• Proceedings of the Ginseng society Conference
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• 2002.10a
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• pp.376-384
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• 2002

Object To find out which of the 27 ginsenosides isolated from Panax ginseng C.A. Mey that may inhibit the proliferation of human osteosaocoma cell line $U_2OS$. Methods Effects of each individual ginsenoside on the proliferation of $U_2OS$ cell were studied by determining the viability of cancer cells during culture with or without the presence of the test compound. DNA assay was determined by flow cytometry. Results Ginsonosides -Ro, $-Rh_l,\;-Rh_2,\;-F_1\;and\;-L_8$ at concentrations of 5 ,umol/L could obviously suppress the proliferation of $U_2OS$ cells while ginsenosides $-Rg_1,\;-F_3,$ -Rf, PPT and PT significantly inhibited the cancer cells. Flow cytometry revealed that ginsenosides $-Ro,-Rg_1-Rf,-F_1-Rh_2,PPT$ and PT induced cell cycle arrest at $G_0/G_1$ phase with obvious decrease of cell count at Sand $G_2+M$ phase, Moreover, ginsenosides $-Rf_1,-Rg_1,\;-F_1$ and PPT induced significantly high rates of cell death as compared with the control. Conclusion These data suggested that ginsenosides inhibited $U_2OS$ proliferation Via cell cycle arrest or induction of cell death.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1176-1182
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• 2021

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method.

• Fashion & Textile Research Journal
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• v.20 no.6
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• pp.656-668
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• 2018

The main object of this study was to understand the body image and body management behavior associated with desiring a thinner and more muscular body. The present study examined whether the drives for thinness and muscularity occur concurrently for both male and female college students, and whether there are gender differences. Moreover, the effects of objectified body consciousness on drive for thinness and drive for muscularity were investigated. A self-administered survey was conducted and a total of 390 data were used for data analysis. Participants included 197 male college students and 193 female students. Results indicated that male students reported significantly lower drive for thinness and higher drive for muscularity than female students. However, the drives for thinness and muscularity were significantly correlated for both male and female college students, and they reported discrepancies between their BMI and self-perceived weight and muscle mass. Findings supported the significant effects of objected body consciousness on the drives for thinness and muscularity for both male and females. Sub-dimensions of objectified body consciousness had differential effects on drive for thinness and muscularity. Specifically, body surveillance and body shame significantly influenced male and female students' drive for thinness, while their control belief did not have significant effects on their drive for thinness. Additionally, body shame emerged as significant unique predictor of drive for muscularity. In terms of gender comparisons, while the effect of body shame was the strongest for the males, the effect of surveillance was as strong as that of body shame for the females.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1691-1697
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• 2018

There has been a steady study of unmanned vehicle. So far, continuous research has brought news of the commercialization of unmanned vehicle. In addition, it has been applied in a variety of fields with another industry. A lot of research has been done, too, to apply inert driving indoors. Using LoRa network, which is a network dedicated to IoT, unmanned vehicle control system that is applied to LoRa network from a small space, or from an office hospital to a factory, is installed to increase efficiency when the performs special tasks. This paper presents solutions to a variety of problems by using LoRa network, which is dedicated to IoT, to recognize an unmanned vehicle as a single object, to communicate with surrounding objects, and to receive information necessary for driving indoors from a cloud server.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.835-837
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• 2016

In this research, Detect viewpoint of the user in other to obtain the coordinates and provided obtain a corresponding stereo images of different positions, Provide a system which can be observed remotely break the spatial limits. For system configuration Designed with a physical action such as left and right movement and rotation of the head is the largest factor in human viewpoint change. Therefore, this system is calculated to analyze user viewpoint, Control system for providing three-dimensional images obtained, It is implemented in network communication for data transmission, As the user observed the object in the same space even though free to observe a target at a remote location, Obtaining a stereo image that corresponds to the viewpoint providing a three-dimensional image, We implemented a system that provides the same visual effect and directly observed.

• Geomechanics and Engineering
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• v.19 no.3
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• pp.255-268
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• 2019

Taking top-coal caving mining face (TCCMF) as research object, this paper considers the combination of top-coal and immediate roof as cushion layer to build the solution model of support resistance based on the theory of elastic foundation beam. Meanwhile, the physical and mechanical properties of coal-rock combination influencing on strata behaviors is explored. The results illustrate that the subsidence of main roof in coal wall increases and the first weighting interval decreases with the increase of top-coal and immediate roof thicknesses as well as the decrease of top-coal and immediate roof elastic modulus. Moreover, the overlying strata reflecting on support has negative and positive relationship with top-coal thickness and immediate roof thickness, respectively. However, elastic modulus has limit influence on the dead weight of top-coal and immediate roof. As a result, it has similar roles on the increase of total support resistance and overlying strata reflecting on support in the limit range of roof control distance. In view of sensitive analysis causing the change of total support resistance, it can be regards as the rank of three components as immediate roof weight > overlying strata reflecting on support > top coal weight. Finally, combined with the monitoring data of support resistance in Qingdong 828, the validity of support resistance determined based on elastic foundation beam is demonstrated, and this method can be recommended to adopt for support type selecting in TCCMF.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.9-16
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• 2019

Companies that want to improve their manufacturing processes have recently introduced the smart factory, which is particularly noticeable. The ultimate goal is to maximize the area of the smart factory that performs the process of the production facility completely with minimal manual control and to minimize errors of reasoning. This research is a part of a project for unmanned production, management, packaging, and delivery management and the detection of the start point of rebars to perform the automatic calibration of the rollers through the tracking of the automated facilities of unmanned production. It must meet the requirement to accurately track the position from the start point to the end point. In order to improve the tracking performance, it is important to set the accurate start point. However, the probability of tracking errors is high depending on environments such as illumination and dust through the conventional time-based detection method. In this paper, we propose a starting point detection method using the average brightness change of high speed IR camera to reduce the errors according to the environments, As a result, its performance is improved by more than 15%.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.147-154
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• 2021

Naval Combat Management System(a.k.a. CMS) is the core combat power of ROK Navy. CMS which has been localized since 1993 has been developed in various categories. However, in the characteristic of defense industry, CMS software has been rarely developed technically and structurally while the environment of computing system has been developed dramatically. A need for a new paradigm of CMS software development was raised. This paper suggests Naval Shield Component Platform(NSCP) as a standard interface architecture of CMS based on SOLID of OOP which is an advanced programming paradigm and introduce its functionality and feature. We expect NSCP's higher reusability, concurrency and maintainability in CMS software development. As a future work, we are going to apply NSCP to the next CMS software development project and evaluate quantitative, qualitative method.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.81-87
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• 2020

This paper creates a standard node for acquiring sensor data from various industrial sensors (IoT/non-IoT) for the establishment of Smart Factory Digital Twin, and provides inter-compatible data by linking zones by group/process to secure data stability and to ensure the digital twin (Digital Twin) of Smart Factory. The process of the Zone Master platform contains interface specifications to define sensor objects and how sensor interactions between independent systems are performed and carries out individual policies for unique data exchange rules. The interface for execution control of the Zone Master Platform processor provides system management, declaration management for public-subscribe, object management for registering and communicating status information of sensor objects, ownership management for property ownership sharing, time management for data synchronization, and data distribution management for Route information on data exchange.