• Journal of Institute of Control, Robotics and Systems
• /
• 제11권2호
• /
• pp.93-102
• /
• 2005

The Nyquist robust stability margin is proposed as a measure of robust stability for systems with Affine TFM(Transfer Function Matrix) parametric uncertainty. The parametric uncertainty is modeled through a Affine TFM MIMO (Multi-Input Multi-Output) description with dead-time, and the unstructured uncertainty through a bounded perturbation of Affine polynomials. Gershgorin's theorem and concepts of diagonal dominance and GB(Gershgorin Bands) are extended to include model uncertainty. Multiloop PI/PID controllers can be tuned by using a modified version of the Ziegler-Nichols (ZN) relations. Consequently, this paper provides sufficient conditions for the robustness of Affine TFM MIMO uncertain systems with dead-time based on Rosenbrock's DNA. Simulation examples show the performance and efficiency of the proposed multiloop design method for Affine uncertain systems with dead-time.

• Proceedings of the Korea Information Processing Society Conference
• /
• 한국정보처리학회 2023년도 추계학술발표대회
• /
• pp.453-455
• /
• 2023

본 연구는 은행에서 리스크 관리 자동화를 위해 고객의 대출 상환 여부 예측 모델을 제안하고자 한다. 예측 모델로 금융 데이터 같은 정형데이터에서 전통적으로 높은 성능을 보인 의사결정나무기반 모델 LightGBM, CatBoost, XGB 와 최근 제안된 정형데이터에서 사용할 수 있는 설명 가능한 딥러닝 기반 모델 TabNet 간의 성능 비교를 진행한다. 다만, 대출 상환 여부 데이터는 불균형 클래스 데이터로 구성되어있어 샘플링을 진행한다. SMOTE, Random Under Sampling, 혼합 방식을 비교해 가장 높은 성능의 샘플링 기법을 제안한다. 대출 상환 여부 예측 결과 TabNet 모델이 의사결정나무모델들보다 좋은 성능을 보여 정형데이터에서 의사결정나무 기반 모델을 딥러닝 모델이 대체 할 수 있는 가능성을 확인했다.

• Radiation Oncology Journal
• /
• 제14권3호
• /
• pp.181-189
• /
• 1996

Purpose : To find the more effective treatment methods that improving the survival of patients with glioblastoma multiforme(GBM), we analyze the prognostic factors and the outcome of therapy in patients with GBM. Materials and Methods : One hundred twently-one patients with a diagnosis of GBM treated at Severance Hospital between 1973 and 1993 were analyzed for survival with respect to patients characteristics, that is, duration of symptom, age, and Karnofsky performance status, as well as treatment related variables such as extent of surgery and radiotherapy. Results : The median survival time(MST) and 2-year overall survival rate (OSR) of the patients with GBM were 13 months and $20.8\%$, respectively. Duration of symptom, age, Karnofsky performance status(KPS), radiotherapy, and extent of surgical resection were associated with improved survial in a univariate analysis. Patients whose duration of symptom was longer than 3 months, had the 2-year OSR of $47.2\%$(p=0.0082), who were younger than age 50, $32.9\%$(p=0.0003) In patients with a KPS of 80 or higher, the 2-rear OSR was $36.9\%$(p=0.0422). Patients undergoing radiotherapy had the 2-year OSR of $22.9\%$(p=0.0030), and surgical resection of $23.3\%$ (p<0.000). A Cox regression model confirmed a significant correlation of duration of symptom, age, radiotherapy, and extent of surgical resection with survival, excluding KPS(P=0.8823). The 2-year OSR were $22.3\%$ and $19.4\%$, combined with chemotherapy or without, respectively(p=0.6028). The duration of symptom of 3 months or shorter, 50 years of age or older, and undergoing stereotactic biopsy only were considered as risk factors, then patients without any risk factors had the MST of 29 months and 2-year OSR of $53.9\%$ compared to 4 months and $0\%$ for Patients who had all 3 risk factors. Most of all treatment failures occurred in the primary tumor site($80.4\%$). Conclusion : The duration of symptom, age, radiotherapy, and extent of surgical resection were a prognostically significant indeuendent variables. To get a better survival, it seems to be reasonable that the study design which improves the local control rates is warranted.

• The Journal of Internal Korean Medicine
• /
• 제29권4호
• /
• pp.1083-1099
• /
• 2008

Objective : We aimed to identify the effects of Bogimakseong-bang(BGMSB) treatment on cBSA-induced MN in a mouse model. Methods : We divided 20 mice into 4 groups. The normal group (NR) had no treatment. We used cBSA to induced MN to the other 3 groups. One group (CT) was treated with cBSA (7mg/kg i.p) only. The second (BG-250) was treated with cBSA (7mg/kg i.p) and BGMSB extract (250mg/kg, p.o). The third group (BG-500) was treated with cBSA(7mg/kg i.p) and BGMSB extract (500mg/kg, p.o). After cBSA and BGMSB extract treatment for 4 weeks, proteinuria, serum albumin, total cholesterol, serum creatinine, BUN, total nucleated spleen cell number and total infiltrated kidney cell number of all groups were measured. CD3e+/CD19+ and CD4+/CD8 cells ratio of peripheral blood, kidney and spleen of all groups were analyzed. $IL-1{\beta}$ and $TNF-{\alpha}$, IL-6, IgG, IgM, and $IFN-{\gamma}$ levels of all groups were gauged. Histological analysis of kidney tissue and immunohistochemical staining (CD4 CD8) of kidneys were observed. Results : Proteinuria significantly decreased and serum albumin increased in groups treated with cBSA and BGMSB extract compared with the control. Total cholesterol decreased but not significantly. CD3e+/CD19cells ratio of peripheral blood decreased. CD3e+/CD19+ and CD4+/CD8 cells percentage of kidney and spleen showed no significant change. Level of $IL-1{\beta}$, $TNF-{\alpha}$ and IL-6 significantly decreased. and $IFN-{\gamma}$ increased but has not significantly. Concentration of IgG and IgM significantly decreased compared with control. Thickness of GBM decreased on histological analysis of kidney. Deposition of CD4 and CD8 decreased on immunohistochemical staining of kidney. Conclusions : According to the above result, BGMSB had a significant effect for treating MN which is cBSA-induced.

• Journal of the Korea institute for structural maintenance and inspection
• /
• 제18권6호
• /
• pp.60-71
• /
• 2014

Cable-stayed bridge, which is one of the representative long-spanned bridge, needs prompt maintenances when a stay cable is damaged because it may cause structural failure of the entire bridge. Many researches are being conducted to develop abnormal behavior detection algorithms for the purpose of shortening the reaction time after the occurrence of structural damage. To improve the accuracy of the damage detection algorithm, ample observation data from various kinds of damage responses is needed. However, it is difficult to measure an abnormal response by damaging an existing bridge, numerical simulation can be an effective alternative. In most previous studies, which simulate the damage responses of a cable-stayed bridge, the damages has been considered as a load variation without regard to its stiffness variation. The analyses of using these simplification could not calculate exact responses of damaged structure, though it may reserve a sufficient accuracy for the purpose of bridge design. This study suggests Gradual Bilinear Method (GBM) which simulate the damage responses of cable-stayed bridge considering the stiffness and mass variation, and develops an analysis program. The developed program is verified from the responses of a simple model. The responses of a existing cable-stayed bridge model are analyzed with respect to the fracture delay time and damage ratio. The results of this study can be used to develop and verify the highly accurate abnormal behavior detection algorithm for safety management of architecture/large structures.

• The Korean Journal of Applied Statistics
• /
• 제37권3호
• /
• pp.323-338
• /
• 2024

This study analyzes the Korean professional volleyball league and predict match outcomes using popular machine learning classification methods. Match data from the 2012/2013 to 2022/2023 seasons for both male and female leagues were collected, including match details. Two different data structures were applied to the models: Separating matches results into two teams and performance differentials between the home and away teams. These two data structures were applied to construct a total of four predictive models, encompassing both male and female leagues. As specific variable values used in the models are unavailable before the end of matches, the results of the most recent 3 to 4 matches, up until just before today's match, were preprocessed and utilized as variables. Logistc Regrssion, Decision Tree, Bagging, Random Forest, Xgboost, Adaboost, and Light GBM, were employed for classification, and the model employing Random Forest showed the highest predictive performance. The results indicated that while significant variables varied by gender and data structure, set success rate, blocking points scored, and the number of faults were consistently crucial. Notably, our win-loss prediction model's distinctiveness lies in its ability to provide pre-match forecasts rather than post-event predictions.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• 제19권6호
• /
• pp.19-30
• /
• 2016

The projection of climate-related range shift is critical information for conservation planning of Korean fir (Abies koreana E. H. Wilson). We first modeled the distribution of Korean fir under current climate condition using five single-model species distribution models (SDMs) and the pre-evaluation weighted ensemble method and then predicted the distributions under future climate conditions projected with HadGEM2-AO under four $CO_2$ emission scenarios, the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. We also investigated the predictive uncertainty stemming from five individual algorithms and four $CO_2$ emission scenarios for better interpretation of SDM projections. Five individual algorithms were Generalized linear model (GLM), Generalized additive model (GAM), Multivariate adaptive regression splines (MARS), Generalized boosted model (GBM) and Random forest (RF). The results showed high variations of model performances among individual SDMs and the wide range of diverging predictions of future distributions of Korean fir in response to RCPs. The ensemble model presented the highest predictive accuracy (TSS = 0.97, AUC = 0.99) and predicted that the climate habitat suitability of Korean fir would increase under climate changes. Accordingly, the fir distribution could expand under future climate conditions. Increasing precipitation may account for increases in the distribution of Korean fir. Increasing precipitation compensates the negative effects of increasing temperature. However, the future distribution of Korean fir is also affected by other ecological processes, such as interactions with co-existing species, adaptation and dispersal limitation, and other environmental factors, such as extreme weather events and land-use changes. Therefore, we need further ecological research and to develop mechanistic and process-based distribution models for improving the predictive accuracy.

• Journal of Climate Change Research
• /
• 제7권3호
• /
• pp.325-334
• /
• 2016

We projected the distribution of Hedera rhombea, an evergreen broad-leaved climbing plant, under current climate conditions and predicted its future distributions under global warming. Inaddition, weexplained model uncertainty by employing 9 single Species Distribution model (SDM)s to model the distribution of Hedera rhombea. 9 single SDMs were constructed with 736 presence/absence data and 3 temperature and 3 precipitation data. Uncertainty of each SDM was assessed with TSS (Ture Skill Statistics) and AUC (the Area under the curve) value of ROC (receiver operating characteristic) analyses. To reduce model uncertainty, we combined 9 single SDMs weighted by TSS and resulted in an ensemble forecast, a TSS weighted ensemble. We predicted future distributions of Hedera rhombea under future climate conditions for the period of 2050 (2040~2060), which were estimated with HadGEM2-AO. RF (Random Forest), GBM (Generalized Boosted Model) and TSS weighted ensemble model showed higher prediction accuracies (AUC > 0.95, TSS > 0.80) than other SDMs. Based on the projections of TSS weighted ensemble, potential habitats under current climate conditions showed a discrepancy with actual habitats, especially in the northern distribution limit. The observed northern boundary of Hedera rhombea is Ulsan in the eastern Korean Peninsula, but the projected limit was eastern coast of Gangwon province. Geomorphological conditions and the dispersal limitations mediated by birds, the lack of bird habitats at eastern coast of Gangwon Province, account for such discrepancy. In general, potential habitats of Hedera rhombea expanded under future climate conditions, but the extent of expansions depend on RCP scenarios. Potential Habitat of Hedera rhombea expanded into Jeolla-inland area under RCP 4.5, and into Chungnam and Wonsan under RCP 8.5. Our results would be fundamental information for understanding the potential effects of climate change on the distribution of Hedera rhombea.

• Geomechanics and Engineering
• /
• 제38권5호
• /
• pp.517-528
• /
• 2024

For numerous tunnelling projects implemented in urban areas due to limited space, it is crucial to take into account the interaction between the foundation, ground, and tunnel. In predicting the deformation of piled foundations and the ground during twin tunnel excavation, it is essential to consider various factors. Therefore, this study derived a prediction model for pile group settlement using machine learning to analyze the importance of various factors that determine the settlement of piled foundations during twin tunnelling. Laboratory model tests and numerical analysis were utilized as input data for machine learning. The influence of each independent variable on the prediction model was analyzed. Machine learning techniques such as data preprocessing, feature engineering, and hyperparameter tuning were used to improve the performance of the prediction model. Machine learning models, employing Random Forest (RF), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM, LGB) algorithms, demonstrate enhanced performance after hyperparameter tuning, particularly with LGB achieving an R² of 0.9782 and RMSE value of 0.0314. The feature importance in the prediction models was analyzed and P_N was the highest at 65.04% for RF, 64.81% for XGB, and P_CTC (distance between the center of piles) was the highest at 31.32% for LGB. SHAP was utilized for analyzing the impact of each variable. P_N (the number of piles) consistently exerted the most influence on the prediction of pile group settlement across all models. The results from both laboratory model tests and numerical analysis revealed a reduction in ground displacement with varying pillar spacing in twin tunnels. However, upon further investigation through machine learning with additional variables, it was found that the number of piles has the most significant impact on ground displacement. Nevertheless, as this study is based on laboratory model testing, further research considering real field conditions is necessary. This study contributes to a better understanding of the complex interactions inherent in twin tunnelling projects and provides a reliable tool for predicting pile group settlement in such scenarios.

• Journal of Wetlands Research
• /
• 제26권2호
• /
• pp.147-159
• /
• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.