检索结果-内蒙古大学图书馆

LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

computer Modeling in engineering & sciences 2024年第9期140卷 2689-2706页

作者： Brij B.Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Computer Engineering Ronin InstituteMontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia School of Science and Technology Hong Kong Metropolitan University(HKMU)Hong KongChina

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall *** balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification *** proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly *** model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and *** study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

关键词： LSTM neural networks anomaly detection smart home health-care elderly fall prevention

来源：评论

学校读者我要写书评

暂无评论

Preliminary Design and Performance Analysis of A Solar-Powered Jumping-Gliding Martian Aircraft

Preliminary Design and Performance Analysis of A Solar-Power...

引用

2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Lou, Bin Xie, Anhuan Zhu, Shiqiang Liu, Yaru Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

A solar-powered jumping-gliding Martian aircraft is proposed as a new tool for Mars exploration, designed especially to perform long-term wide-range detection. A completely new way of continues detection performed by aircraft is created. A preliminary design of the jumping-gliding Martian aircraft is carried out while its flight performance is analyzed afterwards. The conceptual design of a large-aspect ratio foldable wing with a thin fuselage and a jumping device is put forward. The solar energy model, aerodynamic model and weight mode are all built. The energy balance models are established for the periods of detecting mission that the aircraft lands on the ground for harvesting solar energy and flies in the sky. The gliding performance of this aircraft is particularly discussed here. © 2022 IEEE.

关键词： Aspect ratio

来源：评论

学校读者我要写书评

暂无评论

Low-cost Mars Terrain Classification System Based on Coarse-grained Annotation

Low-cost Mars Terrain Classification System Based on Coarse-...

引用

2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Jian Chen, Yeheng Zhu, Shiqiang Li, Yuehua Xie, Tian Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images. © 2022 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Design and Analysis of Printed Conformal Antenna System for Inter and Intra Vehicular (V2V) Communication Utilizations

引用

Progress In Electromagnetics research C 2024年 142卷 143-150页

作者： Raju, Padmanabha Rao, Bathula Sadasiva Jackson, Beulah Islam, Tanvir Madhav, Boddapati Taraka Phani Das, Sudipta Devi, Yalavarthi Usha Department of ECE Sri Vishnu Engineering College for Women AP Vishnupur Bhimavaram India Department of ECE Swarnandhra College of Engineering and Technology Seetarampuram AP Narsapur India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Antennas and Liquid Crystal Research Center Department of ECE Koneru Lakshmaiah Education Foundation AP India Department of Electronics and Communication Engineering IMPS College of Engineering and Technology West Bengal Malda732103 India

A multiple antenna placement system analysis for the improvement of efficiency and capacity for inter and intra vehicular communication is proposed in this article. Four antennas are placed in the four locations of the vehicular body which includes roof, side mirror, rear screen, and dashboard. The constituted antenna occupying the dimension of 40×38.5×0.2 mm3 on flexible substrate material of photo paper and the bending analysis of the model as per the conformal nature on vehicular body is also analyzed and presented in this work. The received power from each receiving antenna response with respect to the transmitter has been analyzed. The channel capacity with respect to the antenna position for V2V communication is analyzed in different areas and in different environmental conditions. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Receiving antennas

来源：评论

学校读者我要写书评

暂无评论

Digital Twin Driven Intelligent Systems and Emerging Metaverse 1

引用

2023年

作者： Enis Karaarslan Ömer Aydin Ümit Cali Moharram Challenger

来源：评论

学校读者我要写书评

暂无评论

Analysis of the causes of power crises and their impacts on energy security 4

Analysis of the causes of power crises and their impacts on ...

引用

4th IEEE International Conference on electrical, computer, and Energy Technologies, ICECET 2024

作者： Fita, Nicolae Daniel Petrilean, Dan Codrut Diodiu, Ioan Lucian Rada, Andrei Cristian Schiopu, Adrian Mihai Muresan-Grecu, Florin Strategic Studies of Energy Security Research Center University of Petrosani Petrosani Romania University of Petrosani Mechanical and Industrial Engineering Transport Depart. Petrosani Romania Blaga University of Sibiu Computer Science and Electrical Engineering Department Lucian Sibiu Romania University of Petrosani Automation Computer Electrical and Power Engineering Depart. Petrosani Romania Strategic Studies of Energy Security European Center Petrosani Petrosani Romania Industrial Engineering Doctoral School University of Petrosani Petrosani Romania

ISBN: (纸本)9798350395914

Without a safety and secure Power System, a state cannot function by the fact that all public systems are interdependent and dependent of electricity. This Power System is composed of several critical power infrastructures of major importance without which the economy and industry of any state in the world could not function. The access to electricity that is created by these critical infrastructures is a common right of every citizen (according with The United Nation 21 Agenda 1992 stated that 'energy is essential for economical and social development and the improvement of quality of life') and it empowers all citizens of a state to find technical and social solutions in times of peace, but also in times of crisis or war, to prevent, correct or managing a blackout. In every state around the world, the Power System is the most important and complex system in that it provides and facilitates critical, domestic and industrial consumers the electricity needed to have a normal life in safety and security. Every Power System is in total interdependence with other systems: food system, water supply system, transport system, health system, financial system, commercial system, administrative system, communication system-information, etc. Through this paper, the authors aimed to take a look at the history, causes and effects of some of the most devastating blackouts that occurred nationally and worldwide between 1977-2018. © 2024 IEEE.

关键词： causes and impacts energy security power crises

来源：评论

学校读者我要写书评

暂无评论

Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment

Machine Vision Observation, Artificial Intelligence Pattern ...

引用

2024 IEEE International Reliability Physics Symposium, IRPS 2024

作者： Chen, Cheng-Shan Yang, Chun-Yen Yang, Shao-Jui Wang, Deng-Yi Kuo, Yaw-Wen Hsiao, Wei-Han Chou, Hsin-Hung Lin, Chia-Feng Li, Yung-Hui Wu, Yewchung Sermon Chen, Hsiang Han, Jung National Yang Ming Chiao Tung University Department of Materials Science and Engineering Hsinchu30010 Taiwan National Chi Nan University Department of Electrical Engineering Nantou54561 Taiwan National Chi Nan University Department of Applied Materials and Optoelectronic Engineering Nantou54561 Taiwan Chang Gung University Department of Electrical Engineering Taoyuan33302 Taiwan National Chi Nan University Department of Computer Science & Information Engineering Nantou54561 Taiwan National Chung Hsing University Department of Materials Science and Engineering Taichung40227 Taiwan Hon Hai Research Institute AI Research Center Taipei11494 Taiwan Yale University Department of Electrical Engineering New HavenCT06511 United States

ISBN: (纸本)9798350369762

This study delves into the degradation of GaN-based LEDs in saline environments, a relatively underexplored area of research. LEDs are known for their longevity, but face challenges under extreme conditions. Utilizing artificial intelligence, machine vision, and material analysis, this study detects early signs of LED degradation[1], [2]. The results highlight the impact of saline exposure on LED performance, with some LEDs continuing to function for up to 30 minutes before failure. Advanced circuits ensure uninterrupted operation. Encompassing electrical engineering, computer science, and materials science, this study provides a comprehensive approach to LED fault detection and protection. © 2024 IEEE.

关键词： III-V semiconductors

来源：评论

学校读者我要写书评

暂无评论

Measuring tree stem diameters and straightness with depth-image computer vision

引用

Journal of Forestry research 2023年第5期34卷 1395-1405页

作者： Hoang Tran Keith Woeste Bowen Li Akshat Verma Guofan Shao Department of Computer Science Purdue University305 N University StWest LafayetteIN 47907USA Hardwood Tree Improvement and Regeneration Center USDA Forest ServiceNorthern Research Station715 West State StreetWest LafayetteIN 47907USA Department of Forestry and Natural Resources Purdue University715 West State StreetWest LafayetteIN 47907USA School of Electrical and Computer Engineering Purdue University610 Purdue MallWest LafayetteIN 47907USA

Current techniques of forest inventory rely on manual measurements and are slow and labor *** developments in computer vision and depth sensing can produce accurate measurement data at significantly reduced time and labor *** developed the ForSense system to measure the diameters of trees at various points along the stem as well as stem *** use,mean absolute error(MAE),and root mean squared error(RMSE)metrics were used to compare the system against manual methods,and to compare the system against itself(reproducibility).Depth-derived diameter measurements of the stems at the heights of 0.3,1.4,and 2.7 m achieved RMSE of 1.7,1.5,and 2.7 cm,*** ForSense system produced straightness measurement data that was highly correlated with straightness ratings by trained *** ForSense system was also consistent,achieving sub-centimeter diameter difference with subsequent measures and less than 4%difference in straightness value between *** method of forest inventory,which is based on depth-image computer vision,is time efficient compared to manual methods and less computationally and technologically intensive compared to Structure-from-Motion(SFM)photogrammetry and ground-based LiDAR or terrestrial laser scanning(TLS).

关键词： Forest inventory Depth sensing computer vision Tree diameter Stem straightness Trunk volume

来源：评论

学校读者我要写书评

暂无评论

Wheat Rust Disease Segmentation from Ground Imagery 25

Wheat Rust Disease Segmentation from Ground Imagery

引用

25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Anwar, Hirra Khan, Muhammad Jawad Fayyaz, Muhammad Mian, Ajmal Saeed Shafait, Faisal School of Mechanical and Manufacturing Engineering Nust Islamabad Pakistan Prince Sattam Bin Abdul Aziz University Department of Electrical Engineering Al-Kharj Saudi Arabia Crop Diseases Research Institute National Agriculture Research Center Islamabad Pakistan University of Western Australia Perth Australia School of Electrical Engineering & Computer Science Nust Islamabad Pakistan National Center of Artificial Intelligence Deep Learning Laboratory Islamabad Pakistan

ISBN: (纸本)9798350379037

Wheat rust disease is a major agricultural threat, causing significant yield losses globally. Precision agriculture and autonomous robot systems represent pivotal advancements in modern farming and disease detection, leveraging technology to enhance crop management, optimize resource use, and improve productivity. These systems rely heavily on accurate and comprehensive data to train effectively. However, current datasets often fall short of meeting the specific needs of these advanced agricultural technologies. This paper addresses this gap by introducing a primary contribution in the form of an open-source dataset focused on wheat rust disease segmentation for precision agriculture. Open-source datasets in literature are either general plant disease datasets or wheat disease classification datasets. Our proposed wheat rust segmentation dataset, Focussed Rust Disease Images (FRDI), provides close-up views of disease manifestations on wheat leaves, capturing the natural variability and complexity of diseases in field conditions, thereby meeting the requirements of precision agriculture systems. This facilitates detailed analysis and effective training of deep learning models designed for disease detection and segmentation tasks for real-world agricultural environments. Furthermore, in our work we present an improved wheat rust disease detection and segmentation technique which is based on the concept of co-salient feature extraction for disease identification within complex and cluttered field images. By integrating localized and generic contextual information, our proposed approach effectively identifies key features and intricate patterns associated with wheat rust disease. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis

引用

IEEE Transactions on Neural Networks and Learning Systems 2024年第8期35卷 10736-10750页

作者： Lin, Chia-Hsiang Liu, Yangrui Chi, Chong-Yung Hsu, Chih-Chung Ren, Hsuan Quek, Tony Q. S. National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu300044 Taiwan National Cheng Kung University Department of Statistics Tainan70101 Taiwan National Central University Center for Space and Remote Sensing Research Taoyuan320317 Taiwan Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Hyperspectral tensor completion (HTC) for remote sensing, critical for advancing space exploration and other satellite imaging technologies, has drawn considerable attention from recent machine learning community. Hyperspectral image (HSI) contains a wide range of narrowly spaced spectral bands hence forming unique electrical magnetic signatures for distinct materials, and thus plays an irreplaceable role in remote material identification. Nevertheless, remotely acquired HSIs are of low data purity and quite often incompletely observed or corrupted during transmission. Therefore, completing the 3-D hyperspectral tensor, involving two spatial dimensions and one spectral dimension, is a crucial signal processing task for facilitating the subsequent applications. Benchmark HTC methods rely on either supervised learning or nonconvex optimization. As reported in recent machine learning literature, John ellipsoid (JE) in functional analysis is a fundamental topology for effective hyperspectral analysis. We therefore attempt to adopt this key topology in this work, but this induces a dilemma that the computation of JE requires the complete information of the entire HSI tensor that is, however, unavailable under the HTC problem setting. We resolve the dilemma, decouple HTC into convex subproblems ensuring computational efficiency, and show state-of-the-art HTC performances of our algorithm. We also demonstrate that our method has improved the subsequent land cover classification accuracy on the recovered hyperspectral tensor. © 2012 IEEE.

关键词： Convex optimization

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：