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I-V Lissajous pattern: a Promising Condition Monitoring Technique for High Voltage Assets 4

I-V Lissajous Pattern: a Promising Condition Monitoring Tech...

引用

4th International conference on High Voltage Engineering and Power Systems, ICHVEPS 2023

作者： Abu-Siada, Ahmed Suwarno, Suwarno Curtin University Electrical and Computer Engineering Discipline Perth Australia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350318678

The current-voltage (I-V) characteristics of critical high voltage assets such as power transformers, overhead transmission lines and underground cables are affected by various factors including the asset's health state along with other operating and environmental conditions. Understanding the current-voltage characteristics of such assets is essential for reliable and efficient operation of power transmission and distribution networks. The measurement of such parameters does not call for additional hardware as it utilises easily accessible voltage and current data at both terminals of the asset. The I-V characteristic can be considered as a unique fingerprint for the asset during its normal operation. variations in the fingerprint can be easily detected using a proper software code. The variation can be observed in real time and analysed using digital image processing.and artificial intelligence-based techniques to identify and quantify any abnormal event or a fault at early stage. This paper discusses the potential of using the I-V Lissajous pattern to detect incipient faults and abnormal events of high voltage assets. Some case studies on power transformer and overhead transmission line are presented. Results show that I-V Lissajous pattern is a promising technique for real time asset management of high voltage critical equipment. © 2023 ieee.

关键词： Power transformers

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Vision Transformer Hashing for image Retrieval

Vision Transformer Hashing for Image Retrieval

引用

2022 ieee International conference on Multimedia and Expo, ICME 2022

作者： Dubey, Shiv Ram Singh, Satish Kumar Chu, Wei-Ta Indian Institute of Information Technology Computer Vision and Biometrics Lab Allahabad India National Cheng Kung University Dept. of Computer Science and Information Engineering Taiwan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recently, Transformer has emerged as a new architecture in deep learning by utilizing self-attention without convolution. Transformer is also extended to Vision Transformer (ViT) for the visual recognition with a promising performance on imageNet. In this paper, we propose a Vision Transformer Hashing (VTS) for image retrieval. We utilize the pre-trained ViT on imageNet as the backbone network and add the hashing head. The proposed VTS model is fine tuned for hashing under six different image retrieval frameworks with their objective functions. We perform the extensive experiments on CIFAR10, imageNet, NUS-Wide, and COCO datasets. The proposed VTS based image retrieval outperforms the recent state-of-the-art hashing techniques with a significant margin. We also find the proposed VTS model as the backbone network is better than the existing networks, such as AlexNet and ResNet. The code is released at https://***/shivram1987/VisionTransformerHashing. © 2022 ieee.

关键词： image retrieval

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Deep Dehazing Powered by image processing.Network

Deep Dehazing Powered by Image Processing Network

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Guisik Kim Jinhee Park Junseok Kwon Korea Electronics Technology Institute Chung-Ang University Seoul Korea School of Computer Science and Engineering Chung-Ang University Seoul Korea

image processing.is a very fundamental technique in the field of low-level vision. However, with the development of deep learning over the past five years, most low-level vision methods tend to ignore this technique. Recent dehazing methods also refrain from using conventional image processing.techniques, whereas only focusing on the development of new deep neural network (DNN) architectures. Unlike this recent trend, we show that image processing.techniques are still competitive, if they are incorporated into DNNs. In this paper, we utilize conventional image processing.techniques (i.e. curve adjustment, retinex decomposition, and multiple image fusion) for accurate dehazing. Moreover, we employ direct learning for stable dehazing performance. The proposed method can perform with low computational cost and easy to learn. The experimental results demonstrate that the proposed method produces accurate dehazing results compared to recent algorithms.

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Air xylophone Using OpenCV

Air xylophone Using OpenCV

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2022 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Sumathi, S. Vijila, D. Shastika, M. St.Joseph's College of Engineering Department of Information Technology Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Writing and drawing within the air with computer vision is one among the foremost fascinating areas in image processing.and pattern recognition. OpenCV may be a widely used image processing.and computer vision technology. It is used for tasks like automatic face recognition, seeing, hand gestures so on. Object detection is employed for detecting things of a particular class (such as individuals, buildings, or cars) in image or video. Everything goes computerized nowadays, including musical instruments. This project is about creating a virtual xylophone instrument using openCV. this may allow the user to virtually play the xylophone within the air without touching any of the keys or the screen and internet. The keys were displayed on the screen. By installing the necessities and using openCV, mallets (xylophone sticks) are automatically identified and therefore the user can play the virtual xylophone by just waving the mallet. It works in three steps. First, detect the required colour object, then track its movement from key to key, and at last, generate the required output/audio. © 2022 ieee.

关键词： computer vision

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Fingerprint Liveness Detection Using Handcrafted Feature Descriptors and Neural Network 11

Fingerprint Liveness Detection Using Handcrafted Feature Des...

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11th ieee Global conference on Consumer Electronics, GCCE 2022

作者： Chen, Zhi-Sheng Pulungan, Reza Li, Yung-Hui Raswa, Farchan Hakim Harjoko, Agus Wang, Jia-Ching Kinarta, Indra Yusuf Lee, Chung-Ting Providence University Department of Computer Science and Information Engineering Taiwan Universitas Gadjah Mada Department of Computer Science and Electronics Indonesia AI Research Center Hon Hai Research Institute Taiwan National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781665492324

Fingerprint recognition is commonly used to verify a user's identity. However, the fingerprint recognition systems in use today can be vulnerable to attacks. For example, some artificial fingerprints can spoof fingerprint recognition systems and use identity information to obtain personal information. For security reasons, fingerprint liveness detectors should be robust to attacks using various materials. As a result, we propose a fingerprint recognition method that uses multi-radius local binary pattern (Multi-radius LBP) and local phase quantization (LPQ) as local texture descriptors and wavelet transforms to remove noise. Classification results are obtained using a multi-layer perceptron (MLP) classifier. This study confirms that the proposed method is useful to improve the average fingerprint liveness detection tasks. On LivDet 2015 dataset, the proposed method achieves 6.73% Average Error Rate(AER). © 2022 ieee.

关键词： Local binary pattern

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Investigation of Machine Learning Algorithms for pattern recognition in image processing 5

Investigation of Machine Learning Algorithms for Pattern Rec...

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5th International conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Kate, Chennaiah Kalpana, C. Sharma, Arvind Yadav, Ajay Singh Kumar, Ashok Kumar, S. Sandeep St. Peter's Engineering College Department of Information Technology Telangana Hyderabad500100 India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul624401 India Government Women Engineering College Department of Electronics and Communication Engineering Rajasthan Ajmer305002 India Srm Institute of Science and Technology Department of Mathematics Uttar Pradesh Ghaziabad201204 India BanasthaliVidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh 522502 India

ISBN: (纸本)9798350321425

In order to recognize patterns in images, this study tests the performance of many 'machine learning algorithms' and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ('deep learning, support vector machines, decision trees, and random forests') and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its 'accuracy, precision, recall, and F1 score', among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms. © 2023 ieee.

关键词： Feature extraction

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EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

EfficientViT-SAM: Accelerated Segment Anything Model Without...

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Zhuoyang Zhang Han Cai Song Han Tsinghua University NVIDIA MIT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present EfficientViT-SAM, a new family of accelerated segment anything models. We retain SAM’s lightweight prompt encoder and mask decoder while replacing the heavy image encoder with EfficientViT. For the training, we begin with the knowledge distillation from the SAM-ViT-H image encoder to EfficientViT. Subsequently, we conduct end-to-end training on the SA-1B dataset. Benefiting from EfficientViT’s efficiency and capacity, EfficientViT-SAM delivers 48.9× measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing performance. Our code and pre-trained models are released at https://***/mit-han-lab/efficientvit.

关键词： Training image segmentation computer vision Codes conferences Computational modeling Graphics processing.units

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Application of image segmentation technology in TCM eye diagnosis 16

Application of image segmentation technology in TCM eye diag...

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16th International conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Peng, Hong Niu, Ning Zhang, Yilin Wang, Guanjun Xue, Chenyang Huang, Mengxing Hainan University Haikou City China North University of China Taiyuan City China

ISBN: (纸本)9781510667341

image segmentation is a critical technology in many fields, such as image processing. pattern recognition, and artificial intelligence. It is also the first and critical step in computer vision technology. Tongue diagnosis combined with deep learning for segmentation and extracting pathological features is relatively mature, but deep learning combined with TCM visualization is sporadic. First, We used the U2Net network1 for segmentation extraction of the sclera in this study. Where the U2Net1 network1 (based on PyTorch) relies on the extensive use of data enhancements to use the available annotation samples more efficiently, and compared with the U-Net network, the U2Net network1 updates an RSU module, each RSU module is a small U-net network, merging multiple U-Net outputs to get the merged Mask target. Finally, we applied classical CNN networks to evaluate the segmentation effect, introducing different evaluation metrics such as Miou, Precision, and Recall. We used the publicly available dataset UBIVIS.V12 for our experiments, where our Miou was as high as 97.3%, and U2Net achieved better results among all the networks, which laid the foundation for our subsequent segmentation and extraction of blood filament features © 2023 SPIE.

关键词： image segmentation

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Research on image recognition Method Based on Improved Neural Network 4

Research on Image Recognition Method Based on Improved Neura...

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4th International conference on Communications, Information System and computer Engineering, CISCE 2022

作者： Yang, Shen Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

In the 1960s, Hubel et al. proposed the concept of receptive field through the study of cat visual cortex cells[1]. In the 1980s, Fukushima [2] proposed the concept of neurocognitive machine based on the concept of receptive field, which can be regarded as the first implementation of convolutional neural network (CNN). The neurocognitive machine will decompose a visual pattern into many sub-patterns (features), and then enter the hierarchical hierarchical connected feature plane for processing. It tries to model the visual system so that it can complete recognition even when the object has displacement or slight deformation. CNN is a variant of multi-layer perceptron (MLP), developed from biologists Huber and Wessel's [3] early research on the visual cortex of cats. There is a complex structure in the cells of the visual cortex, and these cells are very sensitive to the sub-regions of the visual input space, called the receptive field.[4] CNN was proposed by Yann Lecun of New York University in 1998 [5]. In essence, CNN is widely used neural network architecture. The success lies in the local connection and weight sharing adopted by CNN. On the other hand, the complexity of the model is reduced, that is, the risk of overfitting is reduced. In this paper, using CNN as the research method, image recognition is studied by illustrating a set of comparative experiments. In the comparative experiment, I will optimize the network model of previous experiments, and finally effectively and significantly improve the efficiency of model training. © 2022 ieee.

关键词： image recognition

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Bézier Everywhere All at Once: Learning Drivable Lanes as Bézier Graphs

Bézier Everywhere All at Once: Learning Drivable Lanes as B...

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conference on computer Vision and pattern recognition (CVPR)

作者： Hugh Blayney Hanlin Tian Hamish Scott Nils Goldbeck Chess Stetson Panagiotis Angeloudis dRISK.ai Imperial College London

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Knowledge of lane topology is a core problem in au-tonomous driving. Aerial imagery can provide high res-olution, quickly updatable lane source data but detecting lanes from such data has so far been an expensive man-ual process or, where automated solutions exist, undriv-able and requiring of downstream processing. We pro-pose a method for large-scale lane topology extraction from aerial imagery while ensuring that the resulting lanes are realistic and drivable by introducing a novel Bezier Graph shared parameterisation of Bezier curves. We develop a transformer-based model to predict these Bezier Graphs from input aerial images, demonstrating competitive results on the UrbanLaneGraph dataset. We demonstrate that our method generates realistic lane graphs which require both minimal input, and minimal downstream processing. We make our code publicly available at https://***/driskai/BGFormer

关键词： computer vision Head Codes image edge detection Predictive models Transformers Real-time systems

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