Electrical and Electronic Engineering Singapore Institute of Microelectronics A*STAR Singapore

ISBN: (纸本)9798350332674

Compute-in-memory (CiM) is a promising solution for solving the bottleneck of frequent data movement between the memory and processor in Von-Neumann architecture. In conventional multi-bit CiM architecture, when computing N-bit input and N-bit weight MAC operation, 2N 1 cycles are needed for N-bit input modulation and normally 3-4-cycles with complex switch operation are needed for N-bit weight realization, which significantly degrades the final throughput and power efficiency. In this work, a C-2C DAC built in the 8T SRAM CiM array is designed for 4-bit weight and 4-bit input MAC operation, which can be completed in just one cycle. In the final power efficiency evaluation, our 4b/4b/8b CiM architecture attained up to 640 TOPS/W (normalized to 1b/1b/1b precision) which is a 6-10 times improvement as compared to the conventional multi-bit CiM architectures. The proposed architecture with 4b/4b/8b precision can provide 91.47% and 68.10% accuracy on CIFAR-10 and CIFAR-100 dataset classification, respectively. © 2023 IEEE.

关键词： Memory architecture

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Exploring the Potential of LLM-based Chatbots for Task Scheduling in Robot Operations 25

Exploring the Potential of LLM-based Chatbots for Task Sched...

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25th IEEE international conference on Autonomous Robot systems and Competitions, ICARSC 2025

作者： Rema, Catarina Sousa, Armando Sobreira, Heber Costa, Pedro Silva, Manuel F. University of Porto (FEUP) Faculty of Engineering Porto 4200-465 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science CRIIS - Centre for Robotics in Industry and Intelligent Systems Porto 4200-465 Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto 4249-015 Portugal

ISBN: (纸本)9798331538606

The rise of Industry 4.0 has revolutionized manufacturing by integrating real-time data analysis, artificial intelligence (AI), automation, and interconnected systems, enabling adaptive and resilient smart factories. Autonomous Mobile Robots (AMRs), with their advanced mobility and navigation capabilities, are a pillar of this transformation. However, their deployment in job shop environments adds complexity to the already challenging Job Shop Scheduling Problem (JSSP), expanding it to include task allocation, robot scheduling, and travel time optimization, creating a multi-faceted, non-deterministic polynomial-time hardness (NP-hard) problem. Traditional approaches such as heuristics, meta-heuristics, and mixed integer linear programming (MILP) are commonly used. Recent AI advancements, particularly large language models (LLM), have shown potential in addressing these scheduling challenges due to significant improvements in reasoning and decision-making from textual data. This paper examines the application of LLM to tackle scheduling complexities in smart job shops with mobile robots. Guided by tailored prompts inserted manually, LLM are employed to generate scheduling solutions, being these compared to an heuristic-based method. The results indicate that LLM currently have limitations in solving complex combinatorial problems, such as task scheduling with mobile robots. Due to issues with consistency and repeatability, they are not yet reliable enough for practical implementation in industrial environments. However, they offer a promising foundation for augmenting traditional approaches in the future. © 2025 IEEE.

关键词： Chatbots Large Language Model Mobile Robots Task Scheduling

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