• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.268-278
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• 2021

Technologies that are leading the fourth industrial revolution, such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cyber-physical systems (CPS) are developing and generalizing. The demand to improve productivity, economy, safety, etc., is spreading in various industrial fields by applying these technologies. Digital twins are attracting attention as an important technology trend to meet demands and is one of the top 10 tasks of the Korean version of the New Deal. In this study, papers, magazines, reports, and other literature were searched using Google. In order to investigate the contribution or role of geospatial information in the digital twin application, the definition of a digital twin, we investigated technology trends of domestic and foreign companies; the components of digital twins required in manufacturing, plants, and smart cities; and the core techniques for driving a digital twin. In addition, the contributing contents of geospatial information were summarized by searching for a sentence or word linked between geospatial-related keywords (i.e., Geospatial Information, Geospatial data, Location, Map, and Geodata and Digital Twin). As a result of the survey, Geospatial information is not only providing a role as a medium connecting objects, things, people, processes, data, and products, but also providing reliable decision-making support, linkage fusion, location information provision, and frameworks. It was found that it can contribute to maximizing the value of utilization of digital twins.

• Journal of Korean Neurosurgical Society
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• v.64 no.4
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• pp.575-584
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• 2021

Objective : Cervical expansive laminoplasty is an effective surgical method to address multilevel cervical spinal stenosis. During surgery, the spinous processes of C2 and C7 are usually preserved to keep the insertion points of the cervical musculature and nuchal ligament intact. In this regard, dome-like laminectomy (undercutting of C7 lamina) instead of laminoplasty is performed on C7 in selected cases. However, resection of the lamina can weaken the C7 lamina, and stress fractures may occur, but this complication has not been characterized in the literature. The objective of the present study was to investigate the incidence and risk factors for C7 laminar fracture after C7 dome-like laminectomy and its impact on clinical and radiological outcomes. Methods : Patients who underwent cervical open-door laminoplasty combined with C7 dome-like laminectomy (n=123) were classified according to the presence of C7 laminar fracture. Clinical parameters (neck/arm pain score and neck disability index) and radiologic parameters (C2-7 angle, C2-7 sagittal vertical axis, and C7-T1 angle) were compared between the groups preoperatively and at postoperatively at 3, 6, 12, and 24 months. Risk factors for complications were evaluated, and a formula estimating C7 fracture risk was suggested. Results : C7 lamina fracture occurred in 32/123 (26%) patients and occurred at the bilateral isthmus in 29 patients and at the spinolaminar junction in three patients. All fractures appeared on X-ray within 3 months postoperatively, but patients did not present any neurological deterioration. The fracture spontaneously healed in 27/32 (84%) patients at 1 year and in 29/32 (91%) at 2 years. During follow-up, clinical outcomes were not significantly different between the groups. However, patients with C7 fractures showed a more lordotic C2-7 angle and kyphotic C7-T1 angle than patients without C7 fractures. C7 fracture was significantly associated with the extent of bone removal. By incorporating significant factors, the probability of C7 laminar fracture could be assessed with the formula 'Risk score = 1.08 × depth (%) + 1.03 × length (%, of the posterior height of C7 vertebral body)', and a cut-off value of 167.9% demonstrated a sensitivity of 90.3% and a specificity of 65.1% (area under the curve, 0.81). Conclusion : C7 laminar fracture can occur after C7 dome-like laminectomy when a substantial amount of lamina is resected. Although C7 fractures may not cause deleterious clinical outcomes, they can lead to an unharmonized cervical curvature. The chance of C7 fracture should be discussed in the shared decision-making process.

• Applied Chemistry for Engineering
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• v.33 no.6
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• pp.563-573
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• 2022

This work aimed to develop a new equation for turbidity (Turb) simulation and prediction using statistical methods based on principal component analysis (PCA) and multiple linear regression (MLR). For this purpose, water samples were collected monthly over a five year period from Cheurfa dam, an important reservoir in Northwestern Algeria, and analyzed for 12 parameters, including temperature (T°), pH, electrical conductivity (EC), turbidity (Turb), dissolved oxygen (DO), ammonium (NH₄⁺), nitrate (NO₃^-), nitrite (NO₂^-), phosphate (PO₄^3-), total suspended solids (TSS), biochemical oxygen demand (BOD₅) and chemical oxygen demand (COD). The results revealed a strong mineralization of the water and low dissolved oxygen (DO) content during the summer period. High levels of TSS and Turb were recorded during rainy periods. In addition, water was charged with phosphate (PO₄^3-) in the whole period of study. The PCA results revealed ten factors, three of which were significant (eigenvalues >1) and explained 75.5% of the total variance. The F1 and F2 factors explained 36.5% and 26.7% of the total variance, respectively and indicated anthropogenic pollution of domestic agricultural and industrial origin. The MLR turbidity simulation model exhibited a high coefficient of determination (R² = 92.20%), indicating that 92.20% of the data variability can be explained by the model. TSS, DO, EC, NO₃^-, NO₂^-, and COD were the most significant contributing parameters (p values << 0.05) in turbidity prediction. The present study can help with decision-making on the management and monitoring of the water quality of the dam, which is the primary source of drinking water in this region.

• Communications of Mathematical Education
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• v.36 no.4
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• pp.535-558
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• 2022

In the current society, where statistical literacy is recognized as an important ability, statistical education utilizing the statistical problem solving, a series of processes for performing statistics, is required. The result interpretation stage is especially important because many forms of statistics we encounter in our daily lives are the information from the analysis results. In this study, data on private education were provided to pre-service mathematics teachers, and a project was carried out in which they could experience a statistical problem solving process using the population mean estimation. Therefore, this study analyzed the characteristics shown by pre-service mathematics teachers during the result interpretation stage. First, many pre-service mathematics teachers interpreted results based on the data, but the inference was found to be a level of 2 which is not reasonable. Second, pre-service mathematics teachers in this study made various kinds of decisions related to public education, such as improving classes and after-school classes. In addition, the pre-service mathematics teachers in this study seem to have made decisions based on statistical analysis results, but they made general decisions that teachers could make, rather than specifically. Third, the pre-service mathematics teachers of this study were reflective about the question formulation stage, organizing & reducing data stage, and the result interpretation stage, but no one was reflective about the result interpretation stage.

• Analyses & Alternatives
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• v.2 no.1
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• pp.31-69
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• 2018

This study examines the role of science in resolving trade disputes. After the Great East Japan Earthquake of 11 March 2011 that not only jeopardized the people of Japan, but also put the safety of an entire region at risk, the Republic of Korea (Korea) has imposed import bans as well as increased testing and certification requirements for radioactive material on Japanese food products. Japan has challenged these restrictions at the World Trade Organizations Dispute Settlement Body (DSB). This study aims to explain how international trade agreements and previous DSB rulings have dealt with different scientific viewpoints provided by confronting parties. In doing so, it will contrast the viewpoints espoused by Korean and Japanese representatives, and then analyzes the most similar case studies previously ruled on by the DSB, including the case of beef hormones and the case of genetically modified crops including biotech corn, both between the United States and the European Communities (EC). This study finds that science is largely subordinate to national interests in the case of state decision-making within the dispute settlement processes, and science has largely been relegated to a supportive role. Due to the ambiguity and lack of truly decisive decisions in the Appellate Body in science-based trade disputes, this study concludes that the Appellate Body avoids taking a firm scientific position in cases where science is still inconclusive in any capacity. Due to the panel's unwillingness to establish expert review boards as it has the power to do, instead favoring an individual-based system so that all viewpoints can be heard, it has also developed a system with its own unique weaknesses. Similar to any court of law in which each opposing party defends its own interests, each side brings whatever scientific evidence it can to defend its position, incentivizing them to disregard scientific conclusions unfavorable to their position. With so many questions that can arise, combined with the problems of evolving science, questions of risk, and social concerns in democratic society, it is no wonder that the panel views scientific information provided by the experts as secondary to the legal and procedural issues. Despite being ruled against the EC on legal issues in two previous cases, the EC essentially won both times because the panel did not address whether its science was correct or not. This failure to conclusively resolve a debate over whose science is more scientific enabled the EC to simply fix the procedural issues, while continuing to enforce trade restrictions based on their scientific evidence. Based on the analysis of the two cases of disputes, Korea may also find itself guilty of imposing an unwarranted moratorium on Japan's fish exports, only to subsequently pass new restrictions on labelling and certification requirements because Japan may have much scientific evidence at its disposal. However, Korea might be able to create enough uncertainty in the panel to force them to rule exclusively on the legal issues of the case. This will then equip Korea, like the EC in the past, with a way of working around the ruling, by changing whatever legal procedure they need to while maintaining some, if not most, of its restrictions when the panel fails to address its case on scientific grounds.

• The Journal of Bigdata
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• v.7 no.1
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• pp.213-224
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• 2022

In logistics and distribution, Market Basket Analysis (MBA) is used as an important means to analyze the correlation between major sales products and to increase internal operational efficiency. In particular, the results of market basket analysis are used as important reference data for decision-making processes such as product purchase prediction, product recommendation, and product display structure in stores. With the recent development of e-commerce, the number of items handled by a single distribution and logistics company has rapidly increased, And the existing analytical methods such as Apriori and FP-Growth have slowed down due to the exponential increase in the amount of calculation and applied to actual business. There is a limit to examining important association rules to overcome this limitation, In this study, at the Main-Category level, which is the highest classification system of products, the utility item set mining technique that can consider the sales volume of products together was used to first select a group of products mainly sold together. Then, at the sub-category level, the types of products sold together were identified using FP-Growth. By using this sequential layer filtering technique, it may be possible to reduce the unnecessary calculations and to find practically usable rules for enhancing the effectiveness and profitability.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.3-12
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• 2022

In general construction project planning ratio of manpower and quantity of outputs produced, such as the construction estimate standard, is used as the criterion for labor productivity. This method is highly effective in construction projects with repetitive work, however, there is a limit to apply in large-scale projects with high complexity. This is because the influence of non-work time caused by various work interruption factors that act complexly on the productivity of the project is greater than the average labor productivity derived from the performance data of the project. Therefore, this study proposes a productivity measurement method that can evaluate the characteristics of construction works and the cause of non-working time. To this end, first, detailed work processes and their non-work factors for each work type are defined, and the Adv-FMR technique is developed for quantitatively measuring them. Next, based on the concept of obtainable productivity, methods for comparative productivity analysis by work type, evaluating non-work factors, and deriving productivity improvement methods are proposed. Finally, a case study is conducted to validate that the analysis results based on Adv-FMR data can support the decision-making of construction managers on productivity management.

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.310-323
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• 2020

To treat congenital heart disease, it is important to understand the anatomical structure correctly. Three-dimensional (3D) printed models of the heart effectively demonstrate the structural features of congenital heart disease. Occasionally, the exact characteristics of complex cardiac malformations are difficult to identify on conventional computed tomography, magnetic resonance imaging, and echocardiography, and the use of 3D printed models can help overcome their limitations. Recently, 3D printed models have been used for congenital heart disease education, preoperative simulation, and decision-making processes. In addition, we will pave the way for the development of this technology in the future and discuss various aspects of its use, such as the development of surgical techniques and training of cardiac surgeons.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.