基于Transformer复杂运动辨识的机动星凸形扩展目标跟踪方法

针对复杂的机动扩展目标跟踪问题,利用Transformer网络设计了一种有效的星凸不规则形状机动扩展目标跟踪方法。首先,该文研究利用alpha-shape算法建立了星凸形状的变化模型,实现了静态场景下的星凸形扩展目标的形状估计。然后,通过对目标状态转移矩阵进行重新设计,结合Transformer网络对机动扩展目标运动状态转移矩阵进行实时估计,实现了对复杂机动目标运动过程的精准跟踪。进一步地,将估计得到的形状轮廓与运动状态进行融合,最终实现了对星凸形机动扩展目标的实时跟踪。最后,通过构造复杂的机动扩展目标跟踪场景,利用多重性能指标测试算法对形状和运动状态的综合估计性能,验证了算法的有效性。

基于Transformer与距离图谱的泛癌细胞核图像分割

肿瘤细胞的密度、核质比和平均尺寸等指标对癌症的分级和预后有重要的意义.在计算病理学中,细胞核分割是肿瘤微环境分析的基础.通过对分割结果进行统计分析,对新的肿瘤标记物的探索有重大的意义.病理图像背景下的细胞核形态不规则,细胞核染色不均匀,且细胞核边缘之间存在黏连的问题,而现有的深度学习算法在细胞核主体分割正确的情况下,边缘的分割错误不会对总体的损失造成太大的影响,黏连的细胞核很容易被当作同一个分割目标.为了解决细胞核重叠问题,本文提出一种基于Transformer与距离图谱的分割模型,将Transformer中的核心多头自注意力机制与距离图谱引导算法相结合,重视细胞核内部,弱化细胞核边界,提升对图像局部和全局上下文信息的学习能力.本文方法在两个公开数据集上的平均Dice系数为0.7979、精度为0.7561、AJI系数为0.6672、Hausdorff距离为10.11.实验结果表明,相较其他分割算法,本文方法的性能更好,能够有效提高细胞核的分割精度,同时较好地解决了细胞核之间的黏连问题.

Euclidean Distance Transform on the Sea Based on Cellular Automata Modeling

To explore the problem of distance transformations while obstacles existing,this paper presents an obstacle-avoiding Euclidean distance transform method based on cellular automata.This research took the South China Sea and its adjacent sea areas as an example,imported the data of land-sea distribution and target points,took the length of the shortest obstacle-avoiding path from current cell to the target cells as the state of a cellular,designed the state transform rule of each cellular that considering a distance operator,then simulated the propagation of obstacle-avoiding distance,and got the result raster of obstacle-avoiding distance transform.After analyzing the effect and precision of obstacle avoiding,we reached the following conclusions:first,the presented method can visually and dynamically show the process of obstacle-avoiding distance transform,and automatically calculate the shortest distance bypass the land;second,the method has auto-update mechanism and each cellular can rectify distance value according to its neighbor cellular during the simulation process;at last,it provides an approximate solution for exact obstacle-avoiding Euclidean distance transform and the proportional error is less than 1.96%.The proposed method can apply to the fields of shipping routes design,maritime search and rescue,etc.

Improved Relative-transformation Principal Component Analysis Based on Mahalanobis Distance and Its Application for Fault Detection

主要部件分析(PCA ) 广泛地在过程工业被使用了，它能维持最大的差错察觉率。尽管许多问题在 PCA 被处理了，一些必要问题仍然保持未解决。这研究以下列方法为差错察觉性能改进 PCA。第一，一个相对转变计划基于 Mahalanobis 距离(MD ) 被介绍消除数据的尺寸的效果而不是无尺寸的标准化，并且改进精确性和差错察觉的即时性能。理论推导证明那相对转变能直接基于 MD 消除尺寸的效果并且在结果显示出的相对空间，分析和模拟给 PCA 的合理解释它的优势和有效性。第二，一个改进摆平的预言错误(SPE ) 统计数值被给改进标准化 PCA 的差错察觉表演，它能使标准化基于 PCA 的差错察觉方法成为对实际工业过程合适的更多。最后，二个改进方法被联合更有效地检测差错。建议方法被使用在热连续滚动过程检测 looper 系统的单个差错和多差错，模拟结果以易感知，精确性和差错察觉的即时性能为差错察觉性能表明这些改进的有效性。

领域特征融合Transformer的环焊缝缺陷识别方法

环焊缝缺陷的类型识别对于长输管线管道焊缝质量评价以及管道服役寿命评估具有重要意义。由于不同类型缺陷的射线检测图像具有特征差异小、对比度低等特征,现有缺陷类型识别方法所提取的特征表征能力不足,其准确性及可信性难以满足行业需求。为此,提出了一种缺陷领域特征提取与Transformer融合的焊缝缺陷类型识别方法。基于环焊缝缺陷评定技术人员的知识及缺陷领域特征,从缺陷几何特征、缺陷位置特征以及缺陷背景区域特征3个方面定义了14个特征,用于实现不同类型缺陷的特征表征;以Transformer网络为基础,融合上述14个特征提出深度可调节的缺陷类型识别模型与方法。以企业实际环焊缝缺陷数据对模型进行验证。结果表明,与ResNet50网络相比,所提模型对于未熔合、未焊透缺陷,分类精度分别提高18.2%和14.3%;对于咬边和内凹2种形状缺陷,分类精度达到90%以上。证明所提方法可以有效提高缺陷类型识别准确率,可将其扩展到射线检测铸造缺陷、TOFD检测焊缝缺陷识别领域。

Transmission Lines Distance Protection Using Differential Equation Algorithm and Hilbert-Huang Transform

This paper proposed the scheme of transmission lines distance protection based on differential equation algorithms (DEA) and Hilbert-Huang transform (HHT). The measured impedance based on EDA is affected by various factors, such as the distributed capacitance, the transient response characteristics of current transformer and voltage transformer, etc. In order to overcome this problem, the proposed scheme applies HHT to improve the apparent impedance estimated by DEA. Empirical mode decomposition (EMD) is used to decompose the data set from DEA into the intrinsic mode functions (IMF) and the residue. This residue has monotonic trend and is used to evaluate the impedance of faulty line. Simulation results show that the proposed scheme improves significantly the accuracy of the estimated impedance.

Hardware Euclidean Distance Transform Implementation

Based on an efficient algorithm of Euclidean distance transform for binary images, a circuit of O(N2) size is proposed. With in-place calculation, both the intermediate data storing and the result output use the same memory with the input data. This reduces the amount of memory largely. By replacing multipliers with counters, comparators, and adders, the circuit size is further reduced and its calculation speed is improved also.

基于Transformer-Bi-LSTM模型的武器装备剩余寿命预测方法

武器装备担负保卫国土安全的重要使命,其保持稳定运行状态具有重大国防、政治意义;因其装备运行状态不便中断、故障定位过程复杂,使得传统维修方式效率较低;装备使用数据具有连续性、长期性、不平稳性,甚至一些深度学习模型无法处理其中的退化状态历史依赖与关联问题;通过构建元器件层级的剩余寿命预测架构,对特征工程、退化指标构建以及Transformer-Bi-LSTM模型开展研究,采用距离编码技术,实现针对深度学习模型的技术创新,优化模型预测效果;基于某型武器装备主要器件正常试样数据,进行本方法分析验证,在器件已运行时间达到90%设计试验寿命长度时能够进行有效且准确的剩余寿命预测,所提方法满足武器装备器件寿命预警及更换提醒、保障装备战备完好性的应用需求。

Distance Transform Based Enhancement for Linear Interpolated Images

An approach of distance map based image enhancement (DMIE) is proposed. It is applied to conventional interpolations to get sharp images. Edge detection is performed after images are interpolated by linear interpolations. To meet the two conditions set for DMIE, i. e., no abrupt changes and no overboosting, different boosting rate should be used in adjusting pixel intensities. When the boosting rate is determined by using the distance from enhanced pixels to nearest edges, edge-oriented image enhancement is obtained. By using Erosion technique, the range for pixel intensity adjustment is set. Over-enhancement is avoided by limiting the pixel intensities in enhancement within the range.A unified linear-time algorithm for distance transform is adopted to deal with the calculation of Euclidean distance of the images. Its computation complexity is O (N2 ). After the preparation, i. e.,distance transforming and erosion, the images get more and more sharpened while no over-boosting occurs by repeating the enhancement procedure. The simplicity of the enhancement operation makes DMIE suitable for enhancement rate adjusting.

A SKELETONIZATION ALGORITHM BASED ON EUCLIDEAN DISTANCE MAPS AND MORPHOLOGICAL OPERATORS

In this letter a new skeletonization algorithm is proposed. It combines techniques of fast construction of Euclidean Distance Maps(EDMs), ridge extraction, Hit-or-Miss Transformation(HMT) of structuring elements and the set operators. It first produces the EDM image with no more than 4 passes through an image of any kinds, and then the ridge image is extracted by applying a turn-on scheme and performing a rain-fall elimination to accelerate the processing. The one-pixel wide skeleton is finally acquired by carrying out the HMTs of two structure elements and the SUBTRACT and OR operations. Experimental results obtained by practical applications are also presented.

基于Hausdorff Distance算法的配电网故障区段定位方法

为解决配电网小电流接地系统发生单相接地故障时故障区段定位困难的问题,结合故障线路中故障点两侧零序电流方向相反的特点,提出一种基于Hausdorff Distance算法的配电网故障区段定位方法。该方法将零序电流作为故障特征量,首先,对零序电流进行滤波,并通过小波包变换提取故障馈线各检测节点的零序电流小波近似序列;然后,借助Hausdorff Distance算法分析得到各检测节点之间的零序电流小波近似序列偏差矩阵;最后,综合定义一种偏差度表征各区段两侧的差异性,通过比较偏差度大小确定故障区段。仿真结果表明:该方法在不同的故障条件下均能实现正确定位,同时还适用于复杂的配网结构中,便于配电网运行人员迅速对故障线路进行检修和维护,保障电力系统安全可靠运行。

Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric

In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The technique has been tested and found to be more accurate and faster. Characters is classified into 26 pattern classes based on appropriate properties. Features of the handwritten character images are extracted by DWT used with appropriate level of multiresolution technique, and then each pattern class is characterized by a mean vector. Distances from input pattern vector to all the mean vectors are computed by EDM. Minimum distance determines the class membership of input pattern vector. The proposed method provides good recognition accuracy of 90% for handwritten characters even with fewer samples.

基于AED-CEEMD-Transformer的锂离子电池健康状态估计

锂电池健康状态(state of health,SOH)的精确预测评估对电池设备安全稳定运行极为重要,通过对SOH的快速准确预测,可以提高电池设备的安全性并降低出现故障的风险。针对难以精确预测锂离子电池SOH的问题,本文采用电池容量作为SOH的指标,提出一种利用平均欧几里得距离(average euclidean distance,AED)和互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)方法建立基于Transformer网络结构的锂离子电池健康状态估计算法。首先,我们利用AED评估电池数据库中的电池与待预测电池初期循环容量之间的相似度,并选出具有相似容量退化趋势的电池作为训练集以提高模型的训练速度,然后利用CEEMD方法将电池容量曲线分解为容量再生部分以及退化趋势部分,将各个分量分别使用Transformer网络来建立锂电池退化模型,进而得到锂离子电池的SOH预测结果。本文使用分别来自斯坦福大学与马里兰大学的两个具有不同充放电策略与不同测试环境下的锂离子电池数据集来验证了所提出的电池预测算法的准确性。本文所提模型的均方根误差均能控制在4%以内,具有较好的精确性,并通过与基于LSTM、RNN、GRU的常用锂离子电池健康状态估计算法结果的比较,验证了所提出估计方法的优越性。

CTUNING:A REUSE DISTANCE BASED CACHE PERFORMANCE TUNING TOOL

Cache performance tuning tools are conducive to develop program with good locality and fully use cache to decrease the influence caused by speed gap between processor and memory. This paper introduces the design and implementation of a cache performance tuning tool named CTuning, which employs a source level instrumentation method to gather program data access information, and uses a limited reuse distance model to analyze cache behavior. Experiments on 183.equake improve average performance more than 6% and show that CTuning is proficient not only in locating cache performance bottlenecks to guide manual code transformation, but also in analyzing cache behavior relationship among variables, thus to direct manual data reorganization.

“外察”与“内省”:环境威胁引发的绿色消费意愿研究——基于道德情绪的双路径模型

营销学者在探究绿色消费议题方面做了众多开创性工作,然而针对诱因不同的环境威胁是否及通过怎样的心理路径影响消费者绿色消费意愿的研究仍然匮乏。本文将环境威胁分为自然因素引发的环境威胁和人为因素引发的环境威胁两类,通过4组实验检验了两类环境威胁影响消费者绿色消费意愿的心理机制。研究结果证实了无论哪类环境威胁,都会对绿色产品购买行为产生积极影响,但不同类型的环境威胁会通过差异化认知过程和情绪反应影响绿色产品购买行为。当消费者面对自然因素引发的环境威胁时,其“外察”思维模式会起主导作用,此时敬畏感是促进绿色产品购买意愿的中介变量;当消费者面对人为因素引发的环境威胁时,其“内省”思维模式会起主导作用,此时内疚感是促进绿色产品购买意愿的中介变量。研究也证实了环境威胁对道德情绪的影响会受自然联结性和道德提升感的调节。

Simultaneous measurement of distance and speed using frequency-modulated continuous-wave self-mixing interferometry and all-phase fast Fourier transform

In this work,we propose a method using frequency-modulated continuous-wave(FMCW)self-mixing interferometry(SMI)and all-phase fast Fourier transform(APFFT)for simultaneous measurement of speed and distance.APFFT offers superior accuracy in frequency determination by mitigating issues like the fence effect and spectrum leakage,contributing to the high-accuracy measurement for speed and distance.Both simulations and experiments have demonstrated relative errors at the levels of 10^(−4) and 10^(−3) for distance and speed measurements,respectively.Furthermore,factors impacting measurement performance have been discussed.The proposed method provides a high-performance and cost-effective solution for distance and speed measurements,applicable across scientific research and various industrial domains.

基于正弦同源-概率空间协同互补的涌流闭锁方案

为了取消正弦相似性原理中的最小二乘法等中间环节,获得直接固定标准正弦波形所需要的能量信息。借鉴时域概率分布的方法论,从优势互补的角度出发,设计了一种基于Wasserstein距离算法的变压器励磁涌流闭锁方案。该方案采用特征离散化的方式,提取目标对象和模板信号并转化为状态向量作为样本标签,以此来判别两者之间的能量分布差异,从而达到正弦场景同源识别的效果。理论分析与仿真结果表明,该方法实现简便,不需要频域计算。相比于现有正弦相似性原理,降低了保护流程的复杂程度,具有更强的实际应用价值和速动性能。通过PSCAD/MATLAB平台对现场合闸的录波数据进行测试分析,验证了所提方案对促进变压器保护性能的积极意义。

绿色变革型领导对员工自愿绿色行为的影响:自然情感联结与权力距离取向的作用

基于情感事件理论,探究绿色变革型领导对员工自愿绿色行为的影响机制。本研究分三个时点搜集了83个团队305份领导-员工配对数据,并进行多层次分析。结果表明:绿色变革型领导正向预测员工自愿绿色行为,员工自然情感联结在上述关系中发挥中介作用。此外,员工的权力距离取向正向调节绿色变革型领导与员工自然情感联结的关系,以及绿色变革型领导经由自然情感联结对员工自愿绿色行为的积极作用。本研究拓展了学界对员工自愿绿色行为激发因素的认识,同时为企业的绿色管理实践提供了新启示。

基于激光测距仪的智能物联网体感交互感知研究

当前的智能物联网设备在识别用户指令方面存在不足,时常需要用户多次尝试,降低了用户的使用体验和对设备的信任度。为了解决问题,提出了一种基于激光测距仪的智能物联网体感交互感知方法。首先,拟合激光波绘制结果,并利用距离变换算法设定智能物联网的体感交互方式。然后,通过误差传递公式,计算激光测距仪的协方差矩阵,依据此调整参数和测量点。最后,融合激光图像特征,识别操作者的动态指令。实验结果表明,所提方法能够显著提升智能物联网设备对体感指令的采集完整度,正确完成所有体感指令,且平均体感动作类别划分精准度高达97.02%,说明所提方法具有较高的体感交互感知精度。

基于机器视觉的镍板表面气孔分割算法研究

针对金属镍板表面气孔缺陷检测存在分割目标堆叠和分割线偏移等问题,提出了一种基于机器视觉的镍板表面气孔分割方法,以质心标记计算距离图。该算法采用工业相机构建机器视觉系统,获取镍板表面图像。通过分段线性灰度变换抑制不感兴趣区域,其次对G通道分量进行自适应阈值分割、形态学处理等提取气孔区域,然后图像作差提取出极小值标记,以极小值标记的质心计算距离图并分水岭分割。实验结果表明,该算法气孔正确分割率达94.7%,同时能够获得更准确的气孔分割效果。