• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.78-86
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• 2014

This paper proposes a method of tracking an object for a person-following mobile robot by combining a monocular camera and a laser scanner, where each sensor can supplement the weaknesses of the other sensor. For human-robot interaction, a mobile robot needs to maintain a distance between a moving person and itself. Maintaining distance consists of two parts: object tracking and person-following. Object tracking consists of particle filtering and online learning using shape features which are extracted from an image. A monocular camera easily fails to track a person due to a narrow field-of-view and influence of illumination changes, and has therefore been used together with a laser scanner. After constructing the geometric relation between the differently oriented sensors, the proposed method demonstrates its robustness in tracking and following a person with a success rate of 94.7% in indoor environments with varying lighting conditions and even when a moving object is located between the robot and the person.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.91-102
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• 2019

In autonomous driving systems, the ability to classify pedestrians in images captured by cameras is very important for pedestrian safety. In the past, after extracting features of pedestrians with HOG(Histogram of Oriented Gradients) or SIFT(Scale-Invariant Feature Transform), people classified them using SVM(Support Vector Machine). However, extracting pedestrian characteristics in such a handcrafted manner has many limitations. Therefore, this paper proposes a method to classify pedestrians reliably and effectively using CNN's(Convolutional Neural Network) deep features and transfer learning. We have experimented with both the fixed feature extractor and the fine-tuning methods, which are two representative transfer learning techniques. Particularly, in the fine-tuning method, we have added a new scheme, called M-Fine(Modified Fine-tuning), which divideslayers into transferred parts and non-transferred parts in three different sizes, and adjusts weights only for layers belonging to non-transferred parts. Experiments on INRIA Person data set with five CNN models(VGGNet, DenseNet, Inception V3, Xception, and MobileNet) showed that CNN's deep features perform better than handcrafted features such as HOG and SIFT, and that the accuracy of Xception (threshold = 0.5) isthe highest at 99.61%. MobileNet, which achieved similar performance to Xception and learned 80% fewer parameters, was the best in terms of efficiency. Among the three transfer learning schemes tested above, the performance of the fine-tuning method was the best. The performance of the M-Fine method was comparable to or slightly lower than that of the fine-tuningmethod, but higher than that of the fixed feature extractor method.

• Korean Journal of Computational Design and Engineering
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• v.21 no.4
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• pp.397-408
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• 2016

VR and AR are widely used in many applications to provide more immersive visualization and natural interaction for learning and experiencing virtual but realistic tasks. Energy saving is considered as one of the most important issues throughout the world in order to overcome global warming and energy crisis. In particular, the awareness of home energy saving is regarded as a first step to deal with this issue. In this paper, we suggest a new learning environment using virtual and augmented reality (VR/AR) for home energy saving experience. In particular, it presents a comparative study of the user experience in home energy awareness using immersive virtual reality and mobile augmented reality. The first person user experience using immersive VR wearing head mounted display (HMD) and the third person user experience using mobile AR are implemented and systematically compared with each other in terms of the learning effect of energy saving and qualitative usability. Implementation results will be given to show the advantage and effectiveness of the proposed approach.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.145-150
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• 2023

The main purpose of the study is to identify the key aspects of modern innovative research in the field of education. In the modern informatized world, education is becoming a decisive factor in social development and an important component in the development of the human personality, increasing respect for human rights and freedoms. Today it is quite obvious that without the necessary education a person will not be able to provide himself with proper living conditions and realize himself as a person. The high level of education of the population is an important factor that positively influences the creation of favorable conditions for the full realization of the rights and freedoms of man and citizen. Today, active and interactive teaching methods are widely used. The use of interactive teaching methods ensures complete immersion of students in the learning process and is the main source of learning. The radical difference between traditional and interactive learning is that the student not only replenishes and strengthens his knowledge, but also complements and constructs new ones. The methodology includes a number of theoretical methods. As a result of the study, current trends and prerequisites of modern innovative research in the field of education were investigated.

• Korean Medical Education Review
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• v.18 no.3
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• pp.167-173
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• 2016

This study analyzed participation experiences in a voluntarily learning community using both quantitative and qualitative methods. Sixty freshmen and sophomore medical school students in 10 learning communities participated in the study. At the time of the survey, learning communities had been operating for 10 weeks and had weekly in-person meetings. Satisfaction questionnaires and reflective essays were given and analyzed. The results showed that learning community experiences were effective in promoting students' learning motivation, cooperative learning, responsibility, and communication skills. Three essential topics and nine subjects were analyzed in the reflective essays. Three essential topics were conflict with each other due to the difference, forming deep relationships, and sharing and learning together with an in-depth study. The results of this study will contribute to collaborative learning culture and the development of learning communities in medical schools.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.51-57
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• 2022

The purpose of this study is to explore the future direction of university liberal arts classes at a time when many educational institutions have returned to face-to-face instruction as social distancing related to COVID-19 has been eliminated. A survey was conducted with 187 college students who took a liberal arts class that included a combination of online and in-person classes. The results found that learners were generally satisfied with remote learning (87.8%). The reasons for this high level of satisfaction included sufficient comprehension of class content, systematic class progress, and the efficiency and convenience of learning in a remote environment. Satisfaction levels for in-person classes (66%) were relatively lower than those for remote classes, and this is reflected in the preference for class type. Among wholly in-person, wholly remote, and a combination of both, it was found that learners preferred remote classes the most (54.4%). When conducting in-person classes, instructors must devise a class plan that incorporates the advantages of online remote classes.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.6
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• pp.281-287
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• 2020

In this paper, we propose a method for estimating a walking direction by which a mobile robots follows a person using TRT (Tensor RT) pose, which is motion recognition based on deep learning. Mobile robots can measure individual movements by recognizing key points on the person's pelvis and determine the direction in which the person tries to move. Using these information and the distance between robot and human, the mobile robot can follow the person stably keeping a safe distance from people. The TRT Pose only extracts key point information to prevent privacy issues while a camera in the mobile robot records video. To validate the proposed technology, experiment is carried out successfully where human walks away or toward the mobile robot in zigzag form and the robot continuously follows human with prescribed distance.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.193-199
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• 2017

Instead of using only spatial features from a single frame for person re-identification, a combination of spatial and temporal factors boosts the performance of the system. A recurrent neural network (RNN) shows its effectiveness in generating highly discriminative sequence-level human representations. In this work, we implement RNN, three Long Short Term Memory (LSTM) network variants, and Gated Recurrent Unit (GRU) on Caffe deep learning framework, and we then conduct experiments to compare performance in terms of size and accuracy for person re-identification. We propose using GRU for the optimized choice as the experimental results show that the GRU achieves the highest accuracy despite having fewer parameters than the others.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.274-284
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• 2021

Customized services to a sleep induction for better sleepcare are more effective because of different satisfaction levels to users. The EOG data measured at the frontal lobe when a person blinks his eyes can be used as biometric data because it has different values for each person. The accuracy of measurement is degraded by a noise source, such as toss and turn. Therefore, it is necessary to analyze the noisy data and remove them from normal EOG by filtering. There are low-pass filtering and high-pass filtering as filtering using a frequency band. However, since filtering within a frequency band range is also required for more effective performance, we propose a machine learning model for the filtering of EOG data in this paper as the second filtering method. In addition, optimal values of parameters such as the depth of the hidden layer, the number of nodes of the hidden layer, the activation function, and the dropout were found through experiments, to improve the performance of the machine learning filtering model, and the filtering performance of 95.7% was obtained. Eventually, it is expected that it can be used for effective user identification services by using filtering model for EOG data.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.243-248
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• 2013

This paper proposes an effective method using Adaptive Boosting to track a person's head in complex background. By only one way to feature extraction methods are not sufficient for modeling a person's head. Therefore, the method proposed in this paper, several feature extraction methods for the accuracy of the detection head running at the same time. Feature Extraction for the imaging of the head was extracted using sub-region and Haar wavelet transform. Sub-region represents the local characteristics of the head, Haar wavelet transform can indicate the frequency characteristics of face. Therefore, if we use them to extract the features of face, effective modeling is possible. In the proposed method to track down the man's head from the input video in real time, we ues the results after learning Harr-wavelet characteristics of the three types using AdaBoosting algorithm. Originally the AdaBoosting algorithm, there is a very long learning time, if learning data was changes, and then it is need to be performed learning again. In order to overcome this shortcoming, in this research propose efficient method using cascade AdaBoosting. This method reduces the learning time for the imaging of the head, and can respond effectively to changes in the learning data. The proposed method generated classifier with excellent performance using less learning time and learning data. In addition, this method accurately detect and track head of person from a variety of head data in real-time video images.