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Quadcopter UAV Modeling and Automatic Flight control Design

Transactions of Nanjing University of Aeronautics and Astronautics 2017年第6期34卷 627-636页

作者： Bhatia Ajeet Kumar Jiang Ju Zhen Ziyang College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016.P.R.China Jiangsu Key Laboratory of Internet of Things and Control Technologies Nanjing 210016.P.R.China

The mathematical model of quadcopter-unmanned aerial vehicle （UAV） is derived by using two approaches： One is the Newton-Euler approach which is formulated using classical meehanics; and other is the Euler-Lagrange approach which describes the model in terms of kinetic （translational and rotational） and potential energy. The proposed quadcopter＇s non-linear model is incorporated with aero-dynamical forces generated by air resistance, which helps aircraft to exhibits more realistic behavior while hovering. Based on the obtained model, the suitable control strategy is developed, under which two effective flight control systems are developed. Each control system is created by cascading the proportional-derivative （PD） and T-S fuzzy controllers that are equipped with six and twelve feedback signals individually respectively to ensure better tracking, stabilization, and response. Both pro- posed flight control designs are then implemented with the quadcopter model respectively and multitudinous simulations are conducted using MATLAB/Simulink to analyze the tracking performance of the quadcopter model at various reference inputs and trajectories.

关键词： quadcopter unmanned aerial vehicle （UAV） flight control desigm T-S fuzzy inference system propotional-derivative （PD） controller

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Deep learning for smart agriculture:Concepts,tools,applications,and opportunities

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International Journal of Agricultural and Biological Engineering 2018年第4期11卷 32-44页

作者： Nanyang Zhu Xu Liu Ziqian Liu Kai Hu Yingkuan Wang Jinglu Tan Min Huang Qibing Zhu Xunsheng Ji Yongnian Jiang Ya Guo Key Laboratory of Advanced Process Control for Light Industry Ministry of EducationJiangnan UniversityWuxi 214122China School of Internet of Things Jiangnan UniversityWuxi 214122China Chinese Academy of Agricultural Engineering Beijing 100125China Department of Bioengineering University of MissouriColumbiaMO 65211USA Jiangsu Zhongnong IoT Technology Co. LTDYixing 214200China

In recent years,Deep Learning(DL),such as the algorithms of Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN)and Generative Adversarial Networks(GAN),has been widely studied and applied in various fields including *** in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a *** article provides a concise summary of major DL algorithms,including concepts,limitations,implementation,training processes,and example codes,to help researchers in agriculture to gain a holistic picture of major DL techniques *** on DL applications in agriculture is summarized and analyzed,and future opportunities are discussed in this paper,which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly,and further to facilitate data analysis,enhance related research in agriculture,and thus promote DL applications effectively.

关键词： deep learning smart agriculture neural network convolutional neural networks recurrent neural networks generative adversarial networks artificial intelligence image processing pattern recognition

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A layout model of ambiguous path identification stations on freeway based on the greedy algorithm 17

A layout model of ambiguous path identification stations on ...

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17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017

作者： Zhang, Wenliang Zhang, Jian Gao, Zhaohui Huang, Shuaifeng Jiangsu Key Laboratory of Urban ITS Southeast Univ. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou #2 Nanjing Jiangsu210096 China Jiangsu Key Laboratory of Urban ITS Southeast Univ. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou #2 Nanjing Jiangsu210096 China Intelligent Transportation System Research Center Southeast Univ. Si Pai Lou # Collaborative Innovation Center for Technology and Application of Internet of Things Nanjing Jiangsu210096 China Jiangsu Key Laboratory of Urban ITS Southeast Univ. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou #2 Nanjing Jiangsu210096 China

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

To solve ambiguous path and toll splitting problem raised by freeway constantly construction, this paper investigates a path identification toll system (PITS) and its architecture based on 5.8 GHz dedicated short range communication (DSRC) technology. Due to the OD coverage criterion and link independence criterion, a layout scheme of ambiguous path identification points which meet the requirement of precise segmentation of road links was proposed. Also, the layout model with the minimum number of points as the target was established. Then, all paths of road network were found with depth-first searching (DFS) algorithm and layout model was found based on greedy algorithm, to select the maximum coverage number of paths. A freeway network in jiangsu Province was selected as a case study to clarify the model. In view of freeway networking toll collection condition of jiangsu Province, this paper laid the ambiguous path identification points of the whole road network. © ASCE.

关键词： Roads and streets

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Residence-Workplace Spatial Distribution Characteristics Analysis Based on Mobile Phone Data 18

Residence-Workplace Spatial Distribution Characteristics Ana...

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18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018

作者： Du, Shu-Ying Ran, Bin Zhang, Jian Shi, Xiao-Kai Zhang, Xiao-Li Wang, Qing Jiangsu Key Laboratory of Urban ITS Southeast Univ. Si Pai Lou #2 Nanjing Jiangsu210096 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou #2 Nanjing Jiangsu210096 China School of Transportation Southeast Univ. China Collaborative Innovation Center for Technology and Application of Internet of Things China Hohai Univ. China

ISBN: (数字)9780784481523

ISBN: (纸本)9780784481523

The spatial distribution relationship between residence and workplace is essential to urban transportation planning. This paper analyzes residence-workplace spatial distribution in Nanjing by using mobile data. First, the raw data was grouped, positioned, and smoothed. Then, this paper identified residence and workplace locations by selecting possible stations, and calculating cumulative duration. Finally, the indexes including commute indicators, commute space distribution, and commute distance applied to analyze the commute features of three different districts: Xinjiekou, Daishan, and Ruanjiandadao. The results show 87.16% of residents work inside the district and that the commute distance of Xinjiekou residents is only 2.32 km. Up to 41.66% of residents work in the other districts and for Daishan residents the commute distance is 6.49 km. In Ruanjiandaao, 37.14% of workers come from the other districts and the commute distance of workers is 8.36 km. © 2018 American Society of Civil Engineers.

关键词： Spatial distribution

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Floating Population and Resident Population Identification Based on Gaussian Mixture Model Using Origin-Destination Indexes Generated by Cellphone Data 18

Floating Population and Resident Population Identification B...

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18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018

作者： Hong, Yang Ran, Bin Zhang, Jian Ji, Xinkai Yang, Fan School of Transportation Southeast Univ. Si Pai Lou #2 Nanjing Jiangsu210096 China Jiangsu Key Laboratory of Urban ITS Southeast Univ. Nanjing China Research Center for Internet of Mobility Southeast Univ. Nanjing China Jiangsu Prov. Collaborative Innovation Center for Technology and Application of Internet of Things Southeast Univ. Nanjing China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Southeast Univ. Nanjing China

ISBN: (数字)9780784481523

ISBN: (纸本)9780784481523

Floating population differs from permanent population in transportation traits in multiple aspects, including points of interests, travel path selection, time of travel, etc. It is difficult for traditional an origin-destination (OD) survey to obtain travel characteristics for floating population effectively, but cellphone data can provide us with an opportunity to investigate floating population. This paper proposes an algorithm for identifying floating population and permanent population from user cellular data. The mobile data are transferred to sparse trajectory data by preprocessing, and OD pairs are extracted. Numerical indexes describing OD are calculated for each individual and input to a Gaussian mixture model for clustering into permanent population and residential population. A case study in Nanjing showed that the proposed algorithm has high accuracy. © 2018 American Society of Civil Engineers.

关键词： Gaussian distribution

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Expose data as a service in cloud for cooperative intelligent transportation systems-communicating with web application programming interfaces 17

Expose data as a service in cloud for cooperative intelligen...

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17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017

作者： Qu, Junrong Zhang, Jian Liu, Pei Lu, Jiajian Cheng, Yang Jiangsu Key Laboratory of Urban ITS Southeast Univ. Si Pai Lou #2 Nanjing Jiangsu210096 China Jiangsu Key Laboratory of Urban ITS Southeast Univ. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou #2 Nanjing Jiangsu210096 China Jiangsu Key Laboratory of Urban ITS Southeast Univ. Si Pai Lou #2 Nanjing Jiangsu210096 China Collaborative Innovation Center for Technology and Application of Internet of Things China Laboratory Dept. of Civil and Environment Engineering Univ. of Wisconsin-Madison 1241 Engineering Hall 1415 Engineering Dr. MadisonWI53706 United States

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

The accessibility of various traffic data archived by advanced data systems in intelligent transportation system (ITS) has been a critical issue to the efficient utilization of those valuable data. The constant growth in users and data systems brings more challenges in maintaining the performance of traditional data portals. This paper presents an approach in using representational state transfer web APIs for communication between data systems. It offers concise interfaces for data requests, efficient mechanisms for data transfers, and better management of data security and access restriction. The integration of transportation data to the cloud also makes it available to more end users and utilization. The components of a web-based traffic simulation platform demonstrated the consumption and production of web APIs. An ideal schema for transportation data services was suggested for transportation research, as well as an alternative schema due to lack of existing web APIs in the ITS community. © ASCE.

关键词： Data transfer

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An improved model for short-term traffic forecasting considering weather impacts 17

An improved model for short-term traffic forecasting conside...

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17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017

作者： Chen, Xinchao Qin, Si Zhang, Jian Tan, Huachun Xu, Yunxia Dai, Guanchen Chen, Xiaoxuan Jiangsu Key Laboratory of Urban ITS School of Transportation Southeast Univ. Jiangsu China Province Collaborative Innovation Center of Modern Urban Traffic Technologies Jiangsu China Province Collaborative Innovation Center for Technology and Application of Internet of Things China Research Center for Internet of Mobility Southeast Univ. Si Pai Lou #2 Nanjing210096 China School of Highway Chang'an Univ. South Section of S. 2nd Ring Rd. Nanjing China Jiangsu Key Laboratory of Urban ITS School of Transportation Southeast Univ. Jiangsu China Dept. of Transportation Engineering Beijing Institute of Technology Beijing100081 China Jiangsu Key Laboratory of Urban ITS School of Transportation Southeast Univ. Jiangsu China Jiangsu Key Laboratory of Urban ITS School of Transportation Southeast Univ. Jiangsu China TOPS Laboratory Dept. of Civil and Environment Engineering Univ. of Wisconsin-Madison United States

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

Accurate short-term prediction of traffic conditions on freeways has recently become increasingly important because of its vital role in the basic traffic management functions and trip decision making processes. The objective of this research is to utilize traffic and weather data from multiple data sources to develop an integrated model to predict traffic conditions under different rainfall conditions. A set of prediction models are compared and their performances using data from case studies are investigated and reported. The model performance was valuated using prediction errors, which are measured by the relative length of the distance between the predicted state and the observed state. © ASCE.

关键词： Decision making

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Analysis of the Trip Characteristics of Urban Residents Based on Mobile Phone Positioning Data in Nanjing 18

Analysis of the Trip Characteristics of Urban Residents Base...

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18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018

作者： Wang, Qing Zhang, Jian Yang, Fan Du, Shuying Zhang, Han Jiangsu Key Laboratory of Urban ITS Southeast Univ. Si Pai Lou #2 Nanjing Jiangsu210096 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou #2 Nanjing Jiangsu210096 China School of Transportation Southeast Univ. China Collaborative Innovation Center for Technology and Application of Internet of Things China School of Electrical Engineering Southeast Univ. Si Pai Lou #2 Nanjing Jiangsu210096 China

ISBN: (数字)9780784481523

ISBN: (纸本)9780784481523

Trip characteristics reflect the travel demand of residents in an urban area. This paper proposes a complete method to obtain OD and OD data based on mobile positioning data from a systematic point of view. Using the mobile phone data of Nanjing as an example, the method of trip characteristic analysis is tested via mobile data, and the results are displayed by using tools such as QGIS and TRANSCAD software, which shows the method is feasible and makes up for the shortage of traditional information about residents' travel. © 2018 American Society of Civil Engineers.

关键词： Data acquisition

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An Investigation of User Satisfaction with Shared Bicycles in Nanjing 18

An Investigation of User Satisfaction with Shared Bicycles i...

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18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018

作者： Li, Hanchu Zhang, Jian Li, Jing Shi, Zhuangbin Jiangsu Key Laboratory of Urban ITS School of Transportation Southeast Univ. Nanjing210096 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing210096 China Jiangsu Prov. Collaborative Innovation Center for Technology and Application of Internet of Things Nanjing210096 China Research Center for Internet of Mobility Southeast Univ. Si Pai Lou #2 Nanjing210096 China Intelligent Transportation System Research Center Southeast Univ. Jinxianghe Rd. #35 Nanjing Jiangsu210008 China

ISBN: (数字)9780784481523

ISBN: (纸本)9780784481523

The construction and use of shared bicycle systems in China is in the stage of development. In order to improve the system so as to better serve the public, this paper was initiated to investigate the status quo of shared bicycle systems and user satisfaction with shared bicycles. The user satisfaction with shared bicycles in Nanjing was quantitatively analyzed using a factor analysis method based on data obtained from questionnaires in Nanjing. Results indicate that 3 common factors (vehicle factor, service factor, and cost factor) can be extracted from 11 original variables, and that among them, vehicle factor has the greatest impact. Because results suggest the vehicle factor is the key to improvement, some improvement measures are put forward from three aspects: the government, bicycle sharing platform, and the users. © 2018 American Society of Civil Engineers.

关键词： Bicycles

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Evaluation of mobility impact on urban work zones using statistical models

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Journal of Central South University 2017年第6期24卷 1513-1521页

作者： LIU Pei ZHANG Jian QU Jun-rong LU Jia-jian CHENG Yang TAN Hua-chun Jiangsu Key Laboratory of Urban ITS Southeast University Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things Southeast University Traffic Operations and Safety (TOPS) Laboratory Department of Civil and Environment Engineering University of Wisconsin-Madison Department of Transportation Engineering Beijing Institute of Technology

This work correlated the detailed work zone location and time data from the Wis LCS system with the five-min inductive loop detector data. One-sample percentile value test and two-sample Kolmogorov-Smirnov(K-S) test were applied to compare the speed and flow characteristics between work zone and non-work zone conditions. Furthermore, we analyzed the mobility characteristics of freeway work zones within the urban area of Milwaukee, WI, USA. More than 50% of investigated work zones have experienced speed reduction and 15%-30% is necessary reduced volumes. Speed reduction was more significant within and at the downstream of work zones than at the upstream.

关键词： ITS data mobility impact work zone statistical model

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