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学者姓名：陈丹
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Abstract ：
Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.
Keyword ：
deep learning deep learning multi-source data fusion multi-source data fusion semantic segmentation semantic segmentation U-Net U-Net wood defect detection wood defect detection
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| GB/T 7714 | Zhu, Yuhang , Xu, Zhezhuang , Lin, Ye et al. A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation [J]. | SENSORS , 2024 , 24 (5) . |
| MLA | Zhu, Yuhang et al. "A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation" . | SENSORS 24 . 5 (2024) . |
| APA | Zhu, Yuhang , Xu, Zhezhuang , Lin, Ye , Chen, Dan , Ai, Zhijie , Zhang, Hongchuan . A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation . | SENSORS , 2024 , 24 (5) . |
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Surface defect detection of sawn timber is a critical task to ensure the quality of wooden products. Current methods have challenges in considering detection accuracy and speed simultaneously, due to the complexity of defects and the massive length of sawn timbers. Specifically, there are scale variation, large intraclass difference and high interclass similarity in the defects, which reduce the detection accuracy. To overcome these challenges, we propose an efficient multilevel-feature integration network (EMINet) based on YOLOv5s. To obtain discriminative features of defects, the cross fusion module (CFM) is proposed to fully integrate the multilevel features of backbone. In the CFM, the local information aggregation is designed to enrich the detailed information of high-level features, and the global information aggregation is designed to enhance the semantic information of low-level features. Experimental results demonstrate that the proposed EMINet achieves better accuracy with fast speed compared with the state-of-the-art methods.
Keyword ：
information aggregation information aggregation machine vision machine vision multilevel feature integration multilevel feature integration sawn timber sawn timber surface defect detection surface defect detection
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| GB/T 7714 | Zhu, Yuhang , Xu, Zhezhuang , Lin, Ye et al. Surface defect detection of sawn timbers based on efficient multilevel feature integration [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (4) . |
| MLA | Zhu, Yuhang et al. "Surface defect detection of sawn timbers based on efficient multilevel feature integration" . | MEASUREMENT SCIENCE AND TECHNOLOGY 35 . 4 (2024) . |
| APA | Zhu, Yuhang , Xu, Zhezhuang , Lin, Ye , Chen, Dan , Zheng, Kunxin , Yuan, Yazhou . Surface defect detection of sawn timbers based on efficient multilevel feature integration . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (4) . |
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针对基于随机采样的RRT机械臂路径规划算法在全局工作空间下采样效率低、随机性强等问题,提出一种基于采样点优化RRT算法的机械臂路径规划算法.相对于全局工作空间采样,优化算法首先基于非障碍物空间生成随机采样点,以降低算法碰撞检测概率与冗余节点的生成,再结合一定概率的人工势场法产生启发式采样点,使得机械臂臂体于路径规划采样过程中既能保证随机采样的概率完备,又能使采样点更具目标导向性.其次,为使得路径更加简洁平滑,使用冗余节点删除策略剔除路径中的冗余节点来优化最终路径.最后在二维、三维的仿真环境中对优化算法进行对比实验分析,以验证算法在随机采样路径规划算法中的良好性能,并在IRB 1200-7/0.7机械臂上进行避障规划算法实验.仿真和实验结果都表明,所提出的算法在机械臂路径规划中可以获得更高的规划效率和更优的路径.
Keyword ：
人工势场法 人工势场法 启发式采样 启发式采样 快速随机搜索树 快速随机搜索树 机械臂运动规划 机械臂运动规划 采样点优化 采样点优化 非障碍物空间采样 非障碍物空间采样
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| GB/T 7714 | 陈丹 , 谭钦 , 徐哲壮 . 基于采样点优化RRT算法的机械臂路径规划 [J]. | 控制与决策 , 2024 , 39 (08) : 2597-2604 . |
| MLA | 陈丹 et al. "基于采样点优化RRT算法的机械臂路径规划" . | 控制与决策 39 . 08 (2024) : 2597-2604 . |
| APA | 陈丹 , 谭钦 , 徐哲壮 . 基于采样点优化RRT算法的机械臂路径规划 . | 控制与决策 , 2024 , 39 (08) , 2597-2604 . |
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The meter reading with machine vision greatly improves the efficiency of industrial monitoring. However, the pointer and scales of the meter can be occluded by rain or dirt, which greatly reduces the accuracy of the meter reading recognition. To solve this problem, we propose a generative adversarial network (PMS-GAN) with pointer generation and main scale detection for occluded meter reading. Specifically, dilated convolution block is designed to correlate separated pointer features. Then multi-scale feature fusion mechanism is proposed to guarantee the precision of pointer generation and main scale detection with guidance of semantic information. Moreover, feature enhancement mechanism is proposed to construct the long -range relationship for generating pointer under high occlusion. Finally, the reading is accomplished by calculating local angle with generated pointer and detected main scales. Experiments show that PMS-GAN can generate more intact pointer and detect main scales to guarantee the success and accuracy of occluded meter reading.
Keyword ：
Generative adversarial network Generative adversarial network Local angle calculation Local angle calculation Main scale detection Main scale detection Occluded meter reading Occluded meter reading Pointer generation Pointer generation
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| GB/T 7714 | Lin, Ye , Xu, Zhezhuang , Yuan, Meng et al. Pointer generation and main scale detection for occluded meter reading based on generative adversarial network [J]. | MEASUREMENT , 2024 , 234 . |
| MLA | Lin, Ye et al. "Pointer generation and main scale detection for occluded meter reading based on generative adversarial network" . | MEASUREMENT 234 (2024) . |
| APA | Lin, Ye , Xu, Zhezhuang , Yuan, Meng , Chen, Dan , Zhu, Jinyang , Yuan, Yazhou . Pointer generation and main scale detection for occluded meter reading based on generative adversarial network . | MEASUREMENT , 2024 , 234 . |
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在输电线张力放线过程中，走板过转角塔时易因其受力复杂导致其倾斜角与滑车倾斜角不一致而发生跳槽甚至翻转故障。针对此问题，本文建立张力机出口张力与走板倾斜角的数学模型，以便准确控制走板过转角塔时的倾斜角。首先，根据导线的牵展计算得到张力机出口张力控制值；然后考虑到走板在放线过程中的受力情况，建立走板力矩平衡方程；最后，获得张力机出口张力对走板倾斜角的控制模型，并提出张力机出口张力调整策略。在某实际输电线路工程中应用本文提出的力学模型进行仿真验证。结果表明，模型计算的张力机出口张力控制值与实际值误差在5%以内；并且，根据模型分析了走板尺寸、重量及转角塔的位置对走板倾斜角的影响，并结合走板期望倾斜角确定张力机出口张力的最佳调整策略，提高了放线施工的效率和安全。
Keyword ：
姿态调节 姿态调节 张力放线 张力放线 调整策略 调整策略 走板力学模型 走板力学模型 转角塔 转角塔 输电线 输电线
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| GB/T 7714 | 张建勋 , 卞宏志 , 杨大淼 et al. 架空输电线走板与张力机之间的力学模型研究 [J]. | 科技通报 , 2023 , 39 (01) : 53-58,90 . |
| MLA | 张建勋 et al. "架空输电线走板与张力机之间的力学模型研究" . | 科技通报 39 . 01 (2023) : 53-58,90 . |
| APA | 张建勋 , 卞宏志 , 杨大淼 , 徐康 , 陈丹 . 架空输电线走板与张力机之间的力学模型研究 . | 科技通报 , 2023 , 39 (01) , 53-58,90 . |
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| GB/T 7714 | Lin, Ye , Xu, Zhezhuang , Chen, Dan et al. Wood Crack Detection Based on Data-Driven Semantic Segmentation Network [J]. | IEEE-CAA JOURNAL OF AUTOMATICA SINICA , 2023 , 10 (6) : 1510-1512 . |
| MLA | Lin, Ye et al. "Wood Crack Detection Based on Data-Driven Semantic Segmentation Network" . | IEEE-CAA JOURNAL OF AUTOMATICA SINICA 10 . 6 (2023) : 1510-1512 . |
| APA | Lin, Ye , Xu, Zhezhuang , Chen, Dan , Ai, Zhijie , Qiu, Yang , Yuan, Yazhou . Wood Crack Detection Based on Data-Driven Semantic Segmentation Network . | IEEE-CAA JOURNAL OF AUTOMATICA SINICA , 2023 , 10 (6) , 1510-1512 . |
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In the process of tension stringing of transmission lines, the condition of the plate needs manual reporting, so its safety is poor, and its efficiency is low. To solve this problem, this study constructs a plate condition monitoring system of tension stringing based on the LoRa wireless transmission. This system consists of a data acquisition system, a wireless communication transmission system, and a monitoring and warning center. The STM32 is used to process the data collected by the sensors, and the LoRa wireless communication system is designed by way of adaptive dynamic change of LoRa's network ID, and the networking mode of the multi-node relay, and the data is sent to the upper computer from a distance, and the upper computer software is implemented by MFC, which can monitor the condition of the plate in real-time. The test results show that the packet loss rate of LoRa is less than 5%, and the condition data of the plate can be transmitted to the monitoring software through LoRa and displayed. This system has the advantages of strong confidentiality, far transmission, and high safety, which can be used for real-time monitoring of the condition of the plate in the process of tension stringing. © Published under licence by IOP Publishing Ltd.
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| GB/T 7714 | Xu, K. , Chen, D. , Lin, F. et al. Plate Condition Monitoring System Based on LoRa Wireless Transmission [未知]. |
| MLA | Xu, K. et al. "Plate Condition Monitoring System Based on LoRa Wireless Transmission" [未知]. |
| APA | Xu, K. , Chen, D. , Lin, F. , Tan, Q. , Wen, H. . Plate Condition Monitoring System Based on LoRa Wireless Transmission [未知]. |
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由于工业现场设备存在外观相似和部署密集等特点，使得巡检机器人仅依靠机器视觉难以对工业现场的相似设备进行识别，进而影响了自主巡检的准确性和效率。针对上述问题，基于工业物联网的无线信号特征，提出了融合机器视觉与邻近度估计的相似工业设备识别策略。该策略首先通过机器视觉和高效透视N点投影算法估计巡检机器人的初始位姿，进而采用邻近度估计算法实现巡检机器人对邻近工业设备目标的识别。另一方面，该策略还包括了机器人角度校正与位置调整算法，以此保证邻近度估计的精度。实验结果表明，相比于基于机器视觉的传统识别方法，该策略能够在不同设备密度的场景下，提升2%～49%的相似工业设备识别精度，有效地解决巡检机器人对工业现场相似设备的识别问题。
Keyword ：
位姿估计 位姿估计 巡检机器人 巡检机器人 工业物联网 工业物联网 机器视觉 机器视觉 相似工业设备识别 相似工业设备识别 邻近度估计 邻近度估计
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| GB/T 7714 | 徐哲壮 , 黄平 , 陈丹 et al. 融合机器视觉与邻近度估计的相似工业设备识别策略研究 [J]. | 仪器仪表学报 , 2023 , 44 (01) : 283-290 . |
| MLA | 徐哲壮 et al. "融合机器视觉与邻近度估计的相似工业设备识别策略研究" . | 仪器仪表学报 44 . 01 (2023) : 283-290 . |
| APA | 徐哲壮 , 黄平 , 陈丹 , 吴开田 , 李建坤 . 融合机器视觉与邻近度估计的相似工业设备识别策略研究 . | 仪器仪表学报 , 2023 , 44 (01) , 283-290 . |
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Dear Editor, This letter is concerned with wood crack detection which is impor-tant to guarantee the quality of wooden products. In the wood indus-try, the crack detection is one of the most challenging tasks in the wood defects detection, since the detection accuracy may be reduced due to the stains on the boards, the tiny cracks, and some cracks that are similar to the sound region.
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| GB/T 7714 | Ye Lin , Zhezhuang Xu , Dan Chen et al. Wood Crack Detection Based on Data-Driven Semantic Segmentation Network [J]. | 自动化学报（英文版） , 2023 , 10 (6) : 1510-1512 . |
| MLA | Ye Lin et al. "Wood Crack Detection Based on Data-Driven Semantic Segmentation Network" . | 自动化学报（英文版） 10 . 6 (2023) : 1510-1512 . |
| APA | Ye Lin , Zhezhuang Xu , Dan Chen , Zhijie Ai , Yang Qiu , Yazhou Yuan . Wood Crack Detection Based on Data-Driven Semantic Segmentation Network . | 自动化学报（英文版） , 2023 , 10 (6) , 1510-1512 . |
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Reinforcement Learning (RL) has been applied to robotic arm control, which enables the agent to learn an effective policy to solve complex tasks. However, it requires constant interaction with the environment leading to low sample efficiency. In this paper, we propose a robotic arm control approach based on planning via lookahead search, which is a model-based RL algorithm to improve the sample efficiency. The approach builds an environment model in order to obtain the dynamics of the environment. Thus the model can be used to plan future actions by a tree-based search. The experiments show that our approach can solve the task of robotic arm control with less environmental samples. © 2023 IEEE.
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| GB/T 7714 | Chen, Y. , Sun, X. , Zheng, S. et al. Model-Based Reinforcement Learning for Robotic Arm Control with Limited Environment Interaction [未知]. |
| MLA | Chen, Y. et al. "Model-Based Reinforcement Learning for Robotic Arm Control with Limited Environment Interaction" [未知]. |
| APA | Chen, Y. , Sun, X. , Zheng, S. , Huang, W. , Zeng, S. , Chen, D. et al. Model-Based Reinforcement Learning for Robotic Arm Control with Limited Environment Interaction [未知]. |
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