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Enhancing Security in Distributed Drone-Based Litchi Fruit Recognition and Localization Systems

computers, Materials & Continua 2025年第2期82卷 1985-1999页

作者： Liang Mao Yue Li Linlin Wang Jie Li Jiajun Tan Yang Meng Cheng Xiong Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute Shenzhen Polytechnic UniversityShenzhen518055China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable lighting and obstructive foliage. To reinforce security, the tasks of recognition and localization are distributed among multiple drones, enhancing resilience against tampering and data manipulation. This distribution also optimizes resource allocation through collaborative processing. The model remains lightweight and is optimized for rapid and accurate detection, which is essential for real-time applications. Our proposed system, validated with a D435 depth camera, achieves a mean Average Precision (mAP) of 0.943 and a frame rate of 169 FPS, which represents a significant improvement over the baseline by 0.039 percentage points and 25 FPS, respectively. Additionally, the average localization error is reduced to 0.82 cm, highlighting the model’s high precision. These enhancements render our system highly effective for secure, autonomous fruit-picking operations, effectively addressing significant performance and cybersecurity challenges in agriculture. This approach establishes a foundation for reliable, efficient, and secure distributed fruit-picking applications, facilitating the advancement of autonomous systems in contemporary agricultural practices.

关键词： Objective detection deep learning machine learning

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A Constraint Virtual Adversarial Training 19

A Constraint Virtual Adversarial Training

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Yang, Xu Yang, Guoqing Liu, Xianliang Yang, Zhi Zhang, Qiuling Wang, Jianfang Henan Polytechnic University School of Computer Science and Technology Jiaozuo China

ISBN: (纸本)9798350346558

Node classification is widely used in computer vision, natural language processing, recommendation systems, and other fields, and is essentially a semi-supervised learning task. However, due to the additional consideration of connections between nodes, classification based on a graph neural network may be more sensitive to perturbations, as the perturbation from connected nodes exacerbates the impact on classification results. Existing adversarial rarely consider the direction of the perturbations, to lose the topology and influence of the original samples in the surrounding pieces. To counter this, we propose a new method called the constraint virtual adversarial training method based on the graph (G-CAT), which considers the connections and contributions between nodes in the graph structure. We design the influence of different nodes through node attention mechanisms to further constrain the adversarial perturbation further. In particular, our G-CAT is conceptually beneficial to exist graph-based classification methods that can dynamically adapt the adversarial training through virtual labels. Empirical results on three real-world datasets show that G-CAT significantly outperforms state-of-the-art algorithms in classification accuracy. © 2022 IEEE.

关键词： Classification (of information)

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A Multiple Attention Label-Enhanced Model for Legal Article Prediction 4

A Multiple Attention Label-Enhanced Model for Legal Article ...

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4th International Conference on Frontiers technology of Information and computer, ICFTIC 2022

作者： Zhang, Xuanqing Chen, Junyi Wang, Yuke School of Computer Science and Technology Henan Polytechnic University Jiaozuo China

ISBN: (纸本)9798350321951

Legal Article Prediction (LAP) aims to automatically identify the relevant articles according to the content of the prosecution document of each case and the established law articles, which is of great value in legal assistance systems and in improving the work efficiency of legal practitioners. In this paper, we formulate LAP as a multi-label learning problem and present a Multiple Attention Label-Enhanced Model (MALE), which first utilizes two encoders to obtain the base representation of fact description and established law articles, second employs a labelenhanced method fully fusing the information of the two from different perspectives to get the co-dependent representation of the fact description and each law article finally. Experimental results on the CAIL2018 public dataset demonstrate that MALE achieves significant performance improvements over existing neural models in predicting relevant law articles. © 2022 IEEE.

关键词： Forecasting

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A Sequential Recommendation of Hawkes Process Fused with Attention Mechanism 23

A Sequential Recommendation of Hawkes Process Fused with Att...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wen, Xilin Wang, Jianfang Yang, Xu Zhang, Qiuling School of Computer Science and Technology Henan Polytechnic University JiaoZuo China

ISBN: (纸本)9781665494571

Markov Chain (MC) models and recurrent neural networks (RNN) have attracted much attention in recommender systems because of their dynamic capture of user-item sequential patterns. However, most of these models assume that the user interaction history sequence is continuous without considering the time interval between interactive items on modeling. To counter this, we propose a sequential recommendation algorithm for Hawkes Process with Attention Mechanism (AMHP in short). Our model first uses the self-attention model to evaluate the strength of the relationship between different items in the sequence and ensure the dynamic attenuation problem when considering item strength in user history interaction. Specifically, we regard the recurrence of the same attribute items in the user sequence as a self-excitation of the Hawkes model and construct the short-term and long-term time curve function of the corresponding category of items-time potential factors. Results from our experiments over four benchmark datasets demonstrate our model can capture the dynamics of user behavior and preferences over various state-of-the-art sequential models. © 2021 IEEE.

关键词： Recurrent neural networks Correlation Smart cities Heuristic algorithms Music Markov processes Benchmark testing

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Individualization of Head Related Transfer Function Based on PCA and RBF Network 11

Individualization of Head Related Transfer Function Based on...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Chen, Wei Zhang, Hongxu Yu, Jinxia Luo, Fen School of Software Henan Polytechnic University Jiaozuo China School of Computer Science and Technology Henan Polytechnic University Jiaozuo China

ISBN: (纸本)9781450398039

Head-Related Transfer Function (HRTF) describes the acoustic reflection and diffraction effect caused by the influence of the human body (head, torso, etc.) in the transmission of sound waves to the human ear. In Virtual Reality(VR) / Augmented Reality(AR), HRTF is often used to generate virtual 3D audio due to its ability to recreate perceptions of natural sound scenes realistically. However, HRTF varies from person to person due to the differences in anthropometric features. Using non-individualized HRTF to produce 3D sounds may lead to hearing localization bias in users. Therefore, how to obtain individualized HRTF is a hot topic in the field of VR / AR. This paper proposes an effective method to establish the relationship between anthropometric features and HRTF. At first, a method based on multimodal principal component analysis is proposed for the representation of HRTF models with low dimensions. Then a nonlinear mapping representation model between the low-dimensional features of HRTF and anthropometric features is established using Radial Basis Function Neural Network (RBFNN). Objective experiments show that the proposed HRTF Individualization method can reduce the spectral distortion as low as 4.48 dB. The subjective listening experiments based on the principal sagittal plane show that the individualized HRTF obtained using this method can effectively improve the accuracy of subjective listening (about 33%). © 2022 ACM.

关键词： Virtual reality

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Demand-aware mobile bike-sharing service using collaborative computing and information fusion in 5G IoT environment

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Digital Communications and Networks 2022年第6期8卷 984-994页

作者： Xiaoxian Yang Yueshen Xu Yishan Zhou Shengli Song Yinchen Wu School of Computer and Information Engineering Shanghai Polytechnic UniversityShanghai201209China School of Computer Science and Technology Xidian UniversityXi'an710126China

Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)environments.A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation *** the real world,the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike *** addition to pure manual rebalancing,in several works,attempts were made to predict the demand for *** this paper,we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information *** combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station *** carefully analyzed and discovered the group of features that impact the demand of bikes,from historical bike-sharing records and 5G IoT environment *** combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return *** performed sufficient experiments on two real-world *** results confirm that compared to some existing methods,our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.

关键词： Mobile bike-sharing service Demand prediction Collaborative computing Information fusion 5G IoT

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An Entorhinal-Hippocampal Loop Model Based on Non-negative Sparse Coding

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 113-127页

作者： Zhao, Kaixin Ren, Menghui College of Computer Science and Technology Henan Institute of Technology Xinxiang453003 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Manufacturing IoT Big Data Engineering Technology Research Center of Henan Province Xinxiang453003 China

In our study, we investigate how the brain maps environmental spaces into understandable maps through hippocampal place cells and entorhinal cortex grid cells. We uncover that the hippocampus and entorhinal cortex are not just directly connected but also engage in a sophisticated feedback loop crucial for spatial encoding during navigation. We developed an innovative network model based on this feedback mechanism, linking entorhinal inputs to hippocampal spatial encoding cells through a non-negative sparse coding, using grid cells and weak spatial cells as inputs. Our findings demonstrate that the model learns and adjusts the characteristics of spatial encoding cells, successfully learning the specific spatial selectivities of place cells even with inputs from cells carrying weaker spatial information. This highlights the critical role of feedback encoding in refining spatial representations, an indispensable component of the brain’s navigation system. Moreover, experiments comparing our model with others validate our model’s exceptional ability in generating precise spatial encodings, underscoring its superiority. This research not only deepens our understanding of the brain’s navigational mechanisms but also offers a robust computational framework for future neuroscience studies, emphasizing the intricate interplay between feedback loops and spatial encoding in the brain’s navigation system. © The Institution of Engineers (India) 2024.

关键词： Brain

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A Catalytic Copper/Cobalt Oxide Interface for Efficient Hydrogen Generation

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Energy & Environmental Materials 2023年第2期6卷 1-7页

作者： Wenjing Xu Sheli Zhang Ruofan Shen Zhikun Peng Baozhong Liu Jun Li Zhanying Zhang Baojun Li School of Science Jiaozuo Engineering Technology Research Center of Separation and Adsorption MaterialsJiaozuo Teachers CollegeJiaozuo Henan 454000China Research Center of Green Catalysis College of ChemistryZhengzhou UniversityZhengzhou 450001China School of Materials Science and Engineering Henan Polytechnic UniversityJiaozuo 454000China

Metal nanoparticles and metal oxides promisingly provide different catalytic active sites at their *** high-density interfaces is essential to maximize ***,a Cu-Co_(3)O_(4) nanoparticles interfacial structure produced via pyrolysis and moderate oxidation from metal-organic frameworks has been designed to boost the intrinsic *** Cu-Co_(3)O_(4) nanoparticles composites exhibit a turnover frequency of 57.5 min−1 for ammonia borane hydrolysis,far higher than those of monometallic Cu and Co_(3)O_(4) nanoparticles,showing the synergistic effect of Cu and Co_(3)O_(4) nanoparticles at their *** functional theory calculations and in situ Raman spectroscopy reveal the catalytic mechanism of dual active sites,in which Co_(3)O_(4) nanoparticles at Cu-Co_(3)O_(4) interface efficiently bind and activate water molecules and Cu nanoparticles easily activate NH3BH3 *** study opens up a new pathway for achieving high-efficiency noble metal-free catalysts for hydrogen generation and other heterogeneous catalysis.

关键词： ammonia borane metal metal oxide metal-organic framework synergistic effect

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A Novel Construction of Updatable Identity-Based Hash Proof System and Its Applications

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Chinese Journal of Electronics 2023年第3期32卷 564-576页

作者： QIAO Zirui ZHOU Yanwei YANG Bo ZHANG Wenzheng ZHANG Mingwu School of Computer Science Shaanxi Normal University State Key Laboratory of Cryptology Science and Technology on Communication Security Laboratory Henan Key Laboratory of Network Cryptography Technology School of Computere Hubei University of Technology

In the previous works, to further provide the continuous leakage resilience for the identity-based encryption scheme, a new cryptography primitive, called updatable identity-based hash proof system(U-IB-HPS),was proposed. However, most of the existing constructions have some deficiencies, they either do not have perfect key update function or the corresponding security with tight reduction relies on a non-static complexity assumption. To address the above problems, a new construction of U-IB-HPS is created, and the corresponding security of our system is proved based on the static complexity assumption. Also, the corresponding comparisons and analysis of performances show that our proposal not only achieves the perfect key update function and the anonymity, but also has the tight security reduction. In additional, our proposal achieves the same computational efficiency as other previous systems. To further illustrate the practical function of U-IB-HPS, a generic method of non-interactive data authorization protocol with continuous leakage resilience is designed by employing U-IB-HPS as an underlying tool, which can provide continuous leakage-resilient data authorization function for the cloud computing. Hence, the application field of U-IB-HPS is further extended through our study.

关键词： Authorization Cloud computing Protocols Identity-based encryption Complexity theory Computational efficiency Proposals

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Keyword-Based PIR Method for Enhancing Large-Scale Power Credit

Keyword-Based PIR Method for Enhancing Large-Scale Power Cre...

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第43届中国控制会议

作者： Yanan Li Juan Li Menghui Wang School of Software Henan Polytechnic University School of Computer Science and Technology Henan Polytechnic University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Power credit reports are an important means of enhancing enterprises' ***,there are two main problems in existing inquiry *** first problem is that existing methods do not protect the inquiry preference,which is also private *** second problem is that existing private information retrieval methods cannot be extended to larger-scale *** address these issues,a novel inquiry method named "Effi-Retrieval" is proposed to simultaneously ensure the security and efficiency of ***,Paillier homomorphic encryption and oblivious polynomial calculations are used to ensure the security of *** optimal binning strategy,along with the k-anonymity method and hash mapping,is employed to meet the personalized privacy requirements of the querying party in ***,a security analysis of EffiRetrieval is presented,and numerical experiments are conducted to demonstrate the impacts of user privacy requirements and an affordable fee on communication *** ablation experimental results showed that,in comparison to settings without the binning strategy or k-anonymity method,Effi-Retrieval can significantly reduce retrieval time.

关键词： power credit query privacy information retrieval oblivious polynomial computation k-anonymity communication cost

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