Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).

Image Segmentation Based on Support Vector Machine

Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.

Geometric active contour based approach for segmentation of high-resolution spaceborne SAR images

Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion brings adegenerative cycle between the breaking off and the new generation of thinnerand weaker blood vessels. This research aims to develop a suitable retinalvasculature segmentation method for improving retinal screening proceduresby means of computer-aided diagnosis systems. The blood vessel segmentationmethodology relies on an effective feature selection based on SequentialForward Selection, using the error rate of a decision tree classifier in theevaluation function. Subsequently, the classification process is performed bythree alternative approaches: artificial neural networks, decision trees andsupport vector machines. The proposed methodology is validated on threepublicly accessible datasets and a private one provided by Hospital Sant Joanof Reus. In all cases we obtain an average accuracy above 96% with a sensitivityof 72% in the blood vessel segmentation process. Compared with the state-ofthe-art, our approach achieves the same performance as other methods thatneed more computational power.Our method significantly reduces the numberof features used in the segmentation process from 20 to 5 dimensions. Theimplementation of the three classifiers confirmed that the five selected featureshave a good effectiveness, independently of the classification algorithm.

Improvement of Liver Segmentation by Combining High Order Statistical Texture Features with Anatomical Structural Features

Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.

Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold

Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type,scenarios and level of blurriness.In this paper,we propose an effective method for blur detection and segmentation based on transfer learning concept.The proposed method consists of two separate steps.In the first step,genetic programming(GP)model is developed that quantify the amount of blur for each pixel in the image.The GP model method uses the multiresolution features of the image and it provides an improved blur map.In the second phase,the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold.A model based on support vector machine(SVM)is developed to compute adaptive threshold for the input blur map.The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods.The comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.

Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

THE BENDING OF THIN RECTANGULAR PLATES WITH MIXED SUPPORTED SEGMENTS OF STRAIGHT EDGES

In this paper, the exact analytical solution of the rectangular plate having simplysupported segments mixed with free segments of straight edges are first given by means of the method of reciprocal theorem.By comparison .we calculate the same question by finite element method.Thecomparison shows that the analytical solution is correct.

SVM for density estimation and application to medical image segmentation

A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process. Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.

Osteosarcoma Segmentation in MRI Based on Zernike Moment and SVM

Osteosarcoma is primary malignant neoplasms derived from cells of mesenchymal origin, and often has distinct phenotypes at different stages. The location of tumor and reaction zone can be identified by an expert in magnetic resonance imaging (MRI), with MRI being one of the choices for evaluating the extent of osteosarcoma. However, it is still a challenge to automatically extract tumor from its surrounding tissues because of their low intensity differences in MRI. We investigated an approach based on Zernike moment and support vector machine (SVM) for osteosarcoma segmentation in T1-weighted image (TIWI). Firstly, the different order moments around each pixel are calculated in small windows. Secondly, the grayscale and the module values of different order moments are used as a texture feature vector which is then used as the training set for SVM. Finally, an SVM classifier is trained based on this set of features to identify the osteosarcoma, and the segmented tumor tissue is rendered in 3D by the ray casting algorithm based on graphics processing unit (GPU). The performance of the method is validated on T1WI, showing that the segmentation method has a high similarity index with the expert's manual segmentation.

An Intelligent Decision Support System for Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identify and classify only one type of lung cancer.It is crucial to close this gap with a system that detects all lung cancer types.This paper proposes an intelligent decision support system for this purpose.This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives.Its algorithm uses a Convolutional Neural Network(CNN)tool to perform deep learning and a Random Forest Algorithm(RFA)to help classify the type of cancer present using several extracted features,including histograms and energy.Numerous simulation experiments were conducted on MATLAB,evidencing that this system achieves 98.7%accuracy and over 98%precision and recall.A comparative assessment assessing accuracy,recall,precision,specificity,and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics.This study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall results.These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.

柔性光伏系统颤振性能的节段模型试验研究

柔性光伏支架的频率低、质量轻,极易在风荷载作用下发生大幅振动。以某实际柔性光伏项目为工程背景,借鉴桥梁抗风的研究经验,对该柔性光伏支架的颤振性能进行研究。首先,考虑严格的相似比关系,设计并制作了弹性悬挂节段模型系统,并对其动力特性进行了测试;然后,进行不同光伏组件倾角(-39°~+39°)下的弹性悬挂节段模型测振风洞试验,研究光伏组件风致响应随风速的变化规律,得到各倾角下柔性光伏节段模型的颤振临界风速值,探讨2种气动措施对提高柔性光伏支架颤振稳定性的效果。结果表明:在0°~39°的正倾角工况下,柔性光伏支架的颤振临界风速随光伏组件倾角的增大,呈现先减小后增大的趋势,最小颤振临界风速出现在12°~21°光伏组件倾角范围内,折算成实际风速为9.6m/s;在-39°~0°的负倾角工况下,颤振临界风速随光伏组件倾角的增大,也呈现先减小后增大的趋势,最小颤振临界风速出现在-21°~-12°光伏组件倾角范围内,折算成实际风速为10.1m/s;设置中央稳定板的气动措施难以有效提高柔性光伏支架的颤振临界风速。

铁路桥梁大节段预制胶接拼装简支箱梁施工

随着铁路交通的快速发展,桥梁建设面临着工期缩短、成本控制和质量提升的需求。大节段预制胶接拼装技术作为一种创新方法,通过预制箱梁的大节段装配和胶接方式,有效地提升了施工效率和桥梁质量。本文详细介绍了该技术在某铁路特大桥项目中的应用,包括节段梁的设计、预制、悬挂施工、定位及拼装等关键技术环节。实践证明,采用此技术能显著减少现场作业量,缩短工期,并通过精确的施工控制提高结构的耐久性和安全性。本文旨在通过详细阐述大节段预制胶接拼装简支箱梁的设计与施工过程,为铁路桥梁建设提供创新方法与实践案例,以供业界参考和借鉴。

基于粒子图像分割的混合PIV-PTV算法

粒子图像测速法(particle image velocimetry,PIV)因其非接触场测量的特性,已成为空气动力学领域的主要测量工具。复杂流动的速度场往往具有非均匀性,示踪粒子难以在待测空间均匀分布。因此,在应用PIV互相关算法处理粒子稀疏区时,需要采用更大的查询窗口以降低测量的不确定度,但会带来空间分辨率低的实际问题。而粒子追踪测速法(particle tracking velocimetry,PTV)追踪单个示踪粒子的跨帧位移,具有比PIV更高的空间分辨率,但难以适用于粒子浓度高的稠密区。针对PIV、PTV各自的优点,本文发展了一种基于粒子图像分割的混合PIV-PTV测速技术。首先定义了基于维诺多边形的粒子局部浓度量度,用以计算示踪粒子在粒子图像上的局部浓度场;其次通过设定的浓度阈值对粒子进行二分类,使用基于高斯核函数的支持向量机寻找出最优的分类边界,从而实现对粒子图像的粒子稀疏区和稠密区的划分;最后对两个区域分别使用PIV和PTV进行速度场计算,并合并为完整的速度场输出。仿真结果表明,上述方法可实现对粒子图像中的示踪粒子稀疏区和稠密区的自动划分,有效提高速度场测量的空间分辨率。将该方法应用在马赫数Ma=6的湍流边界层近壁测量中,可有效解决高速条件下粒子因强剪切难以进入边界层近壁区的问题,显著提高对近壁流动的解析能力。

岩石隧道掘进机不良地质施工处置技术

岩石隧道掘进机(Tunnel Boring Machine,TBM)凭借其高效优质、作业安全等特点在铁路、公路、地铁、水利水电等领域广泛应用,在普通地质环境下推广顺利。复杂地质环境下致灾源多,如硬岩岩爆、软岩大变形、断层破碎带、涌水突泥等不良地质使TBM施工频繁出现围岩坍塌、支护失效、卡机淹井、设备损伤等问题。本文结合工程案例,从超前主动加固、注浆及锚固、管片支护、撑靴加固4个方面总结现有施工技术,以应对TBM穿越不良地质导致的刀盘卡机、护盾抱死、初期支护变形过大、撑靴打滑等问题,提升TBM在不良地质环境下的适应能力,为类似工程提供参考。

基于反向分段加工的整体叶盘变形控制技术研究

针对航空发动机整体叶盘精加工过程中易产生颤振和加工变形的难题,开展了整体叶盘变形控制技术研究,设计出一种基于反向分段加工的光固化辅助支撑夹具。首先依据工件分段刚性化原则说明反向分段加工对于减少工件加工变形的有效性。其次设计了包括压紧块、仿形块、固定板在内的三段式叶盘加工辅助支撑夹具,通过光敏树脂填充仿形块与叶片之间的缝隙,为整体叶盘的加工提供良好支撑。并对夹具支撑性能进行了测试,验证了夹具的可靠性。最后,采用叶盘模拟件进行了反向分段加工验证试验,结果表明,所设计的夹具减小了叶盘截面线加工误差,最大让刀变形量由0.1 mm下降到了0.05 mm,验证了光固化辅助夹具对整体叶盘加工具有较好的变形控制作用。

融合高斯过程的自支持小样本语义分割

针对小样本语义分割中同类别支持图像与查询图像存在外观差异较大的问题,提出融合高斯过程的自支持匹配小样本语义分割模型。提出的模型在自支持匹配小样本语义分割模型的基础上,首先融入高斯过程,对分布在深层特征空间上的复杂外观进行建模,捕获更多空间细节信息来表示数据分布;随后设计特征增强模块,在空间层对支持特征与查询特征进行信息交互,在通道层进行注意力加权,进一步增强相同类之间的全局相似性,捕获更多目标类别信息;最后利用Gram矩阵量化支持图像和查询图像之间外观差异的大小,从而融合原型匹配的结果,产生更准确的分割图像。实验结果表明:与现有方法相比,所提模型在更强的主干网络下具有较好的分割结果和更少的参数量,在5-shot的设定下,所提模型在PASCAL−5i数据集上平均交并比(mean Intersection over Union,mIoU)达到最优值,提升了0.4%;在COCO−20i数据集上的子集mIoU取得最优值,分别提升了2.2%和1.0%,表明该模型的有效性和先进性。

百米级UHPC简支箱梁桥设计

中山西环高速横栏服务区B匝道桥中采用了105 m跨径超高性能混凝土(UHPC)简支梁桥,桥面宽度为10.5 m。上部结构采用大节段预制吊装+纵横向湿接缝连接的形式。主梁由2片预制斜腹板箱梁组成,梁高4.1 m,箱梁横向中心间距5.2 m,通过湿接缝和横隔板形成整体;主梁每隔6 m设1道横肋,减弱其畸变、翘曲和剪力滞效应;主梁预应力钢束采用体外后张为主、体内先张为辅的方案,即主梁的通长预应力钢束采用后张体外束,保证结构简洁、轻盈,跨中节段增设先张法钢绞线提高截面抗弯承载力并增强结构的延性;后浇带处配筋采用预制梁段纵向钢筋外伸+局部植筋。对结构刚度及承载力、抗裂及应力、端横梁锚固区局部受力及转向块承载力等进行验算,结果均满足规范要求。

基于邻域特征编码优化的液压支架激光点云分割算法

受井下煤尘和易被遮挡的影响,液压支架激光点云数据容易出现残缺。现有点云分割算法难以获取细粒度的点云特征,无法得到完整的点云结构信息,且易在邻域内引入语义信息不相似的点,导致液压支架激光点云分割精度低。针对上述问题,提出了一种基于邻域特征编码优化的液压支架激光点云分割算法。引入了由邻域特征编码模块、邻域特征优化模块和混合池化模块组成的局部邻域特征聚合模块:邻域特征编码模块在传统三维坐标编码的基础上加入极坐标编码和质心偏移来表征局部点云空间结构,提升对残缺点云的特征提取能力;邻域特征优化模块通过特征距离判断并丢弃冗余特征,来优化邻域空间内的特征表达,从而更有效地学习点云局部细粒度特征,增强点云局部上下文信息;混合池化模块结合注意力池化和最大池化,通过聚合邻域内的显著特征和重要特征来获取具有丰富信息的单点特征,减少信息丢失。构建了由2组局部邻域特征聚合模块和残差连接组成的邻域扩张模块,以捕获特征间的长距离依赖关系,扩大单个点的局部感受野,并聚合更多有效特征。实验结果表明,该算法在液压支架激光点云分割数据集上的平均交并比为93.26%,平均准确率为96.42%,可有效区分液压支架不同的几何结构,实现液压支架各部件的准确分割。

深部高应力巷道围岩变形特征及控制技术

以某矿位于121/2煤层底板的2901岩石大巷支护为背景,为有效控制巷道围岩变形,根据大巷围岩力学结构及层位分布特征,以弹塑性区范围、切向径向应力变化、121/2煤距巷道顶板距离及煤层厚度为依据将巷道划分为常规段、较困难段、困难段三区段进行管理。大巷距121/2煤层底板间距6 m以上时为常规段;大巷距121/2煤层底板间距小于6 m区域为较困难段;大巷顶板破碎及煤层厚度变化较大区域为困难段,对三段分别设计合理的支护方式,并进行数值模拟。对支护效果进行十字布点观测,最大顶底板位移量85 mm,最大两帮位移量111 mm,巷道变形得到了很好的控制,满足安全生产的需求。