A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment

In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly,a multi-expert system consisting of color component dispersion,similarity and centroid motion is established to identify flames.The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.

Color Image Quality Assessment Based on Structural Similarity

It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural similarity based image quality assessment was proposed under the assumption that the Human Visual System(HVS)is highly adapted for extracting structural information from an image.While the demand on high color quality increases in the media industry,color loss will make the visual quality different.In this paper,we proposed an improved quality assessment(QA)method by adding color comparison into the structural similarity(SSIM)measurement system for evaluating color image quality.Then we divided the task of similarity measurement into four comparisons:luminance,contrast,structure,and color.Experimental results show that the predicted quality scores of the proposed method are more effective and consistent with visual quality than the classical methods using five different distortion types of color image sets.

P-Indeterminate Vector Similarity Measures of Orthopair Neutrosophic Number Sets and Their Decision-MakingMethod with Indeterminate Degrees

In the complexity and indeterminacy of decision making(DM)environments,orthopair neutrosophic number set(ONNS)presented by Ye et al.can be described by the truth and falsity indeterminacy degrees.Then,ONNS demonstrates its advantages in the indeterminate information expression,aggregations,and DM problems with some indeterminate ranges.However,the existing research lacks some similarity measures between ONNSs.They are indispensable mathematical tools and play a crucial role in DM,pattern recognition,and clustering analysis.Thus,it is necessary to propose some similaritymeasures betweenONNSs to supplement the gap.To solve the issue,this study firstly proposes the p-indeterminate cosine measure,p-indeterminate Dice measure,p-indeterminate Jaccard measure of ONNSs(i.e.,the three parameterized indeterminate vector similarity measures of ONNSs)in vector space.Then,a DMmethod based on the parameterized indeterminate vector similarity measures of ONNSs is developed to solve indeterminate multiple attribute DM problems by choosing different indeterminate degrees of the parameter p,such as the small indeterminate degree(p=0)or the moderate indeterminate degree(p=0.5)or the big indeterminate degree(p=1).Lastly,an actual DM example on choosing a suitable logistics supplier is provided to demonstrate the flexibility and practicability of the developed DM approach in indeterminate DM problems.By comparison with existing relative DM methods,the superiority of this study is that the established DMapproach indicates its flexibility and suitability depending on decision makers’indeterminate degrees(decision risks)in ONNS setting.

Single-channel vector magnetic information detection method based on diamond NV color center

A method of detecting the single channel triaxial magnetic field information based on diamond nitrogen-vacancy(NV)color center is introduced.Firstly,the incident angle of the bias magnetic field which can achieve the equal frequency difference optically-detected magnetic resonance(ODMR)spectrum of diamond NV color center is calculated theoretically,and the triaxial magnetic information solution model is also constructed.Secondly,the microwave time-controlled circuit module is designed to generate equal timing and equal frequency difference microwave pulse signals in one channel.Combining with the optical detection magnetic resonance technology,the purpose of sequentially locking and detecting the four formant signals on one side of the diamond NV color center(m_(s)=-1 state signal)is achieved,and the vector magnetic field information detection is accomplished by combining the triaxial magnetic information solution model.The system can obtain magnetic field detection in a range of 0 mT-0.82 mT.The system's magnetic noise sensitivity is 14.2 nT/Hz^(1/2),and the deviation angle errors of magnetic field detectionθ_(x) andθ_(y) are 1.3° and 8.2° respectively.

LIC color texture enhancement algorithm for ocean vector field data based on HSV color mapping and cumulative distribution function

Texture-based visualization method is a common method in the visualization of vector field data.Aiming at adding color mapping to the texture of ocean vector field and solving the ambiguity of vector direction in texture image,a new color texture enhancement algorithm based on the Line Integral Convolution(LIC)for the vector field data is proposed,which combines the HSV color mapping and cumulative distribution function calculation of vector field data.This algorithm can be summarized as follows:firstly,the vector field data is convoluted twice by line integration to get the gray texture image.Secondly,the method of mapping vector data to each component of the HSV color space is established.And then,the vector field data is mapped into HSV color space and converted from HSV to RGB values to get the color image.Thirdly,the cumulative distribution function of the RGB color components of the gray texture image and the color image is constructed to enhance the gray texture and RGB color values.Finally,both the gray texture image and the color image are fused to get the color texture.The experimental results show that the proposed LIC color texture enhancement algorithm is capable of generating a better display of vector field data.Furthermore,the ambiguity of vector direction in the texture images is solved and the direction information of the vector field is expressed more accurately.

Circuit Compression Based on Similarity for RTL ATPG

This paper analyzes structural characteristics of signal vectors in combinational circuits with RTL description. Then, this paper presents the concept of the Basic Similar Circuit (BSC), a circuit constructed by compressing the bit-width of vectored vectors in the original circuit. BSC shrinks the scale of the original circuit, thus improving the ATPG efficiency. Test patterns are derived from adjustment and assembling of precomputed sub-circuit test sets. Based on the deterministic algorithm, the ATPG method presented in this paper combines deterministic algorithms and undetermined methods.

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

Research on Vector Road Data Matching Method Based on Deep Learning

Most of the existing vector data matching methods use traditional feature geometry attribute features to match, however, many of the similarity indicators are not suitable for cross-scale data, resulting in less accuracy in identifying objects. In order to solve this problem effectively, a deep learning model for vector road data matching is proposed based on siamese neural network and VGG16 convolutional neural network, and matching experiments are carried out. Experimental results show that the proposed vector road data matching model can achieve an accuracy of more than 90% under certain data support and threshold conditions.

Hybrid Color Texture Features Classification Through ANN for Melanoma

Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

基于多阶段递推数据分析的低压台区窃电检测方法

窃电行为不仅会扰乱正常用电秩序,更会影响电网的供电质量和安全运行。针对窃电检测工作中所面临的用户正常用电行为与窃电行为多样化问题,该文提出一种基于多阶段递推数据分析的低压台区窃电检测方法。该方法第1阶段对嫌疑窃电台区进行判定,针对当日线损不是明显激增的情况,提出基于台区线损综合波动率、总分表电流差异率、线损和电流曲线的突变点时间重合度的三步分析法,为窃电嫌疑用户的检测提供了良好的条件;第2阶段提出基于最优特征集的时间序列相似性度量方法,基于欧氏距离度量曲线间数值特征,同时基于动态时间规整(dynamic time warping,DTW)算法度量曲线间的形态特征,实现窃电嫌疑用户的初步筛选;第3阶段提出基于核函数和惩罚参数优化的支持向量机二次深度检测模型(optimize kernel-function and penalty-parameters support vector machine,OKPSVM),其中惩罚参数采用综合改进的粒子群(improved particle swarm optimization,IPSO)算法。通过算例仿真和实际工程应用,整体优化后的支持向量机模型(IPSO-OKPSVM)能够提高深度窃电检测的精准性和适用性。

基于频率响应与图像特征提取的动车组变压器绕组状态诊断方法研究

动车组变压器是保障高速铁路稳定运行的核心设备,频率响应法是目前检测变压器绕组状态的有效方法。为提升车载变压器绕组状态诊断的准确性,结合暂态信号与频率响应法提出基于频率响应与图像特征提取的动车组变压器绕组状态诊断方法。搭建试验车载变压器绕组故障模拟平台,获取不同故障类型和故障位置的频响曲线,利用类Gram矩阵结合幅频和相频曲线信息,再利用密度分层法转换为伪彩色图,提取对应的灰度共生矩阵和灰度差分矩阵特征值,根据鹈鹕优化支持向量机方法对绕组故障进行诊断。试验结果表明:车载变压器绕组故障发生时,伪彩色图能够反映出故障信息,有利于图像分析和特征提取,采用基于频率响应与图像特征提取的动车组变压器绕组状态诊断方法能够识别车载变压器绕组的典型故障类型和位置。

考虑关联波段特性的光谱相似图像分类方法

光谱相似图像分类性能过差会增加光谱信息冗余度,降低地物勘探与军事防御等多种领域的光谱探测效率。为了多元素匀质区分光谱信息与光谱曲线,提出考虑关联波段特性的光谱相似图像分类方法。该方法首先利用光谱匹配消除光谱相似图像白色光源过曝现象。然后提取优化图像的关联波段,并将其作为聚类特征输入支持向量机中。最后根据支持向量机的输出结果,实现光谱相似图像分类。实验结果表明,所提方法分类结果清晰度较高,分类误差或像素块填色错误小,混淆矩阵中同行同列矩形块的分类精度较高。

机器视觉判别牛肉新鲜度的多模型定量分析

色泽是判断生鲜牛肉新鲜度的重要指标之一。随着人工智能的发展,机器视觉为生鲜肉新鲜度判别提供了可量化的解决方案。该研究通过智能手机获取牛肉图像,利用灰度化、二值化、图像分割与提取,获取不同新鲜度牛肉图像在Red-Green-Blue(RGB)、L^(*)a^(*)b^(*)和Hue-Saturation-Intensity(HSI)颜色空间的颜色参数,并与常规牛肉新鲜度评价指标挥发性盐基氮(total volatile base nitrogen,TVB-N)、硫代巴比妥酸(thiobarbituric acid,TBA)、菌落总数(total viable count,TVC)和感官评分(sensory index,SI)相关联,建立多元线性回归(multiple linear regression,MLR)、反向传播神经网络(back propagation neural network,BPNN)和支持向量回归(support vector regression,SVR)牛肉新鲜度定量预测模型。结果表明,MLR模型对TVB-N、TBA、TVC和SI值的预测模型决定系数(R^(2))分别为0.9406、0.9316、0.9582和0.9548。BPNN模型R^(2)为0.9627、0.9641、0.9920和0.9864,SVR模型的R^(2)为0.9712、0.9679、0.9928和0.9883。3种模型中SVR模型新鲜度预测性能最优,且预测相对误差均在±10%之内。在此基础上,建立了基于颜色参数的SVR牛肉货架期预测模型(R^(2)=0.9648),该模型的预测值与真实货架期平均绝对误差<0.5 d,优于传统货架期模型,提供了一种无损、快速确定牛肉新鲜度和货架期的新方法。

基于水资源空间均衡的“四水四定”调控模型构建

为实现未来不同时间尺度下的水资源空间均衡与动态调控,创建了一套完整严谨、可动态调控的“四水四定”模型体系。通过模糊信息粒化窗口的支持向量机模型预测区域未来总用水量,利用基于时间序列相似性分析的自回归支持向量机模型预测区域未来分用水量,并对两类数据进行不确定性分析;构建了复杂回归函数对各类用水指标进行情景预测,经统计检验后将其作为当前用水模式下未来用水指标;构建了“四水四定”水资源承载力模型和水资源空间均衡模型,基于未来总用水量、未来各分用水量、未来用水指标,选用水资源负载系数、用水效益和水土资源匹配系数3个指标,结合基尼系数量化水资源空间均衡度,分析当前用水模式下未来水资源均衡度;构建了最优化模型,以最小化基尼系数为目标函数调整未来用水模式,实现水资源动态调控。所创建的模型体系可以实现未来不同时间尺度下的水资源空间均衡与动态调控。

不同产地与不同生产方式的丹参药材质量相似性评价研究

本研究旨在明确不同产地、不同生产方式丹参药材的质量相似性,建立丹参药材主成分特征图谱以及色差指纹图谱,形成快速、科学的立体质量评价方法,并为丹参临床使用及不同地域药材生产提供数据参考。运用HPLC建立丹参药材指纹特征图谱质量分析方法,通过中药指纹图谱相似度评价系统和ModuleLab Chroman软件对丹参药材质量进行相似性评价及计量分析;采用主成分分析(PCA)、K最近邻法(KNN)等算法及可视化方法对样品丹参药材的色度值总体分布情况进行分析。结果显示,不同产地丹参药材共有11个共有峰,合成特征图谱相似度范围为0.894~0.999,表明不同产地样品化学成分差异较小,各产地丹参药材具有较好的相似度,但根据成分PCA得分载荷图与聚类分析显示,主成分贡献率为85.93%,其中丹酚酸B成分有较高的贡献率,说明丹酚酸B是造成不同产地差异的主要物质;主成分测定显示不同产地与不同生产方式的丹参药材总丹参酮类成分也具有较大差异,总丹参酮含量为1.358~7.515 mg/g,丹酚酸B含量为4.574~112.670 mg/g;药材颜色差异分析结果表明,不同产区丹参药材颜色具有显著差异,特别是广西地区丹参药材颜色差异更加明显,模型识别率达到93.75%。本试验评价方法能快速分辨丹参药材质量差异性,可为进一步进行临床疗效评价提供数据参考,为丹参药材生产方式以及临床应用提供思路。

基于ANFIS-LSSVM的计算颜色恒常性算法研究

计算颜色恒常性是指消除场景光源的影响从而再现物体真实颜色的能力。目前,深度神经网络的应用使颜色恒常性精度显著提高,但大多数深度学习算法训练时间长、计算复杂度高,且需要大量的训练样本。针对此问题,提出了一种结合自适应神经模糊推理系统(ANFIS)和最小二乘支持向量机(LSSVM)的简单有效的方法。该方法分为训练和预测两个阶段:在训练阶段,首先提取图像特征分别训练ANFIS、LSSVM两种初始光源估计模型,接着利用核函数变换将两种模型融合,然后利用预留训练样本进一步训练得到多元线性回归光源估计模型;在预测阶段,提取测试图像特征后,直接由训练所得模型预测得到该测试图像最终的场景光源颜色值。实验结果表明,与深度学习方法相比,本文所提方法计算复杂度较低,即使在小训练样本中也能有很好的光源估计性能。

基于哈希和编辑距离算法的SCD双层向量化与变更校验技术

针对智能变电站配置描述(substation configuration description,SCD)文件变更、智能变电站二次设备配置不清晰导致运维工作量剧增的问题,提出基于哈希和编辑距离算法的SCD双层向量化与变更校验技术。首先,考虑文件变更的启动与校验,应用节点元素、校验码与元素属性构建面向SCD文件解析的双层向量模型;其次,利用哈希算法将构建的文本向量模型转化为哈希字符串向量模型;然后,引入编辑距离算法分别计算原始文件与变更文件的双层向量模型相似度,基于第一层相似度比较构成第二层校验启动判据,基于第二层相似度结合回溯路径方法实现变更校验;最后,通过算例分析验证了所提算法的有效性。

基于光盘衍射系统的衍射效应及其现象探究

本文基于光栅方程矢量化,将传统二维平面光栅衍射的二维作用域延拓至三维空间,并定量分析了三维欧式空间中入射光经光盘衍射后方向具有可预测性.本文通过将光栅方程矢量化,定量解释了在远距条件下光盘表面彩色线状结构出现的必然.引进空间光锥对衍射主极大的上确界进行了定量计算,并将衍射光方向进行了很好的可视化描述.最后结合色散本领、重叠现象、深阱效应和视差角,较好地解决了如何经光盘衍射后得到更纯的彩色线,并给出了CD和DVD两种常见光盘的共性和差异性,发现理论和实验符合较好.

基于防松线分割的螺栓松动全角度检测方法

针对现有无法基于防松线分割进行螺栓松动全角度检测的问题,文章开发了一种基于颜色分割和方向矢量运算的方法。首先,利用一种基于Lab和RGB颜色空间转换下的a分量非线性拉伸和R分量最优阈值分割方法分割防松线图像;其次,利用开运算对图像进行形态学操作;再次,通过一种角度递进最小包围方法确定防松线连通域面积最小外接矩形并确定其方向矢量;然后,基于四象限反正切函数和一定调整求解螺栓松动的全角度;最后,设计实验方案验证文中算法的可行性和精度。实验结果表明,检测算法能够实现螺栓0~360°的松动角度检测,且最大相对误差为1.43%,其精度可以满足工程实践需要,具有良好的应用前景。

基于改进支持向量机的数字档案多标签分类算法

当前的数字档案分类方法存在测试集与训练集的基数之间差值偏大的问题,导致分类结果出现误差。为此,设计一种新的基于改进支持向量机的数字档案多标签分类算法。根据SVM函数确定数字档案信息的标签隶属度。求解深度指标,完成基于改进支持向量机的数字档案标签挖掘。获取数字档案信息样本,计算标签参量之间的相似度水平,实现基于改进支持向量机的数字档案多标签分类算法的设计。实验结果表明,研究方法下的标签测试集基数与训练集基数之间的差值始终小于350个,不会造成严重的数字档案信息错误分类问题。