• Journal of radiological science and technology
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• v.30 no.3
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• pp.205-212
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• 2007

To evaluate the feasibility and efficacy of a pulsatile aortic aneurysm phantoms for in-vitro study. The phantoms consisted of a pulsating motor part(heart part) and an aortic aneurysm part, which mimicked true physiologic conditions. The heart part was created from a high-pressured water pump and a pulsatile flow solenoid valve for the simulation of aortic flow. The aortic aneurysm part was manufactured from paper clay, which was placed inside a acrylic plastic square box, where liquid silicone was poured. After the silicone was formed, the clay was removed, and a silicone tube was used to connect the heart and aneurysm part. We measured the change in pressure as related to the opening time(pulse rate, Kruskal-Wallis method) and pressure before and after the stent-graft implantation(n = 5, Wilcoxon's signed ranks test). The changes in blood pressures according to pulse rate were all statistically significant(p<0.05). The systolic/diastolic pressures at the proximal aorta, the aortic aneurysm, and the distal aorta of the model were $157.80{\pm}1.92/130.20{\pm}1.92$, $159.40{\pm}1.14/134.00{\pm}2.92$, and $147.20{\pm}1.480/129.60{\pm}2.70\;mmHg$, respectively, when the pulse rate was 0.5 beat/second. The pressures changed to $161.40{\pm}1.34/90.20{\pm}1.64$, $175.00{\pm}1.58/93.00{\pm}1.58$, and $176.80{\pm}1.48/90.80{\pm}1.92\;mmHg$, respectively, when the pulse rate was 1.0 beat/second, and $159.40{\pm}1.82/127.20{\pm}1.48$, $166.60{\pm}1.67/138.00{\pm}1.87$, and $161.00{\pm}1.22/135.40{\pm}1.67\;mmHg$, respectively, when it was 1.5 beat/second. When pulse rate was set at 1.0 beat/second, the pressures were $143.60{\pm}1.67/90.20{\pm}1.64$, $147.20{\pm}1.92/84.60{\pm}1.82$, and $137.40{\pm}1.52/88.80{\pm}1.64\;mmHg$ after stent-graft implantation. The changes of pressure before and after stent-graft implantation were statistically significant(p<0.05) except the diastolic pressures at the proximal(p =1.00) and distal aorta(p=0.157). The aortic aneurysm phantoms seems to be useful for the evaluation of the efficacy of stent-graft before animal or clinical studies because of its easy reproducibility and ability to display a wide range of pressures.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.