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

共 37034 条记录，以下是4711-4720 订阅

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Predicting the impact of type changes on Overall Equipment Effectiveness (OEE) through machine learning 1

Predicting the impact of type changes on Overall Equipment E...

引用

1st IEEE international conference on Internet of Digital Reality, IoD 2022

作者： Dobra, Peter Josvai, Janos Doctoral School of Multidisciplinary Engineering Sciences Széchenyi István University Györ Hungary Széchenyi István University Department of Vehicle Manufacturing Györ Hungary

ISBN: (数字)9781665499651

ISBN: (纸本)9781665499651

Nowadays, Industry 4.0 and the Smart manufacturing environment are increasingly taking advantage of Artificial Intelligence. There are more and more sensors, cameras, vision systems and barcodes in the production area, as a result of which the number of data recorded during manufacturing and assembly operations is growing extremely fast. The interpretation and processing of such production-type data by humans is believed less effective. In the Big data domain, machine learning is playing an increasingly important role within data mining. This paper focuses on the product change processes of semi-automatic assembly line batch production and examines the impact of changes on Overall Equipment Effectiveness (OEE) and attempts to determine future values through supervised machine learning. Using decision tree, the effect on the OEE value can be predicted with an accuracy of up to 1%. The presented data and conclusions come from a real industrial environment, so the obtained results are proven in practice. © 2022 IEEE.

关键词： Decision trees

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Implementation of Blockchain and IPFS to safeguard evidentiary data

Implementation of Blockchain and IPFS to safeguard evidentia...

引用

knowledge engineering and Communication systems (ICKECS), international conference on

作者： Supriya Srivastava GeetKiran Kaur Himank Sanjay Singla Dept. of Computer Science & Engineering Chandigarh University Mohali India Dept. of Computer Science & Engineering Chandigarh University Gharuan India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Nowadays due to increasing cases of evidence alteration the need of a robust evidence protection system is increasing rapidly. This paper presents an innovative solution that combines the InterPlanetary File System (IPFS) with blockchain technology to enhance the security, immutability, and transparency of evidential data obtained from crime *** proposed system initiates by utilizing IPFS, a decentralized and distributed file storage protocol, to securely store multimedia evidence, such as photographs. The content-addressed nature of IPFS guarantees tamper-resistant storage, making it an ideal choice for preserving the integrity of sensitive evidentiary materials. The IPFS addresses obtained from IPFS are then embedded within the blockchain, establishing a decentralized and verifiable link between the evidence and its storage location. We use multi-signature wallet to upload the data on blockchain so that we can make it more secure and monitored about who is uploading evidence state data. To verify links and integrity of evidence usage we set up a system of periodical updates of state of evidence whenever accessed. These updates serve as a mechanism to record the state of the evidence at different points in time, adding an additional layer of reliability and accountability.

关键词： knowledge engineering Multimedia systems Blockchains InterPlanetary File System Security Reliability Protection

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A Comprehensive Study on Satellite-based data Communication for Big Earth Observation systems

A Comprehensive Study on Satellite-based Data Communication ...

引用

knowledge engineering and Communication systems (ICKECS), international conference on

作者： N Supritha M S Narasimha Murthy BMSIT&M Viswesvaraya Technological University Bangalore Karnataka India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

For global telecommunications systems to operate effectively, it is crucial that the communication among different satellites be monitored closely. Nearly 2,000 artificial satellites keep orbiting the Earth and they all continuously transmit both analog and digital signals that carry different types of voice, video, and other data to and from one or many locations worldwide. In areas where broadcasting is crucial and where the terrain is challenging along with very few people, satellite communications are renowned to offer the greatest service. In Big Earth Observation systems, disaster management and weather forecasting sectors, quick deployment is crucial. In the global network infrastructure, satellites are regarded as a crucial component of the communications infrastructure. Considering factors like explicit load balancing, interplanetary internet, quality of service, mobility management and packet reordering difficulties, this article reviews and evaluates suggested methods for satellite- based data transmission.

关键词： Technological innovation Satellites Protocols Reviews Satellite broadcasting Earth Observing System Weather forecasting

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Ensemble Model Based on an Improved Convolutional Neural Network with a Domain-agnostic data Augmentation Technique 13

Ensemble Model Based on an Improved Convolutional Neural Net...

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13th international conference on Information and knowledge Technology, IKT 2022

作者： Fatahnaie, Faraz Asghari, Seyyed Amir Azhdehnia, Armin Marvasti, Mohammadreza Binesh Kharazmi University Electrical and Computer Engineering Department Tehran Iran Guilan University Electrical and Computer Engineering Department Rasht Iran

ISBN: (纸本)9798350320626

With the increase of online activities and the growing number of online services, various cyber threats pose a significant challenge to Network Intrusion Detection systems (NIDS). To face these threats, available imbalance sources made researchers develop resampling techniques to have a balance training process. In this paper, a domain-agnostic data augmentation approach followed by random under sampling is used to achieve credible generalized and robust IDS. Moreover, the framework benefits from the potentiality of deep learning models to extract more meaningful features. The final model of the paper was obtained after the ensemble of three improved convolutional neural networks. Each model is trained on a specific subset of the NSL-KDD dataset which is generated by the resampling method. The simulation results illustrate that the model achieves an accuracy of 83.3% which is 6.5% higher, when the original dataset is used. The codes are available at https://***/armin-azh/ensemble. © 2022 IEEE.

关键词： Online systems

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Utilizing Transfer Learning Models to Classify Absence Defects on Aluminum Plates Using Feature-Based Approaches 2nd

Utilizing Transfer Learning Models to Classify Absence Defec...

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2nd international conference on Intelligent manufacturing and Robotics, ICIMR 2024

作者： Pandian, Kiran Zhen, Lim Weng Majeed, Anwar P. P. Abdul Teh, Sze-Hong Goon, Koon Yin Razman, Mohd Azraai Mohd Faculty of Manufacturing and Mechatronics Engineering Technology University Malaysia Pahang Al-Sultan Abdullah Pahang26600 Malaysia Department of Computing and Information Systems Sunway University Bandar Sunway Selangor Darul Ehsan47500 Malaysia Xi’an Jiaotong-Liverpool University Suzhou215123 China TT Vision Technologies Sdn. Bhd. Plot 106 Hilir Sungai Keluang 5 Bayan Lepas Phase 4 Penang11900 Malaysia

ISBN: (纸本)9789819639489

Screw – the small but important elements used in various industry. Its presence plays a significant role as it securely holds the product in place, preventing loosening or collision with the case. Such occurrences could lead to the displacement of small components or compartments, resulting in product failure. The advent of Industry 4.0 has contributed to reducing labor costs and human errors. This research aims to develop a robust classification model for machine vision detection, specifically for identifying the absence or presence of a screw. The model can be integrated into relevant robotics applications. To collect the customized dataset, a 6-degree-of-freedom (DOF) robot. The collected dataset was then categorized into two groups: absent and present. For the training process, a pretrained dataset called ImageNet was employed to facilitate the training process. Transfer learning techniques were used to extract the features required for different machine learning models. Each machine learning model underwent hyperparameter tuning to achieve the highest classification accuracy. The data was divided into training, validation, and testing sets using a sampling ratio of 60:20:20, respectively, before being fed into the various machine learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Screws

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7th international Scientific conference on Intelligent Information Technologies for Industry, IITI 2023

7th International Scientific Conference on Intelligent Infor...

引用

7th international Scientific conference on Intelligent Information Technologies for Industry, IITI 2023

ISBN: (纸本)9783031437885

The proceedings contain 71 papers. The special focus in this conference is on international Scientific conference on Intelligent Information Technologies for Industry. The topics include: About Explainable Machine Learning Models for Anomaly Detection in Cyber-Physical systems;intelligent Graph-Based Correlation of Security Events in Cyber-Physical systems;classifier-Based Combined Measure of Syllable Pronunciation Similarity in Speech Rehabilitation;application of Computer Vision Technologies to Reduce Injuries in the Athletes’ Training;architecture of a Hybrid Clinical Decision Support System;Synthesis of PLL in Capture Mode with a Fuzzy Controller of Quasi-Optimal Structure Based on the Maximum Condition of the Generalized Power Function;automated Discovery of Direct Speech to Increase Quality and Battery Saving in Telephony;temporal Prediction Models for Technological Processes Based on Predictive Analytics;distributed Control System of Protection of Autonomous Object, Based on Hybrid Technology of Artificial Intelligence;algebraic Bayesian Networks: The Generation of the Network Canonical Representation;using Semantic Annotation of Tabular data for Domain knowledge Graph Population;robust Filtering of Nonlinear Stochastic Processes in Machine Learning systems;selecting a Machine Learning Model to Optimize the Burner Digital Twin;modeling of Intuition in Human-Machine Decision-Making Complexes in the Management of Transport systems;intelligent Maintenance and Repair on Railway Transport;traction Induction Motor State Observer Based on an Luenberger Filter;Intelligent Monitoring of Transportation Processes Based on Dynamic Fuzzy PCA Analysis;method for Determining and Fixing the Information Need of Users in the Low-Code Development;intelligent Design of Images of engineering Networks for Spatial Analysis;method of Dynamic Multi-Criteria Quality Assessment and Optimization for Proactive Complex Objects Functioning Schedules.

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7th international Scientific conference on Intelligent Information Technologies for Industry, IITI 2023

7th International Scientific Conference on Intelligent Infor...

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7th international Scientific conference on Intelligent Information Technologies for Industry, IITI 2023

ISBN: (纸本)9783031437915

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An Important Sampling over Graph Convolutional Network for Community Detection 8

An Important Sampling over Graph Convolutional Network for C...

引用

8th international conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Meena, Pawan Pawar, Mahesh Pandey, Anjana University Institute of Technology Rgpv Department of Computer Science & Engineering MP Bhopal India University Institute of Technology Rgpv Department of Information Technology MP Bhopal India

ISBN: (纸本)9781665408165

According to graph theory, the complex network is transformed into graph structure data to process the convolutional neural network efficiently and conveniently. The convolution kernel is defined by extending the convolution operation on the image to the graph structure data, and the convolution layer is used for the graph. Coarse-graining and pooling operations extract features of complex networks with irregular data. When training the network with stochastic gradient descent, an important sampling method is designed to change the distribution of samples to reduce variance, thereby saving gradient calculation time. Experimental results show that this method can obtain better community discovery accuracy with lower computational complexity in social networks, citation networks, and knowledge graph datasets, reducing computational time compared with the existing graph convolutional network. In addition, the memory footprint can be extended to use in large-scale complex networks. © 2022 IEEE.

关键词： Stochastic systems

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Real-Time Sentiment Analysis of Customer Feedback Using Neural Networks and NLP Methods

Real-Time Sentiment Analysis of Customer Feedback Using Neur...

引用

IT Innovation and knowledge Discovery (ITIKD), international conference on

作者： N. Shaafia Tasneem R. Hemalatha Department of computer science and engineering St. Joseph's college of engineering Chennai India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Realistic sentiment analysis will be in need of understanding customer feedbacks system's, facilitating data-depending decision making and improving user experience. This research model utilizing neural based networks and natural language processing systems (NLP) mechanism to realize faster and accurate level of customer sentiment computation on textual data. The proposed strategy investigates state-of-the-art neural network infrastructure to learn useful data-patterns and sentiments from the raw data. On other hand NLP performs factitive in pre-processing, feature extraction and textual-context awareness in interpretation of text. By enabling realistic processing of customer feedbacks, the system gives immediate visibility into customers' preference with their complaints and satisfaction grade. It also show that supervisor learning models in conjunction with NLP will effectively handles the heterogeneity as well as complexity in customer feedbacks. Finally, the proposed system demonstrate the way of real-time sentiment analysis ability can improve customer engagement leading to data-depend business mechanism.

关键词： Hands Sentiment analysis Technological innovation Text analysis Neural networks knowledge discovery Feature extraction Real-time systems User experience Business

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HIT - An Effective Approach to Build a Dynamic Financial knowledge Base 28th

HIT - An Effective Approach to Build a Dynamic Financial K...

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28th international conference on database systems for Advanced Applications, DASFAA 2023

作者： Zhu, Xinyi Xin, Hao Shen, Yanyan Chen, Lei Guangzhou China Computer Science and Engineering HKUST Hong Kong Computer Science and Engineering SJTU Shanghai China

ISBN: (纸本)9783031306716

In recent years, due to their expertise and comprehensiveness in a specific domain, domain-specific knowledge bases (KBs) have attracted more and more attention from both academics and industries. Among these domain-specific KBs, financial KBs have become more and more popular and valuable due to their broad spectrum of downstream applications, such as quantitative investment analysis, financial risk analysis, and financial domain-based KBQA. However, due to their massive volume, high conflicts, and frequent volatile properties, it is pretty challenging to build an error-prone dynamic financial KB. To address these challenges, in this paper, we propose a dynamic financial KB construction pipeline that mainly consists of two fundamental modules, a Human-Interacted (HI) distant supervised evolved relation extraction module targets at obtaining the evolved knowledge with less manual annotations and high extraction accuracy, and a Temporal (T) duplication and conflict resolution module focus on applying a data fusion algorithm to the knowledge fusion task to select high-confidence knowledge without duplication and conflict by incorporating the temporal information. Through extensive experiments, we have demonstrated the effectiveness of HIT. Compared to state-of-the-art solutions, HIT can improve the accuracy by 10.6 % on average for the relation extraction task and by 6.9 % on average for the duplication and conflict resolution task, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Risk assessment

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