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检索条件"机构=Automatic Control and Informatics"

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Determining on-shelf availability based on RGB and ToF depth cameras 23

Determining on-shelf availability based on RGB and ToF depth...

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23rd International Conference on control Systems and Computer Science Technologies, CSCS 2021

作者： Craciunescu, Mihai Baicu, Diana Mocanu, Stefan Dobre, Cristian Faculty of Automatic Control and Computer Science Department of Automatic Control and Applied Informatics Bucharest Romania Rinftech Bucharest Romania

ISBN: (纸本)9781665439398

In this paper, a method of calculating the occupancy of a shelf will be presented. A vision pillar composed of two RGB cameras and two ToF depth cameras will be used to scan a shelf and determine the percentage of emptiness for the products. Since the cameras will scan a small section of the shelf at the time, a stitching method will be applied on both depth and RGB stream. In order to identify categories of products, along with their spatial delimitation within a shelf, a segmentation neural network is used. By combining the segmentation output with the depth information, the distance from the pillar to the product can be determined. Since this type of information does not help a human operator or a supervisor in understanding the state of the shelf or in making an appropriate decision, such as restocking, a method for computing the shelf occupancy is proposed. Optimization must be done to the processing algorithms to enable them to run on an embedded platform, which will allow for the implementation of the proposed architecture on a platform deployed in a production environment. All the computation is done on board of the mobile robot without transmitting the data to an external server. © 2021 IEEE.

关键词： Cameras

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Fuzzy ASC-GAN: A Fuzzy C-means Audio Similarity CycleGAN on Articulation Disorder Voice Conversion

Fuzzy ASC-GAN: A Fuzzy C-means Audio Similarity CycleGAN on ...

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2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Huang, Yi-Wei Chang, Sheng-Wen Chin, Chiun-Li Lin, Guang-Tao Chang, Pei-Hsin Lin, Wan-Xuan Chen, Jun-Ren Zheng, Zhong-Ting Wong, Geng-Kai Chung Shan Medical University Department of Medical Informatics Taiwan Taipei Municipal XiSong Senior High School Taipei Taiwan Feng Chia University Automatic Control Engineering Taiwan

ISBN: (纸本)9798350305791

This paper introduces an innovative approach, Fuzzy Audio Similarity CycleGAN (Fuzzy ASC-GAN), turned at addressing articulation disorders and enhancing dysarthric voice conversion (DVC). By integrating Fuzzy C-means clustering and CycleGAN-VC2, the proposed method focuses on refining both training and transformation processes. Notably, Fuzzy ASC-GAN incorporates a unique strategy that leverages the audio similarity of timbres to adjust the training data of CycleGAN-VC2, ensuring accurate and effective voice conversion. Through repeated and rigorous parameter adjustments and comprehensive performance comparisons, Fuzzy ASC-GAN emerges as the standout performer, achieving an average accuracy of 93.35% across diverse S2T models. The method's distinct advantage lies in its ability to tailor voice conversion about specific characteristics of individual with articulation disorders. This highlights the potential of Fuzzy ASC-GAN to significantly enhance communication for individuals with dysarthric voices, marking a significant advancement in the realm of voice conversion techniques. © 2023 IEEE.

关键词： Fuzzy systems

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Analysis of YouTube Video Comments with NLP Methods 16

Analysis of YouTube Video Comments with NLP Methods

引用

16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Mironela, Pirnau Iustin, Priescu Mihaela, Priescu Catalina Claudiu, Pirnau Daniel, Ghiculescu Liviu 'Titu Maiorescu' University Faculty of Informatics Department of Informatics Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Industrial Engineering and Robotics Manufacturing Engineering Department Bucharest Romania

ISBN: (纸本)9798350371154

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of 'ChatGPT, AI clone, AI robot and Deep Learning.' © 2024 IEEE.

关键词： Deep learning

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Immersive Mixed Reality Framework for Enhanced Human-Robot Collaboration

Immersive Mixed Reality Framework for Enhanced Human-Robot C...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Ionut Sopon Denis-Ioan Plescan Adrian Burlacu Dept. of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper focuses on developing a simulation platform that enhances Human-Robot Interaction through intuitive and safe interchanges between human operators and a UR5e robot by operating a Digital Twin via Mixed Reality. Using the ROS Noetic framework and Unity software for development, in conjunction with Microsoft HoloLens 2, this work presents a system based on Mixed Reality designed for collaborative robotic experiences. Moreover, a key innovation in this research is integrating a ZED camera for real-time human tracking, which enables the simultaneous simulation of humans and robots within a shared mixed environment. This advancement builds upon an existing digital twin of the robot. However, it also significantly enriches the system’s capabilities for ensuring safety and facilitating intuitive interactions, validating the implemented methodology through simulation and real-life experimental results. The paper examines the implications of this development in different industrial and research contexts. It highlights the growing significance of mixed-reality technologies in creating complex human-robot interaction systems that ensure safety and efficiency.

关键词： Technological innovation Service robots Robot vision systems Mixed reality Human-robot interaction Virtual reality Software Real-time systems Safety Digital twins

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Colouring Grayscale Images Using Cycle-Generative-Adversarial Networks

Colouring Grayscale Images Using Cycle-Generative-Adversaria...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Radu-Cosmin Hobincă Lavinia-Eugenia Ferariu Department of Automatic Control and Applied Informatics "Gheorghe Asachi" Technical University of Iaşi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper analyses models that can realistically map the grayscale image domain to the color image domain. We thus focus our attention on a CycleGAN (Cycle-consistent Generative Adversarial Network) neural network architecture, which proves to have good results in the area of translation between domains, and implicitly in the application of image coloring. In addition to exploring CycleGAN for image colorization, this study introduces novel techniques aimed at enhancing its performance. We incorporate a color distribution loss term to ensure reliable color mapping, effectively addressing discrepancies in color distribution between domains. Moreover, an in-depth analysis of generator errors is conducted, unveiling critical insights into model limitations. Leveraging this analysis, we propose a correction network to generate residual images, facilitating more accurate colorization. Through rigorous experimentation across diverse datasets, our approach showcases a remarkable capacity to produce lifelike and coherent colorizations.

关键词： Analytical models Image color analysis Neural networks Refining Color Gray-scale Generative adversarial networks control systems Generators Reliability

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IMPACT OF THE VOLUME OF DEVELOPER HOUSING UNITS ON REAL ESTATE PRICES IN POLAND: CORRELATION AND COHERENCE ANALYSIS 38

IMPACT OF THE VOLUME OF DEVELOPER HOUSING UNITS ON REAL ESTA...

引用

38th ECMS International Conference on Modelling and Simulation, ECMS 2024

作者： Wotzka, Daria Mach, Lukasz Fracz, Pawel Chorkowy, Bartosz Stec, Marzena Dabrowski, Ireneusz faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Poland Faculty of Economics University of Opole Poland Narodowy Bank Polski Regional Branch in Opole Poland Collegium of Management and Finance Warsaw School of Economics Poland

This article conducts a comprehensive correlation analysis to explore the relationship between the development volume by developers and average market prices in Poland's housing sector from 2010 to 2023, utilizing quarterly data on the number of apartments released and their average prices. It employs both linear and nonlinear correlation analysis alongside wavelet coherence analysis. Preliminary correlational analysis offered insights into the basic interdependency patterns, highlighting how developer-supplied apartment numbers impact average prices. Wavelet coherence analysis, a more sophisticated approach, decomposed the data across various frequencies to uncover complex, nonlinear relationship patterns potentially missed by conventional correlation methods. The findings of the study highlight significant connections between the variables that vary over time and space, emphasizing the complexity of the housing market and underscoring the necessity for thorough analysis. These results carry important implications for developers, investors, and housing policy formulation, contributing significantly to understanding Poland's residential real estate market dynamics and supporting further research and strategic real estate industry planning. ©ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.

关键词： Correlation methods

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Velocity Computation for Vehicle Trajectory Tracking: A Comparative Analysis of Performance and Efficiency 24

Velocity Computation for Vehicle Trajectory Tracking: A Comp...

引用

24th International Conference on control Systems and Computer Science, CSCS 2023

作者： Pauca, Georgiana-Sinziana Caruntu, Constantin Florin Gheorghe Asachi Technical University of Iasi Department of Automatic Control and Applied Informatics Iasi Romania Safety and Motion Iasi Romania Software and Central Technologies Continental Automotive Romania Iasi Romania

ISBN: (纸本)9798350313390

Vehicles are a common topic and of great interest in the modern society in which we live. Their automation and the implementation of mechanisms and functionalities make driving easier and easier. The desire to completely exclude driving for every individual who owns a car has captured the interest of engineers and scientists in the automotive field. This paper aims to present such an algorithm that calculates a profile of the maximum velocity with which the car can travel on a certain trajectory. To illustrate the accuracy and to demonstrate the effectiveness of the proposed algorithm, three different scenarios were implemented. For each scenario, three maximum speeds were given with which the car can travel, to demonstrate that regardless of its power, each maneuver will be completed with the same minimum speed. To control the dynamical system, an advanced control method, the predictive control strategy, was implemented, which generated satisfactory results in each presented case. © 2023 IEEE.

关键词： Heuristic algorithms

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Enhanced Loss Function for the Design of Image Transform Models

Enhanced Loss Function for the Design of Image Transform Mod...

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International Symposium on Fundamentals of Electrical Engineering (ISFEE)

作者： Lavinia Ferariu Ioana Axinte Department of Automatic Control and Applied Informatics Gheorge Asachi Technical University of Iasi Romania Ioana Axinte is With the Department of Automatic Control and Applied Informatics Gheorge Asachi Technical University of Iasi Romania

ISBN: (数字)9798350308563

ISBN: (纸本)9798350308570

Deep convolutional neural networks have triggered important progress in image-to-image mapping. The enhanced learning capabilities allow them to understand the input visual layout and perform local transformations customized to image content. This paper discusses an approach based on Generative Adversarial Convolutional Neural Networks (GAN) to help augment training datasets with generated images. The proposed design uses a loss function that integrates terms based on image quality metrics to validate the production of realistic images. This allows the configuration of neural parameters according to the visual perception of the generated images, without imposing a strict pixel-wise similarity with targets. This technique could be helpful for real applications in which supervised training should be done for pairs of input-output images that do not illustrate the same scene. The performance of the proposed approach is illustrated by a transformation of outdoor scenes from summer to winter. The experiments performed for different GAN schemes and quality metrics show improved quality of the generated images when the loss function includes loss terms related to visual perception.

关键词： Training Measurement Image quality Visualization Neural networks Transforms Production

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From Fake to Hyperpartisan News Detection Using Domain Adaptation

From Fake to Hyperpartisan News Detection Using Domain Adapt...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Smadu, Razvan-Alexandru Echim, Sebastian-Vasile Cercel, Dumitru-Clementin Marin, Iuliana Pop, Florin University Politehnica of Bucharest Faculty of Automatic Control and Computers Romania University Politehnica of Bucharest Faculty of Engineering in Foreign Languages Romania National Institute for Research & Development in Informatics ICI Bucharest Romania

ISBN: (纸本)9789544520922

Unsupervised Domain Adaptation (UDA) is a popular technique that aims to reduce the domain shift between two data distributions. It was successfully applied in computer vision and natural language processing. In the current work, we explore the effects of various unsupervised domain adaptation techniques between two text classification tasks: fake and hyperpartisan news detection. We investigate the knowledge transfer from fake to hyperpartisan news detection without involving target labels during training. Thus, we evaluate UDA, cluster alignment with a teacher, and crossdomain contrastive learning. Extensive experiments show that these techniques improve performance, while including data augmentation further enhances the results. In addition, we combine clustering and topic modeling algorithms with UDA, resulting in improved performances compared to the initial UDA setup. © 2023 Incoma Ltd. All rights reserved.

关键词： Classification (of information)

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