• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.11-23
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• 1995

In this paper, a parallel pipelined processor model which acts as a small VLIW processor architecture and a scheduling algorithm for extracting instruction-level parallelism on this architecture are proposed. The proposed model has a dual-instruction mode which has maximum 4 basic operations being executed in parallel. By combining these basic operations, variable instruction set can be designed for various applications. The scheduling algorithm schedules basic operations for parallel execution and removes pipeline hazards by examining data dependency and resource conflict relations. In order to examine operation and evaluate the performance,a C compiler and a simulator are developed. By simulating various test programs with the compiler and the simulator, the characteristics and the performance result of the proposed architecture are measured.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.12
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• pp.75-82
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• 1991

We proposed an optimal parallel implementation of an optimization neural network with linear increase of speedup by using multicomputer system and presented performance analysis model of the system. We extracted the temporal-and the spatial-parallelism from the optimization neural network and constructed a parallel pipeline processing model using the parallelism in order to achieve the maximum speedup and efficiency on the CSP architecture. The results of the experiments for the TSP using the Transputer system, show that the proposed system gives linear increase of speedup proportional to the size of the optimization neural network for more than 140 neurons, and we can have more than 98% of effeciency upto 16-node system.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.317-320
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• 2015

Documenting work-in-progress on construction sites using images captured with smartphones, point-and-shoot cameras, and Unmanned Aerial Vehicles (UAVs) has gained significant popularity among practitioners. The spatial and temporal density of these large-scale site image collections and the availability of 4D Building Information Models (BIM) provide a unique opportunity to develop BIM-driven visual analytics that can quickly and easily detect and visualize construction progress deviations. Building on these emerging sources of information this paper presents a pipeline for model-driven visual analytics of construction progress. It particularly focuses on the following key steps: 1) capturing, transferring, and storing images; 2) BIM-driven analytics to identify performance deviations, and 3) visualizations that enable root-cause assessments on performance deviations. The information requirements, and the challenges and opportunities for improvements in data collection, plan preparations, progress deviation analysis particularly under limited visibility, and transforming identified deviations into performance metrics to enable root-cause assessments are discussed using several real world case studies.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.5
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• pp.213-219
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• 2017

This study investigated the thermal conductivity of $CO_2$ gas mixtures in order to ascertain the effects of particular impurities in $CO_2$ in pipeline transportation. We predicted the thermal conductivity of three $CO_2$ gas mixtures ($CO_2+N_2$, $CO_2+H_2S$, and $CO_2+CH_4$) by utilizing three different methods : Chung et al., TRAPP, and the REFPROP model. We validated predictions by comparing the estimated results with 216 experimental data for $CO_2+CH_4$, $CO_2+N_2$, and $CO_2+C_2H_6$. Following $CO_2$ transportation conditions, we observed that the model developed by Chung et al. showed the lowest mean deviation of 3.07%. Further investigations were carried out on the thermal conductivity of $CO_2$ gas mixtures based on the Chung et al. model including the effects of the operation parameters of pressure, temperature, and mole fraction of impurities.

• Korean Journal of Construction Engineering and Management
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• v.16 no.1
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• pp.35-43
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• 2015

It is important to have enough design and construction duration for infrastructure projects. However, recent water resource project in Korea shows several problems caused by their fast-tract schedule. National Audit Committee report several water resource projects have quality problems caused by insufficient project duration. Especially, water resource projects such as dam and water pipeline construction should have proper time to secure their structure quality. Normal project duration for these projects should be estimated based on previous similar projects' historical data analysis. However there is no standard model which can estimate normal project duration for water resource projects in Korea. There are several normal project duration estimation models for building project developed by public(LH) and private construction companies. However, there is no proper model for water resource projects. So, this study developed normal project duration model for dam and water pipeline projects using historical data and show application of models.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

• Geotechnical Engineering
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• v.5 no.2
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• pp.19-32
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• 1989

The vertical loads of buried Hume pipes were calculated using the finite element method, in which the hyperbolic soil model, the nonlinear hysteretic stress path model and soil-structure interface model were used. The obtained results were compared and discussed with those from the classic methods such as Marston-Spangler's theory and so on. The effects of excavation width and depth to the top of pipe along with soil parameters and type of excavation, which have not been included in the classic methods, were investigated. In addition, a calculation method of the vertical load for buried Hume pipes was proposed and it is presumed to be easily applied in the practical fields.

• Proceedings of the KSRS Conference
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• v.1
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• pp.417-420
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• 2006

In these days, mobile application dealing with information contents on mobile or handheld devices such as mobile communicator, PDA or WAP device face the most important industrial needs. The motivation of this study is the design and implementation of mobile application using high resolution satellite imagery, large-sized image data set. Although major advantages of mobile devices are portability and mobility to users, limited system resources such as small-sized memory, slow CPU, low power and small screen size are the main obstacles to developers who should handle a large volume of geo-based 3D model. Related to this, the previous works have been concentrated on GIS-based location awareness services on mobile; however, the mobile 3D terrain model, which aims at this study, with the source data of DEM (Digital Elevation Model) and high resolution satellite imagery is not considered yet, in the other mobile systems. The main functions of 3D graphic processing or pixel pipeline in this prototype are implemented with OpenGL|ES (Embedded System) standard API (Application Programming Interface) released by Khronos group. In the developing stage, experiments to investigate optimal operation environment and good performance are carried out: TIN-based vertex generation with regular elevation data, image tiling, and image-vertex texturing, text processing of Unicode type and ASCII type.