• Journal of the Korean Geotechnical Society
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• v.32 no.1
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• pp.45-53
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• 2016

Generally, conventional transport infrastructures consist of compacted granular materials. Their stiffness and response greatly depend on the particle sizes and distributions, and application of loading on the surface over a foundation may induce deformation in both the surface and the underlying foundations. Therefore, a better understanding of the deformation characteristics on granular materials and the prediction are needed. For this reason, an attempt to evaluate and predict deformation of coarse materials based on the discrete element method is presented in this paper. An algorithm for particle distribution curve analysis was formulated and incorporated into the discrete element program. The results show that the discrete element model with particle distribution curve is suitable for estimating stress deformation in a pre-peak response. Unlike conventional uniform or random particle distribution, the response can be obtained by the use of the proper model and approach.

• Journal of the Korean Geotechnical Society
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• v.25 no.1
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• pp.5-19
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• 2009

Most of earthquake-induced geotechnical hazards have been caused by the site effects relating to the amplification of ground motion, which is strongly influenced by the local geologic conditions such as soil thickness or bedrock depth and soil stiffness. In this study, an integrated GIS-based information system for geotechnical data, called geotechnical information system (GTIS), was constructed to establish a regional counterplan against earthquake-induced hazards at an urban area of Daejeon, which is represented as a hub of research and development in Korea. To build the GTIS for the area concerned, pre-existing geotechnical data collections were performed across the extended area including the study area and site visits were additionally carried out to acquire surface geo-knowledge data. For practical application of the GTIS used to estimate the site effects at the area concerned, seismic zoning map of the site period was created and presented as regional synthetic strategy for earthquake-induced hazards prediction. In addition, seismic zonation for site classification according to the spatial distribution of the site period was also performed to determine the site amplification coefficients for seismic design and seismic performance evaluation at any site in the study area. Based on this case study on seismic zonations in Daejeon, it was verified that the GIS-based GTIS was very useful for the regional prediction of seismic hazards and also the decision support for seismic hazard mitigation.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• The korean journal of orthodontics
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• v.53 no.4
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• pp.276-285
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• 2023

Objective: The aim of this retrospective study was to compare changes in hard tissue and soft tissue after the four first premolars were extracted with anterior teeth retraction according to the presence or absence of lip incompetence. Methods: Patients who underwent the four first premolars were extracted with anterior teeth retraction were divided into competent (n = 20) and incompetent lip (n = 20) groups. Cephalometric measurements for hard tissue and soft tissue changes were performed pre-treatment and post-treatment. Results: In the competent group, the upper and lower lips retreated by 2.88 mm and 4.28 mm, respectively, and in the incompetent group by 4.13 mm and 5.57 mm, respectively; the differences between the two groups were significant (p < 0.05). A strong positive correlation between retraction of the upper lip and upper incisors was observed in both groups (p < 0.05), whereas a correlation between retraction of the lower lip and lower incisors was only found in the incompetent group. A simple linear regression analysis showed that the pattern of lip retraction following the retraction of the anterior teeth was more predictable in the incompetent group than in the competent group. Conclusions: These findings suggest that the initial evaluation of lip incompetence in patients with skeletal Class II is essential for the accurate prediction of the soft tissue changes following retraction of the anterior teeth in premolar extraction treatment. Therefore, sufficient explanation should be provided during patient consultations.

• Journal of the Korean Society of Urban Environment
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• v.18 no.4
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• pp.401-408
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• 2018

EQPS (Effluent Quality Prediction System, Dynamita, France) was applied to analyze the appropriateness of the design of a bioreactor in A sewage treatment plant. A sewage treatment plant was designed by setting the design concentration of the secondary clarifier effluent to total nitrogen and total phosphorus, 10 mg/L and 1.8 mg/L, respectively, in order to comply with the target water quality at the level of the hydrophilic water. The retention time of the 4-stage BNR reactor was 9.6 hours, which was 0.5 for the pre-anoxic tank, 1.0 for the anaerobic tank, 2.9 for the anoxic tank, and 5.2 hours for the aerobic tank. As a result of the modeling of the winter season, the retention time of the anaerobic tank was increased by 0.2 hours in order to satisfy the target water quality of the hydrophilic water level. The default coefficients of the one step nitrification denitrification model proposed by the software manufacturer were used to exclude distortion of the modeling results. Since the process modeling generally presents optimal conditions, the retention time of the 4-stage BNR should be increased to 9.8 hours considering the bioreactor margin. The accurate use of process modeling in the design stage of the sewage treatment plant is a way to ensure the stability of the treatment performance and efficiency after construction of the sewage treatment plant.

• 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.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.1-11
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• 2020

Article 325 (Prevention of Fire Explosion due to Electrostatic) of the Rule for Occupational Safety and Health Standard specifies that in order to prevent the risk of disasters caused by static electricity, fire, explosion and static electricity in the production process, However, in order to do this, it is absolutely necessary to use a pre-detection technology and a detector for antistatic discharge prediction, which is a precautionary measure by static electricity in a fire / explosion hazard place, but in Korea, And there is no technical standard for the application of the technology of the explosion proof structure of the related equipment. Research methods include domestic and overseas electrostatic discharge detection technology and literature investigation of related equipment explosion proofing technology, domestic and foreign electrostatic discharge detection device production and use situation investigation, advanced foreign technology data analysis and benchmarking. In particular, we sought to verify the results of empirical experiments using electrostatic discharge detection technology through sample purchase and analysis of related major products, development of optimization technology through prototype production, evaluation, and supplementation, and expert knowledge through expert consultation. The results of this study were developed and fabricated two prototypes of electrostatic discharge detector based on the technology / standard related to electrostatic discharge detection technology in Korea and abroad through development of electrostatic discharge detection technology and development and production of detector. In addition, based on the development of electrostatic discharge detection technology, we developed an intrinsic safety explosion proof ib class explosion proof technology applicable to the process of using and handling flammable gas and flammable liquid vapor and combustible dust. In the case of the over voltage and minimum voltage are supplied to the explosion-proof structure ESD detector, check the state of the circuit and the transient and transient currents generated by the coil and capacitor elements during the input and standby of the signal pulse voltage. Explosion-proof equipment-Part 11: Intrinsically safe explosion proof structure The comparative evaluation with the reference curve in Annex A of "i" confirms that the characteristics of the intrinsically safe explosion protection structure are met.

• Journal of Animal Science and Technology
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• v.55 no.1
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• pp.13-18
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• 2013

This study was conducted to estimate the Genomic Estimated Breeding Value (GEBV) using Genomic Best Linear Unbiased Prediction (GBLUP) method in Hanwoo (Korean native cattle) population. The result is expected to adapt genomic selection onto the national Hanwoo evaluation system. Carcass weight (CW), eye muscle area (EMA), backfat thickness (BT), and marbling score (MS) were investigated in 552 Hanwoo progeny-tested steers at Livestock Improvement Main Center. Animals were genotyped with Illumina BovineHD BeadChip (777K SNPs). For statistical analysis, Genetic Relationship Matrix (GRM) was formulated on the basis of genotypes and the accuracy of GEBV was estimated with 10-fold Cross-validation method. The accuracies estimated with cross-validation method were between 0.915~0.957. In 534 progeny-tested steers, the maximum difference of GEBV accuracy compared to conventional EBV for CW, EMA, BT, and MS traits were 9.56%, 5.78%, 5.78%, and 4.18% respectively. In 3,674 pedigree traced bulls, maximum increased difference of GEBV for CW, EMA, BT, and MS traits were increased as 13.54%, 6.50%, 6.50%, and 4.31% respectively. This showed that the implementation of genomic pre-selection for candidate calves to test on meat production traits could improve the genetic gain by increasing accuracy and reducing generation interval in Hanwoo genetic evaluation system to select proven bulls.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2527-2530
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• 2015

Background: The cancer of the prostate risk assessment (CAPRA) score has been defined to predict prostate cancer recurrence based on the pre-clinical data, then pathological data have also been incorporated. Thus, CAPRA post-surgical (CAPRA-S) score has been developed based on six criteria (prostate specific antigen (PSA) at diagnosis, pathological Gleason score, and information on surgical margin, seminal vesicle invasion, extracapsular extension and lymph node involvement) for the prediction of post-surgical recurrences. In the present study, biochemical recurrence (BCR)-free probabilities after open retropubic radical prostatectomy (RP) were evaluated by the CAPRA-S scoring system and its three-risk level model. Materials and Methods: CAPRA-S scores (0-12) of our 240 radical prostatectomies performed between January 2000-May 2011 were calculated. Patients were distributed into CAPRA-S score groups and also into three-risk groups as low, intermediate and high. BCR-free probabilities were assessed and compared using Kaplan-Meier analysis and Cox proportional hazards regression. Ability of CAPRA-S in BCR detection was evaluated by concordance index (c-index). Results: BCR was present in 41 of total 240 patients (17.1%) and the mean follow-up time was $51.7{\pm}33.0$ months. Mean BCR-free survival time was 98.3 months (95% CI: 92.3-104.2). Of the patients in low, intermediate and high risk groups, 5.4%, 22.0% and 58.8% had BCR, respectively and the difference among the three groups was significant (P = 0.0001). C-indices of CAPRA-S score and three-risk groups for detecting BCR-free probabilities in 5-yr were 0.87 and 0.81, respectively. Conclusions: Both CAPRA-S score and its three-risk level model well predicted BCR after RP with high c-index levels in our center. Therefore, it is a clinically reliable post-operative risk stratifier and disease recurrence predictor for prostate cancer.