• Journal of the Earthquake Engineering Society of Korea
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• v.26 no.2
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• pp.71-82
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• 2022

This paper presents the effect on the inelastic behavior and structural performance of concrete and filled steel pipe through a numerical method for reliable judgment under various load conditions of the CJS composite structural system. Variable values optimized for the CJS synthetic structural system and the effects of multiple variables used for finite element analysis to present analytical modeling were compared and analyzed with experimental results. The Winfrith concrete model was used as a concrete material model that describes the confinement effect well, and the concrete structure was modeled with solid elements. Through geometric analysis of shell and solid elements, rectangular steel pipe columns and steel elements were modeled as shell elements. In addition, the slip behavior of the joint between the concrete column and the rectangular steel pipe was described using the Surface-to-Surface function. After finite element analysis modeling, simulation was performed for cyclic loading after assuming that the lower part of the foundation was a pin in the same way as in the experiment. The analysis model was verified by comparing the calculated analysis results with the experimental results, focusing on initial stiffness, maximum strength, and energy dissipation capability.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.488-494
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• 2023

In this paper, scenario-based urban flood prediction for the entire Jinju city was performed, and a simulation domain was constructed using G2D as a 2-dimensional urban flood analysis model. The domain configuration is DEM, and the land cover map is used to set the roughness coefficient for each grid. The input data of the model are water level, water depth and flow rate. In the simulation of the built G2D model, virtual rainfall (3 mm/10 min rainfall given to all grids for 5 hours) and virtual flow were applied. And, a GPU acceleration technique was applied to determine whether to run the flood analysis model in the target area. As a result of the simulation, it was confirmed that the high-resolution flood analysis time was significantly shortened and the flood depth for visual flood judgment could be created for each simulation time.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.181-188
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• 2021

Research and importance of legal services applied with AI so that it can be easily understood and predictable in difficult legal fields is increasing. In this study, based on the decision of the Tax Tribunal in the field of tax law, a model was built through self-learning through information collection and data processing, and the prediction results were answered to the user's query and the accuracy was verified. The proposed model collects information on tax decisions and extracts useful data through web crawling, and generates word vectors by applying Word2Vec's Fast Text algorithm to the optimized output through NLP. 11,103 cases of information were collected and classified from 2017 to 2019, and verified with 70% accuracy. It can be useful in various legal systems and prior research to be more efficient application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.332-334
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• 2021

As fatal accidents due to industrial accidents and deaths due to civil accidents were pointed out as social problems, the Act on Punishment of Serious Accidents Occurred in the Workplace was enacted to ensure the safety of citizens and to prevent serious accidents in advance. Effort is required. In this paper, we propose a distance prediction model in relation to the case where an operator is hit by heavy equipment such as a forklift. For the data, actual forklift trucks and workers roaming environments were directly captured by CCTV, and it was conducted based on the Euclidean distance. It is thought that it will be possible to learn YOLO-v4 by directly building a data-set at the industrial site, and then implement a model that predicts the distance and determines whether it is a dangerous situation, which can be used as basic data for a comprehensive risk situation judgment model.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.505-510
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• 2024

Since rolling contact fatigue cracks can always occur on the rail surface, which is the contact surface between wheels and rails, railway rails require thorough inspection and diagnosis to thoroughly inspect the condition of the cracks and prevent breakage. Recent detailed guidelines on the performance evaluation of track facilities present the requirements for methods and procedures for track performance evaluation. However, diagnosing and grading rail surface damage mainly relies on external inspection (visual inspection), which inevitably relies on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we conducted a deep learning model study for rail surface defect detection using Fast R-CNN. After building a dataset of rail surface defect images, the model was tested. The performance evaluation results of the deep learning model showed that mAP was 94.9%. Because Fast R-CNN has a high crack detection effect, it is believed that using this model can efficiently identify rail surface defects.

• Geomechanics and Engineering
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• v.11 no.2
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• pp.253-267
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• 2016

The stability of surrounding rock will be poor when the tunnel is excavated through nearly horizontal stratum. In this paper, the instability mechanism of local nearly horizontal stratum in super-large section and deep buried tunnel is revealed by the analysis of the macro failure and micro fracture. A structural model is proposed to explain the mechanics of surrounding rock collapse under the action of stress redistribution and shed light on the macroscopic analytical approach of the stability of surrounding rock. Then, some highly effective formulas applied in the tunnel engineering are developed according to the theory of mixed-mode micro fracture. And well-documented field case is made to demonstrate the effectiveness and accuracy of the proposed analytical methods of mixed-mode fracture. Meanwhile, in order to make the more accurate judgment about yield failure of rock mass, a series of comprehensive failure criteria are formed. In addition, the relationship between the nonlinear failure criterion and $K_I$ and $K_{II}$ of micro fracture is established to make the surrounding rock failure criterion more comprehensive and accurate. Further, the influence of the parameters related to the tension-shear mixed-mode fracture and compression-shear mixed-mode fracture on the propagation of rock crack is analyzed. Results show that ${\sigma}_3$ changes linearly with the change of ${\sigma}_1$. And the change rate is related to ${\beta}$, angle between the cracks and ${\sigma}_1$. The proposed simple analytical approach is economical and efficient, and suitable for the analysis of local nearly horizontal stratum in super-large section and deep buried tunnel.

• Journal of Practical Engineering Education
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• v.6 no.1
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• pp.23-29
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• 2014

In this study, there is a modeling for the procedure and operational method of the project based capstone design and related products which are a certificate of graduate qualification, and those results were evaluated by self-review and the performance assessment. Processing of research based on wireless searching robot is described according to the model. Before one semester by the end of to the assessment, the design thesis of capstone results was fixed to 18 groups with two people in each group. 13 teams out of 18 teams are satisfied the criteria of evaluation, and they all got a grade over 60 points and the other teams are not qualified at the first stage of final judgment. The total team mean average is 71.79. The research based on wireless searching robot was earned the highest average point among the other teams which is 96.1.

• Journal of the Korean GEO-environmental Society
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• v.24 no.10
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• pp.33-39
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• 2023

As interest in the use of underground spaces increases, safety against water pressure acting on underground structures is required. In Korea, various buoyancy prevention methods are used to control such underground water pressure, and among them, the vertical drainage method with excellent economic efficiency, constructionability and stability has recently been introduced and applied. However, in the case of the vertical drainage method designed and constructed in the field, it is often designed and constructed depending on numerical analysis, making it difficult to expect practical stability judgment. Accordingly, in this study, an experiment was conducted to measure both pressure by installing a vertical drainage system using a model soil. Based on the measured value by the experiment and the numerical analysis value, we intend to compare and analyze the action positive pressure and use it as basic data for field application.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.212-218
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• 2020

There are dozens of different types of lesions that can be diagnosed through chest X-ray images, including Atelectasis, Cardiomegaly, Mass, Pneumothorax, and Effusion. Computed tomography(CT) test is generally necessary to determine the exact diagnosis and location and size of thoracic lesions, however computed tomography has disadvantages such as expensive cost and a lot of radiation exposure. Therefore, in this paper, we propose a deep learning algorithm for judging the presence or absence of lesions in chest X-ray images as the primary screening tool for the diagnosis of thoracic lesions. The proposed algorithm was designed by comparing various configuration methods to optimize the judgment of presence of lesions from chest X-ray. As a result, the evaluation rate of lesion presence of the proposed algorithm is about 1% better than the existing algorithm.