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Research on On-line Data Prediction of Electrical Equipment Based on Wavelet analysis and Data Fusion 22

Research on On-line Data Prediction of Electrical Equipment ...

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3rd Asia-Pacific conference on image Processing, Electronics and Computers, IPEC 2022

作者： Wang, Guorong Mao, Wentao Tang, Yingzhi Xiao, Yan School Mechatronic Engineering Wuhan Donghu University Wuhan430212 China Wuhan Donghu University Wuhan430212 China School of Computer Science Wuhan Donghu University Wuhan430212 China

ISBN: (纸本)9781450395786

In the process of fault analysis and diagnosis of electrical equipment, it is of great significance to use wavelet transform to detect the fault time of fault signal. In order to overcome this deficiency, wavelet analysis can decompose mixed signals with different frequencies into block signals with different frequency components, which can effectively separate signal from noise, extract features and diagnose faults. According to the virtual instrument solution, this paper comprehensively processes various multi-source fault data information through data fusion technology. In fault pattern recognition, how to make full use of the advantages of various diagnosis methods, combine each other and learn from each other is of great significance to improve the resolution of fault pattern and enhance the comprehensiveness and reliability of fault diagnosis. Data fusion technology is a new frontier discipline to study multi-source information processing and analysis methods. In military, information processing and other fields, the application of multi-sensor data fusion technology to fault diagnosis has attracted more and more attention. This paper discusses the application characteristics of electrical equipment based on wavelet analysis and data fusion, and gives the latest research results and development trend. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Wavelet analysis

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image Texture analysis with Local Binary patterns: A Review 3

Image Texture Analysis with Local Binary Patterns: A Review

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3rd IEEE International Virtual conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： Srilatha, N. Reddy, V. Lokeswara JNT University Anantapauramu CSE Department Anantapuramu India KSRM College of Engineering CSE Department Kadapa India

ISBN: (纸本)9781665426916

Local binary pattern (LBP) and its changed forms have been widely used in the many applications of different fields such as image processing, computer vision, model recognition and texture analysis etc. In this paper, we did extensive research-based study on LBP and its forms. The pros and cons of standalone LBP methods which used in many applications were evaluated in this study. Finally, we interpret based on their defects and focused the gapes of recent forms of LBP model signifier. © 2021 IEEE.

关键词： Local binary pattern

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image processing and Grid-Based analysis for detection of non-uniform Heat Distribution: A comparative study 3

Image processing and Grid-Based analysis for detection of no...

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3rd International conference on Digital Futures and Transformative Technologies, ICoDT2 2023

作者： Khan, Abdul Hakeem Javaid, Ali National University of Sciences and Technology School of Mechanical and Manufacturing Engineering Islamabad Pakistan

ISBN: (纸本)9798350330816

This paper presents a detailed comparative analysis of detecting non-uniform heat distribution over a metal disk, which serves as a baffle for a custom-designed heat exchanger, using thermocouples and a thermal camera (FLIR Lepton 2.5, 60x80 pixels). The experimental setup involves placing K-Type thermocouples in a grid pattern over the surface and positioning the thermal camera at a fixed height/distance. The thermal camera has a stated accuracy of ±5 degrees Celsius. Controlled non-uniform heating is applied to the metal disk, and temperature data are recorded using both the thermocouples and the thermal camera. The study aims to assess the accuracy, spatial resolution, and effectiveness of each method for detecting non-uniform heat distribution over metal surfaces. The findings offer valuable insights into the strengths and limitations of thermocouples and thermal cameras in both industrial processes and academic research. In our case, we gained an in-depth understanding of the thermal distribution of the baffle, providing guidance for process optimization, design refinement, and quality control. © 2023 IEEE.

关键词： Thermocouples

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Few-Shot image Classification for Automatic COVID-19 Diagnosis 11th

Few-Shot Image Classification for Automatic COVID-19 Diagno...

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11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Cores, Daniel Vila-Blanco, Nicolás Mucientes, Manuel Carreira, María J. Universidade de Santiago de Compostela Santiago de Compostela Spain Departamento de Electrónica e Computación Escola Técnica Superior de Enxeñaría Universidade de Santiago de Compostela Santiago de Compostela Spain Santiago de Compostela Spain

ISBN: (纸本)9783031366154

Developing robust and performant methods for diagnosing COVID-19, particularly for triaging processes, is crucial. This study introduces a completely automated system to detect COVID-19 by means of the analysis of Chest X-Ray scans (CXR). The proposed methodology is based on few-shot techniques, enabling to work on small image datasets. Moreover, a set of additions have been done to enhance the diagnostic capabilities. First, a network to extract the lung region to rely only on the most relevant image area. Second, a new cost function to penalize each misclassification according to the clinical consequences. Third, a system to combine different predictions from the same image to increase the robustness of the diagnoses. The proposed approach was validated on the public dataset COVIDGR-1.0, yielding a classification accuracy of 79.10% ± 3.41% and, thus, outperforming other state-of-the-art methods. In conclusion, the proposed methodology has proven to be suitable for the diagnosis of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Islanding Detection in PV-Grid System by Random Forest and Spectrogram Based LBP

Islanding Detection in PV-Grid System by Random Forest and S...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha International conference on

作者： Ritu Singh S. P. Shukla Archana Panda Subhendu Sekhar Sahoo Subhendu Pati Basant K Panigrahi Department of Electrical Engineering Bhilai Institute of Technology Durg India Department of Electronics and Communication Engineering SOA Deemed to be University Bhubaneswar India Department of Electrical Engineering Gandhi Institute for Technology Bhubaneswar India Department of Electrical Engineering ITER SOA University Bhubaneswar India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Islanding is a significant obstacle along with excessive penetration of DG sources. The islanding could cause harm to the customers and their equipment's. The IEEE 1547 DG interconnection regulations stipulate that the DG must be disconnected and the islanding must be observed within two seconds. Basically, this paper presents a novel method for detecting islanding based upon image pattern recognition using random forest (RF) classification technique is proposed. images are utilized to derive the local binary pattern (LBP) characteristics so as to identify non-islanding as well as islanding events. Hence, the time series signal is obtained and transformed into spectrogram images at the point of common connection. These images are utilized to extract the features using histogram of oriented gradient, which is then utilized as a feature vector input for the RF classifier for training as well as testing. It uses rate of change of negative sequence voltage parameter to derive spectrogram image. The RF classifier's effectiveness is assessed with 5-fold cross-validations. The results from classification demonstrates that detection of islanding using RF classifier and LBP feature being used for image pattern recognition achieved excellent results with 98.25% accuracy and detection time of 182 milliseconds.

关键词： Training Accuracy Time series analysis Feature extraction Vectors Regulation pattern recognition Spectrogram Classification tree analysis Testing

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Lesion-Based Chest Radiography image Retrieval for Explainability in Pathology Detection 10th

Lesion-Based Chest Radiography Image Retrieval for Explainab...

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10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Pedrosa, Joao Sousa, Pedro Silva, Joana Mendonca, Ana Maria Campilho, Aurelio Inst Syst & Comp Engn Technol & Sci INESC TEC Porto Portugal Univ Porto FEUP Fac Engn Porto Portugal Ctr Hosp Vila Nova de Gaia Espinho Vila Nova De Gaia Portugal Inst Portugues Oncol Porto Francisco Gentil Porto Portugal Adm Reg Saude Norte ARSN Porto Portugal

ISBN: (纸本)9783031048814;9783031048807

Chest radiography is one of the most common medical imaging modalites. However, chest radiography interpretation is a complex task that requires significant expertise. As such, the development of automatic systems for pathology detection has been proposed in literature, particularly using deep learning. However, these techniques suffer from a lack of explainability, which hinders their adoption in clinical scenarios. One technique commonly used by radiologists to support and explain decisions is to search for cases with similar findings for direct comparison. However, this process is extremely time-consuming and can be prone to confirmation bias. Automatic image retrieval methods have been proposed in literature but typically extract features from the whole image, failing to focus on the lesion in which the radiologist is interested. In order to overcome these issues, a novel framework LXIR for lesion-based image retrieval is proposed in this study, based on a state of the art object detection framework (YOLOv5) for the detection of relevant lesions as well as feature representation of those lesions. It is shown that the proposed method can successfully identify lesions and extract features which accurately describe high-order characteristics of each lesion, allowing to retrieve lesions of the same pathological class. Furthermore, it is show that in comparison to SSIM-based retrieval, a classical perceptual metric, and random retrieval of lesions, the proposed method retrieves the most relevant lesions 81% of times, according to the evaluation of two independent radiologists, in comparison to 42% of times by SSIM.

关键词： Chest radiography image retrieval Deep learning Explainability

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Proceeding of PRIS 2021 - 2021 3rd International conference on pattern recognition and Intelligent Systems

Proceeding of PRIS 2021 - 2021 3rd International Conference ...

引用

3rd International conference on pattern recognition and Intelligent Systems, PRIS 2021

ISBN: (纸本)9781450390392

The proceedings contain 19 papers. The topics discussed include: metric learning for context-aware recommender systems;food safety pre-warning system based on robust principal component analysis and improved Apriori algorithm;toward tracing the source of web attacks targeted at web applications;method of physical inventory checking on cigarette stereoscopic warehouse based on uav;improved correlation filter visual tracker by using scale estimation network;face anti-spoofing by using feature fusion;age estimation from facial images using transfer learning and k-fold cross-validation;synthetic aperture radar image target recognition based on hybrid attention mechanism;a deep learning model capable of producing heatmap probabilities for characters in natural scenes;and research on distortion correction of particleboard surface defect image.

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Hierarchical Feature Fusion Network with Receptive Field Block for Fabric Defect Detection 3

Hierarchical Feature Fusion Network with Receptive Field Blo...

引用

3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Liu, Zhoufeng Liu, Qingjie Li, Chunlei Zhang, Hong Tian, Bo Zhongyuan University of Technology Zhengzhou451191 China Image Processing Center Beihang University Beijing100191 China

ISBN: (纸本)9781510667563

Defect detection as a part of industrial production is essential to monitor product quality. In the last decade, convolutional neural networks have been widely used in industrial defect detection, but existing CNNS-based fabric defect detection models suffer from the problems of not fully utilizing contextual information and inadequate characterization of the underlying features. To address the issues above, we propose a novel hierarchical feature fusion network with receptive field block is proposed for fabric defect detection. The low-level features are efficiently characterized by designing the context-aware feature extraction module (CAFEM) with a short connection. Then the novel receptive field block with five branches(RFB-5) can integrate different scale high-level feature maps, and the holistic attention module (HAM) is adopted to focus on the significant information. Moreover, the low-level features are fused with the high-level features to better represent the fabric texture information. Finally, a joint loss with a boundary IoU loss and a cross entropy loss is adopted to guide the network to learn more detailed information. Experimental results conducted on our built fabric image dataset demonstrate that the proposed method makes it possible to locate the defective areas in the fabric, which is superior to the 7 existing methods. © 2023 SPIE.

关键词： Feature extraction

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Numerical analysis and calculation of urban landscape spatial pattern 3

Numerical analysis and calculation of urban landscape spatia...

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3rd International conference on image Processing and Intelligent Control, IPIC 2023

作者： Zhang, Fan Li, Jialin School of Civil and Architectural Engineering Henan University 475000 China

ISBN: (纸本)9781510668140

In order to plan and design different models of urban landscape, this paper presents numerical methods and calculation methods for different models of urban landscape. Through the analysis of urban green space landscape pattern, it can be found that there are still problems such as the incompatibility of green space and space distribution, the dispersal of some green space, the high fragmentation space, and the urban green space shortage. In the future urban green space development, attention should be paid to the following problems: there is less green space in the central part of the city, and the green space distribution is focused on the poor areas, especially in the northwest part of the city. In future urban development, especially in the context of old urban development, it is necessary to strengthen the construction of green space in the central region, appropriately provide some economic benefits, and increase the green space in the central region of the city by destruction, the construction of small parks and green space, so that the public can live together. Urban ecological green space is insufficient, and construction should be strengthened. City A has good natural resources, among which the ancient Yellow River and the Beijing-Hangzhou Grand Canal pass through the city, and the urban water network is developed. We should focus on building ecological green spaces on both sides of the river and reduce the construction projects of residential quarters within a certain buffer zone. At present, there are many other green spaces in urban green space, most of which are unused construction land or green space, which may be occupied in the process of urban development. How to ensure the quantity and quality of such green spaces will be the key issues for urban builders. © 2023 SPIE.

关键词： Numerical methods

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2 Detection of Grayscale image Implementation Using Multilayer Perceptron 3

2 Detection of Grayscale Image Implementation Using Multilay...

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3rd International conference on Electrical, Electronics, Informatics, and Vocational Education, ICE-ELINVO 2020

作者： Reni, O.N. Marpanaji, E. Electronics and Informatics Engineering Education Yogyakarta State University Indonesia

The human eye can instantly recognize two patterns of color images that are almost the same quickly, but the computer cannot directly recognize any pattern in the image. The problem faced is how the computer can recognize the image pattern entered. pattern recognition is also a technique that aims to classify previously processed images based on similarity or similarity in characteristics. In the Artificial Neural Network there are several methods that can be used to identify image patterns, one of them with the Multilayer Perceptron architecture. Multilayer Perceptron Neural Network is a type of neural network that has the ability to detect or perform analysis for problems that are sufficient or even very complex, such as in language processing problems, recognition of a pattern and processing of an image or image. The results of the study are a system that is able to recognize grayscale image patterns and is able to provide a percentage of pattern recognition in two similar and different images. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Multilayer neural networks

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