检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

73 篇 期刊文献
68 篇 会议

馆藏范围

141 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

49 篇 理学
- 42 篇 物理学
- 5 篇 数学
- 4 篇 化学
- 4 篇 生物学
- 2 篇 大气科学
- 2 篇 地质学
- 2 篇 统计学（可授理学、...
- 1 篇 天文学
- 1 篇 地球物理学
34 篇 工学
- 9 篇 计算机科学与技术...
- 8 篇 机械工程
- 8 篇 电气工程
- 7 篇 软件工程
- 6 篇 电子科学与技术（可...
- 6 篇 生物医学工程（可授...
- 5 篇 生物工程
- 4 篇 光学工程
- 4 篇 仪器科学与技术
- 4 篇 材料科学与工程（可...
- 3 篇 控制科学与工程
- 3 篇 化学工程与技术
- 2 篇 动力工程及工程热...
- 2 篇 信息与通信工程
5 篇 管理学
- 4 篇 管理科学与工程(可...
4 篇 医学
- 4 篇 临床医学
- 2 篇 基础医学(可授医学...
- 2 篇 药学(可授医学、理...
2 篇 农学
- 2 篇 作物学
1 篇 法学
- 1 篇 社会学
1 篇 文学
- 1 篇 新闻传播学

主题

10 篇 monitoring
9 篇 wireless sensor ...
7 篇 electronic noses
7 篇 feature extracti...
6 篇 wireless communi...
6 篇 sensors
6 篇 training
5 篇 temperature sens...
5 篇 radio frequency
4 篇 gas sensor
4 篇 buildings
4 篇 ieee 802.11 stan...
4 篇 microorganisms
3 篇 object detection
3 篇 triboelectric na...
3 篇 support vector m...
3 篇 blood
3 篇 humidity
3 篇 temperature meas...
3 篇 volatile organic...

机构

12 篇 centre of excell...
10 篇 centre of excell...
8 篇 centre of excell...
8 篇 centre of excell...
7 篇 cas center for e...
7 篇 school of nanosc...
6 篇 universiti malay...
5 篇 2centre of excel...
5 篇 project excellen...
5 篇 project delivery...
4 篇 school of mechat...
4 篇 school of microe...
4 篇 3school of manuf...
4 篇 centre of excell...
4 篇 school of mechat...
4 篇 school of comput...
4 篇 centre of excell...
4 篇 centre of excell...
4 篇 centre of excell...
3 篇 center on nanoen...

作者

39 篇 a. zakaria
31 篇 ammar zakaria
23 篇 latifah munirah ...
20 篇 l. m. kamarudin
14 篇 ali yeon md shak...
13 篇 syed muhammad ma...
12 篇 a. y. m. shakaff
11 篇 k. kamarudin
11 篇 a.y.m. shakaff
10 篇 zhou li
8 篇 yubo fan
8 篇 n. yusuf
8 篇 a zakaria
7 篇 kamarulzaman kam...
7 篇 a.h. adom
7 篇 l.m. kamarudin
7 篇 l m kamarudin
7 篇 dongjie jiang
7 篇 zakaria ammar
7 篇 shaharil mad saa...

语言

138 篇 英文
2 篇 其他
1 篇 中文

检索条件"机构=Centre of Excellence for Advanced Sensor Technology"

共 141 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Implementation of Object Tracking using YOLOv7 Pose Estimation for Human Activity Recognition in Fabrication Yard via CCTV Videos 5

Implementation of Object Tracking using YOLOv7 Pose Estimati...

引用

5th International Conference on Smart sensors and Application, ICSSA 2024

作者： Madiahlagan, Sharitta Norzaidi, Farid Azhari Zakaria, Ammar Yeon, Ahmad Shakaff Ali Kamarudin, Latifah Munirah Zakaria, Syed Muhammad Mamduh Syed Ismail, Muhammad Khalid, Hafizal Baharom, Muhammad Azri Ahmad Amin, Ahmad Helmi M. Zolkipli, Khairul Hafiz Abdullah, Asrul Sani Faculty of Electrical Engineering and Technology University Malaysia Perlis Arau Malaysia Centre of Excellence for Advanced Sensor Technology University Malaysia Perlis Arau Malaysia Project Excellence PETRONAS Kuala Lumpur Malaysia Project Delivery and Technology PETRONAS Kuala Lumpur Malaysia

ISBN: (纸本)9798350354294

A new dataset is created in a fabrication yard environment for human activity classification. YOLOv7 object detection model is used to identify 5 classes which are welding, grinding, standing, sitting and walking. Five experiments were conducted, and Experiment 5 achieved the highest mean Average Precision (mAP_0.5) of 0.621. The mAP@0.5 value is the most critical metric as it reflects the model's ability to balance precision and recall across all confidence thresholds. This higher mAP@0.5 suggests that Experiment 5's model is better at accurately identifying. However, YOLOv7 object detection posed limitation to differentiate classes for sitting, standing and walking. The implementation of pretrained models YOLOv7 Object Tracking and YOLOv7 Pose Estimation is done respectively with YOLOv7 Object Detection to improve the detections. The YOLOv7 Object Tracking showed significant improvement in the detections of standing and walking but had limitation for sitting. The YOLOv7 Pose Estimation which uses the heuristic method showed significant improvement in the detections of standing, sitting and walking compared to YOLOv7 Object Tracking. © 2024 IEEE.

关键词： Heuristic methods

来源：评论

学校读者我要写书评

暂无评论

Feasibility Analysis of PPE Detection via YOLOv7 & YOLOv7-Pose to Enhance Workplace Safety 5

Feasibility Analysis of PPE Detection via YOLOv7 & YOLOv7-Po...

引用

5th International Conference on Smart sensors and Application, ICSSA 2024

作者： Bakar, Nor Awatif Abu Norzaidi, Farid Azhari Kamarudin, Latifah Munirah Zakaria, Ammar Zakaria, Syed Muhammad Mamduh Syed Yeon, Ahmad Shakaff Ali Visvanathan, Retnam Zolkipli, Khairul Hafiz Abdullah, Asrul Sani Amin, Ahmad Helmi M. Baharom, Muhammad Azri Ahmad Khalid, Hafizal Faculty of Electrical Engineering & Technology University Malaysia Perlis Arau Malaysia Centre of Excellence for Advanced Sensor Technology University Malaysia Perlis Arau Malaysia Engineering Department IDERIA Sdn Bhd Kangar Malaysia Project Delivery and Technology PETRONAS Kuala Lumpur Malaysia Project Excellence PETRONAS Kuala Lumpur Malaysia

ISBN: (纸本)9798350354294

Ensuring workplace safety in Pre-Fabrication environments is important, particularly in adhering to safety compliance protocols through the correct usage of Personal Protective Equipment (PPE). However, existing systems often fall short in detecting whether PPE is correctly worn or not, posing a risk to workplace safety. By creating a specialized dataset for Pre-Fabrication environments, the system can detect four PPE tools which are hard hat, glove, safety shoes and welding mask. The dataset contains 934 training images and 255 images for testing and validation, respectively. After rigorous training, testing and validation process, the experimental result and performance evaluation show that the PPE detection model achieved 96.8% precision, 95% recall and a mean Average Precision (mAP) of 0.879. However, with the current model, there are several false positive errors from the result where the model falsely detects other objects as PPE. Therefore, this study proposes a dual-model system employing YOLOv7 object detection and pose estimation models to further improve the accuracy of the PPE detection model and reduce false positives. This dual-model system, validated through live CCTV footage, provides a comprehensive solution for enhancing safety protocols in Pre-Fabrication workspaces. © 2024 IEEE.

关键词： Protective clothing

来源：评论

学校读者我要写书评

暂无评论

Improving Personal Protective Equipment Detection in Fabrication Yard Using Image Augmentation with YOLOv7

Improving Personal Protective Equipment Detection in Fabrica...

引用

International Conference on Smart sensors and Application (ICSSA)

作者： Farid Azhari Norzaidi Ammar Zakaria Latifah Munirah Kamarudin Ahmad Shakaff Ali Yeon Retnam Visvanathan Syed Muhammad Mamduh Syed Zakaria Muhammad Azri Ahmad Baharom Hafizal Khalid Asrul Sani Abdullah Khairul Hafiz Zolkipli Ahmad Helmi M Amin Mohd Nizazwajidi Hassan Centre of Excellence for Advanced Sensor Technology Universiti Malaysia Perlis (UniMAP) Arau Perlis Centre of Excellence for Advanced Sensor Technology Universiti Malaysia Perlis Arau Perlis Project Delivery and Technology PETRONAS Kuala Lumpur Malaysia Project Excellence PETRONAS Kuala Lumpur Malaysia

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

Accurate object detection is crucial for industrial safety applications such as monitoring the safety of workers in an industrial location. This study examines the impact of applying data augmentation techniques in tandem with the YOLOv7 object detection model to identify three desired classes which are hard hats, safety shoes, and regular shoes. The utilized dataset is 2300 for training images, 400 for validation images, and 600 for test images, covering various scenarios from the industrial environment and general environment. Five experiments were conducted using different combinations of data augmentation methods which are: padding, flipping, resizing, and cropping. The experiment that applied padding, resizing, and flipping to the original 640×640 images achieved the highest mean Average Precision (mAP_0.5) of 0.8597. Another experiment, which combined multiple augmentations across different resolutions, including padding, flipping, resizing, and cropping, produced the second-highest mAP_0.5 of 0.8568. The baseline experiment without augmentation achieved a mAP_0.5 of 0.6412, while resizing images to 320×320 without other augmentations resulted in a slight decrease to 0.6296. An experiment combining resizing, cropping, and flipping on 320×320 images yielded a mAP_0.5 of 0.8323. Based on the results achieved, the augmentation techniques that were applied significantly enhanced the results achieved compared to the non-augmented baseline runs. The findings highlight the effectiveness of data augmentation in improving the generalization capabilities of object detection models by combining diverse augmentation methods like padding, resizing, and flipping proved to be the most effective combination thus suggesting a synergistic effect for the detection of hard hat, safety shoes and shoes.

关键词： Personal protective equipment Training Accuracy Crops Footwear Object detection Data augmentation Data models Safety Testing

来源：评论

学校读者我要写书评

暂无评论

Polyvinyl Chloride (PVC) Ultra-Wideband Wearable Antenna Under Bending and Wet Conditions

Polyvinyl Chloride (PVC) Ultra-Wideband Wearable Antenna Und...

引用

Communication Engineering and Emerging Technologies (ICoCET), IEEE International Conference on

作者： Hadi Hassan Anvar Shakhirul Mat Salleh Latifah Munirah Kamarudin Mohd Zamri Zahir Ahmad Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering & Technology Advanced Communication Engineering (ACE) Centre of Excellence Universiti Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering & Technology Advanced Sensor Technology Centre of Excellence (CEASTech) Universiti Malaysia Perlis Perlis Malaysia

ISBN: (数字)9798331504144

ISBN: (纸本)9798331504151

This paper presents an investigation into the performance of Polyvinyl Chloride (PVC) Ultra-wideband (UWB) wearable antennas under bending and wet conditions. The study examines the behavior of PVC UWB wearable antennas when subjected to bending, focusing on key performance metrics such as resonance frequency and impedance matching. The results demonstrate that the antenna maintains efficient performance, with S11 values consistently below −10 dB across various bending angles at the frequency range of 3.1 to 10.6 GHz. Furthermore, the study evaluates the impact of wet conditions on the antenna's performance, specifically analyzing the effects of moisture on impedance and transmission coefficients. The findings reveal that the antenna exhibits minimal performance degradation in wet environments, with S11 values remaining below −10 dB after exposure to water, thereby confirming the robustness of PVC material in maintaining antenna functionality under adverse conditions.

关键词： Degradation Transmitting antennas Resonant frequency Bending Reliability engineering Ultra wideband antennas Robustness Resonance Wearable antennas Wideband

来源：评论

学校读者我要写书评

暂无评论

Investigation of The Dynamic Deflection of Conveyor Belts Via Simulation Modelling Methods on Idler Factor 3

Investigation of The Dynamic Deflection of Conveyor Belts Vi...

引用

3rd International Conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Munir, Handy Ali Zakaria, Ammar Ponniran, Asmarashid Rahman, Md Tasyrif Abdul Marimuthu, Thenesh Centre of Excellence for Advanced Sensor Technology Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Malaysia Faculty of Mechanical Engineering Technology Universiti Malaysia Perlis Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia

Belt conveyor is today's very important mechanism in material handling industries and is most commonly used to transport bulk materials such as grain, salt, coal and sand. Belt deflection indicates belt is loosening. It occurs between the idler roll sets due to the self-weight of the belt and the forces from the bulk material. This paper includes detailed analysis of the conveyor belt deflection under pragmatic load conditions. The aim of the project is to develop a Finite Element Method (FEM) simulation model for the deflection analysis of a conveyor belt using load data for bulk material calculated by a coupled Discrete Element Method (DEM) simulation. The scope of this project is on idler roll aspects. Two types of three-trough idlers were used in this project which were made of steel and plastic respectively. A variety of idler spacing were used to determine the most efficient configuration for the belt. This paper will explain the FEM procedures for the modelling of the belt behavior and the working principle of a coupled FEM-DEM analysis. Experimental results were presented and compared based on parameters. Comparisons showed plastic idler-rollers with smaller gap is better than the rest and it could be optimized to improve the performance and efficiency. © Published under licence by IOP Publishing Ltd.

关键词： Belt conveyors

来源：评论

学校读者我要写书评

暂无评论

Implementation of Object Tracking Using YOLOv7 Pose Estimation for Human Activity Recognition in Fabrication Yard via CCTV Videos

Implementation of Object Tracking Using YOLOv7 Pose Estimati...

引用

International Conference on Smart sensors and Application (ICSSA)

作者： Sharitta Madiahlagan Farid Azhari Norzaidi Ammar Zakaria Ahmad Shakaff Ali Yeon Latifah Munirah Kamarudin Syed Muhammad Mamduh Syed Zakaria Muhammad Ismail Hafizal Khalid Muhammad Azri Ahmad Baharom Ahmad Helmi M Amin Khairul Hafiz Zolkipli Asrul Sani Abdullah Faculty of Electrical Engineering and Technology University Malaysia Perlis Arau Malaysia Centre of Excellence for Advanced Sensor Technology University Malaysia Perlis Arau Malaysia Project Delivery and Technology PETRONAS Kuala Lumpur Malaysia Project Excellence PETRONAS Kuala Lumpur Malaysia

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

A new dataset is created in a fabrication yard environment for human activity classification. YOLOv7 object detection model is used to identify 5 classes which are welding, grinding, standing, sitting and walking. Five experiments were conducted, and Experiment 5 achieved the highest mean Average Precision (mAP_ 0.5) of 0.621. The mAP@0.5 value is the most critical metric as it reflects the model's ability to balance precision and recall across all confidence thresholds. This higher mAP@0.5 suggests that Experiment 5's model is better at accurately identifying. However, YOLOv7 object detection posed limitation to differentiate classes for sitting, standing and walking. The implementation of pretrained models YOLOv7 Object Tracking and YOLOv7 Pose Estimation is done respectively with YOLOv7 Object Detection to improve the detections. The YOLOv7 Object Tracking showed significant improvement in the detections of standing and walking but had limitation for sitting. The YOLOv7 Pose Estimation which uses the heuristic method showed significant improvement in the detections of standing, sitting and walking compared to YOLOv7 Object Tracking.

关键词： Legged locomotion Training YOLO Welding Pose estimation Object detection Cameras Object tracking Long short term memory Videos

来源：评论

学校读者我要写书评

暂无评论

Towards Efficient Gait Analysis Using Gyroscope: Preliminary Results and Insights

Towards Efficient Gait Analysis Using Gyroscope: Preliminary...

引用

International Conference on Smart sensors and Application (ICSSA)

作者： Thenmoli Manogar Ahmad Shakaff Ali Yeon Mohd Nor Fakhzan Mohd Kazim Shuhaida Yahud Centre of Excellence for Advanced Sensor Technology University Malaysia Perlis (UniMAP) Arau Perlis Faculty of Electrical Engineering Technology University Malaysia Perlis (UniMAP) Arau Perlis Faculty of Electronic Engineering and Technology University Malaysia Perlis (UniMAP) Arau Perlis

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

Wearable sensor technology, combined with smartphone capabilities, has become a powerful and costeffective solution for gait analysis, offering real-time monitoring of human movements. This study introduces a sensor-based gait event detection system using gyroscope data from the publicly available MotionSense dataset to analyze walking and jogging. The dataset, which includes six subjects of varying demographics, provides a broader understanding of gait patterns. Gyroscope data from the X-axis was used to detect key biomechanical events, such as heel strikes, toe-offs, and mid-swings, as these align with the sagittal plane and are critical in understanding walking dynamics. The gait event detection algorithm The detection algorithm, developed in Python, incorporates advanced signal processing techniques like the Savitzky-Golay filter, to reduce noise while preserving key data. By using a threshold-based and sliding window methods, the system identifies gait events accurately and calculates key gait parameters such as cadence, stance time, swing time, and phase distributions. The results show significant differences between walking and jogging, with jogging exhibiting a higher cadence (82.71-90.23 steps/min) and shorter stance times (0.37-0.46 seconds) compared to walking (52.63-60.15 steps/min, stance time 0.61-0.85 seconds). This research demonstrates that wearable sensor technology offers precise and scalable alternatives to expensive, complex traditional gait analysis systems. The findings show the system's potential for clinical monitoring, rehabilitation, sports science, and smart wearable device development.

关键词： Legged locomotion Event detection Heuristic algorithms Signal processing algorithms Gain measurement Gyroscopes Wearable sensors Monitoring Intelligent sensors Sports

来源：评论

学校读者我要写书评

暂无评论

Feasibility Analysis of PPE Detection Via YOLOv7 & YOLOv7-Pose to Enhance Workplace Safety

Feasibility Analysis of PPE Detection Via YOLOv7 & YOLOv7-Po...

引用

International Conference on Smart sensors and Application (ICSSA)

作者： Nor Awatif Abu Bakar Farid Azhari Norzaidi Latifah Munirah Kamarudin Ammar Zakaria Syed Muhammad Mamduh Syed Zakaria Ahmad Shakaff Ali Yeon Retnam Visvanathan Khairul Hafiz Zolkipli Asrul Sani Abdullah Ahmad Helmi M Amin Muhammad Azri Ahmad Baharom Hafizal Khalid Faculty of Electrical Engineering & Technology University Malaysia Perlis Arau Malaysia Centre of Excellence for Advanced Sensor Technology University Malaysia Perlis Arau Malaysia Engineering Department IDERIA Sdn Bhd Kangar Malaysia Project Delivery and Technology PETRONAS Kuala Lumpur Malaysia Project Excellence PETRONAS Kuala Lumpur Malaysia

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

关键词： Personal protective equipment Training Analytical models Protocols Accuracy Employment Pose estimation Object detection Safety Testing

来源：评论

学校读者我要写书评

暂无评论

Versatile and Easy-to-Operate Grading System for High-Grade Table Grapes: Leveraging Deep Learning, Computer Vision, and IoT

Versatile and Easy-to-Operate Grading System for High-Grade ...

引用

International Conference on Cyberworlds

作者： Muhammad Faris Bin Kamarudzaman Prawit Buayai Yin Suan Tan Latifah Munirah Kamarudin Xiaoyang Mao Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi Graduate Faculty of Interdisciplinary Research University of Yamanashi Kofu JAPAN Centre of Excellence for Advanced Sensor Technology (CEASTech) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

Grapes, ranking among the top fruits globally, undergo essential grading processes to ensure quality and market readiness. However, conventional grading methods rely heavily on subjective human expertise, leading to inconsistencies and inefficiencies. To address this, we present a novel grape grading system integrating computer vision, artificial intelligence (AI), and IoT technologies. Our system utilizes deep neural networks (DNNs) based prediction model to accurately grade the grapes, and a sensing station equipped with cameras and weight sensors to capture images and weight data of grape bunches. Our research focuses on Shine Muscat grapes, a prominent variety in Japan. The system implements a multimodal grading approach that combines image and weight information. Our system offers portability, affordability, and operational versatility that prioritizes the safety of the grape bunch. Experimentation with different DNN architectures and input data configurations reveals the superiority of ResNet-18 for grading classification using multiple images from different angles, achieving $85.71 \%$ accuracy. On the other hand, when the weight information is included with the images, ResNet-50 performs the best with $82.86 \%$ accuracy. Additionally, we analyze mispredictions across grade classes, highlighting the model’s challenges in discerning subtle differences between closely ranked grades.

关键词： Deep learning Computer vision Technological innovation Accuracy Computational modeling Pipelines Predictive models Data models Artificial intelligence Sorting

来源：评论

学校读者我要写书评

暂无评论

Superpixels-Based Automatic Density Peaks and Fuzzy Clustering Approach in COVID-19 Lung Segmentation

Superpixels-Based Automatic Density Peaks and Fuzzy Clusteri...

引用

Biomedical Engineering Conference (NBEC), IEEE National

作者： Ooi Wei Herng Aimi Salihah Abdul Nasir Fatin Nabilah Shaari Abdul Syafiq Abdull Sukor Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Faculty of Electrical Engineering & Technology Centre of Excellence for Sports Engineering (SERC) Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Faculty of Mechanical Engineering & Technology Centre of Excellence for Advanced Sensor Technology (CEASTech) Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia

Clustering algorithms that rely on minimizing an objective function suffer from the drawback of requiring manual setting of the number of clusters. This limitation becomes particularly evident when applied to image segmentation, where the large number of pixels can lead to memory overflow issues. To overcome this challenge, a reference of Automatic Fuzzy Clustering Framework (AFCF) for image segmentation method has been used as the comparison to the Density Peaks Clustering (DPC) algorithm. AFCF used superpixel algorithm to reduce the spatial information of data during computation, DPC algorithm to generate decision graph, and prior entropy-based fuzzy clustering (PEFC) algorithm to achieve fully automatic segmentation method in determining the number of cluster and the clustering result. In this study, 50 open-source healthy, COVID-19 and pneumonia infected radiographs dataset are acquired from the Kaggle and Github. The radiographs dataset that segmented by DPC is down sampling to 100*100 pixels due to overloading computation. At the end of the image segmentation, a segmentation performance evaluation is conducted based on sensitivity, specificity, accuracy, precision, recall, F-score and time consumed. The result shows that AFCF algorithm has the better overall performance with higher accuracy of 92.48% and F-score 0.9455. Meanwhile, the most highlighted evaluation index is drop to the time consume comparison, AFCF has around 2.7 times faster processing speed compare to DPC algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共15页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：