Industrial Fusion Cascade Detection of Solder Joint

With the remarkable advancements in machine vision research and its ever-expanding applications,scholars have increasingly focused on harnessing various vision methodologies within the industrial realm.Specifically,detecting vehicle floor welding points poses unique challenges,including high operational costs and limited portability in practical settings.To address these challenges,this paper innovatively integrates template matching and the Faster RCNN algorithm,presenting an industrial fusion cascaded solder joint detection algorithm that seamlessly blends template matching with deep learning techniques.This algorithm meticulously weights and fuses the optimized features of both methodologies,enhancing the overall detection capabilities.Furthermore,it introduces an optimized multi-scale and multi-template matching approach,leveraging a diverse array of templates and image pyramid algorithms to bolster the accuracy and resilience of object detection.By integrating deep learning algorithms with this multi-scale and multi-template matching strategy,the cascaded target matching algorithm effectively accurately identifies solder joint types and positions.A comprehensive welding point dataset,labeled by experts specifically for vehicle detection,was constructed based on images from authentic industrial environments to validate the algorithm’s performance.Experiments demonstrate the algorithm’s compelling performance in industrial scenarios,outperforming the single-template matching algorithm by 21.3%,the multi-scale and multitemplate matching algorithm by 3.4%,the Faster RCNN algorithm by 19.7%,and the YOLOv9 algorithm by 17.3%in terms of solder joint detection accuracy.This optimized algorithm exhibits remarkable robustness and portability,ideally suited for detecting solder joints across diverse vehicle workpieces.Notably,this study’s dataset and feature fusion approach can be a valuable resource for other algorithms seeking to enhance their solder joint detection capabilities.This work thus not only presents a novel and effective solution for industrial solder joint detection but lays the groundwork for future advancements in this critical area.

Multi-scale Incremental Analysis Update Scheme and Its Application to Typhoon Mangkhut(2018)Prediction

In the traditional incremental analysis update(IAU)process,all analysis increments are treated as constant forcing in a model’s prognostic equations over a certain time window.This approach effectively reduces high-frequency oscillations introduced by data assimilation.However,as different scales of increments have unique evolutionary speeds and life histories in a numerical model,the traditional IAU scheme cannot fully meet the requirements of short-term forecasting for the damping of high-frequency noise and may even cause systematic drifts.Therefore,a multi-scale IAU scheme is proposed in this paper.Analysis increments were divided into different scale parts using a spatial filtering technique.For each scale increment,the optimal relaxation time in the IAU scheme was determined by the skill of the forecasting results.Finally,different scales of analysis increments were added to the model integration during their optimal relaxation time.The multi-scale IAU scheme can effectively reduce the noise and further improve the balance between large-scale and small-scale increments in the model initialization stage.To evaluate its performance,several numerical experiments were conducted to simulate the path and intensity of Typhoon Mangkhut(2018)and showed that:(1)the multi-scale IAU scheme had an obvious effect on noise control at the initial stage of data assimilation;(2)the optimal relaxation time for large-scale and small-scale increments was estimated as 6 h and 3 h,respectively;(3)the forecast performance of the multi-scale IAU scheme in the prediction of Typhoon Mangkhut(2018)was better than that of the traditional IAU scheme.The results demonstrate the superiority of the multi-scale IAU scheme.

Adaptive stabilization for cascade nonlinear systems

An adaptive controller of full state feedback for certain cascade nonlinear systems achieving input-to-state stability with respect to unknown bounded disturbance is designed using backstepping and control Lyapunov function (CLF) techniques. We show that unknown bounded disturbance can be estimated by update laws, which requires less information on unknown disturbance, as a part of stabilizing control. The design method achieves the desired property: global robust stability. Our contribution is illustrated with the example of a disturbed pendulum.

A Case Study on MJO Energy Transport Path in a Local Multi-scale Interaction Framework

A new local kinetic energy(KE)budget for the Madden−Julian Oscillation(MJO)is constructed in a multi-scale framework.This energy budget framework allows us to analyze the local energy conversion processes of the MJO with the high-frequency disturbances and the low-frequency background state.The KE budget analysis is applied to a pronounced MJO event during the DYNAMO field campaign to investigate the KE transport path of the MJO.The work done by the pressure gradient force and the conversion of available potential energy at the MJO scale are the two dominant processes that affect the MJO KE tendency.The MJO winds transport MJO KE into the MJO convection region in the lower troposphere while it is transported away from the MJO convection region in the upper troposphere.The energy cascade process is relatively weak,but the interaction between high-frequency disturbances and the MJO plays an important role in maintaining the high-frequency disturbances within the MJO convection.The MJO KE mainly converts to interaction KE between MJO and high-frequency disturbances over the area where the MJO zonal wind is strong.This interaction KE over the MJO convection region is enhanced through its flux convergence and further transport KE to the high-frequency disturbances.This process is conducive to maintaining the MJO convection.This study highlights the importance of KE interaction between the MJO and the high-frequency disturbances in maintaining the MJO convection.

Multi-Scale Deep Cascade Bi-Forest for Electrocardiogram Biometric Recognition

Electrocardiogram(ECG)biometric recognition has emerged as a hot research topic in the past decade.Although some promising results have been reported,especially using sparse representation learning(SRL)and deep neural network,robust identification for small-scale data is still a challenge.To address this issue,we integrate SRL into a deep cascade model,and propose a multi-scale deep cascade bi-forest(MDCBF)model for ECG biometric recognition.We design the bi-forest based feature generator by fusing L1-norm sparsity and L2-norm collaborative representation to efficiently deal with noise.Then we propose a deep cascade framework,which includes multi-scale signal coding and deep cascade coding.In the former,we design an adaptive weighted pooling operation,which can fully explore the discriminative information of segments with low noise.In deep cascade coding,we propose level-wise class coding without backpropagation to mine more discriminative features.Extensive experiments are conducted on four small-scale ECG databases,and the results demonstrate that the proposed method performs competitively with state-of-the-art methods.

A Transformer-Assisted Cascade Learning Network for Choroidal Vessel Segmentation

As a highly vascular eye part,the choroid is crucial in various eye disease diagnoses.However,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal vessels.Meanwhile,the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks.Common cascaded structures grapple with error propagation during training.To address these challenges,we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this paper.Specifically,we propose TransformerAssisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation.We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information simultaneously.Additionally,we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation process.Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation.Besides,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available dataset.All these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.

基于Web的ARINC661座舱显示内核封装与设计

ARINC661为座舱显示系统(Cockpit Display System,CDS)和用户应用(User Application,UA)提供了接口规范,已经成为现代座舱显示研发的基础。在ARINC661规范中,CDS内核负责加载定义文件实现画面管理,但是目前支持定义文件设计的桌面型软件已经无法满足飞行机组在线评估显示画面的需求。根据CDS的运行机制,封装并设计了基础显示库、显示部件的逻辑存储结构、部件显示层叠规则、数据更新接口及逻辑。经过测试验证,结果表明座舱显示内核的各项功能正常,可以实现Web端的座舱画面显示预览。

一种新的多角度人脸表情识别方法

传统的多角度人脸表情识别方法是对角度特殊的样本采用角度特殊的分类器识别,该方法忽略了不同角度的人脸表情是相同的人脸表情的不同表现形式,而且传统的多角度人脸表情特征提取时间较长以及不能满足增量更新的要求,基于此,提出了一种新的多角度人脸表情识别方法。该方法首先提取回归模型的增量修正特征,然后用PCA进行特征选择,最后采用判别共享高斯过程隐变量模型识别多角度人脸表情。在CMU-PIE和LFPW数据库上的对比实验表明了该方法较传统的多角度人脸表情识别方法好。

支持What-if分析的OLAP多版本管理机制

针对What-if分析中的多版本更新管理需求,根据不同的假设更新实现技术,提出支持What-if分析的内存OLAP系统中的多版本更新策略,其中包括常规的递归假设更新和基于差值的多版本假设更新,采用预合并机制的delta记录合并算法来提高级联假设更新模式下的多版本What-if查询性能。实验表明,与常规的递归假设更新相比,该算法无论在减少多版本更新代价方面还是在减少整体What-if查询时间方面均有效。

关于地理实体编码设计和应用的研究

本文以考虑地理实体编码在地理空间数据更新中的应用为出发点,对地理实体编码的设计进行了初步研究。通过分析和试验建立了一套统一的编码规则,使之能适应于数据生产中要素的自动编码,并为实现要素快速定位、变化要素自动级联更新等应用创造有利的技术支持。

触发器在数据库完整性约束方面的应用

对触发器的概念和功能作了详细的介绍,针对Oracle数据库举例说明了触发器在数据库完整性约束方面的应用。

基于GUID的多尺度核心要素数据级联更新方法研究

福建省核心要素数据包含4个比例尺,每年需要进行2次更新。在多尺度核心要素数据更新时,容易出现数据不一致性的问题,这些问题在数据质量检查时很难被发现,特别是属性的不一致性问题。通过引入GUID型字段可以将更新前后的核心要素数据关联起来,并开发基于GUID字段的变化要素提取程序,用于变化要素的快速提取,从而实现多尺度核心要素数据的级联更新。

下拉式级联菜单局部刷新的一种实现方法

下拉式级联菜单在B/S结构应用系统的建设中比较常见,但这类菜单在选择菜单关联查询时,都会刷新整个页面,交互性不好。阐述采用XSL结合XML、XMLTHHP、XMLDOM等相关技术实现了级联菜单的局部刷新功能,并简单介绍在实际项目中的应用。

多尺度时空数字地形图联动更新方法

随着智慧城市的建设,国家和城市的发展对于基础地理信息数据的现势性、更新生产效率提出了越来越高的要求。因此,及时、快速地更新基础比例尺地形图对于保证基础地理信息数据的现势性具有重要意义。针对多尺度数字地形图,传统的逐个比例尺更新的方法效率低下。本文提出了一种方法:通过赋予要素时间信息来建立基于要素的时空数据库,并研制时空地理信息一体化表达标准体系,研发多比例尺数字地形图联动更新处理与可视化表达平台,实现不同尺度的数字地形图联动更新,搭建维思时空地理信息智能化处理和检查平台。经过实践验证,该方法可行且高效。

广州市地理信息公共服务平台数据更新体系研究

针对传统基础地理信息数据更新周期长、工作量大、数据现势性难以保证等难题、广州市地理信息公共服务平台结合动态变化检测、智能解译、多尺度数据级联更新等关键技术,提出了一套系统化的数据快速更新体系。该体系首先通过多元动态变化检测技术快速提取遥感影像变化区域;然后利用深度学习技术进行遥感影像智能化解译,特征匹配;最后通过智能级联更新技术完成多级比例尺电子地图的联动更新。该数据更新体系解决了基础测绘数据在变化识别及提取、智能化综合、联动更新等方面的技术难题,为广州市地理信息公共服务平台的数据持续更新提供了强有力的技术保障。

Defect Detection Algorithm of Patterned Fabrics Based on Convolutional Neural Network

The background pattern of patterned fabrics is complex,which has a great interference in the extraction of defect features.Traditional machine vision algorithms rely on artificially designed features,which are greatly affected by background patterns and are difficult to effectively extract flaw features.Therefore,a convolutional neural network(CNN)with automatic feature extraction is proposed.On the basis of the two-stage detection model Faster R-CNN,Resnet-50 is used as the backbone network,and the problem of flaws with extreme aspect ratio is solved by improving the initialization algorithm of the prior frame aspect ratio,and the improved multi-scale model is designed to improve detection of small defects.The cascade R-CNN is introduced to improve the accuracy of defect detection,and the online hard example mining(OHEM)algorithm is used to strengthen the learning of hard samples to reduce the interference of complex backgrounds on the defect detection of patterned fabrics,and construct the focal loss as a loss function to reduce the impact of sample imbalance.In order to verify the effectiveness of the improved algorithm,a defect detection comparison experiment was set up.The experimental results show that the accuracy of the defect detection algorithm of patterned fabrics in this paper can reach 95.7%,and it can accurately locate the defect location and meet the actual needs of the factory.

基于肤色和改进AdaBoost算法的人脸检测

提出了一种基于肤色和改进AdaBoost算法的人脸检测方法。从被检图像中分割出肤色区域从而得到包含一系列人脸静态特征的候选人脸区域。针对传统AdaBoost算法在训练过程中的过增益现象提出了一种新的权重更新方法,同时在训练过程中构建级联分类器。通过级联分类器对候选人脸区域进行扫描来准确定位人脸。大量的实验结果表明,所提出的方法在人脸检测上取得了较好的效果。

新型城镇化建设地理空间信息智能处理关键技术及应用

本项目基于新型城镇化智能管理对地理空间信息智能处理技术的迫切需求,以信息感知→信息融合→知识发现→应用服务为主线,构建了现代城镇地理空间信息“感—融—知—用”完整的技术体系,为新型城镇化建设与精细管理提供了原创性的技术支撑,推进了国土空间优化和城镇统一监管,从而创新了新型城镇智能化的管理模式。项目开发了空间数据库管理与更新系统、地理空间信息整合与快速更新软件平台等技术与平台,有效地提高了应用单位的数据质量和项目实施效率。开发了集成地理模拟和空间优化模块的FLUS模型提供给近200所国内外的高校使用,并应用于全国三十多座城市,为空间规划提供了技术支撑。

基于维思时空地理信息处理平台的1∶10000地形图更新

以武汉市1∶10 000数字地形图更新项目为例,介绍了基于时空地理信息处理平台的1∶10 000数字地形图的更新方法与流程,并对更新过程中的多序可视化联动更新进行了研究和探讨,建立了完备的要素级1∶10 000时空地理信息数据库。对项目成果进行了质量检查,成果满足时空云平台管理及规划管理的需要。

一种基于CMT框架的目标跟踪算法研究

论文提出了一种基于CMT(Consensus-based Tracking and Matching of Keypoints for Object Tracking)框架的目标跟踪算法。针对传统的CMT算法采用固定模型的缺点,该文引入时空上下文(Spatio-Temporal Context)跟踪算法,提出一种由跟踪器,检测器与融合器组成的目标跟踪算法。该算法将时空上下文跟踪算法作为跟踪器,使用Kalman滤波预测目标所在位置,减小在图像中特征点检测范围,将CMT中的特征点匹配算法作为检测器,使用融合器分析和评估跟踪器与检测器结果,决策出最终的结果,并执行有效的模型更新策略。