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Autonomous trajectory tracking control method for an agricultural robotic vehicle

International Journal of Agricultural and Biological Engineering 2024年第1期17卷 215-224页

作者： Jin Yan Wenguang Zhang Yong Liu Wei Pan Xiaoyu Hou Zhiyu Liu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Information Engineering Xuzhou University of TechnologyXuzhou 221018JiangsuChina National Key Laboratory of Transient Physics Nanjing University of Science and TechnologyNanjing 210094China School of Mechanical Engineering Nanjing University of Science and TechnologyNanjing 210094China

To address the nonlinearities and external disturbances in unstructured and complex agricultural environments,this paper investigates an autonomous trajectory tracking control method for agricultural ground ***,this paper presents the design and implementation of a lightweight,modular two-wheeled differential drive vehicle equipped with two drive wheels and two caster *** vehicle comprises drive wheel modules,passive wheel modules,battery modules,a vehicle frame,a sensor system,and a control ***,a novel robust trajectory tracking method was proposed,utilizing an improved pure pursuit ***,an Online Particle Swarm Optimization Continuously Tuned PID(OPSO-CTPID)controller was introduced to dynamically search for optimal control gains for the PID *** results demonstrate the superiority of the improved pure pursuit algorithm and the OPSO-CTPID control *** validate the performance,the vehicle was integrated with a seeding and fertilizing machine to realize autonomous wheat seeding in an agricultural *** outcomes reveal that the vehicle of this study completed a seeding operation exceeding 1 km in *** proposed method can robustly and smoothly track the desired trajectory with an accuracy of less than 10 cm for the root mean square error(RMSE)of the curve and straight lines,given a suitable set of parameters,meeting the requirements of agricultural *** findings of this study hold significant reference value for subsequent research on trajectory tracking algorithms for ground-based agricultural robots.

关键词： trajectory tracking autonomy control agricultural robotic vehicle online PSO continuously tuned PID dynamic pure pursuit algorithm

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Power amplifier circuit defect detection based on improved Patch SVDD

Power amplifier circuit defect detection based on improved P...

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作者： Xie, Zhonghui Kong, Wa Zhou, Xinyu Zhang, Wence Zhong, Yujie Xia, Jing School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang China Department of Electronic Information Engineering Hong Kong Polytechnic University Hong Kong

In the manufacturing process of power amplifier (PA), various defects on the circuit surface will seriously affect the circuit performance and its operation. To solve the above problems, this article proposes a circuit defect detection method based on improved patch-based support vector data description algorithm (Patch SVDD), which uses contrastive learning to enhance the feature extraction ability of neural network. An image calibration algorithm is used for calibration preprocessing to enhance the robustness of the model. The Euclidean distance between a query image patch and its nearest normal patch is defined to be an anomaly score. For verification, three defects in the PA circuit, including components missing, solder contamination and component orientation dislocation, were detected. The experimental results show that, compared with conventional method, the area under receiver operating characteristic (AUROC) of the improved anomaly detection model increased from 90.1% to 95.1%, which improves the detection accuracy effectively. © 2023 John Wiley & Sons Ltd.

关键词： Anomaly detection

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Research on Metaverse Security and Forensics

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computers, Materials & Continua 2023年第10期77卷 799-825页

作者： Guangjun Liang Jianfang Xin Qun Wang Xueli Ni Xiangmin Guo Pu Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina School of Intelligent Engineering Nanjing Institute of Railway TechnologyNanjingChina

As a subversive concept,the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic,industrial,and investment ***,while the metaverse brings unprecedented opportunities for transformation to human society,it also contains related *** is a digital living space with information infrastructure,interoperability system,content production system,and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and *** social incentives and governance rules,the metaverse reflects the digital migration of human *** article will conduct an in-depth analysis of the metaverse from the perspective of electronic data ***,from the perspective of Internet development,the background and development process of the metaverse is *** systematically elaborating on the concept and connotation of the metaverse,this paper summarizes the different views of current practitioners,experts,and scholars on the ***,from the perspective of metaverse security,the social risk and crime risks of the metaverse are *** the importance of metaverse forensics is ***,from the perspective of blockchain,smart wearable devices,and virtual reality devices,the objects and characteristics of metaverse forensics have been studied in *** smart wearable devices as an example,this paper gives the relevant experimental process of smart bracelet ***,many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance.

关键词： Metaverse forensics blockchain smart wear virtual reality

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Method of adaptive learning feature model fusion and verification based on metamodels 3

Method of adaptive learning feature model fusion and verific...

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3rd International Symposium on computer Applications and information Systems, ISCAIS 2024

作者： Wang, Chenyu Zhu, Yi Wu, Zixin Hao, Guosheng School of Computer Science and Technology Jiangsu Normal University Xuzhou221000 China Jiangsu Province Education Informatization Engineering Technology Research Center Jiangsu Normal University Xuzhou221000 China The Key Laboratory of the Software Development and Verification Technology The Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9781510681316

As the advancement of computer technique and artificial intelligence technology, education based on computer technology has become a hot topic, and the wave of educational informatization is rising. Among them, adaptive learning has received widespread attention as a type of educational informatization technology. However, the current adaptive learning feature models use different semantics to describe each other, and the models lack meta model level explanations, resulting in a lack of further operability of the models and difficulty in integrating with each other. In order to solve these problems, our paper proposes a fusion and verification method for learning feature adaptive models based on meta models. Above all, an adaptive learning feature meta model is proposed, which defines the construction rules of the adaptive learning feature model at meta model level, so that different models can be fused using the same set of semantic rules. Then, at the meta model level, transform the learning model into a requirement model. Finally, using formal methods, validate the fused feature model, which can make sure of the semantic consistency between the previous and subsequent models. This article demonstrates the entire process through a model fusion experiment. The experimental results indicate that this method is correct and feasible in promoting feature model fusion without adaptive learning. © 2024 SPIE.

关键词： Informatization

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Sharing of Encrypted Lock keys in the Blockchain-Based Renting House System from Time- and Identity-Based Proxy Reencryption

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China Communications 2022年第5期19卷 164-177页

作者： Zhiwei Wang Liping Qian Danwei Chen Guozi sun School of Computer Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Guangxi Key Laboratory of Cryptography and Information Security Department of Computer SciencesGuilin University of Electronic TechnologyGuilin 541004China

To design an efficient protocol for sharing the encrypted lock keys in the renting house system,we introduce a new notion called time-and identitybased proxy reencryption(TIPRE)and the blockchain *** CPA secure TIPRE scheme is constructed from Green et al.’s identity-based proxy reencryption scheme by adding the time *** every time period,a time stamp authority generates a public key embedded with the current time stamp for each *** our protocol for the renting house system,the TIPRE scheme is the primary building block,and the blockchain platform serves instead of a trusted third party,such as a real estate agency between landlords and *** TIPRE scheme allows the landlord to change the lock key at each time period for *** blockchain platform allows the landlords and tenants to directly interact,and all of the interactions are recorded in the blockchain database to provide the desired security requirements,such as nonrepudiation and ***,we provide the secure analysis of our protocol and test its performance by implementing it in the MacBook Pro and the Intel Edison development platforms.

关键词： identity-based proxy reencryption time stamp renting houses system blockchain

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Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

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Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

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A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

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Energy Engineering 2023年第2期120卷 385-408页

作者： Xing Deng Feipeng Da Haijian Shao Xia Wang School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Information Science and Technology Nantong UniversityNantong226019China

Photovoltaic power generating is one of the primary methods of utilizing solar energy resources,with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar *** order to provide reference strategies for pertinent researchers as well as potential implementation,this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches,statistical approaches and optimization techniques for solar power generation and *** learning-related methods,in particular,can theoretically handle arbitrary nonlinear transformations through proper model structural design,such as hidden layer topology optimization and objective function analysis to save information that can increase forecasting accuracy while filtering out irrelevant or less affected data for *** research’s results indicate that RBFNN-AG performed the best when applying the predetermined number of days,with an NRMSE value of 4.65%.RBFNN-AG performs better than sophisticated models like DenseNet(5.69%),SLFN-ELM(5.95%),and ANN-k-means-linear regression correction(6.11%).Additionally,scenario application and PV system investment techniques are provided to evaluate the current condition of new energy development and market trends both domestically and internationally.

关键词： Photovoltaic power generating deep learning PV system

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CASSOR: Class-Aware Sample Selection for Ordinal Regression with Noisy Labels 20th

CASSOR: Class-Aware Sample Selection for Ordinal Regression...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Yuan, Yue Wan, Sheng Zhang, Chuang Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Security Nanjing China

ISBN: (纸本)9789819970216

Ordinal regression aims at solving the classification problem, where the categories are related in a natural order. Due to the difficulty in distinguishing between highly relevant categories, label noise is frequently present in ordinal data. Moreover, the varying degrees of relevance between categories can lead to an inconsistent distribution of misclassification loss across categories, posing a challenge to select clean data consistently from all categories for training. To overcome this limitation, we develop a sample selection method termed ‘Class-Aware Sample Selection for Ordinal Regression’ (CASSOR). To be concrete, we devise a class-specific sample selection strategy in order to adaptively acquire sufficient clean examples for robust model training. Moreover, a label-ranking regularizer is designed to help guide the sample selection process via exploring the ordinal relationship between different examples. As a result, our proposed CASSOR is endowed with strong discrimination abilities on ordinal data. Intensive experiments have been performed on multiple real-world ordinal regression datasets, which firmly demonstrates the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Regression analysis

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Automatic depression recognition by intelligent speech signal processing:A systematic survey

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CAAI Transactions on Intelligence technology 2023年第3期8卷 701-711页

作者： Pingping Wu Ruihao Wang Han Lin Fanlong Zhang Juan Tu Miao Sun Jiangsu Key Laboratory of Public Project Audit School of Engineering AuditNanjing Audit UniversityNanjingChina School of Information Engineering Nanjing Audit UniversityNanjingChina Key Laboratory of Modern Acoustics(MOE) School of PhysicsNanjing UniversityNanjingChina Faculty of Electrical Engineering Mathematics&Computer ScienceDelft University of TechnologyDelftThe Netherlands

Depression has become one of the most common mental illnesses in the *** better prediction and diagnosis,methods of automatic depression recognition based on speech signal are constantly proposed and updated,with a transition from the early traditional methods based on hand‐crafted features to the application of architectures of deep *** paper systematically and precisely outlines the most prominent and up‐to‐date research of automatic depression recognition by intelligent speech signal processing so ***,methods for acoustic feature extraction,algorithms for classification and regression,as well as end to end deep models are investigated and ***,general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.

关键词： acoustic signal processing deep learning feature extraction speech depression recognition

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A Data Security Protection Method for Deep Neural Network Model Based on Mobility and Sharing 18th

A Data Security Protection Method for Deep Neural Network Mo...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Zhao, Xinjian Li, Qianmu Wang, Qi Chen, Shi Li, Tengfei Li, Nianzhe State Grid Jiangsu Information and Telecommunication Company Jiangsu Nanjing210024 China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China State Grid Smart Grid Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Key Laboratory of Information and Network Security Jiangsu Nanjing210003 China

ISBN: (纸本)9789819998920

With the rapid development of digital economy, numerous business scenarios, such as smart grid, energy network, intelligent transportation, etc., require the design and distribution of deep neural network (DNN) models, which typically use large amounts of data and computing resources for training. As a result, DNN models are now considered important assets, but at great risk of being stolen and distributed illegally. In response to the new risks and challenges brought by frequent data flow and sharing, watermarks have been introduced to protect the ownership of DNN models. Watermarks can be extracted in a relatively simple manner to declare ownership of a model. However, watermarks are vulnerable to attacks. In this work, we propose a novel label-based black-box watermark model protection algorithm. Inspired by new labels, we design a method to embed watermarks by adding new labels in the model, and to prevent watermarks from being forged, we use encryption algorithms to generate key samples. We conduct experiments on the VGG19 model using the CIFAR-10 dataset. Experimental results show that the method is robust to fine-tuning attacks, pruning attacks. Furthermore, our method does not affect the performance of deep neural network models. This method helps to solve scenarios such as "internal data circulation, external data sharing and multi-party data collaboration". © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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