检索结果-内蒙古大学图书馆

21st Polish Control Conference, PCC 2023

作者： Kozyra, Andrzej Lipczynska, Aleksandra Koper, Piotr Babisz, Radoslaw Madej, Damian Nowakowski, Konrad Karwatka, Jakub Tomczok, Dominik Department of Measurements and Control Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Heating Ventilation and Dust Removal Technology Faculty of Energy and Environmental Engineering Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783031351693

"Artificial lung" is a device that simulates breathing process of occupants in a room. This allows you to safely test, e.g., the impact of HVAC systems on the spread of pathogens. The paper describes the concept of the device, its construction and performed tests. The project was implemented by students of two faculties: Automatic Control, electronics and computer Science, and Energy and Environmental engineering. Project supervisors from both faculties provided substantive supervision and support for students. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Development and study of the impacts of automated walking aids on the elderly 20

Development and study of the impacts of automated walking ai...

引用

20th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Tangjitjetsada, Maleeya Thianthong, Tawan Rakluea, Chawalit Loedhammacakra, Wisit Rakluea, Paitoon Tangthong, Nipont Mahatthanajatuphat, Chatree Chudpooti, Nonchanutt Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi Thailand Siam Airport Ground Services Co. Ltd Bangkok Thailand Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Bangkok Thailand

ISBN: (纸本)9798350310467

Since Thailand has now completely stepped into an aged society there are more than 20% of the population aged 60 years and over. There was also an increase in those who had problems with their balance. One of the main difficulties for these people is walking on their own. This paper presents an automatic walking aid for people with balance or walking problems. A specially designed automatic walking aid can be adjusted to six levels of height. It has an anti-collision warning system at a distance of up to 40 centimeters and displays GPS coordinates via SMS when falling. The experimental results of the prototype automatic walking aid showed that the walking aid could work as designed and could help people with balance problems walk more comfortably. © 2023 IEEE.

关键词： Walking aids

来源：评论

学校读者我要写书评

暂无评论

Mathematically Modified Adaptive Neuro-Fuzzy Inference System for an Intelligent Cyber Security System

引用

SN computer Science 2023年第5期4卷 453页

作者： Marimuthu, Divyapushpalakshmi Rao, Ganga Rama Koteswara Mehbodniya, Abolfazl Mohanasundaram, Dhavamani Sundaram, Chandru Kavindapadi Maria, Anto Bennet Mani, Devi Department of Computer Science and Engineering GITAM University Bangalore 561203 India Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 52002 India Department of Electronics and Communication Engineering Kuwait College of Science and Technology Kuwait City Kuwait Department of Mathematics Kongu Engineering College Tamil Nadu Perundurai 638060 India Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam 638401 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai 600062 India Department of Computer Science College of Science and Arts (Female) Sarat Abidah Campus King Khalid University Kingdom of Saudi Arabia Abha Saudi Arabia

Cybersecurity is defending sensitive information and crucial systems against Internet attacks. Cyber-Physical Systems (CPS) and the Internet of Things (IoT) are becoming increasingly significant in critical infrastructure, government, and daily life. Using persons, processes, technology, and policies to protect businesses, their sensitive data, and critical systems from Internet threats. Several researchers developed and analyzed the security system for protecting confidential information. The existing security system lacks sufficient accuracy, which results in cyber-attacks. A Mathematically Modified Adaptive Neuro-Fuzzy Inference System (MMANFIS) is used to attain efficiency and handle complex data. The proposed MMANFIS is effective in attaining an accuracy of 96% by retrieving significant features where the MMANFIS classifier accomplishes the classification. To demonstrate the proposed MMANFIS, the output classifier is compared with the results of the current methodologies. Two fuzzy inference-based rules are taken into consideration to clarify this MMANFIS model. The current and proposed optimum feature selection algorithms have examined the classifiers’ effectiveness using full and best features. The proposed MMANFIS approach outperforms in terms of accuracy, which attains 95% accuracy, and the accuracy rate is higher than the existing techniques. The processing time for the attack diagnosis has significantly reduced from 12.220 to 3.079 s while using optimal features of MM as inputs compared to using Gaussian Algorithm (GA) features. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classification Cyber-attacks Fuzzy Mathematical model Membership Neural network Optimal feature

来源：评论

学校读者我要写书评

暂无评论

IoT Enabled Wearable technology Jacket for Tracking Patient Health and Safety System 2

IoT Enabled Wearable Technology Jacket for Tracking Patient ...

引用

2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Santhanalakshmi, M. Radhika, M. Visuvanathan, G. Elavel Dhanalakshmi, S. Kavitha, G. Srinivasan, C. Department of Computer Science Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women Tamil Nadu Chennai India Department of Information Technology R.M.D. Engineering College Tamil Nadu Kavaraipettai India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Electronics and Communication Engineering Sri Muthukumaran Institute of Technology Tamil Nadu Chennai India Tamil Nadu Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Over the years, there have been ongoing efforts to use clothing as a vehicle for distributing digital capabilities. Information technology (IT) is being incorporated into garments to enable people to work more quickly. Because of their increasing availability, accessibility, and, most significantly, cost, wireless body area networks (WBANs) will undoubtedly be used in healthcare to enhance medical data utilization. Whether it has to do with post-operative recovery or Monitoring, applications for their function in the medical field are already being looked into. Wearable technology allows users to complete activities more efficiently by enhancing one or more of their senses with artificial information. The system proposes a method that displays critical information for doctors on a particular cloud for data analysis. In this concept, sensors are attached to someone's coat to collect real-time data. This data is analyzed, uploaded to the cloud using IoT Medium, and notified in the case of an alarming condition. The data access to real-time data, a website built into this module, and an alert notification system to warn the hospital and the family if the numbers are abnormal. A live stream is recorded using the camera, while the GPS module tracks the location. The doctor can take the proper step based on the patient's health. © 2023 IEEE.

关键词： Health monitoring IoT Sensors Wearable jacket Wearable technology

来源：评论

学校读者我要写书评

暂无评论

Image Captioning Using Deep Learning: Bridging the Gap between Vision and Natural Language Processing

Image Captioning Using Deep Learning: Bridging the Gap betwe...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Mudholkar, Rachana Ravindra Srivastava, Kingshuk Ahammed, Javeed Tajne, Niket Anap, Sharayu Kavitha, R. PCET's Pimpri Chinchwad College of Engineering and Research Computer Engineering Pune India Vivekananda Global University Sector 36 Computer Science & Engineering Rajasthan Jaipur303012 India Narasaraopeta Engineering College Department of Electronics & Communication Engineering Andhra Pradesh Narasaraopet522601 India Siu Symbiosis School for Online and Digital Learning Hill Base Campus Maharashtra Pune412115 India Autonomous Cs and DS G H Raisoni College of Engineering and Management Maharashtra Pune India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode638401 India

ISBN: (纸本)9798331508685

The process of writing educational captions for photos has evolved dramatically in recent years thanks to developments in deep learning technologies. The issue of fully understanding image content and creating logical, contextually relevant captions remains a serious difficulty despite notable progress. Image captioning is a crucial task that can be applied to editing tools, virtual assistants, image indexing, and assistance for people with disabilities. AI and Natural Language Processing (NLP) meet up in picture or image subtitling/caption, which is the test of making clear normal language phrases for pictures. Since profound learning strategies, Using Regional Neural Networks (RNNs) and Convolutions Neural Networking (CNN) for image extraction, which are as often as possible upgraded by consideration instruments, have been taken on, this interdisciplinary test has seen critical progressions. Text summarization, template construction, and information retrieval are the three main activities included in the suggested model. The both directions long short-term storage method is used to locate the written content utilizing every word in a phrase, extract the knowledge, and finally incorporate it within the semantically vector. This paper highlights the importance of deep learning as a whole while also examining the development of image inscribing, its important methodology, and state-of-the-art techniques. It additionally discusses the hardships, uses, and expected ways in this field. © 2025 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

LIVES: XAI-based Secure UAV Communication Framework for Essential Items Delivery

LIVES: XAI-based Secure UAV Communication Framework for Esse...

引用

Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Vidhi Ruparelia Sneh Shah Lakshin Pathak Rajesh Gupta Sudeep Tanwar Sahil Sawhney Ankur Gupta Department of Electronics and Communication Engineering Inst. of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science & Engineering Model Institute of Engineering & Technology Jammu & Kashmir India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

The application of Unmanned Aerial Vehicles (UAVs) for securely delivering essential items during extreme conditions includes numerous challenges, particularly in ensuring identification of potential threats. To solve this challenge, we propose a strong framework that makes use of Boosting algorithms, with a particular focus on XGBoost to enhance attack classification in UAV-based delivery systems. The XGBoost model achieved an accuracy of 93%, a precision of 0.9312, a recall of 0.9311, and an F1 score of 0.9283 , which makes it a highly suitable model for security of UAVs. Explainable AI (XAI) is used to further enhance classification including SHAP and LIME to identify the most influential features, such as the barometer and battery levels. Our framework provides a comprehensive solution for secure UAV-based deliveries. The experimental results proves the effectiveness of the proposed model which shows its potential for real-life applications.

关键词： Accuracy Explainable AI Autonomous aerial vehicles Boosting Data engineering Barometers Classification algorithms Batteries Security

来源：评论

学校读者我要写书评

暂无评论

A Semantic-Consistent Conditional Generative Adversarial Model for Zero-Shot Learning in Bearing Fault Diagnosis 13

A Semantic-Consistent Conditional Generative Adversarial Mod...

引用

13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Chen, Jizheng Yang, Rui School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China School of Electrical Engineering Electronics and Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350361674

The scarcity of labeled data remains one of the most significant challenges in industrial fault diagnosis, where the industrial processes lack readily sampled in many fault types. This issue is often called the zero-shot learning problem in industrial fault diagnosis. To address this challenge, this paper proposes a novel fault diagnosis model for rotating machinery, leveraging Generative Adversarial Networks (GANs) and semantic consistent loss. Our approach aims to generate a rich set of samples for fault types that are difficult to collect, thereby enabling the training of an accurate classification model. The proposed model introduces a one-dimensional convolutional neural network to enhance the quality of samples and the accuracy of the model. A semantic consistent discriminator is proposed to ensure that the distribution of generated samples closely aligns with the distribution of actual samples. The performance of the model is validated using the CWRU bearing fault dataset, and the results demonstrate its efficacy in addressing the zero-shot fault diagnosis challenge. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A Novel Dual Temporal Channel Convolutional Network with Exponential Distribution Optimizer for Satellite Image Classification with Google Earth Engine 4

A Novel Dual Temporal Channel Convolutional Network with Exp...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ragavendiran, S. D. Prabu Gowthami, N.R. Gayathri, C. Ramya, D. Padmapriya, D. Aparna, S.V. Suji Builders Engineering College Department of Computer Science and Engineering Tamil Nadu Tirupur638108 India Annamacharya University Department of Civil Engineering Andhra Pradesh Rajampet516126 India Mahendra Institute of Technology Tamil Nadu Namakkal637503 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Medchal Telangana Hyderabad501401 India

ISBN: (纸本)9798331529635

Classification of satellite images using Google Earth Engine entails identifying features on the satellite images to classify the land cover in a particular region through the use of deep learning algorithms. Such a process contributes to monitoring the environment and geographical distribution of resources, planning of cities, and management of resources. However, in many existing approaches, some challenges are worth noting. To address these challenges, this study introduces a new approach, the Dual Temporal Channel Convolutional Network with Exponential Distribution Optimizer (ANDualTCCNet+EDO) for satellite image classification using Google Earth Engine. This approach uses satellite imagery from the Landsat 8 Surface Reflectance dataset, with the input images preprocessed through the Adaptive Tri-Plateau Limit Tri-Histogram Equalization Algorithm (ATri-PLTri-HEA) technique. Following preprocessing, features are extracted from the images using the DenseNet201 method. Then, classification is carried out by A Novel Dual Temporal Channel Convolutional Network (ANDualTCCNet), optimized by the Exponential Distribution Optimizer (EDO) for precise satellite image classification in Google Earth Engine (GEE). The ANDualTCCNet+EDO method is implemented in Python. The proposed method was evaluated using the Landsat 8 Surface Reflectance dataset, achieving a high accuracy of 99.8%, surpassing existing methods. The results presented in this paper show that the use of this method allows for the accurate classification of satellite images in Google Earth Engine. The outcomes of the proposed method improve the method of satellite image classification in Google Earth Engine. This model captures both short- and long-duration temporal characteristics, therefore enhancing spatial-temporal feature acquisition compared to the previous method used in the classification of land cover, especially for temporal dynamics of land covers with different seasonal changes. © 2024 IEEE.

关键词： Equalizers

来源：评论

学校读者我要写书评

暂无评论

Primary Tumor and Lung Cancer Prediction on Dual Energy CT Images for Nodal Metastasis using Deep Learning Technique

Primary Tumor and Lung Cancer Prediction on Dual Energy CT I...

引用

Smart Technologies, Communication and Robotics (STCR)

作者： Ramkumar M Ganesh Babu C Sarath Kumar R S. Vidhya Praveen Hari G S Priyanka G S Department of Electronics and Communication Engineering Sri Krishna College of Engineeirng and Technology Tamilnadu India Department of Electronics and Instrumentation Engineering Bannari Amman Institute of Technology Tamilnadu India Computer Science and Engineering CMS College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamilnadu India Wood PLC Chennai

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

One of the most important clinical responsibilities for lung cancer (LC) survival and recurrence is detecting lymph node metastases (LNM). It is still challenging to identify medical strategies and pretreatment choices for patients with malignancies based on preoperative identification of nodal metastases. The quantitative properties of shape, first-order, second-order, and higher-order textures in computed tomography (CT) are used to identify tumors. Statistical and machine learning (ML) studies are utilized to integrate clinical data and assess the attributes of their individual. This research discovered several radiomic features associated with distant, nodal, and histological spread, which has the great capability as an imaging biomarker for pathological diagnosis and target therapy selection. To detect nodal metastases (Nmets) from primary LC tumors, a new deep prediction approach with a Size-Related Damper Block (SR-DB) has been developed using Gemstone Spectral Imaging (GSI) dual-energy CT. In an implementation, a recommended strategy for Nmet prediction trained on the 40 keV dataset offers an accuracy of 0.89 percent and a sensitivity score of 0.95 percent. The results show that tumor heterogeneity and size aid in determining whether nodal metastasis from the initial tumor is absent or present in the proposed model.

关键词： Deep learning Sensitivity Shape Computed tomography Lung cancer Shock absorbers Metastasis Robots Tumors Radiomics

来源：评论

学校读者我要写书评

暂无评论

Collision Avoidance System using YOLO-based Object Detection and Distance Estimation

Collision Avoidance System using YOLO-based Object Detection...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Shiv Akash Dhinakar Raj Ashita Singh Tarakshraj Singh Shekhawat Manav Muthanna M K. Sivasankaran School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems.

关键词： YOLO Training Road accidents Accuracy Vehicle safety Estimation Size measurement Road safety Reliability Collision avoidance

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：