检索结果-内蒙古大学图书馆

2nd IEEE International conference on image processing and Computer Applications, ICIPCA 2024

作者： Wang, Tan School of Energy and Electrical Engineering Chang'an University Xi'an China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350360240

Aiming at the significant heat generated by high power density batteries in the process of charging and discharging at high current, a design and optimization scheme of battery liquid cooling system based on sliding mode control is proposed in this paper. Firstly, the paper deeply analyzes the important role of the battery thermal management system in ensuring the safety of the battery pack, extending the service life and improving the overall performance, and establishes the effectiveness and superiority of the liquid cooling method. In this study, an efficient and dynamic response liquid battery cooling system was designed. The system uses the fluid cooling medium to directly contact the inside of the battery, and effectively absorbs and takes away a large amount of heat during the battery operation by precisely regulating the flow rate and temperature of the coolant. The core is the introduction of sliding mode control theory, according to the real-time change of battery temperature, dynamically adjust the control strategy of the cooling system, to ensure that the battery temperature is always maintained in the best working range, to prevent overheating phenomenon. In the experimental part, the design process, key parameter selection and simulation verification process of the liquid cooling system based on sliding mode control are described in detail. The results show that compared with the traditional constant cooling strategy, the system has significant advantages in rapid response, temperature stability and energy efficiency. In addition, this study also discusses the problems and challenges that may be encountered in the practical engineering application of the liquid cooling system, puts forward the corresponding solutions, and looks forward to its application prospects in new energy vehicles, energy storage power stations and other fields. In summary, this paper not only provides a new method of battery thermal management, but also provides theoretical basi

关键词： Battery management systems

来源：评论

学校读者我要写书评

暂无评论

SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey 38

SeafloorAI: A Large-scale Vision-Language Dataset for Seaflo...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Nguyen, Kien X. Qiao, Fengchun Trembanis, Arthur Peng, xi Deep-REAL Lab Department of Computer and Information Sciences University of Delaware United States School of Marine Science and Policy University of Delaware United States

A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar image dataset publicly available, they suffer from limitations in terms of environment setting and scale. To bridge this gap, we introduce SeafloorAI, the first extensive AI-ready datasets for seafloor mapping across 5 geological layers that is curated in collaboration with marine scientists. We further extend the dataset to SeafloorGenAI by incorporating the language component in order to facilitate the development of both vision- and language-capable machine learning models for sonar imagery. The dataset consists of 62 geo-distributed data surveys spanning 17,300 square kilometers, with 696K sonar images, 827K annotated segmentation masks, 696K detailed language descriptions and approximately 7M question-answer pairs. By making our data processing source code publicly available, we aim to engage the marine science community to enrich the data pool and inspire the machine learning community to develop more robust models. This collaborative approach will enhance the capabilities and applications of our datasets within both fields. Our code repository are available under the CC-BY-4.0 license. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Review of Intrinsic image Decomposition

A Review of Intrinsic Image Decomposition

引用

image processing and Media Computing (ICIPMC), International conference on

作者： Siyuan Liu xiaoyue Jiang Letian Liu Zhaoqiang xia Sihang Dang xiaoyi Feng School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

Intrinsic image decomposition (IID) aims to extract intrinsic components from natural images, namely shading and reflectance, which is widely applied in computer vision and image processing tasks for the better understanding of the objects and scenes. However, IID is a severely ill-posed problem, extra constraints should be applied to address the decomposition problem. In this paper, we reviewed recent papers about intrinsic image decomposition. The extra cues from lighting, shape, color and textures are used to constrain the ill-posed problem. These papers can be mainly classified into two categories: physics-based methods and learning-based methods. Physics-based methods mainly introduced extra constraints into an optimization framework to solve the IID problem. While learning-based methods mainly depend on an encoder-decoder network with the explicit constraint from the labeled intrinsic images. Additionally, extra priors are introduced into the network by the form of network structure or specific feature. In order to make the IID problem more practical, researchers may explore how to achieve fast and accurate image decomposition with lower computing resources through optimization algorithms and model design.

关键词： Learning systems Accuracy image color analysis Shape Computational modeling image decomposition Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Human Action Recognition using Attention EfficientNet 6

Human Action Recognition using Attention EfficientNet

引用

6th International conference on Signal processing and Information Security, ICSPIS 2023

作者： Karsh, Bhumika Laskar, R.H. Karsh, R.K. Nit Silchar Dept. of Ece Silchar India

ISBN: (纸本)9798350329599

The integration of Human Activity Recognition (HAR) within automated residences and surveillance systems has significantly elevated the significance of this field for advanced investigation. HAR entails the deployment of artificial intelligence (AI)-driven algorithms to autonomously discern human actions. Given the remarkable potency of deep learning (DL) methodologies in deciphering intricate tasks, their supremacy over conventional machine learning (ML) techniques has rendered DL-driven HAR increasingly prevalent. This study centers on the application of the EfficientNet transfer learning architecture with attention mechanism to the realm of HAR image recognition, specifically utilizing skeleton-based representations. The objective was to discern and evaluate the architecture's performance attributes in this context. Notably, our results unveiled an impressive classification accuracy of 99% for EfficientNet. These findings underscore the efficacy of the EfficientNet model in enhancing HAR capabilities, showcasing its potential for pivotal roles in automated settings. © 2023 IEEE.

关键词： image recognition

来源：评论

学校读者我要写书评

暂无评论

Research on Early Warning and Monitoring systems for Contact Between Live Equipment and Foreign Objects

引用

IET image processing 2025年第1期19卷

作者： Yuan, Fang Chen, BoYuan Tan, YunFei Liao, KaiYang xia, WeiMin State Grid Shaanxi Electric Power Company Limited Xi'an Power Supply Company of State Grid Shaanxi Electric Power Co. Ltd. Xi'an China Xi'an University of Technology School of Printing Packaging and Digital Media Xi'an University of Technology Xi'an China

Addressing the safety hazards arising from inadequate clearance between tree growth and power facilities, this paper innovatively proposes an online monitoring and early warning system for distribution networks based on advanced image processing technology. The system integrates three core functions: automatic tree species identification, precise detection of safety distances between tree crowns and live equipment, and real-time foreign object contact warnings. By deploying maintenance-free online monitoring terminals, the system can monitor tree growth around the distribution network around the clock, continuously. It utilizes efficient backend algorithms to intelligently analyse the collected image data, promptly detecting and warning of potential tree-obstacle hazards. Compared to traditional manual inspections and UAV inspections, this system not only significantly improves monitoring accuracy and real-time performance but also overcomes the limitations of UAV inspections, such as high costs and poor real-time performance. The solution proposed in this paper is expected to fundamentally enhance the safe operation level of distribution networks and effectively reduce power accidents such as tripping and short circuits caused by tree obstacles. Future plans include further optimizing system functions to promote the wider application and promotion of the technology. © 2025 The Author(s). IET image processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Distance measurement

来源：评论

学校读者我要写书评

暂无评论

Multi-scale target detection for airfield visual navigation of taxiing aircraft 4

Multi-scale target detection for airfield visual navigation ...

引用

4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Luo, Qijun Chen, Jiagang Zhang, Xuerui Zhang, Tao Civil Aviation University of China Tianjin Intelligent Signal and Image Processing Experiment Tianjin300300 China

ISBN: (纸本)9798350394375

In the context of visual navigation-based civil aircraft assistance systems, the scene target scales captured by onboard cameras exhibit significant variations, and the computational power of airborne platforms is limited. To promptly and accurately identify threat targets in airborne scenic images, a lightweight multi-scale object detection algorithm based on improved YOLOv5s is proposed. Firstly, on the foundation of the Weighted Bidirectional Feature Pyramid Network (BIFPN), a Coordinate Attention (CA) mechanism is integrated to design the CA-BIFPN feature fusion network. This enhances feature representation for small targets and improves the model's learning capability for multi-scale targets. Secondly, a cross-level sub-network module is employed to lightweightly process the neck section, reducing the model's parameter count and thereby improving the overall network's detection speed. Experimental results on a self-constructed scene image dataset demonstrate that the proposed method achieves a detection frame rate of 89FPS, an increase of 5fps. The average precision mean (mAP) on the test set is 70.6%, a 3.4% improvement over the original YOLOv5s. The detection accuracy surpasses typical algorithms such as YOLOv6, YOLOv7, YOLOX, and Faster-RCNN. © 2024 IEEE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

A lossless information embedding algorithm based on data reconstruction

A lossless information embedding algorithm based on data rec...

引用

2022 International conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zhang, Yi Zhou, Quan Sun, Xuemin Huyan, Lang Liu, Juanni Liu, Mengyao Key Laboratory of Science and Technology on Space Microwave China Academy of Space Technology Xi’an China China Academy of Space Technology Xi’an China

ISBN: (纸本)9781510664616

At present, the network connection scale in the communication and transmission system is expanding with the increasing traffic, it is not easy to process the data, which is inefficient and difficult to ensure the quality of service. In order to increase the system transmission capacity and make the processing more efficient, a lossless information embedding algorithm based on data reconstruction is proposed, according to the characteristics of large variance and discrete distribution of different image, text, and voice data in the communication transmission system. Combined with the data characteristics of each group, interval mapping is carried out for segmented data, which is reorganized by certain rules to increase the redundancy of this segment and expand the data embedding range. The proposed algorithm realizes the lossless embedding of information and the original carrier data and embedded information are recovered losslessly. Simulation results show that the algorithm expands the redundancy range of segmented data and increases the capacity of information embedding, which is suitable for different data transmission systems. © 2023 SPIE.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

Face Inpainting Algorithm Combining Face Sketch and Gate Convolution 4

Face Inpainting Algorithm Combining Face Sketch and Gate Con...

引用

4th International conference on Natural Language processing, ICNLP 2022

作者： Wang, Fuping Hu, Yang Liu, Weihua Liu, Ying Xi'an University of Posts and Telecommunications Center for Image and Information Processing Shaanxi Xi'an710121 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Video surveillance contains a lot of facial occlusion, which brings great difficulties to the detection of criminal investigation cases. Current face inpainting algorithms are difficult to meet the uniqueness requirements of face comparison, due to the lack of a priori information within the occluded area. Face sketch drawn by experienced simulated portrait artist according to low-quality video or description of the victim contains lots of useful information. There, this paper proposes a face inpainting algorithm combining face sketch and gate convolution. First, the face sketch, used as guided information, integrates into the occluded face image to complete the missing area. Then, a generative adversarial networks (GAN) with gate convolution is designed for model training, which effectively suppresses the interference of the occlusion area to the inpainting process. The experimental results show that the proposed algorithm obtain the better inpainting results and larger SSIM compared with the other algorithm. The proposed obtain better comprehensive performance. © 2022 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Reconfiguration Design and Implementation of DPRAP-Based Multi-viewpoint image Coding Algorithm 4

Reconfiguration Design and Implementation of DPRAP-Based Mul...

引用

4th International conference on Natural Language processing, ICNLP 2022

作者： xie, xiaoyan Zhang, xihong Wang, Anqi Wang, Shuxin Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Aiming at the problem that the existing texture map and depth map coding algorithm software in Three Dimensional-High Efficiency Video Coding (3D-HEVC) takes a long time to code, and the utilization of special hardware is high, this paper proposes a multi-view image coding framework based on reconfigurable hardware. By reusing the same module algorithm in the coding loop, different module algorithms in the coding loop are reconstructed. Finally, four coding loops of the independent view texture map, depth map, dependent-view texture map, and depth map in multi-view coding are realized by using Dynamic Programmable Reconfigurable Array Processor (DPRAP). Experimental results show that the hardware resources required by the reconfigurable multi-view image coding scheme based on DPRAP are reduced by 7.79% LUTs and 7.88% Registers compared with those before reconstruction, thus achieving the purpose of reducing the use of hardware resources. Compared with the test results of HTM16.0, the PSNR loss of reconstructed images after the implementation of the reconfigurable multi-view coding proposed in this paper is only 0.99%. Therefore, this research has a certain reference value for real-time video coding of the 3D-HEVC algorithm. © 2022 IEEE.

关键词： Textures

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based image Recognition for Electronic Components Identification 5

Deep Learning-Based Image Recognition for Electronic Compone...

引用

5th IEEE International conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Royappa, Angelina Monga, Sandeep Geetha, H. Pavithra, G. Shaik, Bazani Vardhaman College of Engineering Department of IT Telangana Hyderabad India S.A. Engineering College Department of ECE Tamil Nadu Chennai India Oriental Institute of Science and Technology Department of Computer Science and Engineering India St.Joseph's College of Engineering OMR Semmenchery Department of Mathematics Tamil Nadu Chennai India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Ramachandra College Of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

The use of deep learning techniques in the field of picture recognition for the purpose of identifying electronic components. Because of the growing complexity and variety of electronic devices, it is essential for manufacturing, maintenance, and quality control to be able to identify components in a way that is both efficient and accurate. This is accomplished through the utilization of deep learning algorithms, the utilization of convolutional neural networks (CNNs), and advanced image processing techniques in order to improve recognition skills. The suggested model displays higher accuracy in differentiating diverse electronic components, overcoming problems such as differences in size, orientation, and lighting conditions. Moreover, the model succeeds in overcoming these challenges effectively. Based on the findings, it appears that deep learning-based image recognition provides a strong solution for automating the identification process in electronic component analysis. This, in turn, contributes to better efficiency and reliability in the electronics industry. This research makes a contribution to the expanding field of computer vision and highlights the potential of deep learning in the advancement of electrical component identification systems. © 2024 IEEE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：