An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

Research of Image pre-processing Algorithm in Automation Fingerprint Identification

In the paper, a valid method of fingerprint Image pre- processing is introduced. Experiment results show that this kind of algorithm can availably wipe off yawp imported by the incom- plete leave fingerprint - marking of sensor surface when finger- print sensor record fingerprint. Meanwhile, it can extract the ef- fective and uneffective zone of fingerprint effectively, and also further enhance ridge line and vale line of fingerprint so that make the lines of fingerprint clear, continuum, lubricity and has better contrast, at the same time, has quite quick speed, this fingerprint Image pre- processing time can be shorten greatly.

Image pre-processing research of coal level in underground coal pocket

Mathematical morphology is widely applicated in digital image procesing.Vari- ary morphology construction and algorithm being developed are used in deferent digital image processing.The basic idea of mathematical morphology is to use construction ele- ment measure image morphology for solving understand problem.The article presented advanced cellular neural network that forms mathematical morphological cellular neural network (MMCNN) equation to be suit for mathematical morphology filter.It gave the theo- ries of MMCNN dynamic extent and stable state.It is evidenced that arrived mathematical morphology filter through steady of dynamic process in definite condition.

Fingerprint Directional Image Partitioning Based on Genetic Algorithm

In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.

A Novel Adaptive Approach to Process Binary Fingerprint Images Using Directional Morphological Operations

An adaptive algorithm for removing false ridges, bridges and filling gaps in binary fingerprint images based on morphological operations is presented. A novel procedure for structuring elements design based on the specific fingerprint characteristic is described. Using the images from FVC2000 database, we have compared our method proposed here with the approach proposed by other ones. The Experimental results have demonstrated the efficiency of our method.

Analysis of GC×GC fingerprints from medicinal materials using a novel contour detection algorithm:A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm,PeakCET,designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram(GC×GC).This method innovatively combines contour edge tracking with affinity propagation(AP)clustering for peak detection in GC×GC fingerprints,the first in this field.Contour edge tracking signif-icantly reduces false positives caused by“burr”signals,while AP clustering enhances detection accuracy in the face of false negatives.The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin.PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples.Furthermore,this algorithm compares the GC×GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins.The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues.Each sample exhibits unique characteristic components alongside common ones,and vari-ations in content may influence their therapeutic effectiveness.This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional(2D)fingerprint analysis of GC×GC data.

Measuring Fingerprint Image Quality Using the Fourier Spectrum

The fingerprint image quality has a significant effect on the performance of automatic fingerprint identification system. A method for measure of fingerprint image quality based on Fourier spectrum is proposed. First the band frequency which corresponds to the global average period of ridge is searched. Then the quality score of the fingerprint image is computed by measuring relative magnitude of the band frequency components. The method is verified to have good performance by experiments.

A ROBUST METHOD FOR FINGERPRINTING DIGITAL IMAGES

In this paper, a method to fingerprint digital images is proposed, and different watermarked copies with different identification string are made. After determining the number of the customers and the length of the watermark string, this method chooses some values inside the digital image using a characteristic function, and adds watermarks to these values in a way that can protect the product against the attacks happened by comparing two fingerprinted copies.The watermarks are a string of binary numbers -1s and 1s. Every customer will be distinguished by a series of 1s and -1s generated by a pseudo-random generator. The owner of the image can determine the number of customers and the length of the string as well as this method will add another watermarking values to watermark string to protect the product.

Local Orientation Field Based Nonlocal Means Method for Fingerprint Image De-Noising

The de-noising of the fingerprint image is one of the key tasks before the extraction of the minutiae in automatic fingerprint matching. When used for de-noising the fingerprint image, the nonlocal means method can not preserve the local minutiae in the fingerprint image very well. To address this problem, we propose a local orientation field based nonlocal means (NLM-LOF) method in this paper. Experimental results on the simulated and real images show that the proposed method can suppress noise effectively while preserving edges and details in the fingerprint image and it outperforms the state-of-art nonlocal means method in terms of qualitative metrics and visual comparisons.

X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic crisis.In the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected patients.Identifying COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain extent.This paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral infection.This approach includes preprocessing,feature extraction,and classification.However,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature descriptors.Furthermore,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is used.Experimental results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic performance.It is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

Image mathematical morphology and image restoration application in detecting underground bin level

By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used based on neural network. A modified Hopfield network was presented for image restoration. The greed algorithm with n-simultaneous updates and apartially asynchronous algorithm were combined, im- proving convergence and avoiding synchronization penalties. Mathematical morphology was widely applicated in digital image processing. The basic idea of mathematical mor- phology is to use construction element measure image morphology for solving under- stand problem. Presented advanced Cellular neural network that forms MMCNN equa- tion to be suit for mathematical morphology filter. It gave the theory of MMCNN dynamic extent and stable state. It was evidenced that arrived mathematical morphology filter through steady of dynamic precess in definite condition. The results of implementation were applied in detecting undergroug bin level.

Plagiarism Detection in Homework Based on Image Hashing

The problem of high similarity in homework has troubled teachers with time. Previous plagiarism detection systems are mainly realized by string matching which has a limitation, i.e., image homework cannot be detected. To this issue, we propose a new method of plagiarism detection in homework. First,we get fingerprint features of image homework by converting text homework into images. Then, we use image hashing algorithm and hamming distance to calculate the similarity of these features. Finally, we perform the empirical study on course of Computer Network Experiment, the test shows that our method not only reliably keeps the detection speedily, but also consistently ensures precision and false positive rate.

磁共振成像新技术在乳腺良恶性病变鉴别诊断中的应用进展

乳腺癌已成为全球第一大癌症,严重威胁女性健康。磁共振成像技术以其多参数、高软组织分辨率及无电离辐射等优点,已越来越多地应用于乳腺病变的良恶性鉴别。随着近年来磁共振技术的飞速发展,新的成像方法不断涌现,为乳腺病变的良恶性检出提供了更有价值的参考依据。其中,超快速磁共振动态对比增强成像、合成磁共振成像、磁共振指纹打印技术及一系列高级扩散加权成像技术(体素内不相干运动成像、扩散峰度成像和拉伸指数模型成像)均可以从不同角度描述乳腺病变潜在的组织学或病理学特征,为良恶性鉴别提供有力支撑。本文将从成像原理、技术特点以及临床应用等多个层面,系统地阐述上述磁共振成像新技术对乳腺病变良恶性的鉴别诊断价值。

长余辉纳米粒子的合成及其生物成像应用

长余辉发光是一种在激发停止后产生持久余辉发光的特殊光学现象,这种现象在防伪、信息或数据存储、光催化、传感和生物成像等领域中呈现出巨大的前景。长余辉纳米粒子(PLNPs)无需原位激发,能够减少组织的自发荧光和光散射干扰,被广泛用于生物成像领域。此外,发光性能可调节,且具有X射线激发、近红外区域长寿命发光或多模式成像特征的长余辉纳米探针能够应用于深层组织生物成像,为细胞或组织提供更为丰富的成像信息。该综述总结了近年来PLNPs的合成方法及其在生物成像应用中的最新研究进展,重点介绍了PLNPs在指纹成像、细胞和活体成像等方面的应用,同时还讨论了PLNPs在生物成像领域面临的挑战和未来的发展趋势。

一种新型深度分类神经网络黑盒指纹水印算法

提出了一种新型的强鲁棒黑盒指纹水印框架及方法。首先,提出了一种基于数字水印技术的高视觉质量的、具有一定安全性的毒化图像构造方法,将指示用户身份的信息嵌入到毒化图像,实现多用户场景下深度神经网络模型的可追溯性,并降低毒化图像被伪造的概率;其次,提出了毒化特征加强模块来优化模型训练;最后,设计了对抗训练策略,有效地学习到嵌入强度很小的指纹水印。大量的仿真实验表明,所构造的毒化图像中的指纹水印具有非常好的隐蔽性,大幅超越了WaNet等同类最优模型水印方法;以分类性能降低不超过2.4%的代价获得了超过99%的黑盒模型指纹水印验证率;且即便在指纹水印相差1位时亦能准确地进行模型水印版权验证。这些性能总体上优于同类最优的模型水印方法,表明了所提方法的可行性和有效性。

飞行时间二次离子质谱在指纹分析中的研究进展

指纹作为接触类犯罪案件现场最常见的痕迹之一,基于其形态学价值及承载的物质进行分析是个体识别、侦查破案的重要手段。飞行时间二次离子质谱技术(TOF-SIMS)是一种兼具高质量分辨率和高空间分辨能力的表面分析技术,能够同时获得待测物质的质谱信息和成像分布。相较于其他理化分析技术,TOF-SIMS所具备的快速检验、无需前处理、原位近无损分析等优势使其逐渐成为指纹分析领域的前沿课题。该文基于TOF-SIMS在成像增强、物质分析、犯罪信息挖掘等指纹分析领域的研究现状展开综述,分析其在公安实战中的应用前景,以期为该技术在指纹检验领域的推广奠定基础。

基于偏振成像-空间滤波的相位物体上指纹检测

提出了一种结合偏振成像与空间滤波的指纹检测系统,该系统能有效提取淹没在干扰背景中的指纹信息,实现了在相位物体上指纹目标的高质量成像。通过使用聚焦透镜和通孔反射镜,滤除夫琅禾费衍射图样的低频背景,仅采集高频细节信息,结合偏振成像技术,得到指纹图像的偏振参量和偏振度(P_(DOLP))图像,实现清晰的指纹成像。结果显示,添加空间滤波模块和偏振成像技术得到的指纹图像在指纹图像清晰度上提高了约6.8倍,使得细节和纹路更为突出;相较于指纹强度图像,指纹偏振度P_(DOLP)图像能够很大程度上消除相位物体上其他干扰物质的影响,获取完整、丰富的指纹细节信息。这种技术在指纹验证和刑事侦查等领域具有潜在应用价值。

Highly crystallization-induced emissive luminophores with mechanoluminescent features for two-photon harvesting fluorescence imaging and latent fingerprint identification

Fluorescence imaging can be employed in fields of medical treatment,astronomical exploration,and national defense security.Traditional fluorescence imaging often takes the single-photon techniques,which is vulnerable to background interference and photobleaching.Remedially,two-photon fluorescence imaging can achieve much higher-resolution fluorescence imaging for reducing scattering and deeper depth.Hence,by assembling the tetraphenylethylene backbones with nontoxic and non-noble K^(+)ions,compound 1([(Hdma)K(H_(2)ettc)]_(n),H_(4)ettc=4',4''',4''''',4'''''''-(ethene-1,1,2,2-tetrayl)tetrakis(([1,1'-biphenyl]-4-carboxylic acid)))with the crystallization-induced emissions exhibited charming fluorescence imaging under two-photon excitation microscopy(TPEM).Besides,luminescent powders based on compound 1 can achieve high-resolution fingerprint recognition,providing secure access control and identification for a novel authentication method.Compared with the commercial fluorescent dyes coumarin-6,the as-synthesized compound 1 showed great solvent stability,indicating its durability against harsh environment.Moreover,compound 1 shows mechanoluminescent properties for the perturbation of weak supramolecular interactions within ordered arrangements of the H_(2)ettc^(2−)ligands.This novel compound has provided an important insight to the development of twophoton fluorescence imaging and advanced external-stimuli responsive materials.

改进MixNet的CSI图像指纹室内定位方法

为了提升信道状态信息(channel state information,CSI)指纹室内定位的性能,提出了一种改进MixNet的CSI图像指纹室内定位方法.在离线阶段,通过选择定位参考点(reference point,RP)处信号强度指示(received signal strength indication,RSSI)最强的3个接入点(access point,AP),提取其CSI数据并转换为图像;然后利用改进的MixNet模型对图像进行训练并保存模型.其中改进的MixNet引入了坐标注意力(coordinate attention,CA)和残差连接.首先,将MixNet-s中的SE(squeeze-and-excitation)注意力替换为CA,以增强网络的信息表示能力并更精确地获取CSI图像指纹特征.其次,根据MixNet-s模型的特点构建残差连接,以增强网络的表示能力并防止过拟合.最后,通过减小网络深度确保所有网络层得到充分训练;在线阶段,采集目标设备的CSI数据并转换为图像,输入已训练好的改进MixNet模型(命名为MixNet-CA);最后利用加权质心算法根据模型输出的概率值估计目标设备的最终位置.提出方法在室内环境中进行了验证,达到了0.3620 m的平均定位误差.

基于图像处理技术识别伪造指纹的研究

伪造指纹迷惑性强,对社会危害大,急需开发高效、精确的伪造指纹识别技术。采用图像处理技术可以提高伪造指纹的识别效率和准确性。实验制作了复印、打印、硅胶膜和光敏指纹章盖印的指纹,与直接捺印指纹进行人工和系统自动比对纹线特征点,发现伪造指纹能达到与真实指纹同一认定的条件。采用Adobe Photoshop、Open CV计算机视觉库和Image pro plus图像处理软件,对指纹图像的细节进行深入分析,并提取重要特征,如颜色和墨迹分布,来识别和区分真假指纹。通过对比分析指纹图像中的黄色和蓝色墨点,可以识别是捺印指纹还是打印复印指纹。同时,分析指纹颜色对比度和宽度的变化能够识别硅胶膜和光敏指纹章伪造指纹。因此,图像处理技术能显著增强伪造指纹检测的多维度分析能力,为指纹识别领域提供了科学、具象的证据支持。