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Magnetocaloric effect in La_(0.7)Sr_(0.3)MnO_(3)/ta_(2)O_(5) composites

Journal of Advanced Ceramics 2013年第3期2卷 213-217页

作者： Mahmoud Aly hamad Physics Department College of ScienceAl Jouf UniversityAl JoufSkakaP.O.Box 2014Saudi Arabia Physics Department Faculty of ScienceTanta UniversityTantaEgypt

The magnetocaloric effect (MCE) achieved for La_(0.7)Sr_(0.3)MnO_(3)/ta_(2)O_(5) composites has been investigated. The maximum value of magnetic entropy change of La_(0.7)Sr_(0.3)MnO_(3) composites is found to decrease slightly with the further increasing of ta_(2)O_(5) concentration. It is shown that La_(0.7)Sr_(0.3)MnO_(3)/ta_(2)O_(5) composites exhibit much more uniform magnetic entropy change than that of gadolinium. Moreover, the results indicate that the temperature range between 100 K and 400 K can be covered using the La_(0.7)Sr_(0.3)MnO_(3)/ta_(2)O_(5) composites. Therefore, MCE makes the composites promising for room-temperature magnetic cooling applications.

关键词： magnetic materials simulation and modeling

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Crystal structure guided machine learning for the discovery and design of intrinsically hard materials

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Journal of Materiomics 2022年第3期8卷 678-684页

作者： Russlan Jaafreh tamer Abuhmed Jung-Gu Kim Kotiba hamad School of Advanced Materials Science&Engineering Sungkyunkwan UniversitySuwon16419South Korea College of Computing and Informatics Sungkyunkwan UniversitySuwon16419South Korea

In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal *** this purpose,an initial dataset,containing the hardness values of 270 compounds and counterpart applied loads,was employed in the learning *** on various features generated using crystal information,an ML model,with a high accuracy(R^(2)=0.942),was built using extreme gradient boosting(XGB)*** validations conducted by hardness measurements of various compounds,including MSi_(2)(M=Nb,Ce,V,and ta),Al_(2)O_(3),and FeB_(4),showed that the XGB model was able to reproduce load-dependent hardness behaviors of these *** addition,this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements.

关键词： Machine learning XGB algorithm Intrinsic hardness Crystal chemistry OQMD ICSD MP

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3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

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Computers, Materials & Continua 2021年第2期66卷 1757-1770页

作者： Khalil Khan Jehad Ali Kashif Ahmad Asma Gul Ghulam Sarwar Sahib Khan Qui Thanh Hoai ta tae-Sun Chung Muhammad Attique Department of Computer Science and Software Engineering Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur-KPKPakistan Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwon16499South Korea hamad Bin Khalifa University DohaQatar Department of Statistics Shaheed Benazir Bhutto Women UniversityPeshawarPakistan Department of Software Engineering University of Azad Jammu and KashmirPakistan Department of Electronics and Telecommunications Politecnico di TorinoTorino10129Italy Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Department of Computer Engineering Ajou UniversitySuwon16499Korea Department of Software Sejong UniversitySeoul05006South Korea

Face image analysis is one among several important cues in computer *** the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis *** parsing strongly benefits various human face image analysis tasks inducing face pose *** this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing *** have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and *** extract features from gray scale images by using *** train a classifier using the extracted *** use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic *** use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation *** assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.

关键词： Face image analysis face parsing face pose estimation

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