Active contours with normally generalized gradient vector flow external force

Gradient vector flow （GVF） is an effective external force for active contours, but its iso- tropic nature handicaps its performance. The recently proposed gradient vector flow in the normal direction （NGVF） is anisotropic since it only keeps the diffusion along the normal direction of the isophotes; however, it has difficulties forcing a snake into long, thin boundary indentations. In this paper, a novel external force for active contours called normally generalized gradient vector flow （NGGVF） is proposed, which generalizes the NGVF formulation to include two spatially varying weighting functions. Consequently, the proposed NGGVF snake is anisotropic and would improve ac- tive contour convergence into long, thin boundary indentations while maintaining other desirable properties of the NGVF snake, such as enlarged capture range, initialization insensitivity and good convergence at concavities. The advantages on synthetic and real images are demonstrated.

Left Ventricular Systolic Intraventricular Flow Field Assessment in Hyperthyroidism Patients Using Vector Flow Mapping

Summary： Intraventricular hydrodynamics is considered an important component of cardiac function assessment. Vector flow mapping （VFM） is a novel flow visualization method to describe cardiac pathophysiological condition. This study examined use of new VFM and flow field for assessment of left ventricular （LV） systolic hemodynamics in patients with simple hyperthyroidism （HT）. Thirty-seven simple HT patients were enrolled as HT group, and 38 gender- and age-matched healthy volunteers as control group. VFM model was used to analyze LV flow field at LV apical long-axis view. The follow- ing flow parameters were measured, including peak systolic velocity （Vs）, peak systolic flow （Fs）, total systolic negative flow （SQ） in LV basal, middle and apical level, velocity gradient from the apex to the aortic valve （AV）, and velocity according to half distance （V1/2）. The velocity vector in the LV cavity, stream line and vortex distribution in the two groups were observed. The results showed that there were no significant differences in the conventional parameters such as left ventricular ejection fraction （LVEF）, left ventricular end-diastolic diameter （LVEDD） and left atrium diameter （LAD） between HT group and control group （P〉0.05）. Compared with the control group, a brighter flow and more vortexes were detected in HT group. Non-uniform distribution occurred in the LV flow field, and the stream lines were discontinuous in HT group. The values of Vs and Fs in three levels, SQ in middle and basal levels, AV and V1/2 were higher in HT group than in control group （P〈0.01）. AV was positively correlated with serum free thyroxin （FT4） （r=0.48, P〈0.01）. Stepwise multiple regression analysis showed that LVEDD, FT4, and body surface area （BSA） were the influence factors of △V. The unstable left ventricular sys- tolic hydrodynamics increased in a compensatory manner in simple PIT patients. The present study in- dicated that VFM may be used for early detection of abnormal ventricle contraction in clinical settings.

Centerline Extraction for Image Segmentation Using Gradient and Direction Vector Flow Active Contours

In this paper, we propose a fast centerline extraction method to be used for gradient and direction vector flow of active contours. The gradient and direction vector flow is a recently reported active contour model capable of significantly improving the image segmentation performance especially for complex object shape, by seamlessly integrating gradient vector flow and prior directional information. Since the prior directional information is provided by manual line drawing, it can be inconvenient for inexperienced users who might have difficulty in finding the best place to draw the directional lines to achieve the best segmentation performance. This paper describes a method to overcome this problem by automatically extracting centerlines to guide the users for providing the right directional information. Experimental results on synthetic and real images demonstrate the feasibility of the proposed method.

V Flow技术测量颈动脉壁面剪应力的一致性研究

目的评价血流向量成像(V Flow)技术在测量健康成年人颈动脉壁面剪应力(WSS)中的一致性。方法于2021年2月至2021年3月招募健康成年志愿者20人,由2名不同年资的超声医师使用配备V Flow功能的Mindray Resona 7超声仪和3~9 MHz线阵探头进行双侧颈动脉扫查,分别采集双侧颈总动脉远段、颈总动脉分叉至颈内动脉起始部两段动脉的动态V Flow图像,测量两侧颈总动脉远段的近心端、远心端、分叉处及颈内动脉起始部的前、后壁的WSS,使用组内相关系数(ICC)和Bland-Altman图评估组内一致性及组间一致性。结果双侧颈动脉前、后壁的4个不同节段之间WSS值差异均有统计学意义(P<0.05)。高年资超声医师2次测量结果的一致性结果显示,左侧颈总动脉远段的远心端一致性极好(ICC 0.779),右侧颈总动脉远段的近心端(ICC 0.605)、远心端(ICC 0.585)、颈内动脉起始部(ICC 0.457)、左侧颈总动脉分叉处(ICC 0.606)及颈内动脉起始部(ICC 0.702)一致性均较好;不同年资超声医师的测量结果显示,仅右侧颈总动脉分叉处(ICC 0.486)及左侧颈总动脉远段的远心端(ICC 0.576)一致性较好。结论V Flow技术可显示不同位点间颈动脉WSS之间的差异,其组内一致性较好,但在不同年资超声医师间存在一定的差异。

Optimizing the cleanliness in multi-segment disk amplifiers based on vector flow schemes

The objective of maintaining the cleanliness of the multi-segment disk amplifier in Shenguang-II(SG-II) is to reduce laser-induced damage for optics. The flow field of clean gas, which is used for the transportation of contaminant particles,is a key factor affecting the cleanliness level in the multi-segment disk amplifier. We developed a gas–solid coupling and three-dimensional flow numerical simulation model. The three-dimensional and two-phase flow model is verified by the flow-field smog experiment and the particle concentration measurement experiment with the 130-disk amplifier in SG-II. By optimizing the boundary conditions with the same flow rate, the multi-inlet vector flow scheme can not only effectively reduce the purging time, but also prevent the reverse diffusion of contaminant particles in the multi-segment disk amplifier and the deposition of contaminant particles on the surface of the Nd:glass.

Advection-Enhanced Gradient Vector Flow for Active-Contour Image Segmentation

In this paper,we propose a new gradient vector flow model with advection enhancement,called advection-enhanced gradient vector flow,for calculating the external force employed in the active-contour image segmentation.The proposed model is mainly inspired by the functional derivative of an adaptive total variation regularizer whose minimizer is expected to be able to effectively preserve the desired object boundary.More specifically,by incorporating an additional advection term into the usual gradient vector flow model,the resulting external force can much better help the active contour to recover missing edges,to converge to a narrow and deep concavity,and to preserve weak edges.Numerical experiments are performed to demonstrate the high performance of the newly proposed model.

Debris Flow Hazard Assessment Based on Support Vector Machine

Seven factors, including the maximum volume of once flow , occurrence frequency of debris flow , watershed area , main channel length , watershed relative height difference , valley incision density and the length ratio of sediment supplement are chosen as evaluation factors of debris flow hazard degree. Using support vector machine （SVM） theory, we selected 259 basic data of 37 debris flow channels in Yunnan Province as learning samples in this study. We create a debris flow hazard assessment model based on SVM. The model was validated though instance applications and showed encouraging results.

THE SOLUTION FOR TRANSIENT TWO-PHASE FLOW BY SPLIT FLUX VECTOR METHOD

In this paper the transient two-phase flow equations and their eigenvalues are first introduced. The flux vector is then split into subvectors which just contain a specially signed eigenvalue. Using one-sided spatial difference operators finite difference equations and their solutions are obtained. Finally comparison with experiment shows the predicted results produce good agreement with experimental data.

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

流动政体的知识为确定是很重要的二阶段的流动系统的压力落下，稳定性和安全。基于图象多特征熔化和支持向量机器，在二阶段的流动识别流动政体的一个新方法被介绍。第一，煤气液体的二阶段的流动想象包括冒泡的流动，塞子流动，蛞蝓流动，成层的流动，起浪的流动，环形的流动和薄雾流动被数字高速度录像系统在水平试管捕获。图象时刻 invariants 和灰色的水平同现矩阵质地特征用处理技术的图象被提取。改进一个多重分类器系统的表演，不平的集合理论被用于减少无关紧要的因素。而且，支持向量机器被使用这些特徵向量作为流动政体样品，和流动政体减少尺寸训练聪明的鉴定被认识到。测试结果证明与不平的集合理论被减少的图象特征能最优地反映支持向量机器能快速并且精确地识别的七典型流动政体，并且成功的训练之间的差别在水平试管的煤气液体的二阶段的流动的七典型流动政体。图象多特征熔化方法提供了一个新方法识别煤气液体的二阶段的流动，并且比单个特征的完成了更高的鉴定能力。全面鉴定精确性是 100% ，并且处理时间的图象的估计是为联机流动政体鉴定的 8 ms。

Flux vector splitting solutions for coupling hydraulic transient of gas-liquid-solid three-phase flow in pipelines

The gas-liquid-solid three-phase mixed flow is the most general in multiphase mixed transportation. It is significant to exactly solve the coupling hydraulic transient problems of this type of multiphase mixed flow in pipelines. Presently, the method of characteristics is widely used to solve classical hydraulic transient problems. However, when it is used to solve coupling hydraulic transient problems, excessive interpolation errors may be introduced into the results due to unavoidable multiwave interpolated calculations. To deal with the problem, a finite difference scheme based on the Steger- Warming flux vector splitting is proposed. A flux vector splitting scheme is established for the coupling hydraulic transient model of gas-liquid-solid three-phase mixed flow in the pipelines. The flux subvectors are then discretized by the Lax-Wendroff central difference scheme and the Warming-Beam upwind difference scheme with second-order precision in both time and space. Under the Rankine-Hugoniot conditions and the corresponding boundary conditions, an effective solution to those points located at the boundaries is developed, which can avoid the problem beyond the calculation region directly induced by the second-order discrete technique. Numerical and experimental verifications indicate that the proposed scheme has several desirable advantages including high calculation precision, excellent shock wave capture capability without false numerical oscillation, low sensitivity to the Courant number, and good stability.

超声向量血流成像在评估糖尿病患者颈动脉粥样硬化中的应用

目的应用超声向量血流成像(V-Flow)技术观察糖尿病患者及正常人颈动脉壁剪切力(WSS)分布特征,定量分析不同动脉粥样硬化程度颈动脉WSS的差异。资料与方法回顾性分析2019年4—6月在四川省人民医院确诊的糖尿病患者44例,对44例糖尿病患者(88支血管)和15例正常人(30支血管)进行常规颈动脉超声检查及V-Flow成像,根据颈动脉超声检查结果将糖尿病患者分为内-中膜厚度(IMT)正常组、IMT增厚组和斑块组;观察糖尿病患者与正常人颈动脉不同部位WSS的差异,并进一步分析糖尿病患者各组间WSS的变化特点。结果本研究中糖尿病患者颈总动脉分叉处和中段WSS(包括WSS最大值和WSS平均值)均小于正常对照组颈总动脉对应部位WSS(P=0.001、0.003),同时正常对照组和糖尿病组颈总动脉分叉处WSS均明显小于颈总动脉中段,差异有统计学意义(P=0.001)。糖尿病患者中颈总动脉分叉处WSS的大小随动脉粥样硬化程度的增加而变化(P=0.018、0.043),斑块组最高[WSS最大值(2.58±0.69)Pa;WSS平均值(0.86±0.25)Pa],IMT增厚组次之[(2.37±0.69)Pa;(0.77±0.35)Pa],IMT正常组最低[(2.13±0.52)Pa;(0.69±0.20)Pa],颈总动脉中段WSS差异无统计学意义(P>0.05)。结论糖尿病患者颈总动脉分叉处和中段WSS较正常人颈总动脉对应部位降低,随着动脉粥样硬化程度的增加而变化。超声向量血流成像V-Flow技术对评估糖尿病患者颈动脉粥样硬化进程具有一定价值。

源端控制的OpenFlow数据面

为了实现流表的多元快速查找,OpenFlow交换机一般采用TCAM存储和查找流表,从而带来了扩展性、成本和能耗的问题。尽管可以采取流表压缩、引入RAM存储器等方法,但仍无法彻底解决使用TCAM造成的局限性。针对这个问题提出了源端控制的0penFlow数据面模型,即SCOF(source-controlled OpenFlow)。它以一种源路由地址一向量地址(VA)作为数据分组的交换标签,VA完全定义了通信路径。SCOF的数据转发设备是向量交换机,不需要存储和查找流表,只根据VA即可完成数据分组转发。SCOF模型降低了交换机硬件复杂度,简化了流表更新过程,克服了OpenFlow的扩展性问题。

Development of Measurement System for Flow and Shape Using Array Ultrasonic Sensors

In Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, the fuel debris formed in the Reactor Pressure Vessel (RPV) and Primary Containment Vessel (PCV) at Unit 1 - 3. To accelerate and decide further decommissioning steps of the FDNPP, it is crucial to obtain realistic information of the debris and localize contaminated water leakage from PCV. Due to high radiation and dark environment inside the PCV, investigating instruments and techniques should necessarily to meet specification of radiation resistance, waterproofness, dust resistance and so on. This study focuses on development of ultrasonic measurement system using a couple of sectorial array sensors to localize contaminated water leakage and visualize shape of object that repre- senting fuel debris, simultaneously. In this study, Total Focusing Method (TFM) and Ultrasonic Velocity Profiler (UVP) methods are considered to visualize object shape and flow pattern around it, respectively. To demonstrate applicability and reliability of developed measurement system with sectorial array sensors, a mock-up experiment result of simulated water leakage and fuel debris shape were discussed in this paper.

向量血流技术在腹部凸阵超声探头上的应用研究

向量血流成像是一种新型的超声血流测量技术。相对于传统的彩色多普勒和频谱多普勒,它具有不依赖角度校正、可直接获取血流的实时幅值和方向(血流速度矢量)等优势。横向振荡法(transverse oscillation,TO)是实现向量血流成像的有效方法之一。然而,对于结构更加复杂的凸阵探头,目前仍然缺乏基于商用超声机的完整且详细的算法验证过程。该研究先介绍了TO成像过程和成像原理,然后通过仿真实验,计算设定速度值与测量速度值之间的误差,再通过多普勒体模实验,验证设定速度值与测量速度值之间的误差。其中仿真实验中TO向量血流技术测得的速度值为0.48 m/s,与预设值0.50 m/s的误差为−4%;多普勒体模实验中测得的速度值为8.33、11.14、14.44、16.67 cm/s,与实际速度值7.97、10.78、14.06、17.34 cm/s的误差均在±5%内。2个实验都验证了向量血流技术在腹部凸阵探头上应用的可行性。

基于支持向量回归数据驱动的配电网潮流回归

随着大量分布式电源接入配电网,配电网潮流分布由原来的单向流动转为双向流动,配电网潮流分布不均、电压越限等问题频现,研究适用于主动配电网的潮流计算更为重要.由于中低压配电网电气参数往往收集不到,量测系统也不如输电网完备,开关动作导致的拓扑变化难以实时反映到监控系统.实践中难以将基于导纳矩阵的输电网潮流方法应用到中低压配电网中.鉴于此,本文提出一种数据驱动的潮流线性回归模型,以实现不依赖于配电网物理模型的潮流计算与分析.首先建立不同类型的母线已知量与未知量的映射关系;其次,进一步推导该模型母线类型变换的更新方式;然后构建基于支持向量回归(support vector regression,SVR)的潮流回归模型,通过嵌入高斯核函数以及对样本进行聚类更好地拟合潮流的非线性;最后,在多个IEEE标准系统和改进的IEEE33节点系统仿真验证了所提方法的有效性.

基于喷管二次流控制的推力矢量特性研究

通过控制二次流喷口相对位置和宽度来改变喷管出口燃气流动方向是实现推力矢量控制的方法之一。为掌握落压比(NPR)、二次流压比(SPR)以及二次流喷口相对位置(X_(j))和宽度(d)对推力矢量性能的影响,通过数值计算分析了发动机推力矢量角(δ)、推力系数(C_(f))变化规律。结果表明,随着落压比的减小,喷管可以获得更大的推力矢量角度,但推力损失更大;主流落压比不同时,喷管推力矢量效率的最小值所对应的二次流压比不同;随着二次流喷口宽度的增加,喷管的推力矢量角度逐渐增大且随着二次流压比的增大其增幅也随之变大;推力矢量效率在SPR≤0.6时,随着二次流喷口宽度的增大而减小;SPR=0.8后随着二次流喷口宽度的增大先下降后上升,SPR=1.2时随着二次流喷口宽度的增大而增大;随着二次流喷口位置向喷管出口的移动,推力矢量角仅在喷口后的气流分离由闭式变为开式时发生一次突增,而推力系数发生一次突减;随着二次流喷射角度的增大,喷管的推力矢量角度不断增大,喷管的推力系数呈现不断减小的趋势。

Flow-shop网络作业计划模型的解法

将 Flow-Shop网络作业计划的优化模型 Sch( m,n) Lp[1 ] 化为向量矩阵线性空间 ,利用线性空间的理论提出了一个求解

针对恶意软件检测的特征选择与SVM协同优化

提出一种基于改进哈里斯鹰优化SVM和特征选择的恶意软件检测模型。为改进特征子集选取和SVM分类准确率,引入混沌映射、能量因子调节、最优解变异扰动和互利共生对HHO算法的初始种群结构、全局搜索与局部开采切换性能及跳离局部最优能力进行改进;利用改进算法优化SVM参数和特征子集选取,构建恶意软件检测模型。实验结果表明,改进算法在降低特征维度的同时可以有效提升分类准确率,利用高质量特征子集提升恶意软件检测模型的分类能力。

物联网终端信道连续数据流脆弱点快速识别

物联网终端信道连续数据流中潜在的可以被旁路功耗攻击的节点被定义为脆弱点。该类节点一旦被攻击成功,将严重影响物联网终端的通信能力。为此,进行物联网终端信道连续数据流脆弱点快速识别研究。利用简单网络管理协议(Simple Network Management Protocol,SNMP)捕捉物联网终端信道连续数据流并实施离散化处理,提取信息熵、流入流出比、平均流长及单边连接密度四类特征。以提取特征为输入,利用支持向量机识别其是否为脆弱点,通过人工神经网络进一步确定脆弱点的脆弱程度,完成物联网终端信道连续数据流脆弱点快速识别。结果表明,所提方法的连续数据流脆弱点识别质量指数最大、时间复杂度最小,由此说明该方法的识别准确性更高,速度更快。