• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.1
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• pp.1-12
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• 2016

When utilizing a Tunnel Boring Machine (TBM) for tunnelling work, unexpected ground conditions can be encountered that are not predicted in the design stage. These include fractured zones or mixed ground conditions that are likely to reduce the stability of TBM excavation, and result in considerable economic losses such as construction delays or increases in costs. Minimizing these potential risks during tunnel construction is therefore a crucial issue in any mechanized tunneling project. This paper proposed the potential risk events that may occur due to risky ground conditions. A resistivity survey is utilized to predict the risky ground conditions ahead of the tunnel face during construction. The potential risk events are then evaluated based on their occurrence probability and impact. A TBM risk management system that can suggest proper solution methods (measures) for potential risk events is also developed. Multi-Criterion Decision Making (MCDM) is utilized to determine the optimal solution method (optimal measure) to handle risk events. Lastly, an actual construction site, at which there was a risk event during Earth Pressure-Balance (EPB) Shield TBM construction, is analyzed to verify the efficacy of the proposed system.

• 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.

• The KIPS Transactions:PartA
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• v.19A no.2
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• pp.77-84
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• 2012

Speculative execution for improving instruction-level parallelism is widely used in high-performance processors. In the speculative execution technique, the most important factor is the accuracy of branch predictor. Unfortunately, complex branch predictors for improving the accuracy can cause serious thermal problems in 3D multicore processors. Thermal problems have negative impact on the processor performance. This paper analyzes two methods to solve the thermal problems in the branch predictor of 3D multi-core processors. First method is dynamic thermal management which turns off the execution of the branch predictor when the temperature of the branch predictor exceeds the threshold. Second method is thermal-aware branch predictor placement policy by considering each layer's temperature in 3D multi-core processors. According to our evaluation, the branch predictor placement policy shows that average temperature is $87.69^{\circ}C$, and average maximum temperature gradient is $11.17^{\circ}C$. And, dynamic thermal management shows that average temperature is $89.64^{\circ}C$ and average maximum temperature gradient is $17.62^{\circ}C$. Proposed branch predictor placement policy has superior thermal efficiency than the dynamic thermal management. In the perspective of performance, the proposed branch predictor placement policy degrades the performance by 3.61%, while the dynamic thermal management degrades the performance by 27.66%.

• Journal of Korean society of Dental Hygiene
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• v.13 no.1
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• pp.69-81
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• 2013

Objectives : The purpose of this study was to examine whether the subjective oral health awareness and oral health behavior of Korean adults would affect their oral health indexes. It's meant to utilize existing data of epidemiological and alternative indexes in an effort to have a comprehensive and understanding of the relationship between the subjective oral health awareness and oral health behavior of Korean adults. And the following findings were obtained Methods : The subjects in this study were 7,285 adults who were selected from the third-year(2009) raw data of the fourth national health & nutrition survey. Results : As for the relationship between oral health awareness and oral health indexes, there were statistically significant differences in DMFT index, FS-T index, T-health index and CPI index according to self-rated health status, self-rated oral health state, necessity of dental treatment and oral health concern. Concerning the relationship between oral health behavior and the oral health indexes, whether they got a dental checkup over the past year, daily toothbrushing frequency, use or nonuse of oral health supplies and mastication problems made statistically significant differences to DMFT index, FS-T index, T-health index and CPI index. The variables that had a significant impact on oral health were selected from among the variables of oral health awareness and oral health behavior that affected oral health, and the variables were selected as independent variables. And then the oral health indexes were selected as dependent variables, and a multiple regression analysis was carried out by using the selected independent and dependent variables. As a results, it's found that the variables made a 22.4% prediction of DMFT index; 51.3% for FS-T index; 52.0% for T-health index; 47.4% for CPI index. Conclusions : The above-mentioned findings illustrated that the relationship between the subjective oral health awareness and oral health behavior of the Korean adults exercised an influence on their oral health indexes. Accurate and effective oral health plans should be mapped out by grasping the oral health status of adults from diverse angles to facilitate the maintenance and promotion of their oral health status.

• Food Science and Preservation
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• v.16 no.1
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• pp.1-7
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• 2009

Watermelons (Citrulus vulgaris Schrad) are usually sorted manually by weight, appearance, and acoustic impulse, so grading of maturity and internal quality is subject to inaccuracies. It was necessary to develop a nondestructive evaluation technique of internal watermelon quality to reduce human error. Thus, acoustic characteristics related to internal quality factors were analyzed. Among these factors, three (ripeness, presence of an internal cavity, and blood-colored flesh) were selected for evaluation. The number of peaks and the sum of peak amplitudes for watermelons with blood-colored flesh were lower than for normal fruits. The portable evaluation system has an impact mechanism, a microphone sensor, a signal processing board, an LCD panel, and a battery. A performance test was conducted in the field. The internal quality evaluation model showed 87% prediction accuracy. Validation was conducted on 72 samples. The accuracy of quality evaluation was 83%. The quality of samples was evaluated by an inspector using conventional methods (hitting the watermelon and listening to the sounds), and then compared with prototype results. The quality evaluation accuracy of the prototype was better than that of the inspector. This nondestructive quality evaluation system could be useful in the field, warehouse, and supermarket

• Spatial Information Research
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• v.15 no.3
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• pp.267-278
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• 2007

Internationally many models are developed and applied to predict the impact of the climate change, as occurring a lot of symptoms by climate change. Also, in Korea, according to increasing the application of the climate effect model in many research fields, it is required to study the method for preparing climate data and the characteristics of the climate. In this study IDSW (Inverse Distance Squared Weighting), one of the spatial statistic methods, is applied to interpolate. This method estimates a point of interest by assigning more weight to closer points, which are limited to be select by 3 in 100 km radius. As a result, annual average temperature and precipitation had increased by $0.4^{\circ}C$ and 412 mm during 1977 to 2006. They are also predicted to increase by $3.96^{\circ}C$, 319 mm in the 2100 compared to 2007. High variability of temperature and precipitation for last 30 years shows in some part of the Gangwon-do and in the southern part of Korea. Besides in the study of the variable trend, the variability of temperature and precipitation is inclined to increase in Gangwon-do and southern east part, respectively. However, during 2071 to 2100 variability of temperature is predicted to be high in midwest of Korea and variability of precipitation in the east. In the trend of variability, variability of temperature is apt to increase into west south, and variability of precipitation increase in midwest and a part of south.

• The Journal of Engineering Geology
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• v.27 no.2
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• pp.153-164
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• 2017

The geological factors for causing ground subsidence are very diverse. It can be affected by any geological or extrinsic influences, and even within the same geological factor, the soil depression impact factor can be determined by different physical properties. As a result of reviewing a large number of papers and case histories, it can be seen that there are seven categories of ground subsidence factors. The depth and thickness of the overburden can affect the subsidence depending on the existence of the cavity, whereas the depth and orientation of the boundary between soil and rock are dominant factors in the ground composed of soil and rock. In case of soil layers, more various influencing factors exist such as type of soil, shear strength, relative density and degree of compaction, dry unit weight, water content, and liquid limit. The type of rock, distance from the main fracture and RQD can be influential factors in the bedrock. When approaching from the hydrogeological point of view, the rainfall intensity, the distance and the depth from the main channel, the coefficient of permeability and fluctuation of ground water level can influence to ground subsidence. It is also possible that the ground subsidence can be affected by external factors such as the depth of excavation and distance from the earth retaining wall, groundwater treatment methods at excavation work, and existence of artificial facilities such as sewer pipes. It is estimated that to evaluate the ground subsidence factor during the construction of underground structures in urban areas will be essential. It is expected that ground subsidence factors examined in this study will contribute for the reliable evaluation of the ground subsidence risk.

• Journal of Yeungnam Medical Science
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• v.38 no.4
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• pp.337-343
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• 2021

Background: Chromogranin A (CgA) levels have been reported to predict mortality in patients with heart failure. However, information on the prognostic value and clinical availability of CgA is limited. We compared the prognostic value of CgA to that of previously proven natriuretic peptide biomarkers in patients with acute heart failure. Methods: We retrospectively evaluated 272 patients (mean age, 68.5±15.6 years; 62.9% male) who underwent CgA test in the acute stage of heart failure hospitalization between June 2017 and June 2018. The median follow-up period was 348 days. Prognosis was assessed using the composite events of 1-year death and heart failure hospitalization. Results: In-hospital mortality rate during index admission was 7.0% (n=19). During the 1-year follow-up, a composite event rate was observed in 12.1% (n=33) of the patients. The areas under the receiver-operating characteristic curves for predicting 1-year adverse events were 0.737 and 0.697 for N-terminal pro-B-type natriuretic peptide (NT-proBNP) and CgA, respectively. During follow-up, patients with high CgA levels (>158 pmol/L) had worse outcomes than those with low CgA levels (≤158 pmol/L) (85.2% vs. 58.6%, p<0.001). When stratifying the patients into four subgroups based on CgA and NT-proBNP levels, patients with high NT-proBNP and high CgA had the worst outcome. CgA had an incremental prognostic value when added to the combination of NT-proBNP and clinically relevant risk factors. Conclusion: The prognostic power of CgA was comparable to that of NT-proBNP in patients with acute heart failure. The combination of CgA and NT-proBNP can improve prognosis prediction in these patients.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.57-67
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• 2024

This research investigates training methods for large language models to accurately identify sentiments and comprehend information about increasing and decreasing fluctuations in the financial domain. The main goal is to identify suitable datasets that enable these models to effectively understand expressions related to financial increases and decreases. For this purpose, we selected sentences from Wall Street Journal that included relevant financial terms and sentences generated by GPT-3.5-turbo-1106 for post-training. We assessed the impact of these datasets on language model performance using Financial PhraseBank, a benchmark dataset for financial sentiment analysis. Our findings demonstrate that post-training FinBERT, a model specialized in finance, outperformed the similarly post-trained BERT, a general domain model. Moreover, post-training with actual financial news proved to be more effective than using generated sentences, though in scenarios requiring higher generalization, models trained on generated sentences performed better. This suggests that aligning the model's domain with the domain of the area intended for improvement and choosing the right dataset are crucial for enhancing a language model's understanding and sentiment prediction accuracy. These results offer a methodology for optimizing language model performance in financial sentiment analysis tasks and suggest future research directions for more nuanced language understanding and sentiment analysis in finance. This research provides valuable insights not only for the financial sector but also for language model training across various domains.