检索结果-南通市图书馆

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2024年第5期25卷 357-381页

作者： Yakun ZHANG Guofang GONG Huayong YANG Jianbin LI Liujie JING State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310058China China Railway Engineering Equipment Group Co. Lid.Zhengzhou 450016China Maha Fluid Power Research Center Purdue UniversityWest LafayetteIN 47905USA

Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology.

关键词： Intelligent shield machine Tunnel boring machine(TBM) Tunnel boring robot(TBR) self-driving Autonomous control Shield machine

Detection of avian influenza virus H9N_(2) based on self-driving and self-sensing microcantilever piezoelectric sensor

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Chemical Letters 2023年第4期34卷 464-468页

作者： Yawen Zhang Feng Shi Chenguang Zhang Xin Sheng Yunhao Zhong Hui Chong Zhanjun Yang Chengyin Wang College of Chemistry and Chemical Engineering Yangzhou UniversityYangzhou 225002China

In this article, we used the self-excitation and self-inductance characteristics of polyvinylidene fluoride(PVDF) piezoelectric materials, combined with the powerful signal processing and calculation analysis capabilities of integrated circuits, for the first time to explore a set of microcantilever sensor "readout system" without additional driver(self-driving) and can realize self-sensing external signal(self-sensing).It was successfully applied to the unlabeled detection of avian influenza virus(AIV) H9N_(2). The specific force of the antigen-antibody complexes on the surface of the microcantilever leads to the change of the stress of the cantilever, which drives the constructed detection device, and does not require an additional excitation source to drive it, that is, the self-driving part. At the same time, due to the movement of piezoelectric charges in the film caused by the positive piezoelectric effect of the PVDF film, self-inductive charges are generated on the surface of the sensor dielectric. The charge signal is converted into a voltage signal, and the sensing part is completed, that is, self-sensing. The immunosensor has a linear range of100-1000 ng/m L with a detection limit of 2.9 ng/m L. The method will also open up a new avenue for the detection of other analytes based on antigen-antibody responses.

关键词： Micro cantilever sensor self-driving self-sensing Piezoelectric material Avian influenza virus H9N_(2)

Integrated System of Solar Cells with Hierarchical NiCo2O4 Battery-Supercapacitor Hybrid Devices for self-driving Light-Emitting Diodes

在线全文

学校读者我要写书评

暂无评论

Nano-Micro Letters 2019年第3期11卷 92-103页

作者： Yuliang Yuan Yangdan Lu BeiEr Jia Haichao Tang Lingxiang Chen YuJia Zeng Yang Hou Qinghua Zhang Qinggang He Lei Jiao Jianxing Leng Zhizhen Ye Jianguo Lu State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiangUniversityHangzhou310027People’s Republic of China Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineeringand Instrument ScienceZhejiang UniversityHangzhou310027People’s Republic of China Shenzhen Key Laboratory of Laser Engineering College of Optoelectronic EngineeringShenzhenUniversityShenzhen518060People’s Republic of China4College of Chemical and Biological EngineeringZhejiang UniversityHangzhou310027People’s Republic of China Ningbo Research Institute Zhejiang UniversityNingbo315100People’s Republic of China Ocean College Zhejiang UniversityZhoushan316021People’s Republic of China

An integrated system has been provided with a-Si/H solar cells as energy conversion device,NiCo2O4 battery-supercapacitor hybrid(BSH)as energy storage device,and light emitting diodes(LEDs)as energy utilization device.By designing three-dimensional hierarchical NiCo2O4 arrays as faradic electrode,with capacitive electrode of active carbon(AC),BSHs were assembled with energy density of 16.6 Wh kg-1,power density of 7285 W kg-1,long-term stability with 100% retention after 15,000 cycles,and rather low self-discharge.The NiCo2O4//AC BSH was charged to 1.6 V in 1 s by solar cells and acted as reliable sources for powering LEDs.The integrated system is rational for operation,having an overall efficiency of 8.1% with storage efficiency of 74.24%.The integrated system demonstrates a stable solar power conversion,outstanding energy storage behavior,and reliable light emitting.Our study offers a precious strategy to design a self-driven integrated system for highly efficient energy utilization.

关键词： Integrated system NiCo2O4 Battery-supercapacitor hybrid devices self-driving LED

Site selection of self-driving and recreational vehicle camps in China:An investigation using analytic hierarchy process and entropy

在线全文

学校读者我要写书评

暂无评论

Journal of Traffic and Transportation Engineering(English Edition) 2021年第5期8卷 762-777页

作者： Hua Ding Jianwei Wang Wenqiang Liao Ting Liang Hong Dai Department of Urban and Rural Planning School of ArchitectureChang'an UniversityXi'an 710064China Institute of Tourism Planning and Design Chang'an UniversityXi'an 710064China Department of Data Management and Application College of Transportation EngineeringChang'an UniversityXi'an 710064China The Sixth Division China Construction&Development Co.Ltd.Tianjin 300451China School of Geological Engineering and Geomatics Chang'an UniversityXi'an 710064China

self-driving and recreational vehicle(RV)camps are a new form of industry module with the integration of transportation and tourism in China,thus the scientific and reasonable site selection plays an important role in the success of camps’construction and operation.In terms of relying resources and development factors,camps can be divided into five categories:scenic-spot-based,transportation-based,environment-based,project-based and leisure and vacation-based.According to whether it is of excludability and competitiveness,the camps in China mainly embody the attribute of private products.Based on the combination of subjective evaluation and objective calculation,the evaluation model of spatial site selection is constructed and the weight of each index is calculated by using analytic hierarchy process(AHP)method and entropy coefficient method.The results show that traffic condition factor is the priority to the selection of campsite,and whether it is on the popular main self-driving route and the grade of trunk roads are the dominant indices.The second factor taken into consideration is the social factors,in which government policy supports and land cost play a key role.The third factor is the market,in which the urban economic level,partnership with the government and tourist resource conditionsare of great importance.The fourth factor of the campsite selection includes natural elements,in which the quality of ecological environment and water source conditions are mostly considered.In the future,it is suggested that a camp pattern of"public goods"plus"private goods"should be built and the construction of camps in underdeveloped areas should be highly developed so as to form camp spatial network from individual points to a series of campsite and finally the campsite group in China will be set up.

关键词： self-driving RV camp Product attribute Site selection Evaluation model AHP Entropy

End-to-End self-driving Using Deep Neural Networks with Multi-auxiliary Tasks

在线全文

学校读者我要写书评

暂无评论

Automotive Innovation 2019年第2期2卷 127-136页

作者： Dan Wang Junjie Wen Yuyong Wang Xiangdong Huang Feng Pei GAC R&D Center 381 Wushan RoadTianheGuangzhouChina

End-to-end self-driving is a method that directly maps raw visual images to vehicle control signals using deep convolutional neural network(CNN).Although prediction of steering angle has achieved good result in single task,the current approach does not effectively simultaneously predict the steering angle and the speed.In this paper,various end-to-end multi-task deep learning networks using deep convolutional neural network combined with long short-term memory recurrent neural network(CNN-LSTM)are designed and compared,which could obtain not only the visual spatial information but also the dynamic temporal information in the driving scenarios,and improve steering angle and speed predictions.Furthermore,two auxiliary tasks based on semantic segmentation and object detection are proposed to improve the understanding of driving scenarios.Experiments are conducted on the public Udacity dataset and a newly collected Guangzhou Automotive Cooperate dataset.The results show that the proposed network architecture could predict steering angles and vehicle speed accurately.In addi-tion,the impact of multi-auxiliary tasks on the network performance is analyzed by visualization method,which shows the salient map of network.Finally,the proposed network architecture has been well verified on the autonomous driving simu-lation platform Grand Theft Auto V(GTAV)and experimental road with an average takeover rate of two times per 10 km.

关键词： self-driving Multi-auxiliary tasks CNN-LSTM Deep learning

Data Augmentation Technology Driven By Image Style Transfer in self-driving Car Based on End-to-End Learning

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2020年第2期122卷 593-617页

作者： Dongjie Liu Jin Zhao Axin Xi Chao Wang Xinnian Huang Kuncheng Lai Chang Liu School of Mechanical Engineering Guizhou UniversityGuiyang550025China. Faculty of electronic and information engineering Xi’an Jiaotong UniversityXi’an710049China.

With the advent of deep learning,self-driving schemes based on deep learning are becoming more and more popular.Robust perception-action models should learn from data with different scenarios and real behaviors,while current end-to-end model learning is generally limited to training of massive data,innovation of deep network architecture,and learning in-situ model in a simulation environment.Therefore,we introduce a new image style transfer method into data augmentation,and improve the diversity of limited data by changing the texture,contrast ratio and color of the image,and then it is extended to the scenarios that the model has been unobserved before.Inspired by rapid style transfer and artistic style neural algorithms,we propose an arbitrary style generation network architecture,including style transfer network,style learning network,style loss network and multivariate Gaussian distribution function.The style embedding vector is randomly sampled from the multivariate Gaussian distribution and linearly interpolated with the embedded vector predicted by the input image on the style learning network,which provides a set of normalization constants for the style transfer network,and finally realizes the diversity of the image style.In order to verify the effectiveness of the method,image classification and simulation experiments were performed separately.Finally,we built a small-sized smart car experiment platform,and apply the data augmentation technology based on image style transfer drive to the experiment of automatic driving for the first time.The experimental results show that:(1)The proposed scheme can improve the prediction accuracy of the end-to-end model and reduce the model’s error accumulation;(2)the method based on image style transfer provides a new scheme for data augmentation technology,and also provides a solution for the high cost that many deep models rely heavily on a large number of label data.

关键词： Deep learning self-driving end-to-end learning style transfer data augmentation.

The Effect of self-driving Car on Urban Traffic

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

American Journal of Computational Mathematics 2017年第2期7卷 149-156页

作者： Zaiqiang Ku Ting Liao College of Mathematics and Physics Huanggang Normal University Hubei China

Based on the idea of infinitesimal analysis, we establish the basic model of relation between speed and flow. Since putting a certain amount of self-driving car will affect the average speed of mixed traffic flow, we choose the proportion of self-driving car to be a variable, denoted by k. Based on the least square method, we find two critical values of k that are 38.63% and 68.26%. When k 38.63%, the self-driving cars have a negative influence to the traffic. When 38.63% 68.26%, they have significant improvement to the traffic capacity of the road.

关键词： self-driving Car Least Square Method Mixing Speed Traffic Flow

A driving Strategy Model of self-driving Cars

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Computer and Communications 2017年第5期5卷 42-49页

作者： Haoge Liu Zeyu Shen Wei Shang School of Science China University of Geosciences (Beijing) Beijing China

The self-driving cars are highly developed and about to meet the market, but the driving strategies and corresponding behaviors with others still need to be tested. In this paper, based on its characteristics and behaviors of manual-driving vehicles, we propose the driving strategies of manual-driving cars as well as self-driving cars. And we use the cellular automaton to simulate the traffic reality under different conditions, and to evaluate the efficiency of a road when self-driving cars are put into use. This research can be a reference by traffic planning and vehicles performance test, and further research can be designed in a model which can calculate the efficiency of a road when the percentage of self-driving cars are different.

关键词： self-driving Cars driving Strategy Cellular Automaton Analog Simulation Traffic Jam

Artificial Intelligence Applications in the Development of Autonomous Vehicles:A Survey

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 315-329页

作者： Yifang Ma Zhenyu Wang Hong Yang Lin Yang Department of Information Technology and Decision Sciences Old Dominion UniversityNorfolkVA 23529 USA Department of Computational Modeling and Simulation Engineering Old Dominion UniversityNorfolkVA 23529 USA Department of Mechanical and Electrical Engineering Huazhong Agricultural UniversityWuhan 430070China

The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

关键词： Artificial intelligence(AI) autonomous vehicles(AVs) deep learning(DL) motion planning perception self-driving

Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving

在线全文

学校读者我要写书评

暂无评论

Frontiers of Information Technology & Electronic Engineering 2021年第5期22卷 673-686页

作者： Yunpeng WANG Kunxian ZHENG Daxin TIAN Xuting DUAN Jianshan ZHOU Beijing Advanced.Innovation Center for Big Data and Brain Computing School of Transportation Science and EngineeringBeihang UniversityBeijing 100191China

Rule-based autonomous driving systems may suffer from increased complexity with large-scale intercoupled rules,so many researchers are exploring learning-based approaches.Reinforcement learning(RL)has been applied in designing autonomous driving systems because of its outstanding performance on a wide variety of sequential control problems.However,poor initial performance is a major challenge to the practical implementation of an RL-based autonomous driving system.RL training requires extensive training data before the model achieves reasonable performance,making an RL-based model inapplicable in a real-world setting,particularly when data are expensive.We propose an asynchronous supervised learning(ASL)method for the RL-based end-to-end autonomous driving model to address the problem of poor initial performance before training this RL-based model in real-world settings.Specifically,prior knowledge is introduced in the ASL pre-training stage by asynchronously executing multiple supervised learning processes in parallel,on multiple driving demonstration data sets.After pre-training,the model is deployed on a real vehicle to be further trained by RL to adapt to the real environment and continuously break the performance limit.The presented pre-training method is evaluated on the race car simulator,TORCS(The Open Racing Car Simulator),to verify that it can be sufficiently reliable in improving the initial performance and convergence speed of an end-to-end autonomous driving model in the RL training stage.In addition,a real-vehicle verification system is built to verify the feasibility of the proposed pre-training method in a real-vehicle deployment.Simulations results show that using some demonstrations during a supervised pre-training stage allows significant improvements in initial performance and convergence speed in the RL training stage.

关键词： self-driving Autonomous vehicles Reinforcement learning Supervised learning