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Development of a Method for Detecting Cutting Points of Wilted Leaves Using 2D Image Keypoints and 3D Point Cloud

Journal of Institute of Control, Robotics and systems 2024年第11期30卷 1237-1244页

作者： Kang, Byeong Chang Jung, Ho Won Kim, Jong Hyeong Department of Mechanical Design and Robot Engineering Seoul National University of Science and Technology Korea Republic of Department of Mechanical System and Design Engineering Seoul National University of Science and Technology Korea Republic of

In agriculture, removing withered leaves is crucial for maintaining crop quality and preventing pests and diseases. This paper proposes a method for estimating the pose of wilted leaves and removing wilted leaves. While estimating precisely 3D location and pose information is essential for this task, most of previous methods for pose estimation have difficulty in processing time due to the complexity of feature extraction from 3D point cloud. The proposed method utilizes the YOLOv8 keypoint detection model to obtain image information, and YOLOv7 instance segmentation to isolate the region of interest. We calculate the 3D coordinates of the cutting point and the 3D rotation of the wilted leaf by applying RANSAC plane fitting and projection on 3D point cloud, simplifying the information required for the task. The method was validated using five types of model plant pots, achieving an average processing time of 98.584 ms and an accuracy of 3.83 mm. Additionally, the method recorded a 78% success rate in the removal of withered leaves. © 2024, Institute of Control, Robotics and systems. All rights reserved.

关键词： Machine vision

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Lithium-Ion Battery State-of-Charge and State-of-Health Co-Estimation with Feasible Real-Time Implementation 8

Lithium-Ion Battery State-of-Charge and State-of-Health Co-E...

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8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024

作者： Shao, Mingding Chen, Jian Xu, Ke Wang, Yong School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Mechanical Engineering Zhejiang University Hangzhou China Wanxiang Al23 Systems Corp. Department of System Engineering Hangzhou China

ISBN: (纸本)9798331504892

The key to improving the efficiency of lithium-ion battery systems and reducing safety incidents is that the battery management system can accurately estimate the state of lithiumion batteries, including the state of charge and the state of health. At present, the commonly used algorithms do not achieve an effective balance between accuracy and complexity, and it is difficult to apply them in practice. Therefore, on the basis of the existing algorithms, this paper proposes to reduce the order of the equivalent circuit model, simplify the observer polynomial, achieve low computational complexity, and use a processor with limited storage to complete the estimations. The simulation and experimental results confirm that the maximum absolute error of the state of charge and state of health of the lithium-ion battery systems fully meet the requirements of practical use. Compared with the reference method, there is no obvious difference in convergence speed and stability, but the run time is relatively faster. The proposed method has a significant simplification effect. © 2024 IEEE.

关键词： State of charge

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Deep Complex-valued Convolutional Learning for Waveform OFDM Receiver design

Deep Complex-valued Convolutional Learning for Waveform OFDM...

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IEEE Conference on Wireless Communications and Networking

作者： Jiequ Ji Nam Phuong Tran Zehui Xiong Kun Zhu Tony Q. S. Quek Department of Information System and Design Singapore University of Technology Design Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Orthogonal frequency division multiplexing (OFD-M) has been widely used in modern communication networks. Notice that OFDM typically relies on (inverse) Discrete Fourier Transform (DFT/IDFT) for processing its waveforms. In this context, we propose a deep learning-based OFDM receiver that uses a deep complex-valued convolutional neural network (DC-CNN) to recover the information bit stream from synchronized time-domain signals without relying on DFT/IDFT. Specifically, a learned linear transform is designed to utilize the cyclic prefix (CP) of OFDM waveforms instead of DFT/IDFT, which presents the ability of DCCNN for complex communication waveforms. To improve the convergence of the training model for the DCCNN-based receiver, a novel transfer learning scheme is developed to train channel equalization and demodulation in two phases. In addition, both the DCCNN equalizer and DCCNN demodulator are trained and tested at different SNRs for Rayleigh fading and noise, and a mixed multiple fading channel model with various delay spreads is utilized to smooth the training loss. Simulation results suggest that our developed DCCNN channel estimator outperforms conventional estimators such as least square (LS), linear minimum mean square error (LMMSE) and low-rank approximation of LMMSE (ALMMSE) in multipath Rayleigh fading models with varying Doppler spreads and delay spreads.

关键词： Training OFDM Transfer learning Channel estimation Receivers Rayleigh channels Delays Synchronization Time-domain analysis Demodulation

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Optimized Moderator design and Analysis of a Pin-Type Supercritical Carbon Dioxide reactor Based on RMC 29

Optimized Moderator Design and Analysis of a Pin-Type Superc...

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2022 29th International Conference on Nuclear Engineering, ICONE 2022

作者： Zhao, Xingyu Liu, Minyun Cui, Rongyi Huang, Shanfang Wang, Kan Lu, Chuan Department of Engineering Physics Tsinghua University Beijing China Cnnc Key Laboratory on Nuclear Reactor Thermal Hydraulics Technology Nuclear Power Institute of China Chengdu China China Nuclear Power Engineering Co. Ltd Beijing China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9780791886502

This study analyzed an yttrium hydride (YH2) moderated supercritical carbon dioxide cooled reactor loaded with pin-type, beryllium oxide diluted oxide fuel elements to reduce the critical enrichment. The impact of the YH2 on the coolant void reactivity was studied along with a moderator zoning scheme to flatten the radial power distribution. The YH2 was added as hexagonal moderating rods at the center of the fuel assemblies. The core was modeled using the continuous-energy reactor Monte Carlo code, RMC, with the On-The-Fly cross sections treatment. The results showed that the YH2 moderator increased the thermal fission and reduced the critical enrichment of the core with the same diluent volume fraction by more than 30%. The YH2 moderator significantly softened the neutron energy spectrum and reduced the neutron leakage upon core voiding, resulting in both a weaker positive spectral reactivity feedback and a weaker negative leakage reactivity feedback during core depressurization. For an UO2-loaded core, the YH2 gave a lower negative coolant void reactivity, while for a MOX-loaded core with diluent volume fractions smaller than 35%, the spectral feedback was more important and the YH2 strongly reduced the positive coolant void reactivity to less than $1. Arranging the YH2 in the peripheral assemblies reduced the radial power peaking factor to 1.319. The study shows that the YH2 moderator can reduce the critical enrichment, make the core less sensitive to voiding, and can flatten the radial power distribution of a single-enrichment core through moderator zoning. © 2022 by ASME.

关键词： Carbon dioxide

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Thermodynamic Analysis and Optimization of Nuclear Closed Air Brayton Cycle 1

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23rd Pacific Basin Nuclear Conference, PBNC 2022

作者： Fang, Huawei Yi, Jingwei Zhang, Xiaoyu Liang, Tiebo Qian, Yiran Tang, Xin Chen, Weixiong Key Laboratory of Nuclear Reactor System Design Technology Nuclear Power Institute of China Sichuan Chengdu China State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong University Shaanxi Xi’an China

ISBN: (数字)9789811988998

ISBN: (纸本)9789811988981

In order to improve the flexibility and adaptability of micro nuclear power systems, air is considered as an optional working fluid of nuclear Brayton cycle. It has ability to avoid shortage of working fluid under extreme conditions, and ensure the system operating with tight supply. Compared with the traditional open cycles, the closed cycle has advantages of higher cycle efficiency, smaller equipment size and higher power density. Few of closed air power cycle in nuclear power system has been studied by now. In this paper, cycle parameters were analyzed and designed, and optimal cycle performance was compared between cycle constructions. Four closed air Brayton cycles are proposed and modeled in EBSILON 15.0, including simple regenerative cycle (SRC), intercooling regenerative cycle (IRC), reheat regenerative cycle (RRC) and intercooling-reheat regenerative cycle (IRRC). Thermodynamic analysis of the key parameters is conducted, including pressure ratio, turbine inlet temperature, compressor inlet temperature and pressure. In addition, the genetic algorithm (GA) is applied for multi-parameters optimization for the maximal cycle efficiency. Result shows that with the increase of pressure ratio, the efficiency of SRC increases firstly and then decreases. With the increase of turbine inlet temperature, the pressure ratio corresponding to the maximal cycle efficiency increases gradually. Specific work gradually increases with pressure ratio. In higher turbine inlet temperature, the rising velocity of specific work is faster. Cycle efficiency increases with compressor inlet pressure, and this trend tend to be slower in high pressure region. With the increasing of compressor inlet pressure, optimal pressure ratio decreases and finally approaches to 2.0. The cycle minimal cycle pressure was selected as 6 MPa. Maximal efficiency and corresponding pressure ratio increase with turbine inlet temperature increasing and decrease with compressor inlet temperature increasing. Co

关键词： Air

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UWM-Net: A Mixture Density Network Approach with Minimal Dataset Requirements for Underwater Image Enhancement

UWM-Net: A Mixture Density Network Approach with Minimal Dat...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Jun Huang Zongze Li Ruihao Zheng Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

The learning-based underwater image enhancement, which is suitable for batch processing, is a pivotal research direction in underwater image processing. Extensive paired image data are required in existing learning-based methods, which necessitate considerable preprocessing and hinder the application of these methods. To address these limitations, we propose a semi-supervised approach called UWM-Net: firstly, we use a compact dataset of underwater image pairs to train the Mixture Density Network (MDN) with an underwater scene setting; subsequently, U-Net can learn underwater image enhancement more efficiently. The MDN can transform standard images into underwater scenes, reducing the reliance on paired data and making much smaller training datasets. In experimental studies, UWM-Net using only 18 pairs of underwater image data achieves highly competitive results in terms of 3 metrics compared with advanced models.

关键词： Measurement Training Learning systems Image quality Image color analysis Refining Training data

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Towards Lightweight Underwater Depth Estimation

Towards Lightweight Underwater Depth Estimation

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Artificial Intelligence (CAI), IEEE Conference on

作者： Keyu Zhou Jin Chen Shuangchun Gui Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Underwater depth estimation is crucial in the applications of marine robotics. It can provide environment information for target tracking, robot navigation, and 3D reconstruction of underwater terrain. Existing works transform underwater images into in-air conditions to adapt methods that are designed for natural images. However, this may result in expensive computational resources. To overcome this limitation, we propose a lightweight knowledge distillation framework for underwater depth estimation. We utilize a powerful model designed for underwater images as the teacher model and a lightweight CNN model as the student model. We distill global features to enable the student to acquire both local and global information, thereby improving estimation performance. Our framework includes a global transformation module for efficient global feature distillation and a global-local fusion module to combine local and global information for final estimation. Experimental results on the FLSea dataset demonstrate that our student model is lighter than the teacher model while outperforming lightweight in-air models. Our network is 60% lighter than the teacher model and achieves a 3.1% improvement in the δ 1 metric compared to the lightweight in-air model.

关键词： Measurement Adaptation models Three-dimensional displays Target tracking Navigation Computational modeling Estimation

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Decomposition-Based Memetic Algorithm for Multi-Objective Fleet Size and Mix Vehicle Routing Problem

Decomposition-Based Memetic Algorithm for Multi-Objective Fl...

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Congress on Evolutionary Computation

作者： Yunpeng Ba Ruihao Zheng Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The heterogeneous fleet vehicle routing problem (HFVRP) is of great significance in logistics and transportation. This paper considers a crucial and challenging HFVRP variant, namely multi-objective fleet size and mix vehicle routing problem with unlimited fleet, fixed cost, and dependent cost (MO-FSMVRPFD). A decomposition-based memetic algorithm called MOEA/D-ALS is proposed to cope with MO-FSMVRPFD. MOEA/D-ALS uses the multi-objective evolutionary algorithm based on decomposition (MOEA/D) as the backbone. Beyond that, it adopts a replacement strategy based on maximal fitness improvement (MFI). In addition, the local search is conducted on difficult subproblems for further refinement. The experimental studies on 18 instances show that MOEA/D-ALS exhibits a significant performance superiority over two other representative algorithms. The effectiveness of both the MFI strategy and the adaptive local search procedure is also validated.

关键词： Memetics Measurement Costs Vehicle routing Transportation Evolutionary computation Logistics

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Positioning Control for Voice Coil Motor Using Unbalanced Multilevel Inverter Topology

Positioning Control for Voice Coil Motor Using Unbalanced Mu...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Takei, Gaku Obara, Hidemine Haneda, Ryo Nozaki, Takahiro Graduate School of Science and Technology Keio University Yokohama Japan Yokohama National University Department of Physics Electrical and Computer Engineering Yokohama Japan Keio University Department of System Design Engineering Yokohama Japan

ISBN: (纸本)9798331533892

This article proposes a new multi-level inverter (MLI) based on flying capacitor inverter (FCI), with focused on high-precision positioning control for a voice coil motor (VCM). Multi-level inverters (MLIs) have the characteristic that increasing the number of output voltage levels reduces the output voltage error. However, as the number of levels increases, the number of circuit elements increases, and the circuit becomes more complex. In addition, the output voltage amplitude for each level is constant in conventional inverters. As a result, such as high-precision positioning control of VCM, designing an appropriate inverter for a reference wave that contains a large amount of low-voltage components increases the number of circuit elements. Therefore, this paper proposes a MLI with two unbalanced dc-input voltages, referred to as unbalanced MLIs (UMLIs) hereinafter. This topology uses two different dc-input voltages, and it is possible to reduce the output voltage level in extremely low-voltage range compared to other voltage levels. Simulation results when the high-precision positioning control of the VCM confirm that the proposed UMLIs improves the control performance compared to the conventional FCIs with equal number of output voltage levels and uniform level widths. © 2025 IEEE.

关键词： motion control Multi-level inverter voice coil motor

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The study of radiation effect on Raman Distributed optical fiber Temperature Sensor

The study of radiation effect on Raman Distributed optical f...

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2020 International Symposium on Automation, Mechanical and design Engineering, SAMDE 2020

作者： Xiang, Meiqiong Liu, Yanyang Qing, Xianguo He, Zhengxi Zhu, Jialiang He, Peng Xinlv Sci. and Technology on Reactor System Design Technology LaboratoryNuclear Power Institute of China Chengdu610213 China

The rapid development of big data and artificial intelligence technology is inseparable from the acquisition of massive basic data, and the data is acquired by sensors. The Raman Distributed optical fiber Temperature Sensor (RDTS) is a kind of sensor with many advantages, such as: Anti-electromagnetic interference, intrinsic safety, small size, water and high temperature resistance. It has been applied in many fields for fast and continuous temperaturemeasurement. However, due to the rapid attenuation of transmitted light and the temperature error in radiated environment, it has not been applied innuclear field. Therefore, in order to improve the temperature accuracy of RDTS in radiated environment and provide basic data for advanced control. This paper studies the radiation effect on RDTS. Analyzesthe radiation damage mechanism and radiation damage influencing factors of RDTS. Proposes measures to improve the anti-radiation performance and provides four useful calibration methodsfortemperature error. Among them, two calibration methods have been proved right by experiments. © Published under licence by IOP Publishing Ltd.

关键词： Electromagnetic pulse

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