• Journal of the Korea Society for Simulation
• /
• v.30 no.2
• /
• pp.41-48
• /
• 2021

As the rural population continues to decline and aging, the improvement of agricultural productivity is becoming more important. Early prediction of crop quality can play an important role in improving agricultural productivity and profitability. Although many researches have been conducted recently to classify diseases and predict crop yield using CNN based deep learning and transfer learning technology, there are few studies which predict postharvest crop quality early in the planting stage. In this study, a early quality prediction model is proposed for sprout ginseng, which is drawing attention as a healthy functional foods. For this end, we took pictures of ginseng seedlings in the planting stage and cultivated them through hydroponic cultivation. After harvest, quality data were labeled by classifying the quality of ginseng sprout. With this data, we build early quality prediction models using several pre-trained CNN models through transfer learning technology. And we compare the prediction performance such as learning period and accuracy between each model. The results show more than 80% prediction accuracy in all proposed models, especially ResNet152V2 based model shows the highest accuracy. Through this study, it is expected that it will be able to contribute to production and profitability by automating the existing seedling screening works, which primarily rely on manpower.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.1
• /
• pp.685-690
• /
• 2023

It was analyzed that the volume of deep excavation works adjacent to existing underground structures is increasing according to the population growth and density of cities. Currently, many underground structures and tracks are damaged by external factors, and the cause is analyzed based on the measurement results in the tunnel, and measurements are being made for post-processing, not for prevention. The purpose of this study is to analyze the effect on the deformation of the structure due to the excavation work adjacent to the urban railway track in use. In addition, the safety of structures is evaluated through machine learning techniques for displacement of structures before damage and destruction of underground structures and tracks due to external factors. As a result of the analysis, it was analyzed that the model suitable for predicting the structure management standard value time in the analyzed dataset was a polynomial regression machine. Since it may be limited to the data applied in this study, future research is needed to increase the diversity of structural conditions and the amount of data.

• Journal of the Korean Geotechnical Society
• /
• v.26 no.12
• /
• pp.5-17
• /
• 2010

Coarse granular material is used as important fill material in most of large embankments such as railway, road, dam and so on. Therefore, the accurate design parameters of the coarse granular material are necessarily required in design and construction. The behavior of the coarse granular material was not well understood because of the lack of large testing equipment capable of coarse granular material. A large triaxial testing system was developed in this research, capable of large specimens of 500 mm, 300 mm and 150 mm in diameter. In the new large triaxial testing system, the load cell is installed inside the triaxial cell and axial displacement is measured locally on a specimen in order to improve control and measurement in small strain level. Urethane specimens of 300 mm and 50 mm in diameter were prepared. The large triaxial tests were performed on the 300 mm diameter urethane specimens while RC/TS and impact echo tests on the 50 mm diameter urethane specimens to verify this testing system. In this verification test results, we could ascertain the reasonable test results of the KRRI large triaxial testing system.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.633-640
• /
• 2023

In this study, numerical analysis was performed based on field investigation to derive an appropriate reinforcement method by analyzing the displacement behavior characteristics of concrete blocks generated in the direct fixation track on the bridges of the serviced light rail transit. The track of this study was a direct fixation track on a sharp curved track, and the problem of movement of the concrete blocks installed on the bridge deck in the longitudinal and lateral directions occurred. In this study, based on the finite element model using 3D solid elements, the behavior of the direct fixation track that could be occurred under operating load conditions was analyzed. In addition, the reinforcement effect of various reinforcement methods was analyzed. As a result of analyzing the lateral displacement before and after reinforcement, it was analyzed that the maximum lateral displacement after reinforcement under the extreme lateral wheel loads significantly decreased to about 3% (about 0.1mm) compared to before reinforcement. In addition, as a result of examining the generated stress of the filling mortar, bridge decks, and reinforcing bar, it was analyzed that all of them secured a sufficient safety factor of 2.6 or higher, and the optimal conditions for the reinforcement method were derived. Therefore, it is judged that the number of anchoring reinforcements and symmetrical anchor placement reviewed in this study will be effective in controlling the occurrence of lateral displacement of concrete blocks and securing the structural integrity of bridges and concrete blocks.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.1057-1063
• /
• 2023

The urban railway sleeper floating track, the subject of this study, is an anti-vibration track to reduce vibration transmitted to the structure. currently, the replacement cycle of resilience pad for sleeper floating tracks is set and operated based on load. however, most previous studies were conducted on load-based structural safety aspects, such as fatigue life evaluation of sleeper anti-vibration pads and increase in track impact coefficient and track support stiffness due to increase in spring stiffness. therefore, in this study, we measure the vibration acceleration of the ballast for each analysis section and use the results of 7 million fatigue tests to calculate the spring stiffness of the resilience pad for each section. the spring stiffness of the resilience pad calculated for each section was set as the analysis data and the concrete vibration acceleration was derived analytically. the adequacy of analysis modeling was verified as the analyzed concrete bed vibration acceleration for each section was within the field-measured concrete bed vibration acceleration range. using the vibration acceleration curve according to the derived spring stiffness change, the spring stiffness of the resilience pad is estimated from the measured vibration acceleration. therefore, we would like to present a technique that can estimate the spring stiffness of resilience pad of a running track using the vibration acceleration of the measured concrete bed.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.833-838
• /
• 2024

The urban railway sleeper floating track(STEDEF) is a structure that structurally separates the sleepers and the concrete bed using sleeper boots and resilience pads to reduce vibration transmitted to the concrete bed. Recently, the resilience pads of sleeper floating tracks that have been in use for more than 20 years are deteriorating. Accordingly, in order to evaluate the performance of the resilience pad, a static spring stiffness test is being performed after extracting the resilience pad. This evaluation technique is performed after replacing the resilience pad in use. However, the track natural frequency can change depending on the resilience pad spring stiffness and the uplift and subsidence of the concrete bed. In this study, modal testing technique was used to evaluate the track natural frequency. For this purpose, the sleeper boots material, resilience pad spring stiffness, and track natural frequency according to concrete bed uplift and subsidence were measured using modal tests at a laboratory scale. It was analyzed that the natural frequency of the sleeper floating track was directly affected by changes in the spring stiffness of the resilience pad. In addition, the change in natural frequency due to the uplift and subsidence of the concrete bed was also found to be large. Therefore, it is believed that the modal test technique presented in this study can be used to evaluate the resilience pad deterioration and voided sleepers.

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.23-31
• /
• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Geophysics and Geophysical Exploration
• /
• v.11 no.4
• /
• pp.350-360
• /
• 2008

Electrical resistivity is one of physical property of the earth and measured by electrical resistivity survey, electrical resistivity logging and laboratory test. Recently, electrical resistivity is widely used in determination of rock quality in support pattern design of road and railway tunnel construction sites. To get more reliable rock quality data from electrical resistivity, it needs a lot of test and study on correlation of resistivity and rock quality. Firstly, we did rock property test in laboratory, such as P wave velocity, Young's modulus, uniaxial compressive strength (UCS) and electrical resistivity. We correlate each test results and we found out that electrical resistivity has highly related to P wave velocity, Young's modulus and UCS. Next, we accomplished electrical resistivity survey in field site and carried out electrical resistivity logging at in-situ area. We also performed rock classification, such as RQD, RMR and Q-system and we correlate electrical resistivity to RMR data. We found out that electrical resistivity logging data are highly correlate to RMR. Also we found out that electrical resistivity survey data are lower than electrical resistivity logging data when there are faults or fractures. And it cause electrical resistivity survey data to lowly correlate to RMR.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.3
• /
• pp.60-67
• /
• 2016

In cumulative transfer functions, as number of transfer increase, the impact of individual transfer to transfer cost increase linearly or non linearly. This function can effectively explain various passengers's travel behavior who choose their travel routes in integrated transit line networks including bus and railway modes. Using the function, it is possible to simulate general situations such that even though more travel times are expected, less number of transfer routes are preferred. However, because travel cost with cumulative transfer function is known as non additive cost function types in route search algorithms, finding an optimal route in integrated transit networks is confronted by the insolvable enumeration of all routes in many cases. This research proposes a methodology for finding an optimal path considering cumulative transfer function. For this purpose, the reversal phenomenon of optimal path generated in route search process is explained. Also a heuristic methodology for selecting an optimal route among multiple routes predefined by the K path algorithm. The incoming link based entire path deletion method is adopted for finding K ranking path thanks to the merit of security of route optimality condition. Through case studies the proposed methodology is discussed in terms of the applicability of real situations.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.4
• /
• pp.601-610
• /
• 2018

Since the building construction works are repeated vertically in a limited space, there is not a great need for the location information of each activity in the schedule management. On the other hand, civil engineering works such as road and railway projects consist of a large number of earthworks, long bridges, and long tunnels. These types of work should be controlled in a horizontal space according to the linear axis of several tens of kilometers. In other words, since most of the activities are managed in the unit of distance from the start point to the end point, it is possible to improve the efficiency of the schedule management by linking the location information of the activity with the schedule data in the schedule management system. This study presents a methodology for creating a linear schedule chart specific to a project with horizontal work space and compares the convenience with the existing Gantt chart. In addition, the methodology of linking linear schedule chart to the 4D CAD system, which is a typical BIM technology in the construction phase, is presented to improve the usability of BIM. The practical applicability of the proposed methodology was verified statistically.