• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.627-628
• /
• 2021

본 논문은 제조 산업 현장에서의 업무 능력 향상을 위한 XR 교육훈련 콘텐츠의 가변적 시나리오를 제안한다. 가변적 시나리오를 적용한 XR 교육훈련 콘텐츠는 교육훈련 관리자가 자유롭게 시나리오를 가감할 수 있어 동일한 콘텐츠 내에서 다양한 시나리오로 콘텐츠를 구성할 수 있는 특징을 가진다. 이는 기존 하나의 시나리오로 반복되는 교육훈련의 한계를 해결할 수 있으며, 현장에서 발생할 수 있는 돌발적 상황 및 변화되는 업무 프로세스를 효과적으로 진행할 수 있을 것이다.

• Journal of Biomedical Engineering Research
• /
• v.28 no.3
• /
• pp.410-416
• /
• 2007

This paper presents quantitative analysis of a training system based on an unstable platform and a visual interactive system for improving sense of equilibrium. The training system consists of an unstable platform, a force plate, a safety harness, a monitoring device, and a personal-computer. To confirm the effects of the training system, fifteen young volunteers and five elderly volunteers went through a series of balance training using the system. During the training, we measured relevant parameters such as the time a subject maintain his or her center of pressure on a target, the time a subject moves his or her center of pressure to the target, and the mean absolute deviation of the trace before and after training with this system and training programs to evaluate the effects of the training. The results showed that the training system can successfully assess the gradual improvement of the postural control capability of the subject in the system and showed a possibility of improving balance of the subject. Moreover, the significant improvement in the postural capability of the elderly subject suggests that elderly subjects can benefit more from the training using the system for the improvement of sense of equilibrium.

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1145-1157
• /
• 2020

A convolution neural networks (CNNs) has demonstrated outstanding performance compared to other algorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers have proposed a residual network to ease the training for recognition accuracy improvement. In this study, a novel face recognition model based on game theory for call-over in the classroom was proposed. In the proposed scheme, an image with multiple faces was used as input, and the residual network identified each face with a confidence score to form a list of student identities. Face tracking of the same identity or low confidence were determined to be the optimisation objective, with the game participants set formed from the student identity list. Game theory optimises the authentication strategy according to the confidence value and identity set to improve recognition accuracy. We observed that there exists an optimal mapping relation between face and identity to avoid multiple faces associated with one identity in the proposed scheme and that the proposed game-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.

• Current Optics and Photonics
• /
• v.7 no.3
• /
• pp.237-247
• /
• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.

• Journal of the Korea Computer Graphics Society
• /
• v.24 no.4
• /
• pp.1-9
• /
• 2018

We propose an emotional training framework that can determine the initial symptom of schizophrenia by using emotional analysis method through facial expression change. We use Emotion API in Microsoft to obtain facial expressions and emotion values at the present time. We analyzed these values and recognized subtle facial expressions that change with time. The emotion states were classified according to the peak analysis-based variance method in order to measure the emotions appearing in facial expressions according to time. The proposed method analyzes the lack of emotional recognition and expressive ability by using characteristics that are different from the emotional state changes classified according to the six basic emotions proposed by Ekman. As a result, the analyzed values are integrated into the image color transfer framework so that users can easily recognize and train their own emotional changes.

• Journal of Information Processing Systems
• /
• v.11 no.4
• /
• pp.538-555
• /
• 2015

Event detection based on using features from a static grid can give poor results from the viewpoint of two main aspects: the position of the camera and the position of the event that is occurring in the scene. The former causes problems when training and test events are at different distances from the camera to the actual position of the event. The latter can be a source of problems when training events take place in any position in the scene, and the test events take place in a position different from the training events. Both issues degrade the accuracy of the static grid method. Therefore, this work proposes a method called a dynamic grid for event detection, which can tackle both aspects of the problem. In our experiment, we used the dynamic grid method to detect four types of event patterns: implosion, explosion, two-way, and one-way using a Multimedia Analysis and Discovery (MAD) pedestrian dataset. The experimental results show that the proposed method can detect the four types of event patterns with high accuracy. Additionally, the performance of the proposed method is better than the static grid method and the proposed method achieves higher accuracy than the previous method regarding the aforementioned aspects.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.77-87
• /
• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• International journal of advanced smart convergence
• /
• v.11 no.4
• /
• pp.57-67
• /
• 2022

The demand for personal training (PT), through which high exercise effects can be achieved within short-term, has recently increased. PT can achieve an exercise amount improvement effect, only if accurate postures are maintained upon performing PT, and exercise with inaccurate postures can cause injuries. However, research is insufficient on exercise amount comparisons and judging exercise accuracy on PT. This study proposes an exercise accuracy measurement algorithm and compares differences in exercise amounts according to exercise postures through experiments using a respiratory gas analyzer. The exercise accuracy measurement algorithm acquires Euler anglesfrom major body parts operated upon exercise through a motion device, based on which the joint angles are calculated. By comparing the calculated joint angles with each reference angle in each exercise step, the status of exercise accuracy is judged. The calculated results of exercise accuracy on squats, lunges, and push-ups showed 0.02% difference in comparison with actually measured results through a goniometer. As a result of the exercise amount comparison experiment according to accurate posture through a respiratory gas analyzer, the exercise amount was higher by 45.19% on average in accurate postures. Through this, it was confirmed that maintaining accurate postures contributes to exercise amount improvement.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.4
• /
• pp.73-80
• /
• 2016

Most fault prediction models have class imbalance problems because training data usually contains much more non-fault class modules than fault class ones. This imbalanced distribution makes it difficult for the models to learn the minor class module data. Data imbalance is much higher when severity-based fault prediction is used. This is because high severity fault modules is a smaller subset of the fault modules. In this paper, we propose severity-based models to solve these problems using the three sampling methods, Resample, SpreadSubSample and SMOTE. Empirical results show that Resample method has typical over-fit problems, and SpreadSubSample method cannot enhance the prediction performance of the models. Unlike two methods, SMOTE method shows good performance in terms of AUC and FNR values. Especially J48 decision tree model using SMOTE outperforms other prediction models.

• Journal of Information Processing Systems
• /
• v.15 no.6
• /
• pp.1503-1516
• /
• 2019

In this paper, to improve the accuracy of long sentence similarity calculation, we proposed a sentence similarity calculation method based on a system similarity function. The algorithm uses word2vector as the system elements to calculate the sentence similarity. The higher accuracy of our algorithm is derived from two characteristics: one is the negative effect of penalty item, and the other is that sentence similar function (SSF) based on word2vector similar elements doesn't satisfy the exchange rule. In later studies, we found the time complexity of our algorithm depends on the process of calculating similar elements, so we build an index of potentially similar elements when training the word vector process. Finally, the experimental results show that our algorithm has higher accuracy than the word mover's distance (WMD), and has the least query time of three calculation methods of SSF.