Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

The process control-oriented threat,which can exploit OT(Operational Technology)vulnerabilities to forcibly insert abnormal control commands or status information,has become one of the most devastating cyber attacks in industrial automation control.To effectively detect this threat,this paper proposes one functional pattern-related anomaly detection approach,which skillfully collaborates the BinSeg(Binary Segmentation)algorithm with FSM(Finite State Machine)to identify anomalies between measuring data and control data.By detecting the change points of measuring data,the BinSeg algorithm is introduced to generate some initial sequence segments,which can be further classified and merged into different functional patterns due to their backward difference means and lengths.After analyzing the pattern association according to the Bayesian network,one functional state transition model based on FSM,which accurately describes the whole control and monitoring process,is constructed as one feasible detection engine.Finally,we use the typical SWaT(Secure Water Treatment)dataset to evaluate the proposed approach,and the experimental results show that:for one thing,compared with other change-point detection approaches,the BinSeg algorithm can be more suitable for the optimal sequence segmentation of measuring data due to its highest detection accuracy and least consuming time;for another,the proposed approach exhibits relatively excellent detection ability,because the average detection precision,recall rate and F1-score to identify 10 different attacks can reach 0.872,0.982 and 0.896,respectively.

Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

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.

Automatic image segmentation method for cotton leaves with disease under natural environment

In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.

Image Segmentation: A Novel Cluster Ensemble Algorithm

Cluster ensemble has testified to be a good choice for addressing cluster analysis issues, which is composed of two processes: creating a group of clustering results from a same data set and then combining these results into a final clustering results. How to integrate these results to produce a final one is a significant issue for cluster ensemble. This combination process aims to improve the quality of individual data clustering results. A novel image segmentation algorithm using the Binary k-means and the Adaptive Affinity Propagation clustering (CEBAAP) is designed in this paper. It uses a Binary k-means method to generate a set of clustering results and develops an Adaptive Affinity Propagation clustering to combine these results. The experiments results show that CEBAAP has good image partition effect.

DFNet:高效的无解码语义分割方法

针对编解码语义分割网络计算量大、解码结构复杂的问题,提出一种高效无解码的二值语义分割模型DFNet。该模型首先去除主流分割网络中复杂的解码结构和跳跃连接,采用卷积重塑上采样方法重塑特征编码直接得到分割结果,简化网络模型结构;其次在编码器中融合轻量双重注意力机制EC&SA,提高特征编码的通道及空间信息交互,增强网络的编码能力;最后使用PolyCE损失替代常规分割损失,解决正负样本不均衡问题,提高模型的分割精度。在Deep‑Globe道路分割和CrackForest缺陷检测等二值分割数据集上的实验结果表明,本文模型的分割精度F1均值和IoU均值分别达到84.69%和73.95%,且分割速度高达94 FPS,远超主流语义分割模型,极大地提高了分割任务效率。

一种基于冗余位结构CDAC的12 bit SAR ADC

提出一种基于非二进制冗余位结构CDAC的12 bit全差分逐次逼近型模拟数字转换器(SAR ADC)。传统SAR ADC中CDAC的单位电容数量随位数指数增长,且采用全差分结构的电容数量是单端结构的两倍,导致CDAC建立时间过长。为此,设计一种加入冗余位的分段式电容阵列,减少单位电容数量,提高CDAC建立速度。动态比较器的比较速度快,会导致数字码误判,通过加入冗余位弥补比较器对数字码误判的缺陷;采用底板采样技术,避免沟道电荷注入和时钟馈通,提高采样精度;采用SMIC 130 nm CMOS工艺。在电源电压1.2 V、20 MS/s采样率下,对1024点FFT仿真。结果显示:当输入频率(9.824 MHz)接近奈奎斯特频率时,该ADC的整体信噪失真比(SNDR)达到72.42 dB,有效位数(ENOB)达到11.73 bit;无杂散动态范围(SFDR)达到88.4 dBc,功耗为1.29 mW。

基于改进二进制蛇优化算法的配电网故障定位

分布式电源(distributed generation,DG)大规模接入给配电系统带来更多不确定性、随机性,系统运行方式更复杂,传统故障定位方法难以适应新型电力系统构建。提出了一种基于改进二进制蛇优化算法(improved binary snake optimization,IBSO)的新型故障区段定位方法。利用SPM混沌映射生成高质量的随机数序列,以提高算法种群中个体的随机性,并引入了遗传算法的动态变异策略,根据不同的搜索状态和进化阶段来调整变异率和变异方式,提高算法的灵活性和准确性。通过仿真证明,该方法适用于在含有分布式电源的配电网中定位单一和多重故障区段,相比蛇优化算法、传统二进制粒子群算法以及遗传算法在收敛性、快速性和准确性方面更优。

面板数据中方差的共同变点估计

文章对面板数据中方差的共同变点提出了一个含有调节参数的CUSUM(Cumulative Sum)型估计量,证明了变点估计量的相合性,并结合二元分割法将其推广到多个方差共同变点的情形。蒙特卡洛模拟发现,调节参数(γ≠0)下CUSUM型估计量的精确度要高于无调节参数(γ=0)下CUSUM型估计量的精确度。应用外汇汇率进行实证分析,结果也表明调节参数(γ≠0)下CUSUM型估计方法是有效的。

A Likelihood-Based Multiple Change Point Algorithm for Count Data with Allowance for Over-Dispersion

Count data is almost always over-dispersed where the variance exceeds the mean. Several count data models have been proposed by researchers but the problem of over-dispersion still remains unresolved, more so in the context of change point analysis. This study develops a likelihood-based algorithm that detects and estimates multiple change points in a set of count data assumed to follow the Negative Binomial distribution. Discrete change point procedures discussed in literature work well for equi-dispersed data. The new algorithm produces reliable estimates of change points in cases of both equi-dispersed and over-dispersed count data;hence its advantage over other count data change point techniques. The Negative Binomial Multiple Change Point Algorithm was tested using simulated data for different sample sizes and varying positions of change. Changes in the distribution parameters were detected and estimated by conducting a likelihood ratio test on several partitions of data obtained through step-wise recursive binary segmentation. Critical values for the likelihood ratio test were developed and used to check for significance of the maximum likelihood estimates of the change points. The change point algorithm was found to work best for large datasets, though it also works well for small and medium-sized datasets with little to no error in the location of change points. The algorithm correctly detects changes when present and fails to detect changes when change is absent in actual sense. Power analysis of the likelihood ratio test for change was performed through Monte-Carlo simulation in the single change point setting. Sensitivity analysis of the test power showed that likelihood ratio test is the most powerful when the simulated change points are located mid-way through the sample data as opposed to when changes were located in the periphery. Further, the test is more powerful when the change was located three-quarter-way through the sample data compared to when the change point is closer (quarter-way) to the first observation.

GPS BINARY数据向RINEX数据转换方法

将GPS接收机接收到的二进制原始数据流转换成十进制数据,可以用于实时导航定位或者进行标准RINEX格式用于后处理及验证。针对Hemisphere GPS接收机二进制格式数据,进行了程序设计,定义了位段结构体,并结合位运算程序实现二进制数据到十进制数据或标准的RINEX文件数据实时转换,并给出了程序实现中设计的类与相应的结构体。最后结合实例分析验证了该方法的可靠性。

基于局部熵的区域活动轮廓图像分割模型

为解决区域活动轮廓模型不能有效分割灰度不均图像的问题,提出了局部熵约束的区域活动轮廓模型应用于图像分割。首先基于局部熵信息将图像划分为两个特征区域,然后利用局部熵特征信息构造二值拟合能量,并与区域可放缩拟合(Region⁃scalable fitting,RSF)模型相结合,最后得到水平集演化方程。该模型考虑了图像灰度分布的聚集特征和局部区域统计信息,能有效处理灰度不均匀、弱边缘等图像分割问题,且对轮廓初始位置更具鲁棒性,医学图像实验结果验证了模型的有效性。

联合改进LBP和超像素级决策的高光谱图像分类

高光谱图像在有标签样本数目较少的情况下进行分类时,除了利用光谱特征外,空间纹理特征也是必不可少的。本文提出了一种利用多尺度多方向局部二值模式(LBP)描述子获取纹理特征,并结合超像素级指导决策的支持向量机分类方法。首先,本文方法将传统LBP描述子改进为多尺度多方向LBP描述子,一方面充分考虑了邻域像素之间的关系,另一方面在计算时分别考虑了水平垂直方向和对角方向。其次,在利用统计直方图获得纹理特征时,采用了多个尺寸窗口组合的方式,以获得多范围、高精度的纹理特征。第三,对传统的简单线性迭代聚类(SLIC)超像素分割方法进行改进,重新定义了光谱距离并引入了纹理特征距离,获得更精确的超像素分割图。最后,利用超像素分割图结合多数投票策略,对分类结果进行进一步的指导校正。实验表明,本文方法能够更有效的提取纹理特征,再结合超像素分割图的指导决策,进一步提升高光谱图像的分类性能。

基于颜色轮廓的网球收集机器人识别算法研究

针对网球收集机器人工作中识别准确率低下问题,提出一种基于网球颜色和轮廓特征的识别算法。通过分析网球图像的颜色空间,得到网球与背景的二值图像,结合网球区域特征做进一步精准识别。在重叠网球识别上,提出区域分割和轮廓拟合的算法,对重叠区域提取分割、拟合还原网球轮廓。通过实验验证算法可行性,结果表明该算法可在不同环境下精确识别网球。

一种数字水准标尺的新型编码规则及识读方法

为满足高精度、大视距的数字水准测量需求,提出了一种新型二维复合编码的数字水准标尺编码规则,标尺条码由绝对编码和相对编码两列组成。绝对编码由不同宽度的黑白条码组成,沿标尺长度方向上若干个条码组合形成一个码段,采用二-十进制的编码规则唯一确定该码段在标尺上的绝对位置;相对编码由等宽度,相对大尺寸的黑白条码组成,通过同时存在于相对编码和绝对编码中的参考码表示标尺上的位置。针对所提出的编码规则,设计了视线位置的精确识读方法。对所设计水准标尺进行高差、重复性以及远视距实验,结果表明,该二维复合编码的水准测量在30 m以内的测量时间小于1.57 s,条码识别率为100%,最大偏差小于0.09 mm,具有解码快速、识别精确、稳定等优点。

节段模型二元端板合理尺寸估算方法

节段模型风洞试验作为研究桥梁结构的风致振动响应的主要手段,为了保证节段模型周围流场满足二元流动特性,需要设置二元端板减少端部效应,二元端板尺寸的设置以往大多依靠经验,缺乏定量依据,因此提出一种估算节段模型二元端板合理尺寸的方法。采用数值模拟计算试验断面的二维流场,得到断面不同位置无量纲动压差分布函数F(x,y),同时引入修正系数K考虑位置距离对端部效应的影响,在此基础上得到参数P(x,y),P(x,y)反映(x,y)位置处空气展向流动产生的三维绕流对模型气动力的影响。该值越大,端部绕流的影响越严重。由风洞试验和数值模拟结果得到模型宽度方向P值为10,当模型高度方向P值为15时,所得到的端板尺寸即可有效抑制端部效应。由此值可以反算出二元端板的合理尺寸,并通过风洞试验检验了该方法的有效性。该方法可以为定量确定二元端板的合理尺寸提供借鉴。

掩码生成动态调控弱监督视频实例分割

针对全监督视频实例分割网络训练数据高度依赖精细掩码标注,时间和人工成本过高,导致智能机器无法快速适应新场景的问题,提出一种端到端的掩码生成动态调控弱监督视频实例分割(Weakly Supervised Video Instance Segmentation,WSVIS)网络。为克服初始掩码预测层通道维度突降导致的实例激活特征丢失问题,构建多级特征融合模块,利用特征复用策略预测初始实例特征并融合相对位置信息生成初始预测掩码。然后,提出动态调控机制在通道和空间维度上建立掩码特征依赖关系,强化初始预测掩码与实例感知信息之间的动态交互。最后,网络设计二元颜色相似性生成伪亲和标签取代精细掩码标注,联合边界框与掩码一致性损失实现仅边界框标注的弱监督视频实例分割。实验结果表明,在BoxSet和YT-VIS数据集上,WSVIS网络能达到与全监督网络相近的分割精度和分割效果,同时能够满足实时推理要求,为智能机器快速适应新场景实现实时环境感知和理解提供了理论支撑和算法依据。

基于速度-关联约束的风电机组风速感知异常数据识别方法

该文以风速时空关联特性为理论依据,针对风速数据单独清洗构建一种基于速度-关联约束的异常风速数据识别方法。分析了风电场典型异常风速的产生原因和分布特征,根据数据的变化趋势,将异常风速概括为突变型异常数据和渐近型异常数据两类;为提升风速数据清洗方法的准确性,提出一种基于二元形态分割算法的风速数据时序区间分割方法,将全局风速序列在时序上划分为多段分布独立的局部风速子序列,分别对每段风速子序列构建速度-关联约束条件,实现异常风速数据的识别。验证结果表明,所提方法能够有效识别风电场各类异常风速数据,清洗效果好、效率高,具有普适性和鲁棒性。

基于二值化条件随机场卷积网络的极化SAR海陆分割

针对现有SAR海陆分割预测精度较低,采用的分割网络模型普遍较大、难以星上部署等难点,提出了一种基于二值化条件随机场卷积网络的极化SAR海陆分割方法(BiCSNet)。该模型的轻量化主要通过所设计的适用于海陆分割二元任务的二值化卷积模块实现,为了提高轻量化网络的分割精度,BiCSNet还融入了卷积条件随机场实现端到端的网络预测功能。基于我国沿海区域的全极化SAR图像构建的数据集,验证了所提出网络在精度和轻量化两方面的良好性能。

深度学习下动态目标识别算法优化仿真

与静态图像目标识别相比,动态目标识别过程易受复杂背景、未知的运动趋势、障碍物、光照强度等问题的干扰,为了解决上述问题,提出基于深度学习的动态目标识别算法优化研究。采用基于帧间差的高阶统计量算法分割出动态目标的背景区域,采用双向光流预测算法提取动态目标的特征,采用粒子群算法优化深度学习中的BP神经网络模型,将提取的特征输入到模型中,通过模型的训练输出符合要求的目标,完成动态目标的识别。实验结果表明,所提算法的特征提取能力强、识别时间短、识别效果好。