• Journal of the Korean Institute of Intelligent Systems
• /
• v.27 no.2
• /
• pp.157-163
• /
• 2017

Due to economic development and advancement of medical science, an aging society where the proportion of the elderly population increases is coming. Korea's aging speed is growing rapidly compared to that of other developed countries. The physical and mental abilities of elderly people with aging are getting worse more and more. They want a kind of auxiliary system in order to mitigate and prevent their weakness. The supplementary system can greatly contribute to improving the quality of life for elderly people. In particular, some devices that include muscle strengthening and cognitive and balance ability enhancement are useful for the most older people. In this paper, we introduce a development of human care contents based rehabilitation equipment to enhance these functions, and present its usability evaluation. The evaluation is conducted for rehabilitation specialists as well as expected users and their results are analyzed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.6
• /
• pp.1120-1127
• /
• 2008

This paper presents implementation of localization system using UWB (Ultra-Wide Band) sensors and its experimental results along with development of context-aware services. In order for precise measurement of position, we experimented various conditions of pitch angles, yaw angles, number of sensors, height of tags along with measuring errors at each installation. As an application examples of the location tracking system, we developed an intelligent health training management system based on context-aware technology. The system provides appropriate training schedule to a trainee by recognizing position of the trainee and current status of gymnastic equipments and note the usage of the equipment through a personal digital assistant (PDA). Error compensation on position data and moving direction of the trainee was necessary for context-aware service. Hence, we proposed an error compensation algorithm using velocity of the trainee. Experimental results showed that proposed algorithm had made error data reduce by 30% comparing with the data without applying the algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.3
• /
• pp.527-535
• /
• 2023

To develop experts capable of responding to cyber security incidents, numerous institutions have established cyber training facilities to cultivate security professionals equipped with effective defense strategies. However, these challenges such as limited resources, scenario-based content development, and cost constraints. To address these issues, this paper proposes a virtual infrastructure variation generation framework. It provides customized, diverse IT infrastructure environments for each organization, allowing cyber defense trainers to accumulate a wide range of experiences. By leveraging Infrastructure-as-Code (IaC) containers and employing Word2Vec, a natural language processing model, mutable code elements are extracted and trained, enabling the generation of new code and presenting novel container environments.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.686-689
• /
• 1999

발전소 운전원 훈련용 시뮬레이터는 중앙제어실(MCR:Main Control Room) 운전원의 운전결과가 실시간 운영체계하에 발전소 프로세스 모델 및 컴퓨터 시스템과 통합되어 실제 발전소와 같은 반응을 운전원에게 제공하여야 한다. 따라서 시뮬레이터에는 프로세스의 동특성 이외에도 제어시스템의 모델링 및 운전원과 인터페이스되는 제어기들이 실제적으로 구성되어야하며, 이들 제어기와 컴퓨터시스템간 다양한 입력간의 통신 및 동기(Synchronization)가 중요하다. 본 논문에서는 원자력 발전소 시뮬레이터에서 쓰이는 지능형 계기(Intelligent Type Instrument)로 분류된 기기를 살펴보고 시뮬레이션을 위해 개발 구현된 방법들을 기술하고자한다.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.19-31
• /
• 2022

In Machine learning, we usually divide the entire data into training data and test data, train the model using training data, and use test data to determine the accuracy and generalization performance of the model. In the case of models with low generalization performance, the prediction accuracy of newly data is significantly reduced, and the model is said to be overfit. This study is about a method of generating training data based on central limit theorem and combining it with existed training data to increase normality and using this data to train models and increase generalization performance. To this, data were generated using sample mean and standard deviation for each feature of the data by utilizing the characteristic of central limit theorem, and new training data was constructed by combining them with existed training data. To determine the degree of increase in normality, the Kolmogorov-Smirnov normality test was conducted, and it was confirmed that the new training data showed increased normality compared to the existed data. Generalization performance was measured through differences in prediction accuracy for training data and test data. As a result of measuring the degree of increase in generalization performance by applying this to K-Nearest Neighbors (KNN), Logistic Regression, and Linear Discriminant Analysis (LDA), it was confirmed that generalization performance was improved for KNN, a non-parametric technique, and LDA, which assumes normality between model building.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.382-382
• /
• 2020

과거 많은 연구에서 다수의 모형의 결과를 이용한 앙상블 방법론은 인공지능 모형 (artificial neural network)의 예측 능력에 향상을 갖고 온다 논하였다. 본 연구에서는 미계측유역의 저수량(low flow)의 예측을 위하여 Jittering을 기반으로 한 인공지능 모형을 제시하고자 한다. 기본적인 방법론은 설명변수들에게 백색 잡음(white noise)를 삽입하여 훈련되는 자료를 증가시키는 것이다. Jittering을 기반으로 한 인공지능 모형에 대한 효과를 검증하기 위하여 본 연구에서는 Multi-output neural network model을 기반으로 모형을 구축하였다. 다음으로 Jittering을 기반으로 한 앙상블 모형을 variable importance measuring algorithm과 결합시켜서 유역특성치와 예측되는 저수량의 특성치들의 관계를 추론하였다. 본 연구에서 사용되는 방법론들의 효용성을 평가하기 위해서 미동북부에 위치하고 있는 총 207개의 유역을 사용하였다. 결과적으로 본 연구에서 제시한 Jittering을 기반으로 한 인공지능 앙상블 모형은 단일예측모형 (single modeling approach)을 정확도 측면에서 우수한 것으로 확인되었다. 또한, 적은 숫자의 앙상블 모형에서도 그 정확성이 단일예측모형보다 우수한 것을 확인하였다. 마지막으로 본 연구에서는 유역특성치들의 효과가 살펴보고자 하는 저수량의 특성치들에 따라서 일관적으로 영향을 미치거나 그 중요도가 변화하는 것을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.7
• /
• pp.840-845
• /
• 2005

Intelligent agents to learn users' tendency have learn uscrs' tendency by sufficient users information and training time. When the intelligent agents is used in ubiquitous environment, users must wait for intelligent agents to learn, so user may be can't get proper services. In this paper we proposed an intelligent P2P mobile agent system that can learn users' tendency in real-time by sharing users' resource. The system shared users contexts on four places and made feel groups which was composed of similar users. When users' service which had the highest correlation coefficient in the peer groups was suggested, users were satisfied over $80\%$.

• Journal of Industrial Convergence
• /
• v.20 no.5
• /
• pp.61-68
• /
• 2022

In recent years, malicious codes are being produced using the developing information and communication technology, and it is insufficient to detect them with the existing detection system. In order to accurately and efficiently detect and respond to such intelligent malicious code, an intelligent detection model is required, and in order to maximize detection performance, it is important to train with the main characteristic information set of the malicious code. In this paper, we proposed a technique for designing an intelligent detection model and generating the data required for model training as a set of key feature information through transformation, dimensionality reduction, and feature selection steps. And based on this, the main characteristic information was classified by malicious code. In addition, based on the classified characteristic information, we derived common characteristic information that can be used to analyze and detect modified or newly emerging malicious codes. Since the proposed detection model detects malicious codes by learning with a limited number of characteristic information, the detection time and response are fast, so damage can be greatly reduced and Although the performance evaluation result value is slightly different depending on the learning algorithm, it was found through evaluation that most malicious codes can be detected.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.867-871
• /
• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.5
• /
• pp.927-934
• /
• 2023

With the recent growth of the companion animal market, various social issues related to companion animals have also come to the forefront. Notable problems include incidents of dog bites, the challenge of managing abandoned companion animals, euthanasia, animal abuse, and more. As potential solutions, a variety of training programs such as companion animal-focused broadcasts and educational apps are being offered. However, these options might not be very effective for novice caretakers who are uncertain about what to prioritize in training. While training apps that are relatively easy to access have been widely distributed, apps that allow users to directly engage in training and learn through hands-on experience are still insufficient. In this paper, we propose a more efficient AR-based mobile app for companion animal training, utilizing the Unity engine. The results of usability evaluations indicated increased user engagement due to the inclusion of elements that were previously absent. Moreover, training immersion was enhanced, leading to improved learning outcomes. With further development and subsequent verification and production, we anticipate that this app could become an effective training tool for novice caretakers planning to adopt companion animals, as well as for experienced caretakers.