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共 551 条记录，以下是81-90 订阅

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Deep Learning-based Serialized Model and Feature Extraction Algorithm for Solid Oxide Fuel Cell State Prediction 43

Deep Learning-based Serialized Model and Feature Extraction ...

引用

43rd Chinese Control Conference, CCC 2024

作者： Wu, Jiajian Zhu, Kanglin Wu, Han xiang, Guohui Li, xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9789887581581

Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC) prediction. However, the irrationality of the prediction object and the lack of prediction accuracy have hindered the progress of the prediction methods for SOFC systems. Therefore, two algorithms are proposed in this study, a feature selection algorithm based on maximum information coefficient and Bayesian information criterion for identifying key variables that are sensitive to changes in the SOFC system, and a prediction model based on a genetic algorithm optimized for sequence to sequence architecture. The M/BIC algorithm reduces the number of features by 62.5% (from 40 to 15), which contributes to the acceleration of the algorithm training and the subsequent sensor layout optimization. The prediction model incorporates a two-dimensional convolutional neural network and a multi-head attention mechanism to capture more information in the input sequence, an encoder-decoder architecture and a Gated recurrent unit to better predict performance trends. Tests with real experimental data show significant changes in feature dimensionality reduction and prediction accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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A Study on multi-scale landscape character extraction combining GIS and graphic processing algorithms 3

A Study on multi-scale landscape character extraction combin...

引用

3rd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Wang, Shuwei Liu, Yang Li, Guangqi Su, Xueying Wang, Sen Bai, Xuyong Center for Geophysical Survey China Geological Survey Langfang China Technology Innovation Center for Earth Near Surface Detection China Geological Survey China

ISBN: (纸本)9781510690219

This research presents an innovative approach to accurately extract multi-scale landscape characters by combining geographic information systems (GIS) and graphic processing algorithms. It is essential to recognize and analyze landscape characters at different scales. This study uses GIS advantages in spatial analysis and the K-Means clustering algorithm in image enhancement, feature extraction, image segmentation, and color quantification. Through image interpretation and deep learning, the distribution characteristics of buildings in Langfang City are obtained to provide guidance and suggestions for the future construction and development of the city. The proposed method covers data preparation, algorithm design, and system implementation. In addition, the future research direction of improving the method is also discussed, and the possibility of its application in other fields is explored. © 2025 SPIE.

关键词： Photointerpretation

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Application of Database Technology in Video Surveillance System

Application of Database Technology in Video Surveillance Sys...

引用

2022 IEEE International Conference on Knowledge Engineering and Communication systems, ICKES 2022

作者： Zhong, Chongli School of Information Engineering Xi'an Mingde Institute of Technology Shaanxi Xi'an710100 China

ISBN: (纸本)9781665456371

With the development of technology, the technological informationization of the security network video surveillance service industry has become the demand of the times. How to improve the functions of the video surveillance system and build an open security integrated monitoring management platform has become the research point of this article. This article intends to build a video surveillance system based on database technology to meet the multi-functional requirements of the surveillance system. This article mainly uses experimental methods to test the data of the monitoring system designed in this article, and then uses the comparative method to compare the speed of the three methods to calculate the data, and the data results are obtained. According to the experiment, the data processing time of the binary algorithm in the video surveillance system is within 15s. image detection in database technology uses binary algorithms to operate and analyze it more quickly. © 2022 IEEE.

关键词： Informatization

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Design and implementation of interactive image measurement software based on Tkinter and PIL library 25

Design and implementation of interactive image measurement s...

引用

Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering

作者： xiao He Zhulin Wang Shaozheng Zhan National Key Laboratory of Strength and Structural Integrity Xi' an Shaanxi China Aircraft Strength Research Institute of China Xi' an Shaanxi China

ISBN: (纸本)9798400712647

In this paper, we design and implement a high-precision image processing and measurement software based on Python. The software utilizes the Tkinter and PIL libraries to build a graphical user interface and integrates a variety of commonly used image processing algorithms, including grayscale conversion, edge extraction, Gaussian blur, image sharpening, binarization, and median filtering, enabling effective image enhancement and segmentation. In addition, the software provides interactive measurement functionality, allowing users to draw rectangular regions on the image using the mouse and automatically calculate the dimensions of these rectangles. Measurement results can be stored, displayed, and managed, facilitating users in analyzing and processing the data. To evaluate the performance and reliability of the software, we conducted experiments on multiple datasets. The experimental results demonstrate that the software can accurately process images of different types and qualities and provide stable and reliable measurement results. Compared with the existing C-scan measurement software of current equipment, the software developed in this study has certain advantages in terms of functional integration, interactivity, and measurement accuracy. The design and implementation of this software can meet the need to replace the existing C-scan system software, while also providing valuable references and insights for the further development of intelligent and automated image analysis systems.

关键词： PIL

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AutoShape: Automatic Design of Click-Through Rate Prediction Models Using Shapley Value 20th

AutoShape: Automatic Design of Click-Through Rate Prediction...

引用

20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Fang, Yunfei Mu, Caihong Liu, Yi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Collaborative Innovation Center of Quantum Information of Shaanxi Province School of Artificial Intelligence Xidian University Xi’an710071 China School of Electronic Engineering Xidian University Xi’an710071 China

ISBN: (纸本)9789819970216

As a main part of automated machine learning, Neural Architecture Search (NAS) has been employed in exploring effective Click-Through Rate (CTR) prediction models of recommender systems in recent studies. However, these studies update the architecture parameters by different strategies, such as gradient-based methods or evolutionary algorithms, and may suffer from either interference problems or high time-consuming in the search stage. Besides, the blocks in the search space were usually homogeneous, which means they have the same candidate operations to choose. Such design will result in redundancy in the search space, because many structures are inherently invalid and just increase the complexity of searching. To address the above issues, we implement the three-level automatic design of CTR prediction models using NAS via Shapley value, named as AutoShape. For the search space, we divide it into three parts according to the characteristics of the CTR model. Each part comprises distinctive candidate operations and forms a cell as an individual processing level, which improves the stability of the searched models’ effect and reduces the computational complexity. For the search strategy, we leverage Shapley value, a metric derived from cooperative game theory, to estimate the contribution of the operations in each block and the connections between the blocks, which can find effective models and reduce the time cost. Furthermore, experiments on different benchmark datasets show that the structure obtained from the search performs better than several hand-crafted architectures and is more stable than another NAS-based algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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Research on intelligent inspection technologies of expressway maintenance based on edge computing 5

Research on intelligent inspection technologies of expresswa...

引用

5th IEEE International Conference on Information systems and Computer Aided Education, ICISCAE 2022

作者： xi, Hongping Zhang, Lili Yang, Wanli Li, Qiang Huang, Jianping Gansu Luqiao Highway Investment Co. Ltd Lanzhou China Nanjing Digintec Co. Ltd Nanjing China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

Smart inspections of expressway maintenance are collected on road data through vehicle cameras. The vehicle processing equipment combines edge computing to preprocess the collected image to reduce the pressure of data transmission background data operations. The back-end combination of image recognition technology and deep learning theory to filter the collected pavement information, and finally realize road disease recognition. This paper is based on technologies such as images, image enhancement, and data expansion. Through the research of the FASTER RCNN and YOLO-V5 algorithms, the UNET and YOLO-V5 are finally used for object segmentation and recognition. © 2022 IEEE.

关键词： Object detection

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Design of Cloud Platform for Efficient Remote Sensing Big Data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

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Integrated Intelligence and Communication systems (ICIICS), International Conference on

作者： Yan Huang Weimeng Zhang Xi’an Traffic Engineering Institute Xi’an Shaanxi China Xi’an Technical School Xi’an Shaanxi China

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management.

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Optimizing FPGA Hardware Design for Temporal Filtering of High-Definition Video

Optimizing FPGA Hardware Design for Temporal Filtering of Hi...

引用

International Conference on Computers, Communications, and systems (ICCCS)

作者： xingjian Fu College of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

In daily life, noise reduction is an inevitable aspect of video or image processing because noise can affect the subjective quality of viewing. Therefore, video denoising filtering is a crucial operation in video image processing. Among various denoising methods, motion-compensated temporal filtering is a typical and effective filtering technique. However, there is still room for further research in the hardware implementation of this method. Hence, this paper investigates the Multi-hypothesis Motion-Compensated Filter algorithm and proposes an efficient hardware structure for the filtering algorithm. By parallelizing or pipelining various modules, hardware efficiency is effectively improved. By FPGA evaluation, the final experiments demonstrate that the hardware structure for motion-compensated temporal weighted filtering presented in this paper can meet the requirements of video denoising filtering with a resolution of 1920xl080@60fps.

关键词： Filtering Communication systems image processing Noise reduction Noise Filtering algorithms Hardware

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Post refocusing algorithm for high resolution spaceborne SAR image processing in large rolling terrain

Post refocusing algorithm for high resolution spaceborne SAR...

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6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： Yang Gao Tianshun xiang Juanjuan Yang Hongxing Dang xiaomin Tan China Academy of Space Technology Xi’an China

In spaceborne SAR image processing, focusing algorithms are applied with a uniform altitude throughout the observation area. The uniform altitude assumption shows good performance in most spaceborne SAR systems. However, in the high resolution (<0.5m) SAR systems, the defocusing becomes significant in areas with high topographic variations. This letter proposes a new post refocusing algorithm capable of improving the focusing quality of the image with large rolling terrain. Simulate data of spaceborne SAR system are used to demonstrate the performance of the proposed approach.

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Data Optimization Method for Ship Infrared image Recognition

Data Optimization Method for Ship Infrared Image Recognition

引用

2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Zhang, Haoxiang Liu, Chao Ma, Jianguan Sun, Hui University of Electronic Science and Technology of China School of Mathematical Sciences Chengdu China Beijing China

ISBN: (纸本)9798350312492

This study aims to address two challenging problems that affect the accurate and reliable recognition of ship infrared (IR) images in various scenarios: the interference of radiation highlights from specular reflections of target background sunlight and the difficulty of distinguishing ship targets from background confusion under low light conditions. We propose a data optimisation method for ship IR deep learning recognition, which uses processing tools such as median noise reduction and local histogram equalisation image enhancement to improve the performance and stability of ship IR recognition under the above unfavourable conditions. And the effectiveness of the method is verified on high fidelity mid-wave infrared (MWIR) ship simulation data using deep learning image classification algorithms such as AlexNet, VggNet and EfficientNet. To further demonstrate the effectiveness of the method, research has also adopted model feature gradient visualization technique, which indirectly verifies that our method can effectively improve the predictive reliability of ship recognition models. Overall, the data optimization method proposed in this study provides a feasible solution to address the challenges in ship IR image recognition and can provide a valuable reference for military mission-related research. © 2023 IEEE.

关键词： Infrared radiation

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