检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Unmanned systems, ICUS 2023

作者： Fan, Yongsheng Liu, Weixing Liao, Mingrui Zheng, Wenkai Hu, Boqin Bai, Chengchao Aerospace System Engineering Shanghai Shanghai China Harbin Institute of Technology Department of Aerospace Engineering Harbin China

ISBN: (纸本)9798350316308

The use of air-ground coordination system in urban environment can significantly increase the perception ability of the system, meanwhile, conversion of air-ground perspective is the key problem that restricts the realization of autonomous cooperation between UAV (Unmanned Aerial Vehicle) and UGV (unmanned ground vehicle). Considering the characteristics of road traffic signs in the urban environment, this paper proposes an air-ground view conversion method based on the semantic information of road traffic signs. Firstly, we preprocess the aerial view images and ground view images, establish semantic segment layout descriptors from the semantically segmented images according to the encoding rules. Secondly, the descriptors can be matched with each other to solve the homography matrix and calculate the relative position and attitude between the UAV and UGV by decomposing the matrix. Finally, we conduct experiments in the Gazebo simulation environment. The experimental results verify the accuracy and practicability of the method proposed in this paper and the problems and limitations are analyzed. © 2023 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II:Convolutional neural network and long short-term memory integration

引用

Energy and AI 2024年第3期17卷 569-578页

作者： Yiheng Pang Anqi Dong Yun Wang Zhiqiang Niu Renewable Energy Resources Lab(RERL) Department of Mechanical and Aerospace EngineeringUniversity of CaliforniaIrvineCaliforniaUSA92697-3975 College of Engineering School of System Design and Intelligent ManufacturingSouthern University of Science and TechnologyChina Department of Aeronautical and Automotive Engineering Loughborough UniversityUnited KingdomLE113TU

Lithium-ion (Li-ion) batteries have emerged as a cornerstone of electric vehicles (EVs), enabling the road transportation towards net zero. The success of electric vehicles largely hinges on the battery performance and safety. It is challenging to test and predict battery performance and safety issues by conventional methods, which are usually time-consuming and expensive, involving significant human and measurement errors. To enable the quick estimation of battery performance and safety, we developed three data-driven machine learning (ML) models, namely a convolutional neural network (CNN), a long short-term memory (LSTM), and a CNN-LSTM to predict battery discharge curves and local maximum temperature (hot spot) under various operating conditions. The developed ML models mitigated data scarcity by employing a three-dimensional multi-physics Li-ion battery model to generate enormous and diverse high-quality data. It was found the CNN-LSTM model outperforms the others and achieved high accuracy of 98.68% to learn discharge curves and battery maximum temperature, owing to the integration of spatial and sequential feature extraction. The battery safety can be improved by comparing the predicted maximum battery temperature against safe temperature threshold. The proposed data development and data-driven ML models are of great potential to provide digital tools for engineering high-performance and safe EVs.

关键词： Li-ion batteries CNN LSTM Integration Performance Hot spot

来源：评论

学校读者我要写书评

暂无评论

Smart Home Integration and Data Sharing with ActivityPub and UPOD

Smart Home Integration and Data Sharing with ActivityPub and...

引用

2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Zhou, Tao Ni, Yunhao Gao, Xinzhe Chen, Hong Daiichi Institute of Technology Department of Information and Electronics System Engineering Tokyo Japan

ISBN: (纸本)9798350304602

With the widespread adoption of IoT, smart homes have gradually permeated our daily lives. However, most of this data is held by the data platform company. Platform companies sometimes leak data and sometimes discontinue unprofitable services. Concurrently, device compatibility, personal data security, and fragmentation problems are also becoming increasingly serious. In this study, we developed the Ubi-House system that integrates social and IoT data to provide users with personalized smart home management. The system adheres to a 'Human-centric' principle, allowing users to manage their own smart home data and social network data. Through an authorization management feature, users can autonomously decide who to share their data with and to what extent. We advocate for the use of the ActivityPub protocol, which is widely used in decentralized social networks, as the communication protocol for smart homes. This facilitates centralized management of a user's smart home data and social network data. We have attempted to convert the communication protocols of existing smart home devices to the ActivityPub protocol, aiming to achieve interoperability among IoT devices from various companies. Our research addresses the fragmentation issue of smart home data. With the Ubi-House system, users can better control their data and ensure personal data security. Upon user authorization, third-party applications can assist users in understanding their usage habits, environmental conditions, and more, enhancing their comprehension of their own data. The comprehensive data collected by the Ubi-House system can be utilized with digital twin technology to construct virtual user behaviors, forecast device usage, gain a more accurate understanding of user needs, and deliver personalized services. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Wheel-track Robot in Hybrid Driving Mode

引用

Journal of Institute of Control, Robotics and systems 2022年第12期28卷 1170-1175页

作者： Yoon, Gun-Hong Lee, Ji-Woo Kim, Jangseob Shin, Dongwon Department of Mechanical System Engineering Kumoh National Institute of Technology Korea Republic of Department of Mechanical Engineering Graduate School Kumoh National Institute of Technology Korea Republic of

In this paper, we propose a method to design a wheel track robot that can change the driving mode depending on the terrain. The proposed wheel-track robot has a mechanism to change the driving mode, with a parallelogram four-joint link structure that uses wheels to drive on flat ground and tracks to drive on rough ground. In addition, a gyro sensor, a laser sensor, and an ultrasonic sensor are used to recognize the surrounding environment of the robot. An algorithm was developed to allow the robot to change the driving mode by itself and overcome rough terrain, where it was controlled using Arduino Mega 2560. Wheel-track robots were designed and manufactured, where driving tests confirmed that the robots can recognize the environment and change driving modes on their own. © ICROS 2022.

关键词： Robots

来源：评论

学校读者我要写书评

暂无评论

Knowledge and data jointly driven aeroengine gas path performance assessment method

引用

Chinese Journal of Aeronautics 2024年第5期37卷 533-557页

作者： Zhe WANG Xuyun FU Rui ZHANG Zhengfeng BAI Xiangzhao XIA Wei JIANG Department of Mechanical Engineering Harbin Institute of TechnologyWeihai 264209China Weihai Key Laboratory of Intelligent Operation and Maintenance Harbin Institute of TechnologyWeihai 264209China Control System Design Laboratory AECC ShenYang Engine Research InstituteShenyang 110015China

Aeroengines,as the sole power source for aircraft,play a vital role in ensuring flight *** gas path,which represents the fundamental pathway for airflow within an aeroengine,directly impacts the aeroengine's performance,fuel efficiency,and ***,timely and accurate evaluation of gas path performance is of paramount *** paper proposes a knowledge and data jointly driven aeroengine gas path performance assessment method,combining Fingerprint and gas path parameter deviation ***,Fingerprint is used to correct gas path parameter deviation values,eliminating parameter shifts caused by non-component performance ***,coarse errors are removed using the Romanovsky criterion for short-term data divided by an equal-length overlapping sliding ***,an Ensemble Empirical Mode Decomposition and Non-Local Means(EEMD-NLM)filtering method is designed to“clean”data noise,completing the preprocessing for gas path parameter deviation ***,based on the characteristics of gas path parameter deviation values,a Dynamic Temporary Blended Network(DTBN)model is built to extract its temporal features,cascaded with Multi-Layer Perceptron(MLP),and combined with Fingerprint to construct a Dynamic Temporary Blended AutoEncoder(DTB-AutoEncoder).Eventually,by training this improved autoencoder,the aeroengine gas path multi-component performance assessment model is formed,which can sufficiently decouple the nonlinear mapping relationship between aeroengine gas path multi-component performance degradation and gas path parameter deviation values,thereby achieving the performance assessment of engine gas path *** practical application cases,the effectiveness of this model in assessing the aeroengine gas path multi-component performance is verified.

关键词： Performance assessment Aeroengine Fingerprint Gas path parameter deviation values Jointly drive

来源：评论

学校读者我要写书评

暂无评论

Incorporating Lasso Regression to Physics-Informed Neural Network for Inverse PDE Problem

引用

Computer Modeling in engineering & Sciences 2024年第10期141卷 385-399页

作者： Meng Ma Liu Fu Xu Guo Zhi Zhai National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong UniversityXi’an710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an710049China Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01854USA

Partial Differential Equation(PDE)is among the most fundamental tools employed to model dynamic *** PDE modeling methods are typically derived from established knowledge and known phenomena,which are time-consuming and ***,discovering governing PDEs from collected actual data via Physics Informed Neural Networks(PINNs)provides a more efficient way to analyze fresh dynamic systems and establish *** study proposes Sequentially Threshold Least Squares-Lasso(STLasso),a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares(STLS)algorithm,which can complete sparse regression of PDE coefficients with the constraints of l0 *** further introduces PINN-STLasso,a physics informed neural network combined with Lasso sparse regression,able to find underlying PDEs from data with reduced data requirements and better *** addition,this research conducts experiments on canonical inverse PDE problems and compares the results to several recent *** results demonstrated that the proposed PINN-STLasso outperforms other methods,achieving lower error rates even with less data.

关键词： Physics-informed neural network inverse partial differential equation Lasso regression scientific machine learning

来源：评论

学校读者我要写书评

暂无评论

Coexistence of Distributed Sensing and Ultra-simple Coherent RoF based Front-haul for Radio Access Network

引用

Journal of Lightwave technology 2025年第12期43卷 5669-5675页

作者： Liu, Maoqi Wang, Jingchuan Liu, Chen Yu, Changyuan Lu, Chao Huazhong University of Science and Technology National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Wuhan430074 China The Hong Kong Polytechnic University Photonics Research Institute Department of Electrical and Electronic Engineering Hong Kong

The radio access network requires high-speed transmission and multifunctionality to support the increasing demand for advanced communication services. Radio-over-fiber (RoF) technology fulfills these requirements by enabling the delivery of millimeter-wave (MMW) band wireless signals via optical fiber front-haul. This paper presents an ultra-simple RoF system that achieves the coexistence of distributed sensing and telecommunication. A commercially available coherent modulator achieves signal modulation and remote up-conversion by the beat between the residual carrier and the modulated radio signal. The linear-frequency modulated carrier enables distributed sensing while simultaneously serving as the beat tone of RoF communication, showcasing the potential for integrated sensing and communication (ISAC) applications in next-generation radio access networks. We demonstrate the sensing and transmission performance over a 10-km fiber link and a 1-m wireless MMW channel. The system achieves a distributed sensing detection sensitivity of 28 pΕ/√ Hz and supports 1.6 GBaud 16QAM radio transmission in the Ka-band of 28 GHz. © 1983-2012 IEEE.

关键词： Radio access networks

来源：评论

学校读者我要写书评

暂无评论

Social Distance and Face Mask Detection using vision-based sensors and IP cameras 3

Social Distance and Face Mask Detection using vision-based s...

引用

3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Vineela, A. Devi, G. Lavanya Deekshit, G. Vinay GMR Institute of Technology Department of Computer Science and Engineering Rajam India Department of Computer Science and System Engineering Vishakhapatnam India

ISBN: (纸本)9781665494007

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Grey-box Modeling for IPMSMs Considering Nonlinearity of Torque and Current 50

Grey-box Modeling for IPMSMs Considering Nonlinearity of Tor...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ohnuma, Takumi Watanabe, Keiya Mano, Shou Department of Electronic Control System Engineering National Institute of Technology Numazu College Shizuoka Japan Multidisciplinary Engineering Course National Institute of Technology Numazu College Shizuoka Japan

ISBN: (纸本)9781665464543

This paper proposes a grey-box model for IPMSMs (Interior Permanent Magnet Synchronous Motors) that accounts for the nonlinear relationship between currents and torque directly. IPMSMs with high power density typically exhibit complex torque characteristics due to the effects of magnetic saturation and spatial harmonics, necessitating the introduction of various nonlinear compensation tables based on magnetic field analysis to ensure control performance. However, inputting data without considering the physical properties can lead to many variables and increased memory usage, which does not necessarily improve control performance. Consequently, models representing magnetic saturation characteristics with a reduced number of parameters, as well as torque estimation methods, are under investigation. Moreover, advancements in high-precision and rapid-response torque sensors, along with magnetic field analysis technologies, have enabled detailed offline measurements of IPMSM torque, with promising prospects for control applications. In this study, we propose an Extended Torque Coefficient (ETC) matrix to express the grey-box model for control purposes, utilizing accurate map data reflective of the actual machine. In addition, the proposed model can be applied to calculate torque gradient in the current vector space, which is maximize torque control performance, while maintaining the ETC matrix. © 2024 IEEE.

关键词： Permanent magnets

来源：评论

学校读者我要写书评

暂无评论

A Dual-Stage Optimal Controller Design for Controlling AVR system 27

A Dual-Stage Optimal Controller Design for Controlling AVR S...

引用

27th International Computer Science and engineering Conference, ICSEC 2023

作者： Abood, Layla H. Mohamed, Mohamed J. Abood, May H. University of Technology Department of Control and System Engineering Baghdad Iraq Al-Iraqia University Department of Computer Engineering Baghdad Iraq

ISBN: (纸本)9798350342109

In this study a cascade controller is proposed to maintain the Automatic Voltage Regulator (AVR) system, its comprises from two stage the first one is a conventional Proportional and Derivative (PD) controller and the second one is a Fractional Order PID (FOPID) controller is combined together for regulating the terminal voltage in AVR system, the gains of the proposed controller is tuned used a smart optimization algorithm called Squirrel Search algorithm (SSA) and adopt an Integral Time Absolute Error (ITAE) for monitoring for monitoring desired response to obtain a stable and robust response, the system response is tested using Matlab 2019 and the transient analysis is used to calculate overshoot, settling time, rise time, and peak time then compared these values selected classical controllers(PID,FOPID) to verify the robust response of the proposed controller. A robustness analysis verified the system response when there is an external disturbances, positive and negative disturbances signals are added, the controller try to face these signals, it takes 0.15 sec in negative disturbance and 0.1 sec in positive disturbance then in both states force the response to return to its desired response. © 2023 IEEE.

关键词： Controllers

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：