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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

共 11233 条记录，以下是4871-4880 订阅

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Disturbance Identification of Power Quality Based on Markov Transition Field and Deep Residual Network 3

Disturbance Identification of Power Quality Based on Markov ...

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3rd international conference on Energy engineering and Power Systems, EEPS 2023

作者： Zhang, Huaying Wu, Xian Wang, Qing You, Yihong Ding, Wanglong Dong, Kun Zhao, Jianfeng Shenzhen Power Supply Co. Ltd New Smart City High-Quality Power Supply Joint Laboratory of China Southern Power Grid Guangdong Shenzhen China Southeast University School of Electrical Engineering Nanjing China

ISBN: (纸本)9798350313857

Electric energy is an indispensable secondary energy in today's society and an important foundation for the development of the national economy. With the rapid development of renewable energy and the application of a large number of power electronic devices, power quality disturbances(PQDs) are gradually becoming more complicated and diversified. Since unqualified power quality will cause a series of adverse effects on the production user side and electronic equipment, this requires real-time monitoring of power quality and rapid and effective identification and classification of various disturbance problems, thereby effectively improving power quality. Due to the slow computational speed and low identification rate of traditional power quality disturbance recognition algorithms when dealing with large amounts of power quality data, this paper uses the powerful representation learning ability of the deep residual network (Resnet) to classify power quality disturbances. First, the Markov transition field(MTF) is used to transform the one-dimensional power quality disturbance time series signal into a two-dimensional visualization graph, and then the deep residual network is used to extract the disturbance features in the two-dimensional image and perform classification and recognition. The experimental results show that the recognition algorithm based on Markov transition field and deep residual network has good accuracy in the classification of power quality disturbance signals, and the effective recognition rate reaches 99.875%. © 2023 IEEE.

关键词： Power quality

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ETL Data Pipeline to Analyze Scraped Data 3rd

ETL Data Pipeline to Analyze Scraped Data

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3rd international conference on Information technology, InCITe 2023

作者： Gandhi, Rashmi Khurana, Sparsh Manchanda, Harsh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida India

ISBN: (纸本)9789819959969

In this day and age, corporations are moving towards data driven decision making. They are utilizing data to make applications that can assist them with business intelligence. Data assists them with working on their efficiency and income by chipping away at the business challenges. By utilizing data, corporations can generate market patterns, improve client experience and enable improved business decisions. The key problem is the abundance of data and its form. Data needs to be in a form that can be effectively be used to generate tangible insights. The complete cycle of taking data and then generating insights from it is a long one but there are modern solutions to this problem which simplify and improve this process. The scope of this literary research is to examine the use of Data Pipelining to enable better data insights. Data pipelines can be utilized to move data starting with one spot then onto the next using ETL (Extract Transform Load), ELT (Extract Load Transform), data improvement, and continuous data examination. An automated data pipeline is a relatively new solution which can provide impressive results without the need for manual intervention every time. The result of automated pipelines is automatic processing of bigdata from source to destination without any user input. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision making

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Unconventional Risks and Prevention and Control Strategies for Photovoltaic Distribution Grids Containing High Penetration Rates 3

Unconventional Risks and Prevention and Control Strategies f...

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3rd international conference on electrical engineering and Control Science, IC2ECS 2023

作者： Han, Zhentao Wang, Qixiang Song, Zhuoran Ye, Peng Shan, Jinhang Yang, Wenyu State Grid Liaoning Electric Power Co. Ltd. Economic and Technological Research Institute Liaoning Shenyang China State Grid Liaoning Electric Power Co. Ltd Liaoning Shenyang China School of Electric Power Shenyang Institute of Engineering Liaoning Shenyang China

ISBN: (纸本)9798350382426

With the vision of 'double carbon', China has begun to vigorously develop a clean, low-carbon, safe and efficient energy system, and build a new power system with new energy as the main body. With the high penetration of photovoltaic power supply into the distribution network, the coordination between traditional risk and protection can no longer meet the technical requirements of the new distribution network relay protection. Therefore, this paper focuses on the three aspects of the non-conventional risk of PV power supply, its analysis method and the distribution network protection scheme adapted to the access of PV power supply. Firstly, the general structure of PV power supply is introduced, and the working principle of inverter and grid-connected control strategy under normal state are analyzed, and then from the perspective of distribution grid, the method of analyzing the extraordinary risk of distribution grid is proposed, and the symmetrical and asymmetrical fault characteristics of distribution grid and the problems when PV power supply is connected to distribution grid are analyzed. In addition, for the method proposed in this paper, the equivalent model of PV power access to the distribution network is used as the basis to study the fault characteristics of PV power access to the distribution network, analyze the impact of PV power access to the existing current protection of the distribution network, and propose to classify the distribution network into three regions according to the location of PV power access, and then put forward the preventive and control strategies respectively. Finally, the feasibility of the protection scheme proposed in this chapter is verified by simulation. © 2023 IEEE.

关键词： control strategies distribution network fault analysis high penetration photovoltaic power supply

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Realtime Mask Detection of Kitchen Staff Using YOLOv5 and Edge Computing 3

Realtime Mask Detection of Kitchen Staff Using YOLOv5 and Ed...

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3rd international conference on computer, Control and Robotics, ICCCR 2023

作者： Shi, Yunfan Yang, Zheng Bi, Yifei Li, Jingcheng Zhu, Xiaohui Yue, Yong The University of Liverpool Department of Computer Science Liverpool United Kingdom School of Advanced Technology Xi'An Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781665492126

We propose a new end-to-end, edge-device embedded solution for detection of improper mask-wearing of kitchen staff using network cameras. Mask detection on kitchen network cameras is a highly specified task with little ready-to use data. The filming position of network cameras and the enviroment in kitchen leaded to vague images and small targets. Also, the detection model should be light-weighted to be deployed on edge computing devices. To improve accuracy on this task, We constructed a novel dataset from real kitchen cameras of different positions, indoor layouts, light conditions and applied effective data augementation. We conducted transfer learning on our dataset starting from COCO pre-trained YOLOV5s weights. In addition, we optimized the model through parameter tuning and post training model pruning for a single Nvidia Jetson Nano device and achieved high accuracy and sensitivity on multiple HD resolution network camera video feed. Experimental results show that our model achieves a training accuracy of 100 percent mAP(0.5) and test accuracy of 97.6 percent mAP(0.5) with minimal training cost: only 89 epochs on our dataset with early stopping. With an inference speed of 10 FPS on Nvidia Jetson Nano, our solution suffices the application requirements and can handle mutiple parallel HD camera streams simultaneously. Compared with previous research, our solution provides competitive cost efficiency where accurate and sensitive high resolution image detection can be run on a single edge device. © 2023 IEEE.

关键词： Object detection

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Automation of Skin Cancer Detection with Image Processing Using Efficient and Lightweight CNN Models 3

Automation of Skin Cancer Detection with Image Processing Us...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Pal, Ritwika Sagnika, Santwana School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350322842

Skin cancer, if detected and treated at an early stage, is curable. Being one of the most common forms of cancer, the number of patients diagnosed is increasing with time. The conventional method of detecting skin cancer is done by dermatologists with biopsy, which is a gradual process. With the help of Convolutional neural networks(CNNs) which are extensively used for image classification, skin cancer diagnosis can be sped up. However, CNNs require high computation power with a large memory. With the aim of reducing the resources required for this application, the MobileNetV2 model, which is a lightweight CNN, was used to classify skin lesion images taken from the international Skin Imaging Collaboration(ISIC), which is suitable for mobiles with limited memory space, since it takes lesser memory space and reduces computational costs. With the motivation of doing further research, Object detection and Image segmentation algorithms have been explored. The Object detection algorithm of You Only Look Once(YOLO), predicts the bounding box around the skin lesion and labels it as either malignant or benign with a probability score. Different models of YOLOv5 were trained for the purpose of object detection. Finally for image segmentation, which tries to classify every pixel of the image, the U-Net model was trained with two base models, ResNet-50 and MobileNetV2. Its aim is to identify the exact position and shape of the lesion and classify each pixel as either malignant skin lesion, benign skin lesion or normal skin. © 2023 IEEE.

关键词： Image segmentation

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Permittivity inversion of GPR images based on DeepLab 3

Permittivity inversion of GPR images based on DeepLab

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3rd international conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Liu, Hongwei Wang, Jinpeng Talukdar, Masud Yuan, Da Key Laboratory of Intelligent Information Processing Shandong Technology and Business University Yantai Shandong264005 China

ISBN: (纸本)9781510663473

Ground Penetrating Radar (GPR) is usually used to detect unknown underground structure information, but it is very difficult to extract the underground target structure information from original GPR signal. This paper aims to solve the inversion problem by deep learning method. The two-dimensional ground penetrating radar B-scan signal is converted into intuitive underground structure information by neural network. In this paper, the DeepLab network proposed by Google is improved to solve the problem of permittivity inversion of GPR signal images. We verify the network using simulated data, which is generated by Finite Difference Time Domain (FDTD) algorithm. Finally we quantitatively evaluate the performance of our network by comparing it with some existing deep learning inversion networks. © 2023 SPIE.

关键词： Permittivity

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Underwater Image Denoising Using Adaptive Retinal Processes and Smoothening Filters 3

Underwater Image Denoising Using Adaptive Retinal Processes ...

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3rd international conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Singh, Pradeep Kumar Sharan, B. Kushwaha, Vipin Kr. Yadav, Satya Prakash Vats, Satvik Iec College of Engineering & Technology Dept. of Cse Greater Noida201310 India Dept. of Cse Greater Noida201306 India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

The images that were obtained when the camera was submerged in water are an useful source of data that can be used to explore hydrothermal vents that are located on the ocean floor and to acquire a better knowledge of the marine life that lives there. Low contrast, colour distortion, and an unpleasant overall visual appearance are three of the most fundamental obstacles that an underwater photograph must overcome. These problems were caused by the dispersion and refraction of light, which occurred as beams of light went from a less scarce substance to a more dense one. When light is dispersed, the difference in intensity between the colours in the scene is diminished. Not only does the existence of dispersion in the water add to the impact that water has on underwater photography, but the presence of living organisms in the water also contributes to this effect. The research is needed to overcome the challenges of underwater photography, such as light dispersion and color distortion, in order to facilitate accurate exploration of hydrothermal vents and marine life on the ocean floor. The main problem addressed in the paper is the degradation of underwater images due to issues like low contrast and color distortion caused by light dispersion. The paper proposes a solution by introducing an innovative combination of adaptive retinal mechanism and bilateral filter, effectively enhancing image quality by mitigating these challenges. This paper implementation processes a picture by making use of an adaptive retinal mechanism in combination with a bilateral filter for the aim of smoothing. This is done in order to get the desired effect. The MSE and PSNR metrics have both witnessed significant improvements in their quality in recent years. © 2023 IEEE.

关键词： Mean square error

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Mask R-CNN with R-D Net: A Detection and Segmentation Model for Early Gastric Cancer Gastroscopy Images 3

Mask R-CNN with R-D Net: A Detection and Segmentation Model ...

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3rd international conference on Electronic Information engineering and computer Science, EIECS 2023

作者： Liang, Tao Lei, Yi Song, Wenai Sun, Xue Du, Jun Wang, Qing Zhao, Li North University of China School of Software Engineering Taiyuan China Beijing University of Technology Faculty of Information Technology Beijing China Chinese Academy of Medical Sciences Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Vip Department Family Medicine Department Beijing China Chinese Academy of Medical Sciences Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Department of Pathology Beijing China Cross-Strait Tsinghua Research Institute Pharmacovigilance Research Center for Information Technology and Data Science Xiamen China Tsinghua University Department of Automation Beijing China Chinese Academy of Medical Sciences Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Department of Gastroenterology Beijing China

ISBN: (纸本)9798350316674

Gastric cancer is one of the most common malignant tumors of the digestive system. Although gastroscopy can effectively detect early cancers, manual identification and diagnosis is time-consuming and laborious, and the false-negative rate is as high as 4.6-25.8%. Early gastric cancer lesion areas can be detected and segmented using deep learning techniques. We collected and organized 12665 eligible gastroscopy images, and cropped and expanded the original dataset after labeling by professional doctors, and finally obtained 19673 images for model training. We propose ResMSE Net and DWFF Net and apply these two networks to Mask R-CNN to obtain a new model: Mask R-CNN With R-D Net. On the same standard dataset, the mAP@[IoU=0.5] and ACC values of the Mask R-CNN With R-D Net model achieve respectively 45.37% and 67.74%, which are improved by 5.83% and 9.08%, respectively, compared with the original model. In our experiments, we found that Mask R-CNN With R-D Net is difficult to detect targets that are extremely similar to the background, which is the direction of our future work. Secondly, we hope to minimize the number of model parameters and make the model more lightweight under the premise of ensuring accuracy, so that the model can be embedded into gastroscopy detection equipment for real-time detection. © 2023 IEEE.

关键词： deep learning early gastric cancer gastroscopy images

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Revolutionizing Rice Farming: Automated Identification and Classification of Rice Leaf Blight Disease Using Deep Learning 3

Revolutionizing Rice Farming: Automated Identification and C...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Kukreja, Vinay Sharma, Rishabh Vats, Satvik Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350300710

Rice is considered a fundamental dietary component for a significant portion of the global population and is regarded as a crucial crop for ensuring food security on a global scale. Rice crops are susceptible to diverse diseases, among them rice leaf blight disease, which is instigated by the fungus Helminthosporium oryzae. The rice leaf blight disease has the potential to result in substantial reductions in crop yield and poses a significant risk to rice cultivation across various regions globally. The current study introduces a novel approach for the multi-classification of rice leaf blight disease utilizing a hybrid model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques. The model is designed to classify the disease based on six distinct levels of severity. A dataset comprising 6000 images of the rice crop was gathered and subjected to pre-processing and augmentation techniques to enhance the model's efficacy. The proposed model attained a total accuracy of 95.25% in detecting and classifying rice leaf blight disease. Ablation studies were conducted to assess the respective contributions of the CNN and LSTM components toward the model's overall performance. Furthermore, a comparison was made among severity levels to determine the optimal severity level for diagnostic purposes. The model under consideration exhibits the capacity to function as a mechanism for the timely identification and mitigation of rice leaf blight disease, thereby leading to a substantial enhancement in agricultural productivity and guaranteeing food sustainability. © 2023 IEEE.

关键词： Crops

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Endoscopic Bladder Tissue Classification Through Fusion of Handcrafted and Deep Features 3

Endoscopic Bladder Tissue Classification Through Fusion of H...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Tiwari, Raj Gaurang Maheshwari, Himani Agarwal, Ambuj Kumar Sharma, Ochin Bharathiraja, N. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350343632

The proper identification of tissue types during endoscopy may greatly affect both the patient's care and the treatment options that are made, making the categorization of endoscopic bladder tissue a crucial responsibility in the field of urology. Combining handmade characteristics with deep features is the innovative strategy that is presented in this study to improve the accuracy of bladder tissue categorization. To extract handmade characteristics from endoscopic images, the proposed technique makes use of textural feature extraction and the Bag-of-Visual-Word (BoVW) paradigm. This research proposes a hybrid convolutional neural network (CNN) model as a means of further enhancing classification performance. This model takes handcrafted features in addition to CNN-based features as input. This hybrid model is intended to make use of the complementing qualities that both kinds of features possess to provide a depiction of bladder tissue that is more accurate and distinct. During the experimental assessment, the proposed hybrid CNN model is evaluated in comparison to fundamental machine learning approaches as well as alternative deep learning architectures. The findings show that the suggested model can achieve an amazing accuracy rate of 95%, which is higher than the performance of other techniques. This accomplishment demonstrates the ability to integrate handmade cues with deep features for endoscopic bladder tissue categorization. This work has major implications for increasing diagnosis accuracy and the outcomes for patients. © 2023 IEEE.

关键词： Bag-of-Visual-Word model deep features deep learning diagnostic accuracy Endoscopic bladder tissue classification handcrafted features hybrid convolutional neural network model machine learning texture feature extraction urology

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