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IOT technology application model research of transportation industry in China

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Engineering Sciences 2013年第3期11卷 90-96页

作者： Lai Mingyong Zhou Tang Liu Zhengchi School of Business Administration Hunan University internet of things (IOT) Research center Hunan University

The paper studied the connection between internet of things （lOT） technology and transportation industry. Meanwhile, the definition of IOT in transportation was given. Concerning that many problems occurred during the process of traditional intelligent transportation system, the paper proposed a promising model of lOT in transportation. The advantage of the information utilization model from information to function was confirmed through comparative study. Finally, the model presented that a real interconnection of transportation would be achieved based on the unified information collection. It can greatly save cost on technology transfer, exploit potential value of information, and promote the emergence of a sustainable information service market and the industrial upgrade.

关键词： IOT intelligent transportation system sensor network IOT in transportation application model

Key technologies and applications of agricultural energy internet for agricultural planting and fisheries industry

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Information Processing in Agriculture 2023年第3期10卷 416-437页

作者： Xueqian Fu Haosen Niu College of Information and Electrical Engineering China Agricultural UniversityBeijingChina National Innovation center for Digital Fishery China Agricultural UniversityBeijingChina Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural AffairsBeijingChina Beijing Engineering and Technology Research center for internet of things in Agriculture BeijingChina

Energy consumption in the agricultural sector is significant,reaching 20%of the total energy consumption in *** Energy internet,an important extension of Energy internet in the agricultural field,significantly contributes to agricultural *** technologies of Agricultural Energy internet are vital factors supporting its *** article systematically reviews the key technologies of Agricultural Energy internet for two areas:agriculture and *** working mechanisms and power consumption characteristics of some state-of-the-art new-energy agricultural intelligent equipment are *** addition,the principles and profit methods underlying the agro-industrial complementary operation model are ***,against the Agricultural Energy internet background,the development trends of some state-of-theart new energy agricultural intelligent equipment,agro-industrial complementary,and carbon–neutral technology are proposed in this paper,providing novel perspectives on the promotion of the development of Agricultural Energy internet and related technological innovation *** unmanned farm is the main form of the future agricultural system,which is powered by the Agricultural Energy internet based on smart agriculture and a smart *** will become the inevitable trend of modern agriculture to replace oil agriculture with electric *** electricity in farming is mainly generated by renewable *** energy power generation has low carbon emissions and is the future direction for the development of agricultural energy *** addition,the internet of things will be further strengthened to realize automation and intelligence of agricultural energy systems。

关键词： Agricultural Energy internet Agricultural equipment Agricultural planting Fisheries industry Carbon neutrality

Cow behavior recognition based on image analysis and activities

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International Journal of Agricultural and Biological Engineering 2017年第3期10卷 165-174页

作者： Gu Jingqiu Wang Zhihai Gao Ronghua Wu Huarui School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China National Engineering Research center for Information Technology in Agriculture Beijing 100097China Key Laboratory of Agri-informatics Ministry of AgricultureBeijing 100097China Beijing Engineering Research center of Agricultural internet of things Beijing 100097China

For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall *** method of object recognition based on image entropy was proposed,aiming at the identification of motional cow object behavior against a complex *** a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back ***,by combining the continuous image characteristics and movement of cows for 7 d,the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction,improving the accuracy of the identification of characteristics of dairy *** behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior *** experimental results showed that,through target detection,classification and recognition,the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%,and the false negative rates of oestrus and hoof disease were 3.28%and 5.32%,*** method can enhance the real-time monitoring of cows,save time and improve the management efficiency of large-scale farming.

关键词： cow behavior target segmentation image entropy image moment activities intelligent analysis

Triboelectric nanogenerators for smart agriculture

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InfoMat 2023年第2期5卷 80-119页

作者： Shufen Dai Xunjia Li Chengmei Jiang Jianfeng Ping Yibin Ying Laboratory of Agricultural Information intelligent Sensing School of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouPeople's Republic of China Key Laboratory of intelligent Equipment and Robotics for Agriculture of Zhejiang Province HangzhouPeople's Republic of China Key Laboratory of Micro/Nano-Sensing Technology for Biological Information Acquisition ZJU-Hangzhou Global Scientific and Technological Innovation CenterHangzhouPeople's Republic of China

Rapid iterations of sensing,energy,and communication technologies transform traditional agriculture into standardized,intensive,and smart modern ***,the energy supply challenge for the plentiful sensors or other microdevices constraints the extensive application of intelligent technologies in *** nanogenerator(TENG),which efficiently converts mechanical energy into electrical energy through contact electrification and electrostatic induction,is considered a promising way to build next-generation intelligent energy supply *** efficiently harvesting low-frequency mechanical energy from the agricultural environment,including wind,rain,and water flow energy,TENGs can be a strong contender for distributed power for microdevice networks in smart *** addition,highly customizable TENGs can be combined with microdevices in agriculture to enable self-powered agricultural monitoring and production strategy *** deeply exploring the application potential of TENG in agriculture,it is conducive to further promoting unmanned production,refinement,and intelligence of agricultural production and enhancing agriculture's ability to combat natural risks.

关键词： energy harvesting internet of things self-powered sensor smart agriculture triboelectric nanogenerator

Slip-draft embedded control system by adaptively adjusting the battery position for electric tractor

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International Journal of Agricultural and Biological Engineering 2023年第5期16卷 155-164页

作者： Minghui Wang Pucai Ning Ke Su Gejima Yoshinori Wei Wang Yongjie Cui Gongpei Cui College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina SAIC Motor Technical center Shanghai 201804China School of Arts and Design Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Faculty of Agriculture.University of Miyazaki Miyazaki 8892192Japan Key Laboratory of Agricultural internet of things Ministry of Agriculture and Rural AffairsYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and intelligent Service Yangling 712100ShaanxiChina College of Mechanical&Electrical Engineering Henan Agricultural UniversityZhengzhou 450002China

A slip-draft embedded control system was designed and developed for an independent developed 2WD(two-wheel drive)electric tractor,to improve the traction efficiency,operation performance and ploughing depth stability of the electric *** this system,the battery of electric tractor was innovatively equivalent to the original counterweight of the fuel *** through dynamic analysis of electric tractor during ploughing,the mathematical model of adjusting the center of gravity about draft force and slip rate was *** the automatic adjustment of the center of gravity for electric tractor was realized through the adaptive adjustment of battery ***,the system was carried on electric tractor for performance evaluation under different ploughing conditions,the traction efficiency,slip rate and front wheel load of electric tractor were measured and controlled synchronously to make it close to the set *** the comparative experiments of ploughing operation were carried out under the two modes of adaptive adjustment of center of gravity and fixed center of *** test results showed that,based on the developed control system,the center of gravity of electric tractor can be adjusted in real time according to the complex changes of working *** ploughing operation with adjusting adaptively battery position,the average values of traction efficiency,slip rate,front wheel load and relative error of tillage depth of electric tractor were 64.5%,22.2%,2045.0 N and 2.0%*** were optimized by 15.0%,29.5%,19.6%and 80.0%respectively,compared with electric tractor with fixed battery *** slip-draft embedded control system can not only realize the adaptive adjustment of the center of gravity position in the ploughing process of electric tractor,but also improve the traction efficiency and the stability of ploughing depth,which can provide reference for the actual production operation of electric tractor.

关键词： electric tractor embedded control system adjusting adaptively center of gravity position performance test

Quantum-enhanced reinforcement learning for control:a preliminary study

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Control Theory and Technology 2021年第4期19卷 455-464页

作者： Yazhou Hu Fengzhen Tang Jun Chen Wenxue Wang College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China The State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyangLiaoning 110016China Institutes for Robotics and intelligent manufacturing Chinese Academy of SciencesShenyangLiaoning 110169China

Reinforcement learning is one of the fastest growing areas in machine learning,and has obtained great achievements in biomedicine,internet of things(IoT),logistics,robotic control,***,there are still many challenges for engineering applications,such as how to speed up the learning process,how to balance the trade-of between exploration and *** technology,which can solve complex problems faster than classical methods,especially in supercomputers,provides us a new paradigm to overcome these challenges in reinforcement *** this paper,a quantum-enhanced reinforcement learning is pictured for optimal *** this algorithm,the states and actions of reinforcement learning are quantized by quantum *** then,a probability amplifcation method,which can efectively avoid the trade-of between exploration and exploitation via quantized technology,is ***,the optimal control policy is learnt during the process of reinforcement *** performance of this quantized algorithm is demonstrated in both MountainCar reinforcement learning environment and CartPole reinforcement learning environment—one kind of classical control reinforcement learning environment in the OpenAI *** preliminary study results validate that,compared with Q-learning,this quantized reinforcement learning method has better control performance without considering the trade-of between exploration and *** learning performance of this new algorithm is stable with diferent learning rates from 0.01 to 0.10,which means it is promising to be employed in unknown dynamics systems.

关键词： Quantum theory Reinforcement learning Quantum computation State superposition Optimal control

Detection of abnormal hydroponic lettuce leaves based on image processing and machine learning

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Information Processing in Agriculture 2023年第1期10卷 1-10页

作者： Ruizhe Yang Zhenchao Wu Wentai Fang Hongliang Zhang Wenqi Wang Longsheng Fu Yaqoob Majeed Rui Li Yongjie Cui College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Xi’an Agriculture Machinery Management and Extension Station Xi’anShaanxi 710065China Key Laboratory of Agricultural internet of things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and intelligent Service YanglingShaanxi 712100China Faculty of Agricultural Engineering and Technology University of AgricultureFaisalabad 38000Pakistan center for Precision and Automated Agricultural Systems Washington State UniversityProsserWA 99350USA Suide County Lanhuahua Ecological Food Co. Ltd.SuideShaanxi 718000China

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic *** and rotten leaves are main types of abnormal leaves in hydroponic *** study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,*** Linear Regression(MLR),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM).One-way analysis of variance was applied to reduce RGB,HSV,and L*a*b*features number of hydroponic lettuce *** binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models *** showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training *** took about 20.25 s to detect an image with 30244032 pixels by KNN,which was much longer than MLR(0.61 s)and SVM(1.98 s).MLR got detection accuracies of 89.48%and 99.29%for yellow and rotten leaves,respectively,while SVM reached 98.33%and 97.91%,*** was more robust than MLR in detecting yellow and rotten leaves of ***,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.

关键词： Hydroponic lettuce Machine learning Image processing Abnormal leaves Automatic sorting

Towards IoT-based sustainable digital communities

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intelligent and Converged Networks 2022年第2期3卷 190-203页

作者： Fangyu Li Hongyan Yang Xuejin Gao Honggui Han Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemEngineering Research Center of Digital CommunityMinistry of Educationand Beijing Artificial Intelligence InstituteBeijing University of TechnologyBeijing 100124China

Seeking continuous development,a modern community must also be able to adapt to future possible challenges using constrained or limited *** a revolutionary communication paradigm,the internet of things(IoT)empowers the cutting-edge and emerging applications which enable manifold new intelligent services towards a smart *** sophisticated ecosystem of a digital community is made feasible by the IoT infrastructure,which also provides community control with access to a wealth of actual *** addition,IoT platforms empower the ubiquitous computing ability,providing more potentials to the actuators in perception layer in the IoT *** more and more population in the urban areas,sustainability issues have become a key factor to consider in the development of a digital *** give a modern survey in this study on the most recent developments in IoT for sustainable digital *** carefully examining the most recent literature,we specifically highlight the various smart digital community application scenarios,such as smart buildings,energy management,green transportation,trash management,*** also look into a number of major issues facing the use of IoT technology in digital ***,we discuss potential future applications and future research areas for IoT,the critical component of sustainable digital communities.

关键词： internet of things digital communities sustainability smart systems

Dynamic intelligent Supply-Demand Adaptation Model Towards intelligent Cloud manufacturing

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Computers, Materials & Continua 2022年第8期72卷 2825-2843页

作者： Yanfei Sun Feng Qiao Wei Wang Bin Xu Jianming Zhu Romany Fouad Mansour Jin Qi School of internet of things Nanjing University of Posts and TelecommunicationsNanjing210003China College of Automation&College of Artificial Intelligence Nanjing University of Posts and TelecommunicationsNanjing210003China Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Karaga72511Egypt

As a new mode and means of smart manufacturing,smart cloud manufacturing(SCM)faces great challenges in massive supply and demand,dynamic resource collaboration and intelligent *** address the problem,this paper proposes an SCM-oriented dynamic supply-demand(SD)intelligent adaptation model for massive manufacturing *** this model,a collaborative network model is established based on the properties of both the supply-demand and their relationships;in addition,an algorithm based on deep graph clustering(DGC)and aligned sampling(AS)is used to divide and conquer the large adaptation domain to solve the problem of the slow computational speed caused by the high complexity of spatiotemporal search in the collaborative network *** the same time,an intelligent supply-demand adaptation method driven by the quality of service(QoS)is established,in which the experiences of adaptation are shared among adaptation subdomains through deep reinforcement learning(DRL)powered by a transfer mechanism to improve the poor adaptation results caused by dynamic *** results show that the model and the solution proposed in this paper can performcollaborative and intelligent supply-demand adaptation for themassive and dynamic resources in SCM through autonomous learning and can effectively performglobal supply-demand matching and optimal resource allocation.

关键词： Smart Cloud manufacturing supply and demand sides dynamic adaptation Deep Graph Clustering transfer learning reinforcement learning