• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.3
• /
• pp.490-493
• /
• 2021

This paper proposes an embedded system that detects mask and face recognition based on a microprocessor instead of Nvidia Jetson Board what is popular development kit. We use a class of efficient models called Mobilenets for mobile and embedded vision applications. MobileNets are based on a streamlined architechture that uses depthwise separable convolutions to build light weight deep neural networks. The device used a Maix development board with CNN hardware acceleration function, and the training model used MobileNet_V2 based SSD(Single Shot Multibox Detector) optimized for mobile devices. To make training model, 7553 face data from Kaggle are used. As a result of test dataset, the AUC (Area Under The Curve) value is as high as 0.98.

• Journal of Korea Multimedia Society
• /
• v.17 no.1
• /
• pp.1-7
• /
• 2014

Hippocampus is an important part of brain which is related with early memory storage and spatial navigation. By observing the anatomy of hippocampus, some brain diseases effecting human memory (e.g. Alzheimer, schizophrenia, etc.) can be diagnosed and predicted earlier. The diagnosis process is highly related with hippocampus segmentation. In this paper, hippocampus segmentation using Active Shape Model, which not only works based on image intensity, but also by using prior knowledge of hippocampus shape and intensity from the training images, is proposed. The results show that ASM is applicable in segmenting hippocampus from whole brain MR image. It also shows that adding more images in the training set results in better accuracy of hippocampus segmentation.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.7
• /
• pp.31-37
• /
• 2017

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.6
• /
• pp.2709-2729
• /
• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.11
• /
• pp.75-84
• /
• 2018

Social engineering attack means to get information of Social engineering attack means to get information of opponent without technical attack or to induce opponent to provide information directly. In particular, social engineering does not approach opponents through technical attacks, so it is difficult to prevent all attacks with high-tech security equipment. Each company plans employee education and social training as a countermeasure to prevent social engineering. However, it is difficult for a security officer to obtain a practical education(training) effect, and it is also difficult to measure it visually. Therefore, to measure the social engineering threat, we use the results of social engineering training result to calculate the risk by system asset and propose a attack graph based probability. The security officer uses the results of social engineering training to analyze the security threats by asset and suggests a framework for quick security response. Through the framework presented in this paper, we measure the qualitative social engineering threats, collect system asset information, and calculate the asset risk to generate probability based attack graphs. As a result, the security officer can graphically monitor the degree of vulnerability of the asset's authority system, asset information and preferences along with social engineering training results. It aims to make it practical for companies to utilize as a key indicator for establishing a systematic security strategy in the enterprise.

• The Journal of Korean Association of Computer Education
• /
• v.20 no.4
• /
• pp.37-46
• /
• 2017

Since 2018, the level of informatics education that is mandatory in junior high schools depends on the subject matter expertise of Informatics & Computer teachers. The purpose of this study is to analyze whether secondary teacher training institutes provide curriculum that guarantees the subjectivity of Informatics & Computer teachers. In order to achieve the goal, this study first compares curriculum courses for educating Informatics & Computer teachers of Korea secondary teacher training institutes with subjects based on the content system of J07-CS, the informatics education in Japan. Second, we compare the basic subjects offered by the Ministry of Education with the vocational subjects. Third, we analyzed the basic subjects of each university. As a result of the study, the number of informatics-related courses opened by Korean secondary teacher training institutions was insufficient compared to the number of subjects in J07-CS. Even though the standard of comparison was limited to basic subjects, the content elements were insufficient, and the ratio of the basic subjects of each university was low. In order to achieve the goal of informatics education from 2018, it is urgent to improve the curriculum of secondary education teachers.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.9
• /
• pp.269-277
• /
• 2024

This study aims to explore the key factors for the systematic development and activation of a MyData-based platform for SW·AI education and training programs recently initiated by the government. To achieve this, a research model based on the Value-based Adoption Model (VAM) was established, and a survey was conducted with 178 participants who had experience in SW·AI education and training programs. The research model was validated using confirmatory factor analysis and Partial Least Squares Structural Equation Modeling (PLS-SEM). The main findings of the study are as follows: First, transparency and self-determination significantly influenced perceived benefits, while technical effort and security significantly influenced perceived risks. Second, perceived benefits positively affected the intention to use the platform, whereas perceived risks did not show a significant impact. Based on these results, this study suggests implications for the systematic development and activation of a MyData-based platform in the field of SW·AI education and training.

• Korean Management Science Review
• /
• v.14 no.2
• /
• pp.161-184
• /
• 1997

This research examines the importance of IT training/education, present situation and possible suggestion for the successful training/education. The research method adopts a comparative analytical approach based on questionnaire survey responses from three work groups - managers, employees, and union representatives - drawn from five sample Korean banks. The evidence indicates that all three groups agree that IT improves banking efficiency and reduces job repetitiveness, but their job satisfaction level with IT-based work is surprisingly very low. The main reasons are mainly lack of training/education and poor user manuals. Also the research shows that most respondents would like to get further training/education to more adequately fit them for their jobs. Those from banks which invested in continuing training/education revealed more positive work attitudes and higher job satisfaction.

• Journal of Korea Multimedia Society
• /
• v.22 no.1
• /
• pp.44-54
• /
• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• The Journal of Information Systems
• /
• v.31 no.1
• /
• pp.1-24
• /
• 2022

Purpose The purpose of this study is to investigate the factors that affect the user satisfaction and continuous use intention of the improved ATCIS in the Korean Army. Design/methodology/approach Based on the various theories in relation to IT continuance, user satisfaction was identified as the main factor with regard to the continuous use intention of the improved ATCIS. In addition, computer self-efficacy, education-training, and system quality were hypothesized as antecedent variables to user satisfaction, and information security stress was set as a moderating variable for these relationships. Findings Survey results show that computer self-efficacy, education and training, and system quality had a positive effect on user satisfaction, and information security stress was found to moderate these relationships. The effects of computer self-efficacy and education-training on user satisfaction were higher in the group with low information security stress. However, the relationship between system quality and user satisfaction was higher in the group with high information security stress. User satisfaction is found to have a positive effect on the continuous use intention even with habit considered as a control variable.