• International journal of advanced smart convergence
• /
• v.11 no.3
• /
• pp.49-55
• /
• 2022

Hair styling has a significant influence on human social perception. An increasing number of people are learning hair styling and obtaining hair designer licenses. However, it takes a considerable amount of money and time to learn professional hairstyle and beauty techniques for hair styling. Since COVID-19, there has been a growing need for offline and video lectures due to the decline in onsite training opportunities. This study provides a field practice environment in which real hair beauty is performed in a virtual space. Further, the hairstyle that is most similar to the user's hair taken with a webcam or mobile phone is determined through an image matching system using the speeded up robust features (SURF) method. The matching hairstyle was created into a three-dimensional (3D) hair model. The created 3D hair model uses a head-mounted display (HMD) and a controller that enables finger tracking through mapping to reproduce the haircutting scissors' motion while providing a feeling of real hair beauty.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.881-882
• /
• 2020

최근 딥 러닝 (deep learning) 기술의 큰 발전으로 기존 기계 학습 분야의 기술들이 성공적으로 해결하지 못하던 많은 문제들을 해결할 수 있게 되었다. 이러한 딥 러닝의 학습 과정은 매우 많은 연산을 요구하기에 다수의 노드들로 모델을 학습하는 분산 학습 (distributed training) 기술이 연구되었다. 대표적인 분산 학습 기법으로 파라미터 서버 기반의 분산 학습 기법들이 있으며, 이 기법들은 파라미터 서버 노드가 학습의 병목이 될 수 있다는 한계를 갖는다. 본 논문에서는 이러한 파라미터 서버 병목 문제를 해결하는 파라미터 샤딩 기법에 대해 소개하고, 각 기법 별 학습 성능을 비교하고 그 결과를 분석하였다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.07a
• /
• pp.695-696
• /
• 2023

본 논문은 뇌-컴퓨터 인터페이스(BCI)를 활용한 게임 개발을 통해 집중력 부족 문제를 해결하기 위한 방안을 제시한다. BCI 기술은 사용자의 뇌파 신호를 분석하여 게임에 적용할 수 있으며, 그에 따라 뇌파 신호를 활용한 집중력 향상을 도모해 볼 수 있는 게임을 설계하였다. Unity 게임 개발 환경과 Emotiv Insight 장비를 사용하여 게임을 구현하였으며, 사용자는 뇌파 신호를 통해 플레이어를 제어하여 게임을 즐길 수 있다. 연구 결과는 뇌파 기반 게임이 사용자의 집중력 향상에 도움을 줄 수 있는 잠재력을 보여준다.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 1998.10a
• /
• pp.613-622
• /
• 1998

This study investigates the factors that affect the adoption of object orientation . One major research question is addressed : What are the factors that significantly influeence the adoption of object orientation ? This study especially explores the impact of independent variables on the level of actual usage of object orientation. The independent variables are classified into four categories as follow ; (1) Individual factors : amount of experience in using the structures methods and level of openness toward new technologies ; (2) Managerical factors : perceived management support and training : (3) Organizatinal factors : number of IS professionals in the working group and that in organization ; and (4) Environmental factors ; accessibility to technology champions and software hardware environment supporting object orientation. A Questionnaire measuring the above variables was utilized to investigate the effect of these variables on the dependent variable, the actual usage of object orientation. The structured questionnaire was administered to Data Processing Management Association (DPMA) professionals in U. S. The results of this study revealed several important findings with most results being consistent with expectations based on related theory. The personal openness toward new technologies, perceived management support, training, and hardware/software environment were highly related to the usage of object orientation. This study suggested an empiriclal basis for understanding the early adoption of object orientation in organization.

• 한국정보교육학회:학술대회논문집
• /
• 2010.08a
• /
• pp.51-57
• /
• 2010

When you look at computer training techniques, generally you will see an unbalanced focus towards the software applications being used. Office programs, including software applications, or simply the ability to operate has been assigned in the attempt to develop more information. Omitted, while a students thought process dealing with computer applications is usually clear and effective, the functional oriented tasks involved are time consuming. In order to keep up with the pace of today changing requirements, creativity and problems solving ability is a necessity. These are the areas in which both our training techniques are inefficient, and the resulting ability of student's is unsatisfactory. In this study of 5thand6thgradelevelcomputerteachingtechniques.

• Proceedings of the KIEE Conference
• /
• 2003.11b
• /
• pp.171-174
• /
• 2003

In this paper, a Learning module for a reconfigurable neural network processor(ERNIE) was proposed for an On-chip learning. The existing reconfigurable neural network processor(ERNIE) has a much better performance than the software program but it doesn't support On-chip learning function. A learning module which is based on Back Propagation algorithm was designed for a help of this weak point. A pipeline structure let the learning module be able to update the weights rapidly and continuously. It was tested with five types of alphabet font to evaluate learning module. It compared with C programed neural network model on PC in calculation speed and correctness of recognition. As a result of this experiment, it can be found that the neural network processor(ERNIE) with learning module decrease the neural network training time efficiently at the same recognition rate compared with software computing based neural network model. This On-chip learning module showed that the reconfigurable neural network processor(ERNIE) could be a evolvable neural network processor which can fine the optimal configuration of network by itself.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.1
• /
• pp.52-58
• /
• 2005

We have developed a Group Method of Data Handling (GMDH) type algorithm for designing multi-layered neural networks. The algorithm is general enough that it will accept any number of inputs and any sized training set. Each neuron of the resulting network is a function of two of the inputs to the layer. The equation for each of the neurons is a quadratic polynomial. Several forms of the equation are tested for each neuron to make sure that only the best equation of two inputs is kept. All possible combinations of two inputs to each layer are also tested. By carefully testing each resulting neuron, we have developed an algorithm to keep only the best neurons at each level. The algorithm's goal is to create as accurate a network as possible while minimizing the size of the network. Software was developed to train and simulate networks using our algorithm. Several applications were modeled using our software, and the result was that our algorithm succeeded in developing small, accurate, multi-layer networks.

• Journal of Information Processing Systems
• /
• v.9 no.3
• /
• pp.395-404
• /
• 2013

In order to classify a web page as being benign or malicious, we designed 14 basic and 16 extended features. The basic features that we implemented were selected to represent the essential characteristics of a web page. The system heuristically combines two basic features into one extended feature in order to effectively distinguish benign and malicious pages. The support vector machine can be trained to successfully classify pages by using these features. Because more and more malicious web pages are appearing, and they change so rapidly, classifiers that are trained by old data may misclassify some new pages. To overcome this problem, we selected an adaptive support vector machine (aSVM) as a classifier. The aSVM can learn training data and can quickly learn additional training data based on the support vectors it obtained during its previous learning session. Experimental results verified that the aSVM can classify malicious web pages adaptively.

• Journal of the Korean Society of Physical Medicine
• /
• v.10 no.1
• /
• pp.23-35
• /
• 2015

PURPOSE: People with physical disabilities such as cerebral palsy usually experience obstacles when interacting with computer through conventional keyboard because of their motor disabilities. The purpose of this study is empirically compare of text entry(alphabet and word) speed and accuracy using the three different keyboard type on four students(male 2 and female 2) with cerebral palsy. METHODS: This research design used a replicated single-case experimental approach to compare the individual performance. An alternating treatments design was used to examine the effectiveness of standard QWERTY keyboard and alternative keyboard(mini and big keyboard) on computer access for students with cerebral palsy. To avoid changes in posture that influence a keyboard character entry training and evaluation was carried out using his sitting in a wheelchair. Compass software program used in this study as an assessment tool to measure speed and accuracy when performance of text entry(alphabet and word). This was repeated until the stable status of reaction time. RESULTS: As a result, the alternative keyboard seems to be the most effective device for students with cerebral palsy to perform text entry. But various factors such as peculiarity of motor disabilities, experience and preferences of the user are heavily related. CONCLUSION: Thus, we must perform the objective and systematic assessment for computer access and if sustained training is accomplished, it could to improve speed and accuracy of text entry(alphabet and word).

• IEMEK Journal of Embedded Systems and Applications
• /
• v.14 no.4
• /
• pp.207-218
• /
• 2019

The purpose of this study is to develop a model that can systematically study the whole learning process of machine learning. Since the existing model describes the learning process with minimum coding, it can learn the progress of machine learning sequentially through the new model, and can visualize each process using the tensor flow. The new model used all of the existing model algorithms and confirmed the importance of the variables that affect the target variable, survival. The used to classification training data into training and verification, and to evaluate the performance of the model with test data. As a result of the final analysis, the ensemble techniques is the all tutorial model showed high performance, and the maximum performance of the model was improved by maximum 5.2% when compared with the existing model using. In future research, it is necessary to construct an environment in which machine learning can be learned regardless of the data preprocessing method and OS that can learn a model that is better than the existing performance.