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FCOS-LSC:A Novel Model for Green Fruit Detection in a Complex Orchard Environment

Plant Phenomics 2023年第3期5卷 546-563页

作者： Ruina Zhao Yujie Guan Yuqi Lu Ze Ji Xiang Yin Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinan 250358China School of Engineering Cardiff UniversityCardiff CF243AAUK School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Zhenjiang 212013China

To better address the difficulties in designing green fruit recognition techniques in machine vision systems,a new fruit detection model is *** model is an optimization of the FCOS(full convolution one-stage object detection)algorithm,incorporating LSC(level scales,spaces,channels)attention blocks in the network structure,and named *** method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions,lighting conditions,and capture ***,the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature *** feature pyramid network(FPN)is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected ***,the attention mechanisms are added to each of the 3 dimensions of scale,space(including the height and width of the feature map),and channel of the generated multiscale feature map to improve the feature perception capability of the ***,the classification and regression subnetworks of the model are applied to predict the fruit category and bounding *** the classification branch,a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit *** proposed FCOS-LSC model has 38.65M parameters,38.72G floating point operations,and mean average precision of 63.0%and 75.2%for detecting green apples and green persimmons,*** summary,FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition using intelligent agricultural ***,FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models.

关键词： connected designing weights

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INFORMER NEURAL NETWORK-BASED PREDICTION OF DEGRADATION TRAJECTORY OF ENERGY METER CLOCK BATTERY LIFE 14

INFORMER NEURAL NETWORK-BASED PREDICTION OF DEGRADATION TRAJ...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Zhang, Lingyu Chen, Yongqi Qin, Xinkun He, Wanlin Qi, Jia Sun, Yongquan School of Computer Science and Technology Harbin University of Science and Technology Harbin China School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin China School of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781837242672

Accurate prediction of the life degradation trajectory of the clock battery, a critical component of the power meter, is essential for assessing the reliability of the device. Addressing the computational inefficiency and limited generalization capabilities of traditional empirical recession models, this paper introduces a prediction model based on the Informer neural network. The battery degradation data are initially segmented and reassembled using a sliding window strategy. The encoder-decoder structure is then employed to achieve multilevel feature extraction from the input sequences. By setting various prediction starting points, the model demonstrates high sensitivity in predicting battery degradation under temperature conditions identical to those in the training set. Additionally, the model is capable of predicting the degradation trajectories of batteries under different temperature conditions, such as 60℃. © The Institution of engineering & technology 2024.

关键词： Decoding

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Deformation Mechanism in Central China Revealed by Regional Earthquake Tomography

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Seismological Research Letters 2025年第3期96卷 1561-1572页

作者： Hua, Yujin Zhang, Shuangxi Liu, Yu Liu, Li Wu, Tengfei Wei, Yu Li, Bin Department of Earth Sciences and Engineering Taiyuan University of Technology Taiyuan China Department of Geophysics School of Geodesy and Geomatics Wuhan University Wuhan China School of Geophysics and Measurement-Control Technology East China University of Technology Nanchang China College of Civil Engineering Taiyuan University of Technology Taiyuan China School of Civil Engineering and Architecture Hubei Polytechnic University Huangshi China China Huadian Corporation Ltd. Beijing China

The central China region (CCN) has experienced significant deformation and complex structural arrangements. We conducted research on the deformation pattern of the CCN through the 3D velocity structure of the crust and uppermost mantle obtained via body-wave tomography. This velocity structure strongly correlates with the region’s topographic features. The relationship between velocity distributions and topography indicates that compression in the east–west direction, which can lead to crustal and mantle shortening, may play a crucial role in shaping topography. The various structures between the Qinling mountain range in the west and the Tongbai–Dabie mountain range in the east highlight distinct geological processes occurring along the Qinling–Dabie orogenic belt. In addition, we propose that reservoir water has infiltrated the crystalline basement rock layer up to 15–20 km beneath the Three Gorges Reservoir. Furthermore, the Three Gorges Dam serves as a solid foundation to prevent reservoir water from seeping downstream underground. © Seismological Society of America.

关键词： Crystalline rocks

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Research on hyperspectral image classification based on improved deep cross-domain few-shot learning 5

Research on hyperspectral image classification based on impr...

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5th International Conference on Video, Signal and Image Processing, VSIP 2023

作者： Luo, Xinhua Wang, Qingyan Liang, Xintao Zhang, Zhen School Of Measurement And Control Technology And Tele Communications Engineering Harbin University Of Science And Technology Harbin150080 China

ISBN: (纸本)9798400709272

In this study, an enhanced method and system for hyperspectral image classification are presented, based on deep cross-scene few-shot learning. This pertains to the domain of remote sensing image processing technology and addresses the prevalent issues of inadequate classification performance in existing techniques for hyperspectral image categorization. The core aspects of this invention encompass the following: utilization of two mapping layers to standardize the input dimensions between the source and target domains;the deployment of an embedded feature extractor to incorporate the image cubes from both the source and target domains into a space-spectral embedding environment simultaneously, ensuring that like samples are closely aligned and dissimilar ones are distanced. Through gauging the distances between each class of unlabeled and labeled samples in this space-spectral embedding zone, learning with a few number of examples in both the source and target domains is achieved. Furthermore, a conditional domain discriminator is employed to mitigate domain shifts between domains, thus solidifying the domain stability of the extracted spatial-spectral embedding features. This innovative approach allows for high-precision hyperspectral data categorization, even when only a few examples are available. © 2023 ACM.

关键词： Image enhancement

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Fruit recognition system based on Raspberry Pi and YOLOv5 4

Fruit recognition system based on Raspberry Pi and YOLOv5

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4th International Conference on Computer Science and communication technology, ICCSCT 2023

作者： Huang, Chunxin Liu, Changyuan Zhao, Yiwen School of Measurement Control Technology and Communications Engineering Harbin University of Science and Technology Heilongjiang Harbin150006 China

ISBN: (纸本)9781510671232

With China’s booming economy, agricultural modernization has become an unstoppable trend. In order to solve the problem of real-time detection of multiple kinds of fruits, a real-time detection system of multiple kinds of fruits based on Raspberry Pi was developed. We trained the YOLOv5 network on a homemade multi-species fruit dataset. In the end, it not only realized the recognition of various types of fruits but also was able to output their location coordinates in the image. At the same time, we used the edge computing device Intel Neural Compute Stick 2 to realize the feed-forward inference operation on the neural network, so that the problem of low frame rate, when the Raspberry Pi performs real-time detection on a variety of fruits, is solved. In the real scenario, the real-time fruit detection model based on YOLOv5s network can effectively achieve the target detection of multiple types of fruits with 99.86% detection mAP, 99.93% testing accuracy and 10.37 FPS average real-time fruit detection frame rate. The system has many advantages, such as high accuracy, good robustness, fast calculation speed, etc. In addition, the embedded system can be further extended to realize the development of many intelligent products related to fruit recognition, such as intelligent fruit-sacking robots. Therefore, it can be concluded that the real-time detection system of multi-species fruits based on Raspberry Pi is helpful to improve the efficiency of farming, and the product can contribute to the modernization of agriculture in China. © 2023 SPIE.

关键词： Fruits

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Microwave Photonics-Based Small Hydrostatic Pressure Sensing Assisted by Convolutional Neural Network

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IEEE Sensors Journal 2025年第11期25卷 20948-20955页

作者： Li, Songlin Xue, Ting Wu, Yan Li, Zhuping Wu, Bin Tianjin University Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin300072 China Tianjin University State Key Laboratory of Precision Measuring Technology and Instruments Tianjin300072 China

Hydrostatic pressure measurement is essential in many industries, such as oil and gas production, chemical processing, and environmental monitoring. Due to the minimal impact of small hydrostatic pressure on ordinary silica optical fibers, research on its measurement utilizing optical fiber sensing technology remains limited. In this paper, a novel microwave photonics technique, termed optical carrier-based microwave interferometry (OCMI), is utilized for small hydrostatic pressure sensing with the assistance of a convolutional neural network (CNN). The theory of OCMI-based phase demodulation is established, and numerical simulations are conducted to investigate the factors affecting the axial displacement of the fiber core. In practical experiments, the phase demodulation method is applied to small hydrostatic pressure measurements;however, the results are suboptimal. Therefore, the CNN is developed to assist in the implementation of accurate small hydrostatic pressure sensing. The small hydrostatic pressures predicted by the well-trained CNN model are in good agreement with the actual values, with an error of less than 0.25 kPa. In addition, the prediction results from multiple Fabry-Perot interferometers demonstrate the feasibility and effectiveness of utilizing CNN for OCMI-based small hydrostatic pressure sensing. The introduction of machine learning broadens the application scope of the OCMI technique, allowing it to be employed for distributed sensing of a wider range of physical, chemical, and biological quantities. © 2001-2012 IEEE.

关键词： Interferometry

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A high-power and high-efficiency mini generator for scavenging energy from human foot movement

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Science China(Technological Sciences) 2023年第12期66卷 3381-3392页

作者： WU Hui QIAN Shuo HOU XiaoJuan ZHAO JuanHong ZHANG Jie SONG XiaoGuang LIU YanLi SHI ShuZheng GENG WenPing MU JiLiang HE Jian CHOU XiuJian Science and Technology on Electronic Test and Measurement Laboratory North University of ChinaTaiyuan 030051China School of Software North University of ChinaTaiyuan 030051China School of Information and Communication Engineering North University of ChinaTaiyuan 030051China School of Mechanical Engineering Hebei University of ArchitectureZhangjiakou 075000China

Harvesting energy from human movement and converting it into electricity is a promising method to address the issue of sustainable power supply for wearable electronic *** traditional energy harvesters for practical applications is difficult due to their low output *** this paper,an energy harvester with high power and efficiency is reported based on the principle of electromagnetic *** adopts a tiny compound mechanism comprising symmetrical lever-sector gear,which can amplify the vertical displacement of the human heel of 1.44 times without affecting the flexibility and comfort of human *** lever-sector gear and gear acceleration mechanism can achieve high output power from the tiny vertical movements of the *** results demonstrated that the average power and energy harvesting efficiency of the device are 1 W and 63%,***,combining a novel controllable electric switch and energy management circuit allows the energy harvester to be worn by individuals with different weights and functions as a continuous real-time power supply for various electronic devices(mobile phones,smartwatches,etc.).Therefore,this research provides a new approach for the highly efficient harvesting of human motion energy and sustainable power supply of wearable electronics.

关键词： human movement energy lever-sector gear compound mechanism high power electromagnetic energy harvester wearable electronics

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Deterministic Event-Based Physical Watermarks Against Replay Attacks in Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 61-69页

作者： Zhao, Xudong Xing, Wei Zhang, Junfeng Wang, Xinyu Zong, Guangdeng Zhao, Ning Hainan University School of Information and Communication Engineering Haikou570288 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116622 China Tiangong University School of Control Science and Engineering Tianjin300387 China Bohai University College of Control Science and Engineering Jinzhou121013 China

Cyber-physical systems (CPSs) are of great importance in various applications and have garnered significant attention. Ensuring their security is a crucial concern, while this paper focuses on the security of CPSs against replay attacks during the transmission of sensor measurements over a wireless network. It is widely recognized that incorporating physical watermark signals is an effective approach to mitigate replay attacks. However, traditional physical watermarks are time-based and result in a notable degradation in the performance of Linear Quadratic Gaussian systems. With this in mind, a novel event-based physical watermarking technique is devised to address the performance degradation and detect replay attacks. The approach involves a deterministic physical watermark schedule that utilizes the innovation sequence to determine whether to incorporate physical watermarks or not. We provide evidence that the probability of successfully embedding physical watermarks increases when the system is targeted by malicious attacks. Additionally, we demonstrate that the deterministic event-based watermarking approach is more effective than the traditional time-based watermarking method. Finally, simulation examples are included to validate and illustrate the theoretical results. © 2023 IEEE.

关键词： Cyber attacks

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