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

作者： Hu, Junming Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province Xihua University Chengdu China Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan Xihua University Chengdu China School of Automobile and Transportation Xihua University Chengdu China

ISBN: (纸本)9781837242672

Enhancing vehicle reliability is of paramount importance during the research and development phase, with reliability growth testing playing a crucial role in this process. Traditional reliability growth testing often treats vehicle failures under varying environmental conditions with uniform significance, overlooking the specific impacts these conditions may have on failure occurrence. This paper aims to illuminate the influence of environmental factors on vehicle failures. To explore the relationship between vehicle failures and the contexts in which they occur, Principal Component Analysis (PCA) was employed. The findings indicate that a diversified testing approach, which assigns different levels of importance to various testing scenarios, is recommended. This approach is likely to yield a more nuanced understanding of vehicle performance and foster a more effective strategy for enhancing reliability. © The Institution of engineering & technology 2024.

关键词： FAILURE DATA ANALYSIS PRINCIPAL COMPONENT ANALYSIS RELIABILITY GROWTH TESTING TRACKED VEHICLE

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BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment

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Plant Phenomics 2022年第1期4卷 75-93页

作者： Meili Sun Liancheng Xu Xiude Chen Ze Ji Yuanjie Zheng Weikuan Jia School of Information Science and Engineering Shandong Normal UniversityJinanChina Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Zhenjiang 212013China National Engineering Research Center for Apple Shandong Agriculture UniversityTaian 271018China School of Engineering Cardiff UniversityCardiff CF243AAUK

Despite of significant achievements made in the detection of target fruits,small fruit detection remains a great challenge,especially for immature small green fruits with a few *** closeness of color between the fruit skin and the background greatly increases the difficulty of locating small target fruits in the natural orchard *** this paper,we propose a balanced feature pyramid network(BFP Net)for small apple detection.

关键词： Net pyramid locating

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Multi-Path Fusion Transformer Network for Hyperspectral Remote Sensing Image Classification

Multi-Path Fusion Transformer Network for Hyperspectral Remo...

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2024 International Conference on measurement, Communication, and Virtual Reality, MCVR 2024

作者： Li, Jiacheng Wang, Wei School of Measurement-Control Technology and Communication Engineering Harbin University of Science and Technology 52 Xuefu Road Heilongjiang Harbin150000 China

ISBN: (纸本)9781510691773

To address the challenges of insufficient spectral feature extraction and feature redundancy in hyperspectral image (HSI) classification, this paper proposes a Multi-Path Fusion Transformer Network (MPFT) that integrates Vision Transformer, hybrid residual convolution, and attention mechanisms. The proposed approach effectively utilizes both spatial and spectral information of pixels to overcome these limitations. The MPFT architecture comprises a Data Processing Module (DPM), a Dual-Stream Feature Extraction Module (DSFE), and a Vision Transformer (ViT) module. Specifically, the DPM performs dimensionality reduction and merges HSI information in three-dimensional space, while the DSFE extracts deeper features, which are subsequently processed by the ViT module for classification. Experiments conducted on three benchmark datasets—Indian Pines, University of Pavia, and Salinas—demonstrate that MPFT significantly outperforms mainstream models. Notably, MPFT achieved improvements in Overall Accuracy (OA) by up to 5.52%, Average Accuracy (AA) by up to 6.39%, and Kappa coefficient by up to 6.49% compared to state-of-the-art methods. These results highlight that MPFT not only efficiently extracts spatial-spectral features from HSI but also demonstrates superior effectiveness and robustness in various classification tasks. © 2025 SPIE.

关键词： Data streams

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Dynamic Programming Based Strategies of Future Crop Plant 24

Dynamic Programming Based Strategies of Future Crop Plant

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2024 5th International Conference on Big Data Economy and Information Management, BDEIM 2024

作者： Zhang, Beining Wang, Xueying Deng, Yongkang Chen, Kai Liu, Qinglang Measurement and Control Technology and Instruments School of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China School of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China

ISBN: (纸本)9798400711862

In this paper, we develop a decision model based on dynamic programming to create an optimal crop plan for 2024-2030 in rural northern China. Considering different marketing scenarios, uncertainties, and crop substitutions and complementarities, this paper solves the problem with three sub-problems. First, the optimal cropping plan for 2024-2030 is calculated based on the cropping scenario for 2023. It is assumes that expected sales, cropping costs, yields and marketing prices are stable. Secondly, a multi-objective planning model was developed. It aimed at profitability and stability and took into account uncertainties. Finally, the planting plan was further optimised and it considered crop substitutability and complementarity, and the relationship between expected sales and selling prices and planting costs. Through dynamic planning and Monte Carlo simulation, this paper takes into account uncertainty and crop relationships and it solves the optimal crop planting plan for the years 2024-2030 with a maximum profit of about $7.737 million. Therefore, this paper proposes a decision model based on dynamic programming and it takes into account various factors of the planting plan for the next seven years. An effective method is provided for solving the crop planting strategy problem in sustainable agricultural development. © 2024 Copyright held by the owner/author(s).

关键词： Dynamic Programming K-means Clustering Monte Carlo Simulation Spearman Correlation

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The Research Focuses on Enhancing the RTDETR Model for Improved Detection of PCB Defects

The Research Focuses on Enhancing the RTDETR Model for Impro...

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Advanced Algorithms and control engineering (ICAACE), International Conference on

作者： Gang Wang Yaohui Pan Yu Jun School of Mechanical Engineering Jilin Communications Polytechnic Changchun China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In response to the low accuracy, slow speed, and compatibility issues commonly exist in current PCB circuit board defect detection methods, this paper proposes an improved circuit board defect detection model called RTDETR-CGRFPN based on RTDETR. This model is designed with a Rectangular Self-Calibration Module for spatial feature reconstruction and pyramid context extraction. Additionally, considering the limited background information of PCB circuit boards and the performance limitations of general object detection models, FuseBlock Multi and Dynamic Interpolation Fusion modules are added to the original model for multi-scale feature fusion through dynamic interpolation. Furthermore, dynamic interpolation and multi-feature fusion are utilized to further enhance target detection capabilities in complex backgrounds. Moreover, Pyramid Context Extraction module is also introduced to enhance the model's perception of context and improve its ability to detect small defects. Experimental results demonstrate that our proposed algorithm achieves an average accuracy rate of 98.1% with a recall rate of 99.1% on the test dataset. In comparison, the original RTDETR-r18 algorithm has an average accuracy rate of 95.0% with a recall rate of 90.9%. Clearly, our proposed algorithm outperforms in these two metrics significantly. Additionally, this model maintains relatively few parameters while ensuring performance improvement and computational efficiency simultaneously; thus enabling quick and accurate defect detection in PCB circuit boards potentially providing technical support for fields such as smart factories.

关键词： YOLO Training Interpolation Accuracy Computational modeling Printed circuits Feature extraction Integrated circuit modeling Defect detection Context modeling

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A Lightweight Anchor-Free Detector Using Cross-Dimensional Interactive Feature Balance for SAR Ship Detection 23

A Lightweight Anchor-Free Detector Using Cross-Dimensional I...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Jiaming Shen Lin Bai Haijuan Wang Bo Ji Wenming Cheng Guocai Zhang School of Electronics and Control Engineering Changan University China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd China

ISBN: (纸本)9798400709272

In recent years, the deep convolutional neural network (CNN) model has rapidly developed, and it is widely used in SAR remote sensing target detection. Many methods utilize preset anchor boxes for target classification and boundary box coordinate regression. However, these methods encounter challenges when deployed to edge devices. Firstly, in SAR images, ships are sparsely and unevenly distributed, rendering most anchor boxes redundant. Secondly, the target detection model relying on anchor boxes demands extensive computation during anchor box generation, typically running on hardware-rich environments. Nevertheless, edge devices lack the necessary hardware environment to meet the deployment requirements of the CNN model. To address these issues, this paper proposes a lightweight anchor-free detector called Cross-Dimensional Interactive Feature Balance (CDIFB-Net). We adopt a key point strategy to predict the bounding box, eliminating the reliance on anchors. CDIFB-Net employs a partial convolution (PConv) based model as a lightweight feature extraction network to tackle the model's lightweight problem. Additionally, considering the characteristics of large target scale differences and unbalanced target distribution in SAR ship images, and to reduce the precision decline problem caused by lightweight, this paper introduces a cross-dimensional interactive feature balance pyramid (CDI-FBP), which balances semantic information of different levels through feature pyramid aggregation and averaging. The cross-dimensional interaction module (CDIM) establishes the relationship between the ship target and the background. Experiments conducted on the SAR Ship dataset (SSDD) and High-resolution SAR Image dataset (HRSID), along with inference experiments in different hardware environments, validate the effectiveness of the network for SAR ship detection. The experimental results demonstrate that the proposed CDIFB-Net maintains fast reasoning, low delay, and excellent detectio

关键词： CDI-FBP Convolutional neural network lightweight ship detection

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Path Optimization of Complex and Large-Scale Automated Antenna Assembly Lines

Path Optimization of Complex and Large-Scale Automated Anten...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Ma, Zeyu Wang, Hongjun Zhang, Mingliang Jiang, Fan Li, Bin School of Mechanical Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science Cooperation Base of High-End Equipment Intelligent Perception Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China Beijing Institute of Radio Measurement Beijing100854 China School of Information Management Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9783031494208

Considering issues such as low assembly efficiency and the inability of the assembly process index and product characteristics to be fully reflected in the traditional construction mechanical arm assembly process, the concept of a digital twin was introduced. The study involved the modeling and simulation technology of the construction mechanical arm assembly process based on the digital twin, and included some path optimization. Furthermore, according to the virtual model, a genetic algorithm was employed to optimize the path in Matlab. Additionally, the Hamiltonian cycle algorithm was utilized to enhance the efficiency of the robot and decrease the working time. Taking the assembly line of a screw arm as an example, it was demonstrated that the assembly simulation based on digital twinning can monitor the assembly process in real time. It can also optimize its configuration to improve assembly efficiency and implement an effective closed-loop feedback mechanism to enhance production efficiency, improve production quality, and reduce production cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cyber Physical System

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Digital Twin-Based Service System Platform

Digital Twin-Based Service System Platform

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ma, Zeyu Wang, Hongjun Zhang, Mingliang School of Mechanical Electrical Engineering Beijing University of Information Science and Technology Beijing100192 China Beijing International Science Cooperation Base of High-End Equipment Intelligent Perception Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China Beijing Institute of Radio Measurement Beijing100854 China School of Information Management Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9783031702341

This paper discusses the integration and application of digital twin technology within the manufacturing sector, underscoring its pivotal role in advancing the Industry 4.0 revolution. Digital twin technology, which creates dynamic virtual replicas of physical systems, facilitates enhanced simulation, analysis, and optimization of real-world processes. The implementation of this technology, illustrated through examples from NASA and the U.S. Air Force Research Laboratory, demonstrates its significant contributions across the entire product lifecycle, including design, manufacturing, service operation, and maintenance. The paper introduces a comprehensive framework for a digital twin-based production line service system, leveraging the Internet of Things (IoT) technology and flexible simulation to enable real-time user-application interaction, front-end operation responsiveness, and efficient data management. This system enhances operational efficiency, reduces time-to-market, and improves issue prediction and mitigation through virtual-real mapping and debugging. An application case using MongoDB highlights the technology's adaptability and effectiveness in managing complex data structures. The study concludes that digital twin technology is instrumental in driving smart manufacturing, innovation, and maintaining a competitive advantage in the evolving industrial landscape. Key references provide a foundation for understanding the technology's development and application, reinforcing the digital twin's role as a transformative element in manufacturing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： NASA

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FPGA-Oriented Target Detection Accelerator Design

FPGA-Oriented Target Detection Accelerator Design

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Weizhe Huang School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

Target detection is widely applied in fields such as face recognition, autonomous driving, and industrial automation. However, when deploying target detection models based on convolutional neural networks on resource-constrained embedded platforms, issues like high computational complexity, long execution times, and significant on-chip memory usage arise. To address these challenges, this paper presents a target detection accelerator design based on Field Programmable Gate Array (FPGA). Firstly, the computational workload of convolutional neural networks is reduced by merging convolutional layers with batch normalization layers. Secondly, to mitigate on-chip storage space concerns, on-chip data fixed-point quantization is employed to decrease on-chip memory consumption. Subsequently, to tackle the issue of long execution times, a convolution calculation module based on the Winograd algorithm is designed, which reduces computation latency. Finally, a dual-buffer pipeline architecture is devised to optimize memory bandwidth. Experimental results demonstrate that the proposed solution accelerates YOLOv4-tiny on the ZYNQ-7020, achieving a performance of 21.35 GOP/s. The energy efficiency is 32.56 times higher than CPU and 9.76 times higher than GPU. Additionally, the convolution calculation module in this paper reduces inference time by 15% compared to the sliding window convolution calculation method.

关键词：

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FPGA Based High Throughput LK Optical Flow Acceleration Design 3

FPGA Based High Throughput LK Optical Flow Acceleration Desi...

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3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Liang, Wei Sun, Ting Xing, Fei Dong, Xinran Xiao, Heng Li, Lin School of Instrument Science and Opto-Electronics Engineering Beijing Information Science and Technology University Beijing100192 China Department of Precision Instrument Tsinghua University Beijing100084 China Space Optoelectronic Measurement and Perception Lab. Beijing Institute of Control Engineering Beijing100190 China

ISBN: (纸本)9781510680531

In the field of machine vision, algorithms for estimating optical flow in sequential images have been proposed for many years. However, due to the advancements in image resolution and video frame rates of sensor, the optical flow algorithm running on a CPU or a GPU may struggle to meet real-time processing and low power consumption requirements. This paper presents an FPGA-based accelerated method for high-speed LK optical flow estimation. We implemented a serial input and multi-window output buffer structure to enhance the algorithm's parallel processing capability and improve processing speed. Additionally, we designed a multi-clock convolution structure to reduce resource consumption. Tested on the Middlebury dataset, the FPGA-based LK optical flow algorithm developed in this paper can achieve a processing speed of 330 frames per second for image sequences with a resolution of 640x480. It also maintains a relatively high level of accuracy and has low power consumption. © 2024 SPIE.

关键词： Optical flows

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