• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.211-217
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• 2010

This paper presents numerical investigations of the physical habitat changes induced by the hydropeaking in the downstream river of dam. For the two-dimensional ecohydraulic simulations, River2D model is used. Pirami (Zacco platypus) is selected as the target fish for investigating the impact of the hydropeaking. For validation of the model, the water surface elevations are simulated with two different water discharges. The computed results are compared with field data in the literature, and the result shows that the model successfully simulates the water flows. The weight usable area (WUA) of Pirami with the life cycle and the composite suitability index with different water discharges are computed and discussed. The results show that habitat for Pirami appears to be best in the bend region downstream of the dam. The discharge of the maximum WUA for adult Pirami is computed to be about 9 $m^3/s$. Also, the WUA computed in a condition of hydropeaking during seven days are presented. The averaged discharge of the hydropeaking appears to be about 20% larger than the drought flow, but the WUA by the hydropeaking is computed to be 60-100% smaller. This result shows that the hydropeaking reduces quantity of habitat available to fish.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.547-560
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• 2023

Recently, research on predicting ground classification using machine learning techniques, TBM excavation data, and ground data is increasing. In this study, a multi-classification prediction study for uniaxial compressive strength (UCS) was conducted by applying random forest model based on a decision tree among machine learning techniques widely used in various fields to machine data and ground data acquired at three slurry shield TBM sites. For the classification prediction, the training and test data were divided into 7:3, and a grid search including 5-fold cross-validation was used to select the optimal parameter. As a result of classification learning for UCS using a random forest, the accuracy of the multi-classification prediction model was found to be high at both 0.983 and 0.982 in the training set and the test set, respectively. However, due to the imbalance in data distribution between classes, the recall was evaluated low in class 4. It is judged that additional research is needed to increase the amount of measured data of UCS acquired in various sites.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.79-90
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• 2024

This study focused on user trust in ChatGPT, a generative AI technology, and explored the factors that affect usage status and intention to continue using, and whether the influence of trust varies depending on the purpose. For this purpose, the survey was conducted targeting people in their 20s and 30s who use ChatGPT the most. The statistical analysis deploying IBM SPSS 27 and SmartPLS 4.0. A structural equation model was formulated on the foundation of Bhattacherjee's Expectation-Confirmation Model (ECM), employing path analysis and Multi-Group Analysis (MGA) for hypothesis validation. The main findings are as follows: Firstly, ChatGPT is mainly used for specific needs or objectives rather than as a daily tool. The majority of users are cognizant of its hallucination effects; however, this did not hinder its use. Secondly, the hypothesis testing indicated that independent variables such as expectation- confirmation, perceived usefulness, and user satisfaction all exert a positive influence on the dependent variable, the intention for continuance intention. Thirdly, the influence of trust varied depending on the user's purpose in utilizing ChatGPT. trust was significant when ChatGPT is used for information retrieval but not for creative purposes. This study will be used to solve reliability problems in the process of introducing generative AI in society and companies in the future and to establish policies and derive improvement measures for successful employment.

• Korean Journal of Radiology
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• v.24 no.12
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• pp.1190-1199
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• 2023

Objective: This study aimed to investigate the feasibility of ultrafast magnetic resonance imaging (MRI) and radiomic features derived from breast MRI for predicting the upstaging of ductal carcinoma in situ (DCIS) diagnosed using percutaneous needle biopsy. Materials and Methods: Between August 2018 and June 2020, 95 patients with 98 DCIS lesions who underwent preoperative breast MRI, including an ultrafast sequence, and subsequent surgery were included. Four ultrafast MRI parameters were analyzed: time-to-enhancement, maximum slope (MS), area under the curve for 60 s after enhancement, and time-to-peak enhancement. One hundred and seven radiomic features were extracted for the whole tumor on the first post-contrast T1WI and T2WI using PyRadiomics. Clinicopathological characteristics, ultrafast MRI findings, and radiomic features were compared between the pure DCIS and DCIS with invasion groups. Prediction models, incorporating clinicopathological, ultrafast MRI, and radiomic features, were developed. Receiver operating characteristic curve analysis and area under the curve (AUC) were used to evaluate model performance in distinguishing between the two groups using leave-one-out cross-validation. Results: Thirty-six of the 98 lesions (36.7%) were confirmed to have invasive components after surgery. Compared to the pure DCIS group, the DCIS with invasion group had a higher nuclear grade (P < 0.001), larger mean lesion size (P = 0.038), larger mean MS (P = 0.002), and different radiomic-related characteristics, including a more extensive tumor volume; higher maximum gray-level intensity; coarser, more complex, and heterogeneous texture; and a greater concentration of high gray-level intensity. No significant differences in AUCs were found between the model incorporating nuclear grade and lesion size (0.687) and the models integrating additional ultrafast MRI and radiomic features (0.680-0.732). Conclusion: High nuclear grade, larger lesion size, larger MS, and multiple radiomic features were associated with DCIS upstaging. However, the addition of MS and radiomic features to the prediction model did not significantly improve the prediction performance.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.772-783
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• 2023

Objective: Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma. Materials and Methods: This study included diffuse glioma cases from The Cancer Genome Atlas (training set: 141 patients) and Asan Medical Center (validation set: 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification: group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-codeleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model. Results: Both OS and PFS were stratified according to the five imaging-based risk subgroups (P < 0.001) and three imaging-based risk groups (P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889). Conclusion: Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.99-106
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• 2002

Researches on integration of design and construction information from earlier era focused on the conceptual data models. Development and prevalent use of commercial database management system led many researchers to design database schemas for enlightening of relationship between non-graphic data items. Although these researches became the foundation fur the proceeding researches. they did not utilize the graphic data providable from CAD system which is already widely used. 4D CAD concept suggests a way of integrating graphic data with schedule data. Although this integration provided a new possibility for integration, there exists a limitation in data dependency on a specific application. This research suggests a new approach on integration for design and construction information, 'Product Model Centered Integration Methodology'. This methodology achieves integration by preliminary research on existing methodology using 4D CAD concept. and by development and application of new integration methodology, 'Product Model Centered Integration Methodology'. 'Design Component' can be converted into digital format by object based CAD system. 'Unified Object-based Graphic Modeling' shows how to model graphic product model using CAD system. Possibility of reusing design information in latter stage depends on the ways of creating CAD model, so modeling guidelines and specifications are suggested. Then prototype system for integration management, and exchange are presented, using 'Product Frameworker', and 'Product Database' which also supports multiple-viewpoints. 'Product Data Model' is designed, and main data workflows are represented using 'Activity Diagram', one of UML diagrams. These can be used for writing programming codes and developing prototype in order to automatically create activity items in actual schedule management system. Through validation processes, 'Product Model Centered Integration Methodology' is suggested as the new approach for integration of design and construction information.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.322-340
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• 2021

Due to climate change and its consequential spring temperature rise, flowering time of Robinia pseudoacacia has advanced and a simultaneous blooming phenomenon occurred in different regions in South Korea. These changes in flowering time became a major crisis in the domestic beekeeping industry and the demand for accurate prediction of flowering time for R. pseudoacacia is increasing. In this study, we developed and compared performance of four different models predicting flowering time of R. pseudoacacia for the entire country: a Single Model for the country (SM), Modified Single Model (MSM) using correction factors derived from SM, Group Model (GM) estimating parameters for each region, and Local Model (LM) estimating parameters for each site. To achieve this goal, the bloom date data observed at 26 points across the country for the past 12 years (2006-2017) and daily temperature data were used. As a result, bloom dates for the north central region, where spring temperature increase was more than two-fold higher than southern regions, have advanced and the differences compared with the southwest region decreased by 0.7098 days per year (p-value=0.0417). Model comparisons showed MSM and LM performed better than the other models, as shown by 24% and 15% lower RMSE than SM, respectively. Furthermore, validation with 16 additional sites for 4 years revealed co-krigging of LM showed better performance than expansion of MSM for the entire nation (RMSE: p-value=0.0118, Bias: p-value=0.0471). This study improved predictions of bloom dates for R. pseudoacacia and proposed methods for reliable expansion to the entire nation.

• The Korean Journal of Archival Studies
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• no.16
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• pp.39-96
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• 2007

Traditionally, the concept of transfer is that physical records such as paper documents, videos, photos are made a delivery to Archives or Records centers on the basis of transfer guidelines. But, with the automation of records management environment and spreading new records creation and management applications, we can create records and manage them in the cyberspace. In these reasons, the existing transfer system is that we move filed records to Archives or Records centers by paper boxes, needs to be changed. Under the needing conditions of a new transfer paradigm, the fact that the revision of Records Act that include some provisions about electronic records management and transfer, is desirable and proper. Nevertheless, the electronic transfer provisions are too conceptional to apply records management practice, so we have to develop detailed methods and processes. In this context, this paper suggest that a electronic records transfer process model on the basis of international standard and foreign countries' cases. Doing transfer records is one of the records management courses to use valuable records in the future. So, both producer and archive have to transfer records itself and context information to long-term preservation repository according to the transfer guidelines. In the long run, transfer comes to be the conclusion that records are moved to archive by a formal transfer process with taking a proper records protection steps. To accomplish these purposes, I analyzed the 'OAIS Reference Model' and 'Producer-Archive Interface Methodology Abstract Standard-CCSDS Blue Book' which is made by CCSDS(Consultative committee for Space Data Systems). but from both the words of 'Reference Model' and 'Standard', we can understand that these standard are not suitable for applying business practice directly. To solve this problem, I also analyzed foreign countries' transfer cases. Through the analysis of theory and case, I suggest that an Electronic Records Transfer Process Model which is consist of five sub-process that are 'Ingest prepare ${\rightarrow}$ Ingest ${\rightarrow}$ Validation ${\rightarrow}$ Preservation ${\rightarrow}$ Archival storage' and each sub-process also have some transfer elements. Especially, to confirm the new process model's feasibility, after classifying two types - one is from Public Records center to Public Archive, the other is from Civil Records center to Public or Civil Archive - of Korean Transfer, I made the new Transfer Model applied to the two types of transfer cases.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.5 s.45
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• pp.11-19
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• 2005

Seismic design of a building is usually performed by using the linear static procedure. However, the actual behavior of the building subjected to earthquake is inelastic and dynamic in nature. Therefore, inelastic dynamic analysis is required to evaluate the safety of the structure designed by the current design codes. For the validation, a RC special moment resisting frame building was chosen and designed by IBC 2003 representing new codes. Maximum plastic rotation and dissipated energy of some selected members were calculated for examining if the inelastic behavior of the building follows the intention of the code, and drift demand were calculated as well for checking if the building well satisfies the design drift limit. In addition, the effect of including internal moment resisting frames (non lateral resisting system) on analyses results was investigated. As a result of this study, the building designed by IBC 2003 showed the inelastic behavior intended in the code and satisfied the design drift limit. Furthermore, the internal moment resisting frames should be included in the analytical model as they affect the results of seismic analyses significantly.