• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.571-576
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• 2024

Ontologies are created by domain experts, but the same content may be expressed differently by each expert due to different understandings of domain knowledge. Since the ontology standardization is still lacking, multiple ontologies can be exist within the same domain, resulting in a phenomenon called the ontology heterogeneity. Therefore, we propose a novel ontology matching method that combines SCBOW(: Siames Continuois Bag Of Words) and BERT(: Bidirectional Encoder Representations from Transformers) models to solve the ontology heterogeneity issue. Ontologies are expressed as a graph and the SimRank algorithm is used to solve the one-to-many problem that can occur in ontology matching problems. Experimental results showed that our approach improves performance by about 8% over traditional matching algorithm. Proposed method can enhance and refine the alignment technology used in ontology matching.

• Journal of Gastric Cancer
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• v.16 no.3
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• pp.167-176
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• 2016

Purpose: The purpose of this study was to determine the effect of a prior gastrectomy on the difficulty of subsequent colonoscopy, and to identify the surgical factors related to difficult colonoscopies. Materials and Methods: Patients with a prior gastrectomy who had undergone a colonoscopy between 2011 and 2014 (n=482) were matched (1:6) to patients with no history of gastrectomy (n=2,892). Cecal insertion time, intubation failure, and bowel clearance score were compared between the gastrectomy and control groups, as was a newly generated comprehensive parameter for a difficult/incomplete colonoscopy (cecal intubation failure, cecal insertion time >12.9 minutes, or very poor bowel preparation scale). Surgical factors including surgical approach, extent of gastrectomy, extent of lymph node dissection, and reconstruction type, were analyzed to identify risk factors for colonoscopy performance. Results: A history of gastrectomy was associated with prolonged cecal insertion time ($8.7{\pm}6.4$ vs. $9.7{\pm}6.5$ minutes; P=0.002), an increased intubation failure rate (0.1% vs. 1.9%; P<0.001), and a poor bowel preparation rate (24.7 vs. 29.0; P=0.047). Age and total gastrectomy (vs. partial gastrectomy) were found to be independent risk factors for increased insertion time, which slowly increased throughout the postoperative duration (0.35 min/yr). Total gastrectomy was the only independent risk factor for the comprehensive parameter of difficult/incomplete colonoscopy. Conclusions: History of gastrectomy is related to difficult/incomplete colonoscopy performance, especially in cases of total gastrectomy. In any case, it may be that a pre-operative colonoscopy is desirable in selected patients scheduled for gastrectomy; however, it should be performed by an expert endoscopist each time.

• Korean Journal of Construction Engineering and Management
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• v.19 no.3
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• pp.97-104
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• 2018

This paper aims to evaluate the performance of S University's Premier Global Construction Engineer Training Program. The net impact of the program is evaluated through a quasi-experiment design approach. The competency level of an individual participant is compared with that of a non-participating expert with similar professional background. The results show that the training program contributed to a significant improvement in the professional competencies of the participating students. The competency level was regressed on the subjects of curriculum. The achievements are attributable to a group of subjects focused on the skills for project development such as PPP, feasibility study, and project financing. Another group of subjects found to have significantly contributed to the improved competencies can be categorized as subjects focused on nurturing global perspectives. The paper shows it is possible to quantify the contribution of the program and the results provide a set of information that can be useful in designing and operating similar programs.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Journal of the Korea Society for Simulation
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• v.33 no.1
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• pp.1-17
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• 2024

Forest management is known to beneficially influence stand structure and wood production, yet quantitative understanding as well as an illustrative depiction of the effects of different management approaches on tree growth and stand dynamics are still scarce. Long-term management of beech forests must balance public interests with ecological aspects. Efficient forest management requires the reliable prediction of tree growth change. We aimed to develop a novel hybrid simulation approach, which realistically simulates short- as well as long-term effects of different forest management regimes commonly applied, but not limited, to German low mountain ranges, including near-natural forest management based on single-tree selection harvesting. The model basically consists of three modules for (a) natural seedling regeneration, (b) mortality adjustment, and (c) tree growth simulation. In our approach, an existing validated growth model was used to calculate single year tree growth, and expanded on by including in a newly developed simulation process using calibrated modules based on practical experience in forest management and advice from the local forest. We included the following different beech forest-management scenarios that are representative for German low mountain ranges to our simulation tool: (1) plantation, (2) continuous cover forestry, and (3) reserved forest. The simulation results show a robust consistency with expert knowledge as well as a great comparability with mid-term monitoring data, indicating a strong model performance. We successfully developed a hybrid simulation that realistically reflects different management strategies and tree growth in low mountain range. This study represents a basis for a new model calibration method, which has translational potential for further studies to develop reliable tailor-made models adjusted to local situations in beech forest management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2529-2539
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• 2013

The Korean construction industry has led the miraculous economic boost of Korea by providing solid domestic infrastructures such as highway, roads, and airports. It also played a critical role in global construction market and eaned more than 500 billions dollars in terms of their accumulated international orders. However, domestic construction market has significantly decreased in recent years due to the domestic political environments and global economic crisis. Therefore, the importance of international construction market cannot be more emphasized to the Korean construction market in order for the sustainable growth. There has been, however, little research in the area of identifying required competency elements for the Korean construction industry to stay successful in the global market. The main purpose of this study is to identify elements of core competency to increase global competitiveness for Korean construction industry. Core global construction competency elements were derived from the internal and external environmental analyses along with the extensive literature review, expert interviews and a survey. This study utilized the Importance-Performance Analysis (IPA) and a gap analysis in providing insights on the status competitiveness of the Korean construction industry in terms of required global core competency elements. The analysis shows that project management and financial management are the main areas for improvements required to engineering contractors while construction contractors need to take a more balanced approach among technical, project management, and financial management in order to increase their global competencies.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Journal of the Society of Disaster Information
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• v.8 no.3
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• pp.310-318
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• 2012

Actual condition by items based on the level of execution of Construction Company certified by Construction Safety and Health Management Systems (KOSHA 18001) was investigated, analyzed and evaluated reflecting various opinions fincluding safety experts, top management, audit experts, and construction engineers. Currently, the maintenance is being managed through internal audit after the safety and health management system has been certified, but it is difficult to identify the degree of continuous improvement. In order to present the standards to see the level of quantified system, this study was conducted. The purpose of this study is to present the system maturity evaluation tool to be used to reduce occupational accidents through proper establishment and continuous improvement of national health and safety management system. Results of this study are summarized through identification of current condition of implementation of KOSHA 18001 system, development of maturity measurement tool and verification as follows: First, priority of implementation for activities of headquarters and on-site was determined by importance of activities such as the risk assessment, safety and health accident prevention activities, performance assessment and monitoring, resource management and support, and management review and improvement in order. In addition, the expert group presented that association with continuous improvement activities could establish the system by presenting strengths, weaknesses and improvement subjects of system.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.303-310
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• 2021

Cracks in bridges are important factors that indicate the condition of bridges and should be monitored periodically. However, a visual inspection conducted by a human expert has problems in cost, time, and reliability. Therefore, in recent years, researches to apply a deep learning model are started to be conducted. Deep learning requires sufficient data on the situations to be predicted, but bridge crack data is relatively difficult to obtain. In particular, it is difficult to collect a large amount of crack data in a specific situation because the shape of bridge cracks may vary depending on the bridge's design, location, and construction method. This study developed a crack detection model that generates and trains insufficient crack data through a Generative Adversarial Network. GAN successfully generated data statistically similar to the given crack data, and accordingly, crack detection was possible with about 3% higher accuracy when using the generated image than when the generated image was not used. This approach is expected to effectively improve the performance of the detection model as it is applied when crack detection on bridges is required, though there is not enough data, also when there is relatively little or much data f or one class.