• Korean Journal of Adult Nursing
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• v.15 no.1
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• pp.105-115
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• 2003

Purpose: The research purposes analyzed the study participation and lecture evaluation of applicants in the cyber hospice specialist education course to find problems of nurse education application on the web. Method: Study participants were 125 nurses for participation and 68nurses for lecture evaluation. The data was analysed by descriptive statistics. Result: The results obtained from this study were as follows 1) The residence distribution of study participants was spread out across the nation. Equal distribution of education was accomplished without a difference among provinces. 2) The average study duration in the study participation was about one hour and a quarter minutes a week, and number of access to lecture notes was 65.8 times. But in a discussion room and a cooperative room, the system using rate was very low, so we considered the idea to come up with a more effective application way. 3) The participant's lecture evaluation of cyber education were generally satisfied about the quality of lecture, time, contents etc. Conclusion: This study shows the possible implication for nursing fields using a web-based learning program for reeducation in a variety of fields, so nursing cyber application can be considered to come up with this more effective method.

• Journal of Drive and Control
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• v.20 no.4
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• pp.1-8
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• 2023

In recent years, robots have been utilized in various industries to reduce workload and enhance work efficiency. The following mobility offers users convenience by autonomously tracking specific locations and targets without the need for additional equipment such as forklifts or carts. In this paper, deep learning techniques were employed to recognize individuals and assign each of them a unique identifier to enable the recognition of a specific person even among multiple individuals. To achieve this, the distance and angle between the robot and the targeted individual are transmitted to respective controllers. Furthermore, this study explored the control methodology for mobility that tracks a specific person, utilizing Simultaneous Localization and Mapping (SLAM) and Proportional-Integral-Derivative (PID) control techniques. In the PID control method, a genetic algorithm is employed to extract the optimal gain value, subsequently evaluating PID performance through simulation. The SLAM method involves generating a map by synchronizing data from a 2D LiDAR and a depth camera using Real-Time Appearance-Based Mapping (RTAB-MAP). Experiments are conducted to compare and analyze the performance of the two control methods, visualizing the paths of both the human and the following mobility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.775-792
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• 2015

Person re-identification is an important and challenging task in computer vision with numerous real world applications. Despite significant progress has been made in the past few years, person re-identification remains an unsolved problem. This paper presents a novel appearance-based approach to person re-identification. The approach exploits region covariance matrix and color histograms to capture the statistical properties and chromatic information of each object. Robustness against low resolution, viewpoint changes and pose variations is achieved by a novel signature, that is, the combination of Log Covariance Matrix feature and HSV histogram (LCMH). In order to further improve re-identification performance, third-party image sets are utilized as a common reference to sufficiently represent any image set with the same type. Distinctive and reliable features for a given image set are extracted through decision boundary between the specific set and a third-party image set supervised by max-margin criteria. This method enables the usage of an existing dataset to represent new image data without time-consuming data collection and annotation. Comparisons with state-of-the-art methods carried out on benchmark datasets demonstrate promising performance of our method.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.

• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1508-1517
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• 2021

In CCTV environments, a lot of learning image data is required to monitor illegal dumping of garbage with a typical image-based object detection using deep learning method. In this paper, we propose a system to monitor unauthorized dumping of garbage by learning the articular points of the person using only a small number of images without immediate use of the image for deep learning. In experiment, the proposed system showed 74.97% of garbage dumping detection performance with only a relatively small amount of image data in CCTV environments.

• Journal of Society of Preventive Korean Medicine
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• v.11 no.2
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• pp.113-120
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• 2007

The Internet is increasingly changing our lives and enables the ordinary person to have access to never-ending quantities of information and knowledge. Technology and the Internet empower individuals and facilitate a more active role in the educational process. The webcast is a media file distributed over the Internet using streaming media technology and is used extensively in the commercial sector for investor relations presentations, in e-learning, and for related communications activities. And relating to computers, technology, and news are particularly popular and many new shows are added regularly. Especially e-learning is a general term used to refer to computer-enhanced learning and has the ability to level the learning playing field. So that the e-learning experience will be second nature to the growing Internet population. And this study intends to develop web-based education.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.303-304
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• 2021

It is an important issue to detect automatically whether a mask is worn or not for corona prevention. It is known that mask wearing detection can be solved by learning the face data set. However, the search for whether a person is wearing a mask can be detected in a simpler way using OpenCV. In this paper, we describe that it is possible to easily detect whether a single person is wearing a mask or not with a general PC camera using OpenCV learning data results and simple OpenCV functions. Through experiments, the proposed method was shown to be effective.

• International Journal of Contents
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• v.4 no.2
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• pp.19-23
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• 2008

This paper presents a novel method to construct a personalized e-Learning system based on dynamic estimations of item parameters and learners' abilities, where the learning content objects are of the same intrinsic quality or homogeneously distributed and the estimations are carried out using IRT(Item Response Theory). The system dynamically connects the test and the corresponding learning procedures. Test results are directly applied to estimate examinee's ability and are used to modify the item parameters and the difficulties of learning content objects during the learning procedure is being operated. We define the learning unit 'Node' as an amount of learning objects operated so that new parameters can be re-estimated. There are various content objects in a Node and the parameters estimated at the end of current Node are directly applied to the next Node. We offer the most appropriate learning Node for a person's ability throughout the estimation processes of IRT. As a result, this scheme improves learning efficiency in web-base e-Learning environments offering the most appropriate learning objects and items to the individual students according to their estimated abilities. This scheme can be applied to any e-Learning subject having homogeneous learning objects and unidimensional test items. In order to construct the system, we present an operation scenario using the proposed system architecture with the essential databases and agents.

• Asia Pacific Journal of Corpus Research
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• v.1 no.2
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• pp.15-26
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• 2020

This corpus-based study investigated third language acquisition by Taiwanese college students in learning Spanish clitic pronouns at beginning and intermediate levels. It examined the acquisition sequences of Spanish clitic pronouns of the Chinese-speaking learners whose second language was English and third language was Spanish. The results indicated that indirect object pronouns (OP) preceded direct OP (case), first person preceded third person OP (person), masculine preceded feminine OP (gender), and animate preceded inanimate OP (animacy). The findings presented similar patterns as those of previous studies on English-speaking learners of Spanish. In further comparisons of the target forms in Chinese, English, and Spanish, the results suggested that L1 Chinese had strong influence on L3 Spanish, which accounts for the challenges that Taiwanese learners of Spanish face as they learn the Spanish clitic pronouns in the beginning stage.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.35-42
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• 2021

In this paper, we propose a deep learning-based person re-identification method using a three-dimensional RGB-Depth Xtion2 camera considering joint coordinates and dynamic features(velocity, acceleration). The main idea of the proposed identification methodology is to easily extract gait data such as joint coordinates, dynamic features with an RGB-D camera and automatically identify gait patterns through a self-designed one-dimensional convolutional neural network classifier(1D-ConvNet). The accuracy was measured based on the F1 Score, and the influence was measured by comparing the accuracy with the classifier model (JC) that did not consider dynamic characteristics. As a result, our proposed classifier model in the case of considering the dynamic characteristics(JCSpeed) showed about 8% higher F1-Score than JC.