• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.47-50
• /
• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.05a
• /
• pp.483-486
• /
• 2020

Age estimation and gender classification for human is a classic problem in computer vision. Almost research focus just only one task and the models are too heavy to run on low-cost system. In our research, we aim to apply multitasking learning to perform both task on a lightweight model which can achieve good precision on embedded system in the real time.

• Journal of IKEEE
• /
• v.26 no.1
• /
• pp.56-61
• /
• 2022

Various efforts have been made worldwide to respond to environmental problems such as climate change. Research on artificial intelligence (AI)-based energy management has been widely conducted as the most effective way to alleviate the climate change problem. In particular, buildings that account for more than 20% of the total energy delivered worldwide have been focused as a target for energy management using the building energy management system (BEMS). In this paper, we propose a multi-armed bandit (MAB)-based energy management algorithm that can efficiently decide the energy consumption level of the lighting system in each room of the building, while minimizing the discomfort levels of occupants of each room.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.6
• /
• pp.303-310
• /
• 2023

A single vision system limits the ability to accurately understand the spatial constraints and interactions between robots and dynamic workers caused by gantry robots and collaborative robots during production manufacturing. In this paper, we propose a 3D pose registration method for dynamic workers based on a multi-domain vision system for safety monitoring in manufacturing environments. This method uses OpenPose, a deep learning-based posture estimation model, to estimate the worker's dynamic two-dimensional posture in real-time and reconstruct it into three-dimensional coordinates. The 3D coordinates of the reconstructed multi-domain vision system were aligned using the ICP algorithm and then registered to a single 3D coordinate system. The proposed method showed effective performance in a manufacturing process environment with an average registration error of 0.0664 m and an average frame rate of 14.597 per second.

• The Journal of the Korea Contents Association
• /
• v.5 no.4
• /
• pp.197-203
• /
• 2005

Interactivity is very important at ubiquitous e-Learning system oriented multi platform because online study is accomplished by multi media. In this thesis promotion for interactivity is designed at online study. By cyber education with supposed promotion used for a feedback. From now on a special research of study design should be made for interactivity and effectiveness using human sensibility ergonomics.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2012.07a
• /
• pp.13-14
• /
• 2012

본 논문에서는 멀티 터치 테이블을 중심으로 u-Learning 시스템을 제안한다. 멀티 터치 테이블에서 수업 진행 중 교사는 모바일로 멀티 터치 테이블을 제어함으로써 수업을 진행 및 관리하고, 학생들은 마커를 이용하여 멀티 터치 테이블과의 인터랙션을 통해 웹과 자동화된 과제 시스템을 이용함으로써 능동적인 학습참여를 할 수 있게 된다.

• Journal of Korea Multimedia Society
• /
• v.21 no.12
• /
• pp.1459-1466
• /
• 2018

Robots which detect human and recognize action are important factors for human interaction, and many researches have been conducted. Recently, deep learning technology has developed and learning based robot's technology is a major research area. These studies require a database to learn and evaluate for intelligent human perception. In this paper, we propose a multi-modal sensor-based image database condition considering the security task by analyzing the image database to detect the person in the outdoor environment and to recognize the behavior during the running of the robot.

• Journal of Fisheries and Marine Sciences Education
• /
• v.15 no.2
• /
• pp.184-192
• /
• 2003

When the present Learning System for Computer-Related Subjects Using WBI is implemented on the Web with the above characteristics to help students to study computer subjects without any limitations of time or space, they can easily attain the goals of learning, have computer-utilizing abilities or information capacity, and enhance their capabilities for self-initiative learning. This system enables the learners to carry out 'plan-do-see' for the contents of learning initiatively. The learners can study the practice part of the curriculum using multi-media, such as motion pictures, voices, images, and sound effects, vividly with a sense of actual presence. It helps the students to have an active attitude toward leaning afterward. without meeting the teacher or without any storage media, the leaners can submit their assignments or materials for performance evaluation via the Internet.

• ETRI Journal
• /
• v.38 no.6
• /
• pp.1207-1217
• /
• 2016

The general method of machine learning has encountered disadvantages in terms of the significant amount of time and effort required for feature extraction and engineering in natural language processing. However, in recent years, these disadvantages have been solved using deep learning. In this paper, we propose a mention pair (MP) model using deep learning, and a system that combines both rule-based and deep learning-based systems using a guided MP as a coreference resolution, which is an information extraction technique. Our experiment results confirm that the proposed deep-learning based coreference resolution system achieves a better level of performance than rule- and statistics-based systems applied separately

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.8
• /
• pp.958-964
• /
• 2016

In this paper, we propose an i-vector based language recognition system to identify the spoken language of the speaker, which uses multiple discriminative deep neural network (DNN) models analogous to the multi-class support vector machine (SVM) classification system. The proposed model was trained and tested using the i-vectors included in the NIST 2015 i-vector Machine Learning Challenge database, and shown to outperform the conventional language recognition methods such as cosine distance, SVM and softmax NN classifier in open-set experiments.