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Multispectral point cloud superpoint segmentation

Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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On the Use of Quality Flags for Digital Transformers in the Sampled Values Protocol IEC 61850 25

On the Use of Quality Flags for Digital Transformers in the ...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Plotnikov, Igor V. Ponomarev, Stanislav V. Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia Faculty of Radio Engineering and Electronics Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9798350389234

This work is devoted to a detailed description of the quality flags of the Sampled Values protocol of the IEC 61850 standard for digital current and voltage transformers. The relevance of this article is due to the fact that the quality flags are not strictly and in detail described in the standard itself, therefore, manufacturers of equipment for digital current and voltage transformers fill in the quality flags if there are problems with data or device at their own discretion. The first part provides information on the structure of interaction between elements of a digital measuring system, describes the advantages of using quality flags in digital data transmission protocols relative to traditional transformers. In the second part, special attention is paid to the interpretation of quality flags in the IEC 61850 standard and the lack of a clear formulation of their applicability in real devices. The third part offers new interpretation of these quality flags with an example of use based on the developed measuring system. At the end of the article, evidence of the correct operation of the described quality flags is provided using a test bench and equipment for analyzing the Sampled Values protocol. © 2024 IEEE.

关键词： Distribution transformers

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Development of a Software Complex for Energy Facilities Based on an Embedded Linux System 25

Development of a Software Complex for Energy Facilities Base...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Osyko, Georgii S. Ponomarev, Stanislav V. Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia Faculty of Radio Engineering and Electronics Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9798350389234

Supervisory Control and Data Acquisition is the main and currently remains the most promising method of automated control of complex dynamic systems. In this article the development of a software package installed on embedded Linux-based systems for managing and monitoring energy facilities is discussed. The main part of the article consists of 3 sections. The first section describes the structural diagrams of the entire software package. The UML diagrams of individual software modules designed for processing and transferring data to other devices are presented. The second section describes the structure and appearance of the web interface, describes possible scenarios for user interaction with the interface. At the end, the results of testing performed using a specially created test bench are presented. The entire software base of the developed complex was implemented by the authors of this article. The developed software package has unique characteristics in terms of functionality in conditions of limited resources, and therefore is fully suitable for managing energy facilities based on embedded systems. © 2024 IEEE.

关键词： Embedded software

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Analysis of the Performance Impact in Industrial Control Process of Wastewater Systems when using Distributed Ledger Technology Sensor Authentication 8

Analysis of the Performance Impact in Industrial Control Pro...

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8th International Symposium on Electrical and electronics engineering, ISEEE 2023

作者： Bogdan-Daniel, Portasa Larisa, Diaconu 'Dunarea de Jos' University of Galati Faculty of Automation Computer Sciences Electronics and Electrical Engineering Galai Romania

ISBN: (纸本)9798350301670

In recent years, the integration of advanced technologies in industrial control processes has gained significant attention, particularly in the domain of wastewater systems. One emerging technology with promising potential is Distributed Ledger Technology (DLT), which offers secure and transparent data management through blockchain-based solutions. This paper presents an in-depth analysis of the performance impact that arises when incorporating DLT-based sensor authentication in industrial control processes of wastewater systems. The study aims to evaluate the benefits and challenges associated with this integration, providing insights into the effectiveness and efficiency of DLT-based sensor authentication in ensuring data integrity and enhancing the overall control process performance. © 2023 IEEE.

关键词： Authentication

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Emotion Recognition in Human Thermal Images with Artificial Intelligence Technology 28

Emotion Recognition in Human Thermal Images with Artificial ...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Pavel, Sorin Moldovanu, Simona Aiordachioaie, Dorel Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania

ISBN: (纸本)9798350339918

This paper presents a case study related to emotion recognition based on human thermal image processing. Three states are considered for human faces: normal, sad, and happy. The thermal images are pre-processed for important feature selection and extraction by using the random forest (RF) classifier. All and selected features set is used as input to a feedforward backpropagation neural network (FFBPN), with binary outputs. The input images are organized in pairs, i.e., normal with sad and normal with happy. The preliminary results show particularly good classification accuracy. © 2023 IEEE.

关键词： Emotion Recognition

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First Order Features Extraction from Thermal Images for Human State Recognition 8

First Order Features Extraction from Thermal Images for Huma...

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8th International Symposium on Electrical and electronics engineering, ISEEE 2023

作者： Moldovanu, Simona Pavel, Sorin Aiordachioaie, Dorel Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania

ISBN: (纸本)9798350301670

Due to the common limitation of the human visual system, internal features of thermal images cannot be fully discovered. To overcome these drawbacks, a lot of studies analyzed the facial expressions corroborating the features extracted from thermal images with machine learning tools. This paper proposed the first-order features extracted from the image histogram and a binary classification performed by the k-nearest neighbor algorithm (K-NN). The proposed method tests separately the performance of different input data, firstly, the features were extracted from raw images and the robustness of the K-NN algorithm led to an accuracy of 92.6%. Secondly, the features were extracted sequentially from corrupted images with noise. The novelty of the proposed method consists of verifying the quality classification when the images have bad quality, in this case, the accuracy decreased to 70.7%, as a result, the classification of facial expressions is sensitive to the quality of images. © 2023 IEEE.

关键词： Emotion Recognition

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Diabetic Retinopathy Image Classification Using Machine Learning and Local Binary Patterns Features 8

Diabetic Retinopathy Image Classification Using Machine Lear...

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8th International Symposium on Electrical and electronics engineering, ISEEE 2023

作者： Miron, Mihaela Culea-Florescu, Anisia Moldovanu, Simona Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Computer Science and Information Technology Galati Romania Dunarea de Jos University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Electronics and Telecommunications Galati Romania

ISBN: (纸本)9798350301670

Diabetic Retinopathy (DR) is a condition caused by diabetes that affects the blood vessels in the retina. Detecting the disease early and providing appropriate treatment are crucial in slowing its progression. Therefore, there is great potential in utilizing Machine Learning (ML) to improve the identification and monitoring of DR development in patients. Our study aims to explore the performance of six ML algorithms, namely Random Forest (RF), Adaptive Boosting (AB), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), and Quadratic Discriminant Analysis (QDA), in two binary classifications involving three classes: non-diabetic retinopathy (NoDR), moderate retinopathy (MR), and severe retinopathy (SV). These ML algorithms were applied to ten features extracted using local binary patterns (LBP). The first classification task involved distinguishing between NoDR and MR, while the second task involved differentiating between NoDR and SV. The RF technique achieved the highest classification accuracy, with 0.912 for the first task and 0.94 for the second task. © 2023 IEEE.

关键词： Local binary pattern

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Analysis of Electric Arc Furnace Influence on Power Quality in Low and Medium Voltage Networks 24

Analysis of Electric Arc Furnace Influence on Power Quality ...

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24th EEEIC International Conference on Environment and Electrical engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Grabowski, Dariusz Klimas, MacIej Lange, Andrzej Silesian University of Technology Faculty of Electrical Engineering Department of Electrical Engineering and Computer Science Gliwice Poland University of Warmia and Mazury Faculty of Technical Sciences Department of Electrical and Power Eng. Electronics and Automation Olsztyn Poland

ISBN: (纸本)9798350355185

This paper focuses on analyzing power quality issues resulting from electric arc furnace operation. It highlights the challenges posed by the stochastic nature of the arc and proposes strategies to mitigate its impact on the power grid. Through measurements carried out on the medium and low voltage side of the furnace, fluctuations in parameters such as voltage, current, and power factor were observed, indicating significant disturbances in power quality. Mitigation strategies, including e.g. installing compensators, are suggested to stabilize the network and improve power quality, enhancing efficiency and reducing energy consumption and electrode usage. © 2024 IEEE.

关键词： Electric arc furnaces

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