• Structural Engineering and Mechanics
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• v.50 no.4
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• pp.541-561
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• 2014

This paper approaches the issue of seismic vulnerability assessment strategies for facade walls of traditional masonry buildings through the development of a methodology and its subsequent application to over 600 building facades from the old building stock of the historic city centre of Coimbra. Using the post-earthquake damage assessment of masonry buildings in L'Aquila, Italy, an analytical function was developed and calibrated to estimate the mean damage grade for masonry facade walls. Having defined the vulnerability function for facade walls, damage scenarios were calculated and subsequently used in the development of an emergency planning tool and in the elaboration of an access route proposal for the case study of the historic city centre of Coimbra. Finally, the methodology was pre-validated through the comparison of a set of results obtained from its application and also resourcing to a widely accepted mechanical method on the description of the out-of-plane behaviour of facade walls.

• The Korean Journal of Nuclear Medicine
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• v.38 no.2
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• pp.143-151
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• 2004

Recent progress in the development of non-invasive imaging technologies continues to strengthen the role of molecular imaging biological research. These tools have been validated recently in variety of research models, and have been shown to provide continuous quantitative monitoring of the location(s), magnitude, and time-variation of gene expression. This article reviews the principles, characteristics, categories and the use of radionuclide reporter gene imaging technologies as they have been used in imaging cell trafficking, imaging gene therapy, imaging endogenous gene expression and imaging molecular interactions. The studios published to date demonstrate that reporter gene imaging technologies will help to accelerate pre-clinical model validation as well as allow for clinical monitoring of human diseases.

• Nuclear Medicine and Molecular Imaging
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• v.40 no.6
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• pp.279-292
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• 2006

Recent progress in the development of non-invasive imaging technologies continues to strengthen the role of biomedical research. These tools have been validated recently in variety of research models, and have born shown to provide continuous quantitative monitoring of the location(s), magnitude, and time-variation of gene delivery and/or expression. This article reviews the use of PET technologies as they have been used in imaging biological processes for molecular imaging applications. The studies published to date demonstrate that noninvasive imaging tools will help to accelerate pre-clinical model validation as well as allow for clinical monitoring of human diseases.

• Korean Journal of Computational Design and Engineering
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• v.10 no.3
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• pp.199-209
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• 2005

In this paper, a digital simulation model for an automotive assembly line is constructed by adapting a digital manufacturing methodology. Applied methodology is a simulation for a plant level of the assembly production line. The first significance of this methodology is a validation of the production planning based on various scenarios. The second is pre-verification for the new production plan or production method. The third is a visualization of the production process. Several models were implemented and those models were verified. Then, it was possible to find a most efficient production scenario and production method.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.485-492
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• 2018

In this paper, we propose facial expression recognition using CNN (Convolutional Neural Network), one of the deep learning technologies. The proposed structure has general classification performance for any environment or subject. For this purpose, we collect a variety of databases and organize the database into six expression classes such as 'expressionless', 'happy', 'sad', 'angry', 'surprised' and 'disgusted'. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. In the existing CNN structure, the optimal structure that best expresses the features of six facial expressions is found by adjusting the number of feature maps of the convolutional layer and the number of nodes of fully-connected layer. The experimental results show good classification performance compared to the state-of-the-arts in experiments of the cross validation and the cross database. Also, compared to other conventional models, it is confirmed that the proposed structure is superior in classification performance with less execution time.

• Nuclear Medicine and Molecular Imaging
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• v.40 no.2
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• pp.96-105
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• 2006

Molecular imaging has its root in nuclear medicine and gene therapy monitoring. Therefore, recent progress in the development of non-invasive imaging technologies, particularly nuclear medicine, should allow molecular imaging to play a major role in the field of gene therapy. These tools have recently been validated in gene therapy models for continuous quantitative monitoring of the location, magnitude, and time-variation of gene delivery and/or expression. This article reviews the use of radionuclide imaging technologies as they have been used in imaging gene delivery and gene expression for gene therapy applications. The studios published to date lend support that noninvasive imaging tools will help to accelerate pre-clinical model validation as well as allow for clinical monitoring of human gene therapy.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.11-23
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• 2021

House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.

• Smart Media Journal
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• v.13 no.6
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• pp.90-97
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• 2024

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.12
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• pp.1128-1139
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• 2013

KOMPSAT-5(KOrea Multi-Purpose SATellite-5) has a benefit of continuously conducting its mission in all weather and even night by loading SAR(Synthetic Aperture Radar) payload, which is different from optical sensor of KOMPSAT-2 satellite. During IOT(In-Orbit Test) periods, SAR image calibration should be conducted through ground target of which location and RCS is pre-determined. Differently from the conventional corner reflector, active transponder has a capability to change its internal transfer gain and delay, which allows active transponder to be shown in a pixel of SAR image with very high radiance and virtual location. In this paper, the development of active transponder is presented from design to I&T(Integration and Test).

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.3
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• pp.261-269
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• 2015

In this study, numerical analysis of curved channel steam-methanol reformer was conducted using the computational fluid dynamics (CFD) commercial code STAR-CCM. A pre-numerical analysis of reference model with a cylindrical channel reactor was performed to validate the combustion model of the CFD commercial code. The result of advance validation was in agreement with reference model over 95%. After completing the validation, a curved channel reactor was designed to determine the effects of shape and length of flow path on methanol conversion efficiency and generation of hydrogen. Numerical analysis of the curved-channel reformer was conducted under various flow rate ($10/15/20{\mu}l/min$). As a result, the characteristics of flow and mass transfer were confirmed in the cylindrical channel and curved channel reactor, and useful information about methanol conversion efficiency and hydrogen generation was obtained for various flow rate.