• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Journal of Chest Surgery
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• v.41 no.3
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• pp.295-304
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• 2008

Background: Various experimental trials for the development of bioprosthetic devices are actively underway, secondary to the limited supply of autologous and homograft tissue to treat cardiac diseases. In this study, porcine bioprostheses that were treated with glutaraldehyde (GA), ethanol, or sodium dodecylsulfate (SDS) were examined with light microscopy and transmission electron microscopy for mechanical and physical imperfections before implantation, Material and Method: 1) Porcine pericardium, aortic valve, and pulmonary valve were examined using light microscopy and JEM-100CX II transmission electron microscopy, then compared with human pericardium and commercially produced heterografts. 2) Sections from six treated groups (GA-Ethanol, Ethanol-GA, SDS only, SDS-GA, Ethanol-SDS-GA and SDS-Ethanol-GA) were observed using the same methods. Result: 1) Porcine pericardium was composed of a serosal layer, fibrosa, and epicardial connective tissue. Treatment with GA, ethanol, or SDS had little influence on the collagen skeleton of porcine pericardium, except in the case of SDS pre-treatment. There was no alteration in the collagen skeleton of the porcine pericardium compared to commercially produced heterografts. 2) Porcine aortic valve was composed of lamina fibrosa, lamina spongiosa, and lamina ventricularis. Treatment with GA, ethanol, or SDS had little influence on these three layers and the collagen skeleton of porcine aortic valve, except in the case of SDS pre-treatment. There were no alterations in the three layers or the collagen. skeleton of porcine aortic valve compared to commercially produced heterografts. Conclusion: There was little physical and mechanical damage incurred in porcine bioprosthesis structures during various glutaraldehyde fixation processes combined with anti-calcification or decellularization treatments. However, SDS treatment preceding GA fixation changed the collagen fibers into a slightly condensed form, which degraded during transmission electron micrograph. The optimal methods and conditions for sodium dodecylsulfate (SDS) treatment need to be modified.