• Particle and aerosol research
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• v.20 no.3
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• pp.103-114
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• 2024

Liquid-spray cleaning has recently been considered an eco-friendly cleaning method in the semiconductor industry because it efficiently cleans contaminated wafers without using any chemicals, relying instead on direct momentum transfer through dropwise impaction. Previous researches are mainly divided into two groups, such as modelling studies predicting the cleaning effect of single-droplet impact and experimental works for measuring particle removal efficiency (PRE) that essentially accompanies multiple droplet impacts. Here, we developed a Monte Carlo model to connect the single-droplet based model to the ensemble effect of multiple droplet impacts in real cleaning experiments, and thereby predict the PREs from the impaction conditions of droplets and the diameters of target particles. Additionally, we developed a two-fluid supersonic nozzle system, capable of spraying 10-60 ㎛ droplets under control of impact velocity, with aims to validate the model predictions of PREs for 15-130 nm contaminant particles on a Si wafer. We confirmed that the model predictions are in agreement with the experimental data within 7% and the cleaning time needs to be controlled for ensuring the efficient removal of particles.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1369-1373
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• 2014

Objectives: To study application of the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) with $^{18}F$-FDG PET/CT for predicting prognosis of esophageal squamous cell cancer (ESC) patients. Methods: Eighty-six patients with ESC staged from I to IV were prospectively enrolled. Cisplatin-based chemoradiotherapy (CCRT) or palliative chemoradiotherapy were the main treatment methods and none received surgery. $^{18}F$-FDG PET/CT scans were performed before the treatment. SUVmax, MTV, and TLG were measured for the primary esophageal lesion and regional lymph nodes. Receiver operating characteristic curves (ROCs) were generated to calculate the P value of the predictive ability and the optimal threshold. Results: MTV and TLG proved to be good indexes in the prediction of outcome for the ESC patients. An MTV value of 15.6 ml and a TLG value of 183.5 were optimal threshold to predict the overall survival (OS). The areas under the curve (AUC) for MTV and TLG were 0.74 and 0.70, respectively. Kaplan-Meier analysis showed an MTV less than 15.6 ml and a TLG less than 183.5 to indicate good media survival time (p value <0.05). In the stage III-IV patient group, MTV could better predict the OS (P < 0.001), with a sensitivity and specificity of 0.80 and 0.67, respectively. Conclusions: Pre-treatment MTV and TLG are useful prognostic factors in nonsurgical ESC.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.76-92
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• 2018

This study aimed to establish database of rainfall inundation area by rainfall scenarios and conduct a real time prediction for urban flood mitigation. the data leaded model was developed for the mapping of inundated area with rainfall forecast data provided by korea meteorological agency. for the construction of data leaded model, 1d-2d modeling was applied to Gangnam area, where suffered from severe flooding event including september, 2010. 1d-2d analysis result agree with observed in term of flood depth. flood area and flood occurring report which maintained by NDMS(national disaster management system). The fitness ratio of the NDMS reporting point and 2D flood analysis results was revealed to be 69.5%. Flood forecast chart was created using pre-flooding database. It was analyzed to have 70.3% of fitness in case of flood forecast chart of 70mm, and 72.0% in case of 80mm flood forecast chart. Using the constructed pre-flood area database, it is possible to present flood forecast chart information with rainfall forecast, and it can be used to secure the leading time during flood predictions and warning.

• Journal of Korean Neurosurgical Society
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• v.63 no.6
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• pp.827-833
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• 2020

Objective : Spine surgery is associated with higher morbidity and mortality rates in elderly patients. The modified Frailty Index (mFI) is an evaluation tool to determine the frailty of an individual and how preoperative status may impact postoperative survival and outcomes. This study aimed to determine the usefulness of mFI in predicting postoperative complications in patients aged ≥75 years undergoing surgery with instrumentation. Methods : We retrospectively reviewed the perioperative course of 137 patients who underwent thoracolumbar-instrumentation spine surgery between 2011 and 2016. The preoperative risk factors were the 11 variables of the mFI, as well as body mass index (kg/㎠), preoperative hemoglobin, platelet, albumin, creatinine, anesthesia time, operation time, estimated blood loss, and transfusion amount. The 60-day occurrences of complication rates were used for outcome assessment. Results : Major complications after spinal instrumentation surgery occurred in 34 of 138 patients (24.6%). The mean mFI score was 0.18±0.12. When we divided patients into a pre-frail group (mFI, 0.09-0.18; n=94) and a frail group (mFI ≥0.27; n=44), only the rate of sepsis was statistically higher in the frail group than in the pre-frail group. There were significantly more major complications in patients with low albumin levels or in patients with infection or who had experienced trauma. The mFI was a more useful predictor of postoperative complications than the American Society of Anesthesiologists physical status score. Conclusion : The mFI can successfully predict postoperative morbidity and mortality in patients aged ≥75 years undergoing spine surgery. The mFI improves perioperative risk stratification that provides important information to assist in the preoperative counselling of patients and their families.

• Tunnel and Underground Space
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• v.23 no.1
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• pp.13-30
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• 2013

Increase of pore fluid pressure resulting from injection of $CO_2$ may reactivate pre-existing faults, and the induced seismic activities can raise the safety issues such as seal integrity, restoration of storage capacity, and, in the worst case, removal of previously injected $CO_2$. Thus, fault stability and potential for $CO_2$ leakage need to be assessed at the stage of site selection and planning of injection pressure, based on the results of large-scale site investigations and numerical modeling for various scenarios. In this report, studies on the assessment of fault stability during injection of $CO_2$ were reviewed. The seismic activities associated with an artificial injection of fluids or a release of naturally trapped high-pressure fluids were first examined, and then site investigation methods for the magnitude and orientation of in situ stresses, the distribution and change of pore fluid pressure, and the location of faults were generally summarized. Recent research cases on possibility estimation of fault reactivation, prediction of seismic magnitude, and modeling of $CO_2$ leakage through a reactivated fault were presented.

• The Korean Journal of Nuclear Medicine
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• v.29 no.1
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• pp.22-30
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• 1995

To predict preoperatively the safety of permanent occlusion of an internal carotid artery with $^{99m}Tc$-HMPAO brain single photon emission computed tomography(SPECT) from an objective point of view, Twenty-four patients underwent balloon test occlusion (BTO) of the internal carotid arteries because of neck and skull base tumors. The authors assessed the uptake of both middle cerebral artery territories before and during BTO with $^{99m}Tc$-HMPAO brain SPECT using semiquantitative analysis method and compared the results with other factors(neurologic examination, arterial stump pressure and electroenceph-alogram). Nineteen patients had not experienced neurological deteriorating or any problem during BTO. Their comparative uptakes of the middle cerebral artery territories were 95 to 101% of the pre-BTO state. The remaining five patients showed severe neurologic symptoms such as transient hemiplegia and unconsciousness. Their comparative uptake of the middle cerebral artery territories were 77 to 85% of the pre-BTO state, and were well matched with other factors. $^{99m}Tc$-HMPAO brain SPECT before and during BTO seems to be a simple and objective method for prediction of permanent neurologic deficits when the comparative uptake of middle cerebral artery territories during BTO is lower than 85% of that before BTO.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.2
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• pp.148-155
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• 2005

An accurate prediction of blooming date is crucial for many authorities to schedule and organize successful spring flower festivals in Korea. The Korea Meteorological Administration (KMA) has been using regression models combined with a subjective correction by forecasters to issue blooming date forecasts for major cities. Using mean monthly temperature data for February (observed) and March (predicted), they issue blooming date forecasts in late February to early March each year. The method has been proved accurate enough for the purpose of scheduling spring festivals in the relevant cities, but cannot be used in areas where no official climate and phenology data are available. We suggest a thermal time-based two-step phenological model for predicting the blooming dates of spring flowers, which can be applied to any geographic location regardless of data availability. The model consists of two sequential periods: the rest period described by chilling requirement and the forcing period described by heating requirement. It requires daily maximum and minimum temperature as an input and calculates daily chill units until a pre-determined chilling requirement for rest release. After the projected rest release date, it accumulates daily heat units (growing degree days) until a pre- determined heating requirement for flowering. Model parameters were derived from the observed bud-burst and flowering dates of cherry tree (Prunus serrulata var. spontanea) at KMA Seoul station along with daily temperature data for 1923-1950. The model was applied to the 1955-2004 daily temperature data to estimate the cherry blooming dates and the deviations from the observed dates were compared with those predicted by the KMA method. Our model performed better than the KMA method in predicting the cherry blooming dates during the last 50 years (MAE = 2.31 vs. 1.58, RMSE = 2.96 vs. 2.09), showing a strong feasibility of operational application.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.368-378
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• 2021

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.