• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.494-501
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• 2020

This study is aimed at exploring the effects of forest experience activities on promoting children's community spirit. To achieve this, a pre-post survey was empirically carried out with 40 children at Kindergarten A in the city of Chungnam. The comprehensive findings showed a significant difference between the experimental group, which had forest experience activities, and the control group, which had outdoor activities based on the existing Nuri curriculum. Based on a pre-test for intimacy, emotion, mutual public awareness, and participation consciousness as sub-factors of community spirit, which adopted all the research hypotheses, the results suggest that the forest kindergarten will become an educational place for children. Consequently, personality education using nature in forest kindergartens can become an excellent goal, helping to boost the development of children's sensitivity and emotional stability through awakening the five senses; building up self-awareness, self-reliance, and trust; learning consideration and respect for others; and developing positive attitudes, sociality, potential, imagination, and creativity through forest activities with their peers.

• Smart Media Journal
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• v.11 no.5
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• pp.26-37
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• 2022

In this paper, images are automatically acquired to control the initial disease of strawberries among facility cultivation crops, and disease analysis is performed using the EfficientNet model to inform farmers of disease status, and disease diagnosis service is proposed by experts. It is possible to obtain an image of the strawberry growth stage and quickly receive expert feedback after transmitting the disease diagnosis analysis results to farmers applications using the learned EfficientNet model. As a data set, farmers who are actually operating facility cultivation were recruited and images were acquired using the system, and the problem of lack of data was solved by using the draft image taken with a cell phone. Experimental results show that the accuracy of EfficientNet B0 to B7 is similar, so we adopt B0 with the fastest inference speed. For performance improvement, Fine-tuning was performed using a pre-trained model with ImageNet, and rapid performance improvement was confirmed from 100 Epoch. The proposed service is expected to increase production by quickly detecting initial diseases.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Journal of Korean Home Economics Education Association
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• v.17 no.3 s.37
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• pp.109-121
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• 2005

The Purpose of this research was to find out differences in motivation and achievement by gender between traditional group and ARCS group. The subjects were 217 first graders in six different classes from a middle school in Daegu. Each Class had Five lessons during 5 weeks. The collected data were analyzed using descriptive statistics, ANOVA and ANCOVA by SPSS 10.0 program. The results of this study are as follows : First, the ARCS group showed significantly higher score than the control group in motivation. But no significant difference was found between boys and girls and in interactive effects. Statistically significant differences were found in three factors of motivation - attention, relevance and satisfaction - from the ARCS model, but no significant difference in confidence. Second, there was no significant difference in students' achievement.

• Journal of the Korea Society for Simulation
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• v.22 no.1
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• pp.53-61
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• 2013

The purpose of this paper is to examine how the humans learn and perceive the weight of objects corresponding to visual information in virtual environment. We conducted two kinds of load-on-tasks with two virtual objects that have same weight but different visual cues; have same visual cues but changed weight by trails. We found that the subject could not generate appropriate force for the smaller and changed weight objects in the beginning of the trials. the discrepancy between the expected weight and actual force consequences sue to visually invoked size and previous experience made subjects perceive the small object were heavier. one the other hand, after the tasks were repeated, the subject responded the weights were the same or very similar when the mismatch between the expected weight and the actual weight became vanished. this means that the sensorimotor feedback influences the anticipatory control scheme and weight perception aggressively in virtual environment.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.75-81
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• 2021

Since plant growth is greatly influenced by moisture, it is important to control the soil to have optimal moisture for the plant being grown. Recently, researches on automatically analyzing plant growth information including soil moisture using spectral images are being conducted. However, hyperspectral images are difficult to use due to huge amount of data appearing in spectral bands. In this paper, we propose a method to solve the complexity of hyperspectral images using a CNN. Since the proposed method automatically analyzes the entire band of the target hyperspectral using deep learning, there is no need to make an effort to find a specific band for analysis of each image. In order to show the effectiveness of the proposed system, we conduct an experiment to analyze moistures using hyperspectral images obtained from soil.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1171-1181
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• 2022

Front Collision-Avoidance Assist (FCA) or Smart Cruise Control (SCC) is installed in a modern vehicle, and the amount of data exchange between ECUs increases rapidly. Therefore, Automotive Ethernet, especially SOME/IP, which supports wide bandwidth and two-way communication, is widely adopted to overcome the bandwidth limitation of traditional CAN communication. SOME/IP is a standard protocol compatible with various automobile operating systems, and improves connectivity between components in the vehicle. However, no encryption or authentication process is defined in the SOME/IP protocol itself. Therefore, there is a need for a security study on the SOME/IP protocol. This paper proposes a deep learning-based intrusion detection system in SOME/IP and performs six attacks to confirm the performance of the intrusion detection system.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.310-315
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• 2017

In a centralized control structure, the software defined network controller manages all openflow enabled switched in a data plane and controls the telecommunication between all hosts. In addition, the network manager can easily deploy the network function to the application layer with a software defined network controller. For this reason, many methods for network management using a software defined network concept have been proposed. The main policies for network management are related to traffic Quality of Service and resource management. In order to provide Quality of Service and load distribution for network users, we propose an efficient routing method using a naive bayesian algorithm and transmission delay estimation module. In this method, the forwarding path is decided by flow class and estimated transmission delay result in the software defined network controller. With this method, the load on the network node can be distributed to improve overall network performance. The network user also gets better dynamic Quality of Service.