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作者： hafiz muhammad jamsheed nazir 华南理工大学

学位级别：硕士

绘画是绘画中的一个重要类型。等人为提供了人们普遍使用的人像传输系统。肖像摄影风格在其中最为重要,绝大部分学者正试图为调整肖像风格作出努力,但极少数关注到绘画风格。绘画风格极为有趣,它通过人物图解法,精致显示人脸及人体构造... 详细信息

绘画是绘画中的一个重要类型。等人为提供了人们普遍使用的人像传输系统。肖像摄影风格在其中最为重要,绝大部分学者正试图为调整肖像风格作出努力,但极少数关注到绘画风格。绘画风格极为有趣,它通过人物图解法,精致显示人脸及人体构造的细微差别。最近,公众风格定位试图将语义材料融入旧式方法中。通过这一方法,在语义一致的领域进行风格转换时,能够高度保留原始内容。在本文,我们建议采用高级框架以获得良好或高阶的语义绘画风格转换.我们介绍一种新式技术,以解决语义绘画风格转化展示、特征重建以及特征摄影的问题。重建部分考虑的是如何通过特征重建解决及优化内容和风格损坏的问题。这将大大降低在传输过程视像丢失的程度。解释器将重建部分转换为风格化图像。通过细致衔接原始画面和绘画风格版本,这一技术不仅可以作为渐进式优化系统获得合理结果并且能够帮助发现何种方法更好更快速。我们相信本文能够阐明与这一问题相关的两个重要的研究方法并明晰随之而来的研究内容.我们的未来方法不仅仅限于头部摄影或是特定的风格,因为我们的方法还考虑语义绘画风格变换的问题。图片的语义内容将展现不同的风格,从这个角度来看,将实现一个难度较高的图片处理目的。以往的技术问题大致为难以清晰展示或体现图片的语义信息,因此通过这一技术,能够很好地保持图片风格和内容的连贯性。在这里,我们将使用一种不同于卷积神经网络(Convolutional Neural Networks,以下简称“CNNs”)的技术为其优化,从而清晰展现具有优良风格地图片信息。我们展示的是一种高阶绘画风格,并提供了一些与原始图片内容和风格相关的例子。通过引入这一技术,我们能够添加丰富的知名的绘画风格,将主体照片内容联合起来,以创造出顶级卓越的高级图片。这一方法能通过学习使用(CNNs)技术展示高水准的视觉效果,如清晰的图片信号。它能够帮助实现超级照片的合成。(CNNs)技术的运作在照片合成和绘画风格转换中发挥着很大的作用。对于绝大多数使用者而言,可以通过自动创建照片标签或使用可行解决方案进行语义绘画风格分割以实现有趣的目标。通过这一方法,将创设极具敏感度的内容反应系统。因此,我们认为可以规避一些细节的不足以实现图片的优化,使图片外观更为真实可信,并设置这些语义绘画风格的选择范围,如具有语义绘画风格转换以及使用(CNNs)技术调整成清晰的于一绘画风格转换的展示等.

关键词：卷积神经网络技术(CNNs) 语义绘图绘图风格风格转换

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Effect of Measurement Error on the Multivariate CUSUM Control Chart for Compositional Data

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1207-1257页

作者： muhammad Imran Jinsheng Sun Fatima Sehar Zaidi Zameer Abbas hafiz Zafar nazir School of Automation Nanjing University of Science and TechnologyNanjing210094China School of Economics and Statistics Guangzhou UniversityGuangzhou510006China Department of Mathematics and Statistics East China Normal UniversityShanghai200062China Department of Statistics Government Ambala Muslim Graduate CollegeSargodha40100Pakistan Department of Statistics University of SargodhaSargodha40100Pakistan

Control charts(CCs)are one of the main tools in Statistical Process Control that have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous *** errors(M.E’s)are involved in the quality characteristic of interest,which can effect the CC’s *** authors explored the impact of a linearmodel with additive covariate M.E on the multivariate cumulative sum(CUSUM)CC for a specific kind of data known as compositional data(CoDa).The average run length(ARL)is used to assess the performance of the proposed *** results indicate that M.E’s significantly affects themultivariate *** authors haveused theMarkov chainmethod to study the impact of different involved parameters using six different cases for the variance-covariance matrix(VCM)(i.e.,uncorrelated with equal variances,uncorrelated with unequal variances,positively correlated with equal variances,positively correlated with unequal variances,negatively correlatedwith equal variances and negatively correlated with unequal variances).The authors concluded that the error VCM has a negative impact on the performance of themultivariate CUSUM-CoDa CC,as the ARL increases with an increase in the value of the error *** subgroup size m and powering operator b positively impact the proposed CC,as the ARL decreases with an increase in m or *** number of variables p also has a negative impact on the performance of the proposed CC,as the values of ARL increase with an increase in *** the implementation of the proposal,two illustrated examples have been reported formultivariate CUSUM-CoDaCCs inthe presence ofM.E’*** themanufacturingprocessof uncoated aspirin tablets,and the other is based on monitoring the machines involved in the muesli manufacturing process.

关键词： MCUSUM chart CoDa measurement error manufacturing process performance

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Thermal Decomposition Kinetics of Some Di- and Triorganotin（IV） Carboxylates Derived from para-Nitrophenylethanoic Acid

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Chinese Journal of Chemistry 2011年第7期29卷 1359-1365页

作者： Danish, muhammad Ahmad, nazir Ali, Saqib muhammad, Niaz Department of Chemistry University of Gujrat Hafiz Hayat Campus Gujrat-50700 Pakistan Department of Chemistry University of Sargodha Srgoadha 40100 Pakistan Department of Chemistry Quaid-i-Azam University Islamabad-45320 Pakistan

Thermogravimetric （TG） investigations of organotin（IV） carboxylates with the general formula RmSnL4-m （where R=CH3, C2H5, n-C4H9, C6H5, cyclo-C6H11, n-C8H17, m=2, 3, and L=para-nitrophenylethanoate anion） have been performed. Derivative thermogravimetry （DTG） and differential thermal analysis （DTA） techniques, Horowitz-Metzger method and the fundamental thermodynamic relations are used to evaluate the thermokinetic pa- rameters of each thermal degradation pattern. Results reveal that the thermal stability is functional to Sn--C and Sn--O bonds. In the case of R2SnL2, activation energy, reaction order and pre-exponential factor associated with the bulk degradation processes increase as the alkane chain length increases. Hence, Oct2SnL2 is thermally more stable than Bu2SnL2, which in turn is more resistant to thermal dissociation than Et2SnL2. The same phenomenon is not observed for R3SnL compounds because their degradation is highly irregular. Furthermore, R2SnL2 has larger values of kinetic parameters than those of corresponding triorganotin（IV） para-nitrophenylethanotes. Thermodynamic parameters of these compounds also reinforce the above facts.

关键词： organotin(IV) caroxylate thermogravimetry decomposition kinetics activation energy thermodynamics

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Defocus blur detection using novel local directional mean patterns(LDMP)and segmentation via KNN matting

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Frontiers of Computer Science 2022年第2期16卷 110-122页

作者： Awais KHAN Aun IRTAZA Ali JAVED Tahira nazir hafiz MALIK Khalid Mahmood MALIK muhammad Ammar KHAN Department of Computer Science University of Engineering and TechnologyTaxila 47050Pakistan Department of Electrical and Computer Engineering University of MichiganDearbornMI 48128USA Department of Software Engineering University of Engineering and TechnologyTaxila 47050Pakistan Department of Computer Science and Engineering Oakland UniversityMI 48309USA

Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp regions that wrap significant information and require effective methods for information *** defocus blur detection and segmentation methods have several limitations i.e.,discriminating sharp smooth and blurred smooth regions,low recognition rate in noisy images,and high computational cost without having any prior knowledge of images i.e.,blur degree and camera ***,there exists a dire need to develop an effective method for defocus blur detection,and segmentation robust to the above-mentioned *** paper presents a novel features descriptor local directional mean patterns(LDMP)for defocus blur detection and employ KNN matting over the detected LDMP-Trimap for the robust segmentation of sharp and blur *** argue/hypothesize that most of the image fields located in blurry regions have significantly less specific local patterns than those in the sharp regions,therefore,proposed LDMP features descriptor should reliably detect the defocus blurred *** fusion of LDMP features with KNN matting provides superior performance in terms of obtaining high-quality segmented regions in the ***,the proposed LDMP features descriptor is robust to noise and successfully detects defocus blur in high-dense noisy *** results on Shi and Zhao datasets demonstrate the effectiveness of the proposed method in terms of defocus blur *** and comparative analysis signify that our method achieves superior segmentation performance and low computational cost of 15 seconds.

关键词： defocus blur detection local directional mean patterns image matting sharpness metrics blur segmentation

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