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共 446 条记录，以下是11-20 订阅

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Application of Process Mining to Production Lines Using Industrial Internet of Things 5

Application of Process Mining to Production Lines Using Indu...

引用

5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Yapakçi, Beyza Akdaǧcik, Zeynep Ayvaz, Bora Ergenç, Ali Fuat Istanbul Technical University Big Data and Business Analytics Prog. Istanbul Turkey Ku Leuven Business Engineering Dept. Leuven Belgium Istanbul Technical University Control and Automation Engineering Dept. Istanbul Turkey

ISBN: (纸本)9798350314694

Process mining has emerged as a powerful solution for analyzing production processes in the manufacturing sector, yet its integration with the Internet of Things (IoT) remains a challenge. Drawing from IoT ontologies and business process context models, we introduced a practical methodology for constructing event logs from industrially gathered IoT data. The study was conducted in a controlled laboratory environment to emulate a discrete production model through an industrial communication and database setup. Employing a systematic process mining framework encompassing data preparation, pre-processing, production process analysis, and interpretation steps, we used industrial controllers and Industrial Internet of Things (IIoT) tools to simulate a sauce production process. The generated data was analyzed using ProM software, fortified by visualization techniques such as the 'log as log skeleton' method, for enhancing process flow comprehension and pinpointing ways for optimization. The study results unveiled a systematic linear structure by employing the Alpha Miner method for process model construction. Notably, the analysis exposes extended duration in the production process, prompting further investigation and refinement. The resultant model provides valuable insights into the actual performance of the process and emphasizes the range of potential process scenarios, making it easier to pinpoint deviations from the ideal model. Through the demonstration of the IIoT data integration, the result significantly contributes to the growing enthusiasm for process-oriented methodologies within the manufacturing sector as well as providing an example approach to transform captured IoT data into event logs via contextualization. Furthermore, the study result provides the groundwork for future research and improvement endeavors to enhance manufacturing practices and outcomes. © 2023 IEEE.

关键词： data analysis Industrial Internet of Things manufacturing process Mining production process

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Ontological Analysis of the Digital Crime Conceptual Model 18

Ontological Analysis of the Digital Crime Conceptual Model

引用

18th IEEE International Conference on Computer Science and Information Technologies, CSIT 2023

作者： Vlasenko, Lidiia Lutska, Nataliia Zaiets, Nataliia Lysenko, Vitaliy State University of Trade and Economics dept. of Software Engineering and Cyber Security Kyiv Ukraine National University of Food Technologies dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine dept. of Automation and Robotic Systems Kyiv Ukraine

ISBN: (纸本)9798350360462

A conceptual ontological model of Digital Crime has been developed, consisting of five non-empty classes. Identification and classification were carried out according to the experience of domestic and foreign experts. The central place of the model is occupied by objects of the Digital Crime class, which have their own attributes and are connected by various links with other objects. Objects of some classes are filled in by users, others are formed by knowledge engineers. Based on the ontological analysis, SWRL-rules are constructed that automatically form links between objects of the custom classes Digital crime and Digital evidence with objects of the Type of crime and Criminal Liability classes. The last class contains articles of the Criminal Code of Ukraine on criminal liability for digital crimes. The ontological model is developed taking into account possible requests to it. Such queries can be formed on the basis of direct relationships between human and automatic concepts. And also contain logical chains to concepts that do not contain an explicit link in the ontology. It is assumed that the model can be expanded and supplemented. The ontological model of Digital Crime can be used to share information, develop new systems and applications, improve information retrieval and ensure its accordance. © 2023 IEEE.

关键词： Ontology

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Application of Visual Servo in Tracking and Grasping Moving Target 5

Application of Visual Servo in Tracking and Grasping Moving ...

引用

5th International Conference on control, Robotics and Informatics, ICCRI 2022

作者： Nguyen, Duc Tam-Hong Nguyen, Vinh-Hao VNU-HCM Dept. of Control Engineering and Automation Ho Chi Minh City Viet Nam

ISBN: (数字)9781665468008

ISBN: (纸本)9781665468008

This paper presents a visual servoing system for the task of tracking and grasping a moving target in 3D space. A multiple-cue feature visual tracking module combining known 3D CAD models of the object is used to extract the target pose in the RGB-D images. An Extended Kalman Filter (EKF) is developed to further compensate for the delay of the system and estimate the object motion. A two-stage controller for the visual servo is proposed with the first part being able to control the gripper to track the target, while the second part is a pose based visual servo (PBVS) scheme for the grasping motion. Experiment results show that the presented system is able to perform real time tracking- grasping tasks to a moving target. © 2022 IEEE.

关键词： Extended Kalman filters

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INN: A Deep Correction Network for High-Dynamic Inertial Navigation 4

INN: A Deep Correction Network for High-Dynamic Inertial Nav...

引用

4th International Conference on Industrial automation, Robotics and control engineering, IARCE 2024

作者： Gu, Tao Cong, Li Yang, Jing Guo, Yuzhu Wang, Lina School of Automation Science and Electrical Engineering Beihang University Beijing China School of Electronic Information Beihang University Engineering dept. Beijing China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing China

ISBN: (纸本)9798350380323

Inertial navigation has gained extensive application because it solely relies on built-in Inertial Measurement Unit (IMU) devices. Nevertheless, due to its inherent accumulative integral error, inertial navigation is often integrated with other external devices in practical situations, which unfortunately leads to unavoidable privacy breaches. To address these issues, an Inertial Navigation Neural Network (INN) is designed to generate high-precision position estimations without relying on any other auxiliary information. The INN model consists of attitude correction and velocity regression modules. The attitude module employs a convolutional network to correct the attitude information, and the obtained rotation matrices are then fed into a recurrent neural network to regress incremental velocities and produce high-precision positions. The proposed model is validated on the simulated trajectories based on the Precise Strapdown Inertial Navigation System (PSINS) toolbox. Experimental studies demonstrated that the proposed method has a significant corrective effect on high-dynamic strapdown inertial navigation settings. The accumulated position errors are an order of magnitude smaller than those of traditional track reckoning algorithms and achieved a comparable performance with integrated navigation. © 2024 IEEE.

关键词： Inertial navigation systems

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Transfer and Bayesian Learning Approaches for Land Use and Land Cover Classification

Transfer and Bayesian Learning Approaches for Land Use and L...

引用

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Zainab Malik Aisha Sir Elkhatem Gokhan Bilgin Dept. of Computer Engineering Yildiz Technical University Istanbul Turkey Dept. of Control and Automation Engineering Yildiz Technical University Istanbul Turkey

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Land use and land cover (LULC) classification is essential for understanding the impact of both human activities and natural processes on the Earth’s surface. This classification plays a critical role in deforestation, urban planning, and damage assessment. However, traditional machine learning techniques often face challenges in achieving consistent accuracy in diverse datasets. In this study, we implement transfer learning and Bayesian learning approaches to enhance the accuracy and robustness of LULC classification. Transfer learning leverages knowledge from previous classification problems, while Bayesian learning addresses uncertainties in the classification process. Using the Remote Sensing Image Classification Benchmark (RSI-CB128) dataset, the study evaluates the performance of Bayesian Convolutional Neural Networks (CNN), Mobile Neural Networks (MobileNet), Inception Neural Networks (InceptionNet), Densely Connected Convolutional Networks (DenseNet) and Efficient Neural Networks (EfficientNet). The study also investigates the impact of varying the Bayesian layers on model performance, finding that a medium number of layers optimally captures model uncertainty. The results indicate that the transfer learning models, particularly MobileNet, achieved the highest accuracy of 97. 48% compared to 90. 33% for Bayesian CNN. These findings suggest that transfer learning techniques are highly effective for LULC classification, providing a reliable method for practical applications in remote sensing.

关键词： Deforestation Accuracy Uncertainty Computational modeling Transfer learning Neural networks Urban planning Land surface Bayes methods Convolutional neural networks

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

引用

5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods 4

Forecasting Breakdowns of Electric Motors of a Sugar Factory...

引用

4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Zaiets, Nataliia Vlasenko, Lidiia Lutska, Nataliia Zhyltsov, Andrii Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350395532

The paper demonstrates the process of developing mathematical models for identifying breakdowns of electric motors using machine learning methods. The authors have developed three mathematical models for identifying breakdowns of electric drives with a power of 55 kW, 1500 rpm on the example of sugar production: model for quadratic discriminant analysis;binary classification decision tree;feedforward neural network model. The best structures and parameters of the models were determined by machine learning methods. The performance of the models on the training and test sets was more than 96% by F-score. It was proposed to use all three models independently for diagnosing breakdowns of electric drives, while the decision maker makes the final decision on the replacement or repair of electrical equipment. This approach is justified and effective because of the diversity of models. The developed models, except for assessing the state of the engine, can also be used for simulation modelling, forecasting, and serve as information support for process operators in organizing equipment maintenance. The availability of real-time information about the state of equipment in production will allow timely repair or replacement of equipment, thereby reducing the risks of stopping production and increasing the resource efficiency of technological processes. © 2023 IEEE.

关键词： Decision trees

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Local Polynomial Models for Real-Time Tracking of Humidity Sensor Signals 3

Local Polynomial Models for Real-Time Tracking of Humidity S...

引用

3rd International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Nikovski, Plamen Titova, Tanya Mihov, Vasil University of Food Technologies Dept. of Electronics And Electrical Engineering Plovdiv Bulgaria University of Food Technologies Dept. of Automation Information And Control Systems Plovdiv Bulgaria

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

Sensor signal tracking has great practical importance for solving a number of problems such as data fusion and sensor integration in multi-sensor systems (MSS), robotics, fault detection (FD), and diagnosis, etc. The Kalman filter is a widely used algorithm that can be applied in these cases if an appropriate model of the signal is available. The present study shows that under a step change in humidity, the filter can estimate the signal from a humidity sensor by means of a second-order local polynomial model. This gives a reason to expect similar behavior not only for sudden but also for all other changes. The proposed online signal tracking solution is simple to implement and can be used not only for humidity sensors, but also for sensors that measure quantities such as temperature, pressure, level, etc. © 2022 IEEE.

关键词： Polynomials

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NIS Test for Selecting the Order of Local Polynomial Model 3

NIS Test for Selecting the Order of Local Polynomial Model

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3rd International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

This work presents an approach that uses normalized innovations squared (NIS) test to determine the order of a local polynomial trend model of a humidity sensor signal. Compared to the classical Akaike information criterion which balances model fit and model complexity NIS test allows to determine the lowest order of the model at which a consistent Bayesian estimation is possible. This approach is useful when signal processing algorithms, such as those for data fusion in multi-sensor systems, contain a Kalman filter, because a successful NIS test is a prerequisite for normal filter operation. The results of the study show that the response of the SHT31-DIS sensor under a step change in humidity is adequately described by a second-order local polynomial model, and this implies a consistent estimation for other input variations. © 2022 IEEE.

关键词： Polynomials

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Electric Vehicles Modeling: A Review 15

Electric Vehicles Modeling: A Review

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： de Oliveira, Victor Manuel Riva dos Remédios, Vinícius Ferreira Donadel, Clainer Bravin dos Santos, Walbermark Marques Có, Márcio Almeida de Melo, Vinícius Secchin Nunes, Reginaldo Barbosa Electrical Engineering Department Federal Institute of Espírito Santo Vitória Brazil Control and Automation Eng Dept. Federal Institute of Espírito Serra Santo Brazil

ISBN: (纸本)9798350314182

The increasing concern about climate change has been leading researches in many areas to investigate solutions for this issue. An emerging subject related to it is electric vehicles (EV). Although it is an important topic, there is still a lack of review papers about EVs modeling. In this context, this paper aims to bring a review about EV modeling to fulfill this gap. A wide bibliography review is made to cover different kinds of EVs modeling, in many aspects. As a conclusion, in some areas, authors do not diverge from each other, while in others may exist a large range of different ways to model the EVs. In this case, researchers should choose the modeling that fits the best for their research, which may vary in simplicity and computational effort. © 2023 IEEE.

关键词： Battery Electric Vehicles Modeling Simulation

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