• Journal of the Society of Naval Architects of Korea
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• v.59 no.5
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• pp.271-279
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• 2022

In order to study the self-propulsion test and analysis techniques for the submerged body with pumpjet propulsors in the Large Cavitation Tunnel (LCT), at the Korea Research Institute of Ships and Ocean Engineering, a set of test equipment was designed and manufactured. The pumpjet propulsor is composed of rotor, stator and duct which results in the strong interaction between the components. To measure the thrust and torque for duct and stator, a ring-shaped sensor was applied. The test equipment including pumpjet is installed on the stern of the submerged body. As the whole pumpjet including duct and stator was considered as the propulsor from pumpjet open-water test, the self-propulsion test was conducted in the same way. The total thrust, combined thrust of rotor, duct and stator was used for the pumpjet self-propulsion test analysis. Accordingly, the self-propulsion test and analysis were conducted in the same way as those of the conventional propeller. The full-scale performances of the pumpjet propulsor were compared with those of the reference propeller. On the basis of the present study, it is thought that the pumpjet propulsor would be designed optimally.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.137-147
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• 2022

In this study, for students who gave up their studies midway through college, we investigate the reasons for dropping out of college, analyze the factors that caused students to drop out, and suggest improvement measures to effectively increase the recruitment rate of enrolled students. This study explores measures to increase student enrollment in community colleges. For this purpose, it conducted a survey of students who dropped out of G College between 2018 and 2020 from June 28 to July 9, 2021. Its analysis is based on the results of 53 students who participated in the survey. First, our results suggest that programs to develop interpersonal relationships between students, faculty, and staff are necessary. Such programs will cultivate a culture of solidarity and collective identity among students, which in turn will reinforce positive experiences in college. Moreover, by developing systems to form relationships between faculty and students, colleges can have a feedback mechanism, such as an open-study program, through which they learn what the students want and need. Through this effort, colleges can help their students adjust to campuses and enhance student satisfaction in college. Second, it is necessary to develop various extracurricular programs not only for freshmen but existing students and to run hobby and leisure activity programs. To have continuous, standing extracurricular programs instead of one-time events, colleges should set up specific goals, delivery methods and strategies. Third, colleges should renovate old and outdated buildings and facilities on campus to enhance the quality of campus life. Moreover, more comprehensive improvement of facilities and a campus environment by having various convenient and leisure facilities that meet the needs and demands of students. Fourth, it is suggested to develop programs or systems that help students to more fully engage in campus lives and activities, which in turn increases confidence and self-efficacy among students. Through such programs, students can better adjust to their majors and, therefore, will be less likely to drop out of college.

• Industry Promotion Research
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• v.7 no.1
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• pp.1-8
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• 2022

We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of the features are independent variables. After applying various machine learning methods to the data, it has been found that just like many other reports from the artificial intelligence arena random forest performs better than logistic regression. But recently well performed artificial neural network and gradient boost do not perform as expected due to the lack of data. Finally Kaplan-Meier and Cox proportional hazard model have been employed to explore the relationship of the dependent variable (t_i, δ_i) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.45-52
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• 2020

Traditional methods of detecting security vulnerabilities in source-code require a lot of time and effort. If there is good data, the issue could be solved by using the data with machine learning. Thus, this paper proposes a source-code vulnerability detection method based on machine learning. Our method employs the code2vec model that has been used to propose the names of methods, and uses as a data set, Juliet Test Suite that is a collection of common security vulnerabilities. The evaluation shows that our method has high precision of 97.3% and recall rates of 98.6%. And the result of detecting vulnerabilities in open source project shows hopeful potential. In addition, it is expected that further progress can be made through studies covering with vulnerabilities and languages not addressed here.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.1
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• pp.5-7
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• 2022

KEPCO's plan is undergoing a trial operation to replace the open-loop section with ring main units configuration where underground distribution lines are installed, by linking the multi-way circuit breakers auto (MCA) on the power side of each pad-mounted transformer. However, ring main units application mentioned above may cause the ripple effects, when implementing the configuration without a study of protection coordination. Because ring main units with classical pre-set protection devices contribution in fault condition didn't consider yet. For the reliable ring main units operation, it is necessary to resolve several protection issues such as the protection coordination with substation side, prevention of the transformer inrush current. These issues can radically deteriorate the distribution system reliability Hence, it is essential to design proper protection coordination to reduce these types of problems. This paper presents a scheme of ring main units' configuration and MCA's settings of time-current curves to preserve the performance of protection coordination among the switchgears considering constraints, e.g. prevention of the ripple effects (on the branch section when a transformer failure occurs and the mainline when a branch line failure occurs). It was confirmed that the propagation of the failure for each interrupter segment could be minimized by applying the proposed TCC and the interrupter settings for the MCAs (branch, transformer). Further, it was verified that the undetected area of the distribution automation system (DAS) could be supplemented by having the MCA configurated ring main units operate first, instead of the internal protection equipment in the transformer such as the fuse, STP when a transformer failure occurs.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1346-1351
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• 2009

In this paper we propose a framework for an Object Relational IFC Server (OR-IFC Server). Enormous amounts of information are generated in each project. Today, many BIM systems are developed by various CAD software vendors. Industry Foundation Classes (IFC) developed by International Alliance for Interoperability (IAI) is an open standard data model for exchanging data between the various BIM tools. The IFC provides a foundation for exchanging and sharing of information directly between software applications and define a shared building project model. The IFC model server is a database management system that can keep track of transactions, modifications, and deletions. It plays a role as an information hub for storing and sharing information between various parties involved in construction projects. Users can communicate with each other via the internet and utilize functions implemented in the model server such as partial data import/export, file merge, version control, etc. IFC model servers using relational database systems have been developed. However, they suffered from slow performance and long transaction time due to a complex mapping process between the IFC structure and a relational-database structure because the IFC model schema is defined in the EXPRESS language which is object-favored language. In order to simplify the mapping process, we developed a set of rules to map the IFC model to an object-relational database (ORDB). Once the database has been configured, only those pieces of information that are required for a specific information-exchange scenario are extracted using the pre-defined information delivery manual (IDM). Therefore, file sizes will be reduced when exchanging data, meaning that files can now be effectively exchanged and shared. In this study, the framework of the IFC server using ORDB and IDM and the method to develop it will be examined.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.677-686
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• 2023

In this paper, we study the design and implementation of a smart home outing care system that recognizes the user's purpose of going out and delivers useful information that can help when going out. RSS service data of the Korea Meteorological Administration can be transmitted in real time using ESP8266, and a system that can provide weather information to users after analyzing the data using Arduino MEGA is implemented. Using App Inventor, you can pack the necessary items without forgetting, and you can change the settings according to the desired weather and purpose. The position of the microphone was placed outside to increase awareness by 12%, and the sensitivity of the pressure sensor was set to a maximum of 210 kΩ. If there is an obstacle between the doors, the doors open automatically. An ultrasonic sensor was placed on the ceiling of the drawer to recognize an object within the range of 0.5cm to 10cm to check the existence of an object, and a camera was installed to research a security reinforcement system.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.655-661
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• 2005

For a hydro power plant project, the headrace tunnel having a finished diameter of 3.3m was constructed in volcanic rocks with well-developed vertical joint and high groundwater table. The intake facility was located 20.3 km upstream of the powerhouse and headrace tunnel of 20 km in length and penstock of 440 m in height connected the intake and the powerhouse. The typical caldera lake, Lake Toba set the geology at the site; the caving of the ground caused tension cracks in the vertical direction to be developed and initial stresses at the ground to be released. High groundwater table(the maximum head of 20 bar) in the area of well-connected vertical joints delayed the progress of tunnel excavation severely due to the excessive inflow of groundwater. The excavation of tunnel was made using open-shield type TBM and mucking cars on the rail. High volume of water inflow raised the water level inside tunnel to 70 cm, 17% of tunnel diameter (3.9 m) and hindered the mucking of spoil under water. To improve the productivity, several adjustments such as modification of TBM and mucking cars and increase in the number of submersible pumps were made for the excavation of severe water inflow zone. Since the ground condition encountered during excavation turned out to be much worse, it was decided to adopt PC segment lining instead of RC lining. Besides, depending on the conditions of the water inflow, rock mass condition and internal water pressure, one of the invert PC segment lining with in-situ RC lining, RC lining and steel lining was applied to meet the site specific condition. With the adoption of PC segment lining, modification of TBM and other improvement, the excavation of the tunnel under severe groundwater condition was successfully completed.

• Structural Engineering and Mechanics
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• v.84 no.3
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• pp.423-435
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• 2022

In this article, we present torsion-bending analysis of a composite FGM beam with an open section, according to the advanced and refined theory of 1D / 3D beams based on the 3D Saint-Venant's solution and taking into account the edge effects. The (initially one-dimensional) model contains a set of three-dimensional (3D) displacement modes of the cross section, reflecting its 3D mechanical behaviour. The modes are taken into account depending on the mechanical characteristics and the geometrical form of the cross-section of the composite FGM beam. The model considered is implemented on the CSB (Cross-Section and Beam Analysis) software package. It is based on the RBT/SV theory (Refined Beam Theory on Saint-Venant principle) of FGM beams. The mechanical and physical characteristics of the FGM beam continuously vary, depending on a power-law distribution, across the thickness of the beam. We compare the numerical results obtained by the three-beam theories, namely: The Classical Beam Theory of Saint-Venant (Classical Beam Theory CBT), the theory of refined beams (Refined Beam Theory RBT), and the theory of refined beams, using the higher (high) modes of distortion of the cross-section (Refined Beam Theory using distorted modes RBTd). The results obtained confirm a clear difference between those obtained by the three models at the level of the supports. Further from the support, the results of RBT and RBTd are of the same order, whereas those of CBT remains far from those of higher-order theories. The 3D stresses, strains and displacements, obtained by the present study, reflect the 3D behaviour of FGM beams well, despite the initially 1D nature of the problem. A validation example also shows a very good agreement of the proposed models with other models (classical or higher-order beam theory) and Carrera Unified Formulation 1D-beam model with Lagrange Expansion functions (CUF-LE).

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.