• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.11-19
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• 2022

Recently, various researches on medical image generation have been suggested, and it becomes crucial to accurately evaluate the quality and diversity of the generated medical images. For this purpose, the expert's visual turing test, feature distribution visualization, and quantitative evaluation through IS and FID are evaluated. However, there are few methods for quantitatively evaluating medical images in terms of fidelity and diversity. In this paper, images are generated by learning a chest CT dataset of non-small cell lung cancer patients through DCGAN and PGGAN generative models, and the performance of the two generative models are evaluated in terms of fidelity and diversity. The performance is quantitatively evaluated through IS and FID, which are one-dimensional score-based evaluation methods, and Precision and Recall, Improved Precision and Recall, which are two-dimensional score-based evaluation methods, and the characteristics and limitations of each evaluation method are also analyzed in medical imaging.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.8-14
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• 2023

The majority of IoT devices already employ AIoT, however there are still numerous issues that need to be resolved before AI applications can be deployed. In order to more effectively distribute IoT edge resources, this paper propose a machine learning-based approach to managing IoT edge resources. The suggested method constantly improves the allocation of IoT resources by identifying IoT edge resource trends using machine learning. IoT resources that have been optimized make use of machine learning convolution to reliably sustain IoT edge resources that are always changing. By storing each machine learning-based IoT edge resource as a hash value alongside the resource of the previous pattern, the suggested approach effectively verifies the resource as an attack pattern in a distributed AIoT context. Experimental results evaluate energy efficiency in three different test scenarios to verify the integrity of IoT Edge resources to see if they work well in complex environments with heterogeneous computational hardware.

• Journal of Creative Information Culture
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• v.5 no.3
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• pp.307-317
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• 2019

This study is an example of the education of block coding and physical computing teaching tool for preservice teachers at the college of education. The students were familiar with coding and improved their coding skills in solving various problems through 'Entry' that support block coding. In addition, the students configured the computing system with various input / output devices of the physical computing teaching tool and controlled things through programming and produced the educational portfolio to experience the whole process of problem analysis, design, implementation, and testing in coding. We applied Flow based coding and Pair programming as the teaching methods, and the results of the survey to measure the effectiveness of the study show that students have a good understanding of the entry and physical computing teaching tool and using the combination of the entry and physical computing teaching tool were more effective in learning than the Entry-only coding. In addition, it was confirmed that the effect of Pair programming applied in the physical computing teaching tool.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.161-166
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• 2023

In recent years, new and variant hacking of binary codes has increased, and the limitations of techniques for detecting malicious codes in source programs and defending against attacks are often exposed. Advanced software security vulnerability detection technology using machine learning and deep learning technology for binary code and defense and response capabilities against attacks are required. In this paper, we propose a malware clustering method that groups malware based on the characteristics of the taint information after entering dynamic taint information by tracing the execution path of binary code. Malware vulnerability detection was applied to a three-layered Few-shot learning model, and F1-scores were calculated for each layer's CPU and GPU. We obtained 97~98% performance in the learning process and 80~81% detection performance in the test process.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.107-114
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• 2022

In this paper, we have enhanced the risk prediction of hypertension using the feature selection method in the Korean National Health and Nutrition Examination Survey (KNHANES) database of the Korea Centers for Disease Control and Prevention. The study identified various risk factors correlated with chronic hypertension. The paper is divided into three parts. Initially, the data preprocessing step of removes missing values, and performed z-transformation. The following is the feature selection (FS) step that used a factor analysis (FA) based on the feature selection method in the dataset, and feature importance (FI) and multicollinearity analysis (MC) were compared based on FS. Finally, in the predictive analysis stage, it was applied to detect and predict the risk of hypertension. In this study, we compare the accuracy, f-score, area under the ROC curve (AUC), and mean standard error (MSE) for each model of classification. As a result of the test, the proposed MC-FA-RF model achieved the highest accuracy of 80.12%, MSE of 0.106, f-score of 83.49%, and AUC of 85.96%, respectively. These results demonstrate that the proposed MC-FA-RF method for hypertension risk predictions is outperformed other methods.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.47-55
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• 2023

This paper presents a method for predicting the threat index of combat systems using Gradient Boosting Regressors and Support Vector Regressors among machine learning models. Currently, combat systems are software that emphasizes safety and reliability, so the application of AI technology that is not guaranteed to be reliable is restricted by policy, and as a result, the electrified domestic combat systems are not equipped with AI technology. However, in order to respond to the policy direction of the Ministry of National Defense, which aims to electrify AI, we conducted a study to secure the basic technology required for the application of machine learning in combat systems. After collecting the data required for threat index evaluation, the study determined the prediction accuracy of the trained model by processing and refining the data, selecting the machine learning model, and selecting the optimal hyper-parameters. As a result, the model score for the test data was over 99 points, confirming the applicability of machine learning models to combat systems.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.73-80
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• 2022

Facial expression is an important means of representing characteristics in movies and animations, and facial capture technology can support the production of facial animation for 3D characters more quickly and effectively. Blendshape techniques are the most widely used methods for producing high-quality 3D face animations, but traditional blendshape often takes a long time to produce. Therefore, the purpose of this study is to achieve results that are not far behind the effectiveness of traditional production to reduce the production period of blend shape. In this paper, in order to make a blend shape, the method of using the cross-model to convey the blend shape is compared with the traditional method of making the blend shape, and the validity of the new method is verified. This study used kit boy developed by Unreal Engine as an experiment target conducted a facial capture test using two blend shape production techniques, and compared and analyzed the facial effects linked to blend shape.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.259-262
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• 2022

정보화 시대 스마트폰이 대중화되고 실시간 인터넷 사용이 가능해짐에 따라, 본인을 식별하기 위한 사용자 인증이 필수적으로 요구된다. 대표적인 사용자 인증 기술로는 아이디와 비밀번호를 이용한 비밀번호 인증이 있지만, 키보드로부터 입력받는 이러한 인증 정보는 시각 장애인이나 손 사용이 불편한 사람, 고령층과 같은 사람들이 많은 서비스로부터 요구되는 아이디와 비밀번호를 기억하고 입력하기에는 불편함이 따를 뿐만 아니라, 키로거와 같은 공격에 노출되는 문제점이 존재한다. 이러한 문제점을 해결하기 위하여, 자신의 신체의 특징을 활용하는 생체 인증이 대두되고 있으며, 그중 목소리로 사용자를 인증한다면, 효과적으로 비밀번호 인증의 한계점을 극복할 수 있다. 이러한 화자 인식 기술은 KT의 기가 지니와 같은 음성 인식 기술에서 활용되고 있지만, 목소리는 위조 및 변조가 비교적 쉽기에 지문이나 홍채 등을 활용하는 인증 방식보다 정확도가 낮고 음성 인식 오류 또한 높다는 한계점이 존재한다. 상기 목소리를 활용한 사용자 인증 기술인 화자 인식 기술을 활용하기 위하여, 사용자 목소리를 학습시켰으며, 목소리의 주파수를 추출하는 MFCC 알고리즘을 이용해 테스트 목소리와 정확도를 측정하였다. 그리고 악의적인 공격자가 사용자 목소리를 흉내 내는 경우나 사용자 목소리를 마이크로 녹음하는 등의 방법으로 획득하였을 경우에는 높은 확률로 인증의 우회가 가능한 것을 검증하였다. 이에 따라, 더욱 효과적으로 화자 인식의 정확도를 향상시키기 위하여, 본 논문에서는 목소리에 잡음을 섞는 방법으로 화자를 인식하는 방안을 제안한다. 제안하는 방안은 잡음이 정확도에 매우 민감하게 반영되기 때문에, 기존의 인증 우회 방법을 무력화하고, 더욱 효과적으로 목소리를 활용한 화자 인식 기술을 제공할 것으로 사료된다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.177-182
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• 2024

In this paper, the purpose of this paper is to predict and prevent the risk of crowd concentration in advance for possible future crowd accidents based on the Itaewon crush accident in Korea on October 29, 2022. In the case of a single CCTV, the administrator can determine the current situation in real time, but since the screen cannot be seen throughout the day, objects are detected using YOLOv4, which learns images taken with CCTV angle, and safety accidents due to crowd concentration are prevented by notification when the number of clusters exceeds. The reason for using the YOLO v4 model is that it improves with higher accuracy and faster speed than the previous YOLO model, making object detection techniques easier. This service will go through the process of testing with CCTV image data registered on the AI-Hub site. Currently, CCTVs have increased exponentially in Korea, and if they are applied to actual CCTVs, it is expected that various accidents, including accidents caused by crowd concentration in the future, can be prevented.

• Archives of design research
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• v.20 no.3 s.71
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• pp.151-162
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• 2007

How can new media devices and networks provide an effective response to the world's growing sector of cultural and historically-minded travelers? This study emerged from the question of how mobile handsets can change the nature of cultural and historical tourism in ubiquitous city environments. As wireless network and mobile IT have rapidly developed, it becomes possible to deliver cultural and historical information on the site through mobile handset as a tour guidance system. The paper describes the development of a new type of mobile tourism platform for site-specific cultural and historical information. The central objective of the project was to organize this cultural and historical walking tour around the mobile handset and its unique advantages (i.e. portability, multi-media capacity, access to wireless internet, and location-awareness potential) and then integrate the tour with a historical story and role-playing game that would deepen the mobile user's interest in the sites being visited, and enhance his or her overall experience of the area. The project was based on twelve locations that were culturally and historically significant to Korean War era in Busan. After the mobile tour game prototype was developed for this route, it was evaluated at the 10th PIFF (Pusan International Film Festival). After use test, some new strategies for developing mobile "edutainment content" to deliver cultural historical contents of the location were discussed. Combining 'edutainment' with a cultural and historical mobile walking tour brings a new dimension to existing approaches of the tourism and mobile content industry.