• Smart Media Journal
• /
• v.6 no.4
• /
• pp.58-64
• /
• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.6
• /
• pp.842-849
• /
• 2022

With the development of deep learning technology, there are many cases of using DNNs in embedded systems such as unmanned vehicles, drones, and robotics. Typically, in the case of an autonomous driving system, it is crucial to run several DNNs which have high accuracy results and large computation amount at the same time. However, running multiple DNNs simultaneously in an embedded system with relatively low performance increases the time required for the inference. This phenomenon may cause a problem of performing an abnormal function because the operation according to the inference result is not performed in time. To solve this problem, the solution proposed in this paper first reduces the computation by applying the Tucker decomposition to DNN models with big computation amount, and then, make DNN models run in parallel as much as possible in the unit of hidden layer inside the GPU. The experimental result shows that the DNN inference time decreases by up to 75.6% compared to the case before applying the proposed technique.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.11 no.6
• /
• pp.205-212
• /
• 2003

This paper may provide a basic design data for the safer car seat mechanism and the quality of the material used by finding out the passenger's dynamic behavior when protected by seat belt during collision. A computer simulation with finite element method is used to accomplish this objective. At first, a detailed geometric model of the seat is constructed using CAD program. The formation of a finite element from a geometric data of the seat is carried out using Hyper-Mesh that is the commercial software for mesh generation and post processing. In addition to seat modeling, the finite element model of seat belt and dummy is formed using the same software. Rear impact analysis is accomplished using Pam-Crash with crash pulse. The part of the recliner and right frame is under big stress in rear crash analysis because the acceleration force is exerted on the back of the seat by dummy. The stress condition of the part of the bracket is checked as well because it is considered as an important variable on the seat design. Front impact model which including dummy and seal belt is analyzed. A Part of anchor buckle of seat frame has high stress distribution because of retraction force due to forward motion of dummy at the moment of collision. On the basis of the analysis result, remodeling and reanalysis works had been repeatedly done until a satisfactory result is obtained.

• Journal of Advanced Navigation Technology
• /
• v.4 no.2
• /
• pp.191-200
• /
• 2000

Recently, NORAD reported that only 6% of the total space objects cataloged in the table as above 10cm objects were being operated for the space missions and the others were just non-operated objects, such as rocket body, useless satellites which were finished their missions, and other fragments of space debris. A major contributor to the orbital debris background has been object breakup. Breakups generally are caused by explosions and collisions. Several international research groups and big countries' governments are trying to develop advanced technology for de-orbiting and to design new future satellites' modeling. The future need to be considered continuously that kind of technology and designing to preserve space and global environmental safety and to maintain welfare of mankind forever.

• Journal of Korea Artificial Intelligence Association
• /
• v.1 no.1
• /
• pp.1-6
• /
• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• Economic and Environmental Geology
• /
• v.31 no.3
• /
• pp.235-247
• /
• 1998

A new technique of simultaneons inversion for 3-D seismic velocity structure by using direct, reflected, and refracted waves is applied to the center of the Korean Peninsula including Pyongnam Basin, Kyonggi Massif, Okchon Fold Zone, Taebaeksan Fold Zone, Ryongnam Massif and Kyongsang Basin. Pg, Sg, PmP, SmS, Pn, and Sn arrival times of 32 events with 404 seismic rays are inverted for locations and crustal structure. 5 ($1^{\circ}$ along the latitude)${\times}6$ ($0.5^{\circ}$ along the longitude) ${\times}8$ block (4 km each layer) model was inverted. 3-D seismic crustal velocity tomography including eight sections from the surface to the Moho, eight profiles along latitude and longitude and the Moho depth distribution was determined. The results are as follows: (1) the average velocity and thickness of sediment are 5.15 km/sec and 3-4 km, and the velocity of basement is 6.12 km/sec. (2) the velocities fluctuate strongly in the upper crust, and the velocity distribution of the lower crust under Conrad appears basically horizontal. (3) the average depth of Moho is 29.8 km and velocity is 7.97 km/sec. (4) from the sedimentary depth and velocity, basement thickness and velocity, form of the upper crust, the Moho depth and form of the remarkable crustal velocity differences among Pyongnam Basin, Kyonggi Massif, Okchon Zone, Ryongnam Massif and Kyongsang Basin can be found. (5) The different crustal features of ocean and continent crust are obvious. (6) Some deep index of the Chugaryong Rift Zone can be located from the cross section profiles. (7) We note that there are big anisotropy bodies near north of Seoul and Hongsung in the upper crust, implying that they may be related to the Chugaryong Rift Zone and deep fault systems.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.23 no.6
• /
• pp.469-484
• /
• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.3049-3067
• /
• 2018

Although there is a growing body of research on organizational commitment profiles based on a person-centered approach, it is not widely applied to the commitment research conducted by Korean organizational scholars yet. Therefore, in this paper, we introduced the concept and analytical methods, such as cluster analysis and latent profile analysis (LPA), of the person-centered approach. In addition, we also performed both cluster analysis and LPA to identify types of organizational commitment profiles of Korean employees based on the combination of affective, continuance and normative commitment on the sample from a range of different fields in South Korea (n = 349). Both analyses extracted two comparable sets of 6 commitment profiles. These six profiles were then contrasted with employee turnover intention. Finally, implications for commitment theory, practices and future research issues were discussed.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.28 no.6
• /
• pp.675-682
• /
• 2015

Current design codes such as AASHTO LRFD or Korean Highway Bridge Design Code recommend of using static force for designing bridge column against vehicle collisions. However, there was an accident that the bridge was collapsed shortly after vehicle impact on bridge pier in Nebraska(near Big Spring, 2003). It was found that the second largest cause of bridge collapse is collision after hydraulic causes. It can be thought that the possibility of truck-bridge collision are getting increasing as the size of truck increases and traffic condition are becoming improved. However, dynamic behavior under the impact loading seldom considered in bridge design procedure due to computational cost and time. In this study, in order to reduce the computational cost for dynamic impact analysis, model reduction method was developed. Obtained results of residual displacement were compared with the results of direct impact simulations.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.32 no.5
• /
• pp.322-330
• /
• 2020

Modeling for outliers in time series was carried out using the MSL and extreme high, low tide levels (EHL, HLL) data set in the Busan and Mokpo stations. The time-series model is seasonal ARIMA model including the components of the AO (additive outliers) and LS (level shift). The optimal model was selected based on the AIC value and the model parameters were estimated using the 'tso' function (in 'tsoutliers' package of R). The main results by the model application, i.e.. outliers and level shift detections, are as follows. (1) The two AO are detected in the Busan monthly EHL data and the AO magnitudes were estimated to 65.5 cm (by typhoon MAEMI) and 29.5 cm (by typhoon SANBA), respectively. (2) The one level shift in 1983 is detected in Mokpo monthly MSL data, and the LS magnitude was estimated to 21.2 cm by the Youngsan River tidal estuary barrier construction. On the other hand, the RMS errors are computed about 1.95 cm (MSL), 5.11 cm (EHL), and 6.50 cm (ELL) in Busan station, and about 2.10 cm (MSL), 11.80 cm (EHL), and 9.14 cm (ELL) in Mokpo station, respectively.