Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,w...Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.展开更多
In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe ope...In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.展开更多
The traditional automated guided vehicle(AGV) on goods delivery faces the challenges when task space expands beyond 2 D plans. 3 D environments such as uneven terrain, ramps, and staircase are typical in construction ...The traditional automated guided vehicle(AGV) on goods delivery faces the challenges when task space expands beyond 2 D plans. 3 D environments such as uneven terrain, ramps, and staircase are typical in construction site. Thus, the key to introducing this technology into construction industry is to improve AGV’s stability and autonomous navigation ability in more complex three-dimensional environments. In this paper, mobileman, a novel tracked autonomous guide vehicle, is introduced. Compared with other construction robots, mobileman maximizes its load capacity on the basis of assuring accessibility. Furthermore, its modular designs and self-balancing platform enable it to cope with more complex challenging scenarios, such as staircase with 35-degree sloped staircase, while another modular design featured automated loading and unloading functionality. The mobile base specifications were presented in section two, and modular designs and exploration of the navigation system on construction site were illustrated in the rest of sections.展开更多
下一个兴趣点推荐(next POI recommendation)作为基于位置社交网络的主要应用之一,为用户和服务提供商带来了显著的实用价值。现有的POI推荐模型主要依赖于目标用户的历史签到数据进行推荐,没有充分利用其他用户移动轨迹数据的潜在价值...下一个兴趣点推荐(next POI recommendation)作为基于位置社交网络的主要应用之一,为用户和服务提供商带来了显著的实用价值。现有的POI推荐模型主要依赖于目标用户的历史签到数据进行推荐,没有充分利用其他用户移动轨迹数据的潜在价值,也未有效提取和融合时空-类别信息的特征。为了解决上述问题,提出了一种融合人群移动轨迹和时空-类别的下一个兴趣点推荐模型(GGCN-STC)。依据用户的移动轨迹构建区域轨迹图,提出了门控图卷积神经网络对共同移动轨迹进行建模;将签到序列中的时空-类别信息进行多维度的特征融合;利用自注意力机制捕获用户偏好,为用户提供更准确的POI推荐。在两个真实数据集上进行实验比较与分析,结果表明该模型优于其他模型。展开更多
针对当前注塑车间生产效率低、产品质量不稳定以及人工操作依赖性强等问题,笔者研究了注塑车间在自动化和智能化技术方面的发展现状与趋势。通过分析传感器技术、机器视觉、工业机器人、物联网(Internet of Things,IoT)、机器学习(Machi...针对当前注塑车间生产效率低、产品质量不稳定以及人工操作依赖性强等问题,笔者研究了注塑车间在自动化和智能化技术方面的发展现状与趋势。通过分析传感器技术、机器视觉、工业机器人、物联网(Internet of Things,IoT)、机器学习(Machine Learning,ML)与人工智能(Artificial Intelligence,AI)、大数据与云计算等技术的应用,探讨了其如何在注塑生产过程中提高生产效率、优化工艺参数、提升质量控制和减少人工干预;然而,尽管智能化技术已取得显著成效,但在数据标准化、技术集成和算法精准性等方面仍存在挑战,且对高素质技术人才的需求急剧增加。最后,笔者展望了注塑车间自动化生产线未来的发展方向,特别是在柔性化、绿色化和集成化方面的深入革新,探讨了如何通过技术突破推动车间向更高效、绿色、灵活和智能的方向发展,以期为行业转型升级提供有价值的参考。展开更多
基金funded by the National Natural Science Foundation of China(62273213,62472262,62572287)Natural Science Foundation of Shandong Province(ZR2024MF144)+1 种基金Natural Science Foundation of Shandong Province for Innovation and Development Joint Funds(ZR2022LZH001)Taishan Scholarship Construction Engineering.
文摘Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.
基金Supported by Fundamental Research Funds for the Central Universities of China(Grant No.2023JBMC014).
文摘In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.
文摘The traditional automated guided vehicle(AGV) on goods delivery faces the challenges when task space expands beyond 2 D plans. 3 D environments such as uneven terrain, ramps, and staircase are typical in construction site. Thus, the key to introducing this technology into construction industry is to improve AGV’s stability and autonomous navigation ability in more complex three-dimensional environments. In this paper, mobileman, a novel tracked autonomous guide vehicle, is introduced. Compared with other construction robots, mobileman maximizes its load capacity on the basis of assuring accessibility. Furthermore, its modular designs and self-balancing platform enable it to cope with more complex challenging scenarios, such as staircase with 35-degree sloped staircase, while another modular design featured automated loading and unloading functionality. The mobile base specifications were presented in section two, and modular designs and exploration of the navigation system on construction site were illustrated in the rest of sections.
文摘下一个兴趣点推荐(next POI recommendation)作为基于位置社交网络的主要应用之一,为用户和服务提供商带来了显著的实用价值。现有的POI推荐模型主要依赖于目标用户的历史签到数据进行推荐,没有充分利用其他用户移动轨迹数据的潜在价值,也未有效提取和融合时空-类别信息的特征。为了解决上述问题,提出了一种融合人群移动轨迹和时空-类别的下一个兴趣点推荐模型(GGCN-STC)。依据用户的移动轨迹构建区域轨迹图,提出了门控图卷积神经网络对共同移动轨迹进行建模;将签到序列中的时空-类别信息进行多维度的特征融合;利用自注意力机制捕获用户偏好,为用户提供更准确的POI推荐。在两个真实数据集上进行实验比较与分析,结果表明该模型优于其他模型。
文摘针对当前注塑车间生产效率低、产品质量不稳定以及人工操作依赖性强等问题,笔者研究了注塑车间在自动化和智能化技术方面的发展现状与趋势。通过分析传感器技术、机器视觉、工业机器人、物联网(Internet of Things,IoT)、机器学习(Machine Learning,ML)与人工智能(Artificial Intelligence,AI)、大数据与云计算等技术的应用,探讨了其如何在注塑生产过程中提高生产效率、优化工艺参数、提升质量控制和减少人工干预;然而,尽管智能化技术已取得显著成效,但在数据标准化、技术集成和算法精准性等方面仍存在挑战,且对高素质技术人才的需求急剧增加。最后,笔者展望了注塑车间自动化生产线未来的发展方向,特别是在柔性化、绿色化和集成化方面的深入革新,探讨了如何通过技术突破推动车间向更高效、绿色、灵活和智能的方向发展,以期为行业转型升级提供有价值的参考。
