检索结果-内蒙古大学图书馆

A Review of Smart Materials for the Boost of Soft Actuators,Soft Sensors,and Robotics Applications

Chinese Journal of Mechanical engineering 2022年第2期35卷 12-27页

作者： Yufei Hao Shixin Zhang Bin Fang Fuchun Sun Huaping Liu Haiyuan Li School of mechanical engineering University of Science and Technology BeijingBeijing 100083China School of Engineering and Technology China University of Geosciences(Beijing)Beijing 100083China Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and TechnologyTsinghua UniversityBeijing 100084China School of Automation Beijing University of Posts and TelecommunicationsBeijing 100876China

With the advance of smart material science,robotics is evolving from rigid robots to soft *** to rigid robots,soft robots can safely interact with the environment,easily navigate in unstructured fields,and be minimized to operate in narrow spaces,owning to the new actuation and sensing technologies developed by the smart *** the review,different actuation and sensing technologies based on different smart materials are analyzed and *** to the driving or feedback signals,actuators are categorized into electrically responsive actuators,thermally responsive actuators,magnetically responsive actuators,and photoresponsive actuators;sensors are categorized into resistive sensors,capacitive sensors,magnetic sensors,and optical waveguide *** introducing the principle and several robotic prototypes of some typical materials in each category of the actuators and *** advantages and disadvantages of the actuators and sensors are compared based on the categories,and their potential applications in robotics are also presented.

关键词： Smart material Soft robot Actuator Sensor

来源：评论

学校读者我要写书评

暂无评论

Exploring the Effect of Different Features Enhancement Methods on Wild Animal Classification 21

Exploring the Effect of Different Features Enhancement Metho...

引用

21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Li, Xu Li, Jianping Khoury College of Computer Science Northeastern university BostonMA02115 United States School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798331519254

This study investigates the impact of various feature enhancement methods on the accuracy of a deep learning model used for the classification of wild animal. We specifically compare three attention-based mechanisms, Convolutional Block Attention Module (CBAM)., Dual Attention Network (DANet), and Bottleneck Attention Module (BAM), each enhancing feature focus through different attention structures. Using the Animal Image Dataset from Kaggle, we evaluate the classification performance across 90 animal species. The CBAM achieves the highest test accuracy of 92.2 %, while DANet got 91.58% and the accuracy for BAM is 88.5%. In addition, a model applied all three methods sequentially was also examined, its redundancy and overfitting lead to a slight decrease in accuracy, which is 89.73%. Our findings suggest that the CBAM's sequential attention mechanism highlight the important features in the most effective way, making it a suitable choice for complex multi-class image classification tasks. © 2024 IEEE.

关键词： Invertebrates

来源：评论

学校读者我要写书评

暂无评论

RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

RPF-ELD: Regional Prior Fusion using Early and Late Distilla...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Haosen Zhang, Gengyuan Zhao, Yingnan Lai, Fang Cui, Wenwei Xue, Jiexiao Wang, Qihang Zhang, Hao Lin, Yi Beijing Jiaotong University School of Computer Science and Information Technology Beijing China Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China Harbin Engineering University School of Computer Science and Technology Harbin China University of Denver Department of Computer Science Denver United States University of York Wentworth College York United Kingdom Institute of Heilongjiang The Second Surveying and Mapping Harbin China Southeast University School of Information Science and Engineering Nanjing China Harbin Institute of Technology Faculty of Computing Harbin China Harbin Medical University School of Interdisciplinary Medicine and Engineering Harbin China

ISBN: (纸本)9798350386226

Breast cancer is one of the main factors responsible for the deaths of women worldwide. Ultrasound imaging is a key method for early detection of breast cancer, which can help patients gain valuable treatment time and improve their chances of survival. The computer-aided system of breast cancer recognition has started to receive attention due to the lack of experienced sonographers. Presently, most breast cancer recognition methods typically suffer from uncertain locations and proportions of tumor regions in ultrasound images. In this paper, we propose a novel Regional Prior Fusion framework using Early and Late Distillation (RPF-ELD), inspired by the knowledge distillation of the teacher-student framework, for breast cancer recognition in ultrasound images. Firstly, to enhance the concentration of the tumor regions, a high-performing prior-fused model is trained as the teacher model using ultrasound images with the corresponding regional prior information. Next, a diagnostic model is trained as the student model under the prior-fused model distillation using early and late features to implicitly obtain the regional prior knowledge. Finally, the diagnostic model recognizes the categories of breast cancer from only ultrasound images using the experience from distilled prior knowledge. Two publicly released datasets are used to evaluate the proposed RPF-ELD framework. Experimental results demonstrate that the proposed RPF-ELD surpasses current state-of-the-art methods. © 2024 IEEE.

关键词： Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

A survey on deep learning for polyp segmentation: techniques, challenges and future trends

引用

Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

来源：评论

学校读者我要写书评

暂无评论

A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

引用

40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Usage of Machine Learning and Artificial Intelligence in Industry 4.0 and Banking Sector 5

Usage of Machine Learning and Artificial Intelligence in Ind...

引用

5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Balakrishna, S. Arulkumar, V. Srihari, M. Rohith, Cmr Sri Krishna College of Engineering and Technology Computer Science and Business Systems Tamilnadu Coimbatore India Sgi Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9781665474672

In the banking sector, increased digitization of the economy has forced the businesses to re-evaluate their conventional business models, which is an indication that there is a need for quick and effective change in the banking industry to meet the demands of their clients while offering safe, simple, and connected services to the generation z customers. Industrial revolution 4.0 has affected and changed the banking industry more than 1.0 and 2.0 combined. This paper discusses how the industrial revolution 4.0 has affected and changed the financial and banking sector and primarily revolves around commercial and investment banks rather than reserve banks. It provides a vivid view starting from the digital economy to digitization of banks to the latest trends in the technology incorporated by the banks, to how it has changed the financial markets, to security advancements and finally to how it has changed the banking industry and how it has affected lives. © 2023 IEEE.

关键词： Industry 4.0

来源：评论

学校读者我要写书评

暂无评论

A Novel Self-Attentive Neural Network Hardware Accelerator for Edge Computing 4

A Novel Self-Attentive Neural Network Hardware Accelerator f...

引用

4th IEEE International Conference on Data science and computer Application, ICDSCA 2024

作者： He, Fangzhou Ding, Ke He, Xiao Chen, Mingzhe Mao, Kai Zhang, Qian State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China Chongqing University of Science and Technology Chongqing China Chongqing Changan Automobile Co. Ltd. Chongqing China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (纸本)9798350368239

High-precision floating-point self-attentive neural networks have high computational complexity and power consumption, posing significant challenges for deployment on edge devices. To tackle these problems, this paper propose a novel deep neural network hardware accelerator with unique vector tiling systolic array and vector ALU designs, where vector tiling systolic array is used to accelerate matrix multiplications, and vector ALU is used to process non-linear operations. By utilizing a systolic array with a systolic pipeline data path, matrix multiplication is accelerated. The vector tiling mechanism ensures that output results are immediately fed back into the arrays for further accumulation, without additional memory access or data loading delays. This approach significantly reduces arithmetic cycle overhead. Extensive experiments show that our method achieve 70×, 160×, 6× and 9× increase on peak energy efficiency ratio from Intel i3-2320 CPU, Intel i5-9400 CPU, RTX 2060 GPU and RTX 3060 GPU, respectively. The peak arthmetic of hardware accelerator is 2.41 TOPs in 8-bit fixed-point computation. ©2024 IEEE.

关键词： Vectors

来源：评论

学校读者我要写书评

暂无评论

CONTC: A Traffic Control System for Container Overlay Networks 31

CONTC: A Traffic Control System for Container Overlay Networ...

引用

31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zeng, Deze Gu, Lin Chen, Quan Lei, Yu School of Computer Science China University of Geosciences Hubei Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350399738

To enable inter-container communication of services on different hosts, container overlay network, the most widely used container network mode, provides a layer of virtual network between containers to transparent the physical device heterogeneous. However, overlay network produces two-layered packet encapsulation, and current container network management system cannot identify the source containers from the two-layered encapsulated packets or control the network traffics of different services. To tackle this issue, a traffic control system for container overlay networks (CONTC) is proposed and implemented by redesigning the packets processing procedure in overlay network model, enabling accurate network packet identification, multi-level network resource management and user-centric customized control. Extensive experiment results are conducted on different system settings and practical service cases to show that CONTC can provide accurate network traffic control for data flows with different network protocols and different packet sizes at both the service level and the container level. The results based on open-source microservice benchmarks also validate the correctness and effectiveness of CONTC by reducing the tail latency of single-service and multi-service environments by 37.53% and 22.33%, respectively. © 2023 IEEE.

关键词： Containers

来源：评论

学校读者我要写书评

暂无评论

Video Streaming Caching and Transcoding for Heterogeneous Mobile Users 29

Video Streaming Caching and Transcoding for Heterogeneous Mo...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Cai, Jinbo Xiao, Mingjun Sun, He Shao, Junjie Zhao, Yu Zhang, Tongxiao University of Science and Technology of China School of Computer Science and Technology Suzhou Institute for Advanced Study Hefei China Northeastern University at Qinhuangdao School of Computer and Communication Engineering China

ISBN: (纸本)9798350330717

With the increasing prevalence of video streaming media, users have higher requirements for the quality of video streams, which can be quantified as Quality of Experience (QoE). Caching and transcoding video chunks on edge servers are effective methods to improve users' QoE and reduce the cost of retrieving video chunks. In this paper, we investigate how to cache and transcode video chunks for mobile users to maximize their QoE, while taking into consideration the cooperation between edge servers, users' mobility, and heterogeneous preferences, simultaneously. We employ a Multi-Agent Reinforcement Learning (MARL) framework and propose an MARLbased Cache replacement and Transcoding (MACT) mechanism. More specifically, we formulate the problem as a multi-agent game and prove this game is a state-based potential game. Then, we decompose this multi-agent game into individual agent decision problems to be solved. Through extensive simulations, we demonstrate that the proposed MACT mechanism achieves about 19.3% performance improvement, compared to the state-of-the-art works. © 2023 IEEE.

关键词： edge caching multi-agent reinforcement learning quality of experience Video streaming

来源：评论

学校读者我要写书评

暂无评论

Proximity Detection Based Low-Cost and Handheld IoT Device for Tracking Lost Objects 10

Proximity Detection Based Low-Cost and Handheld IoT Device f...

引用

10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Sachan, Kanishk Kumar Natarajan, Anisha School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India

ISBN: (纸本)9798331533229

This paper presents a novel device leveraging the ESP32 micro controller to determine the proximity of a Wi-Fi signal source such as a smartphone hotspot or Wi-Fi router based on the received signal strength indicator (RSSI) values. The device connects to a smartphone hotspot, monitors RSSI values, and provides audio feedback proportional to the distance from the signal source. The proposed implementation can be employed as a low-cost handheld device for locating a misplaced Wi-Fi enabled smart device and also as a proximity tracker for items like bags. This paper discusses the design, implementation and potential applications of the proposed system. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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：