• Journal of the HCI Society of Korea
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• v.13 no.4
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• pp.5-14
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• 2018

The Empathy for Dementia using Virtual Reality (EDuVR) system, developed by the Jeju Provincial Dementia Center, is a 3D video system capturing the experience of dementia in a first-person perspective using 360 degree vritual reality (VR) technology. It was developed to create a greater understanding of dementia and to help people empathize with individuals with dementia through an immersive VR experience. The EDuVR shows how a dementia patient has impairments in memory, orientation, language, judgment and problem solving, as well as problems with activities of daily living. The present study reported the effectiveness of the EDuVR experience in changing public awareness of, and attitude toward, dementia. Sixty-six participants were assigned to the EDuVR (n = 34) or the conventional education (n = 32) groups, and two types of questionnaires - attitude and awareness questionnaires - were administered to the subjects before and after the EDuVR experience or education. The simulator sickness and presence questionnaires were administered to the EDuVR group to assess cybersickness and presence of the VR experience. As a results, the attitude and awareness toward dementia patients changed positively in both the EDuVR and the conventional education groups, and these changes did not differ between two groups. Only one person reported a significant level of cybersicness after experiencing the EDuVR system. These results suggest that the EDuVR enhances the level of understanding and empathy for dementia and would be a useful tool for improving awareness in the general public.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.19-25
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• 2022

This study examined the impact of direct structured approach of students who demonstrate little or no sense of basic engineer concepts in physics courses. This direct structured instruction is one of the methodologies that focuses on explicit and systematic practices in which an instructor set clear learning outcomes and clarifies the direction of the instruction. 90 participants were randomly selected and tested on the areas of problem-solving skills, reasoning, working memory, and processing speed. 20% of the participants were found to be students with basic engineering disabilities. On the other hand, in the direct structured group, 51.7% and 58.0% of the sample group (90 students) showed a 6.3% increase from the mid-term to final examinations, respectively. The subgroups with 50% or lower grades were decreased from 26.7% to 24.5%. However, five students with the lowest grade of 20% were selected as students with learning disabilities in the study and the average scores of mid-term and final exams were increased by 8.6%, which was 17.9% and 26.5%, respectively at the end of the study. As a result, it showed that direct structured approach for students with learning disabilities in the engineer concepts was effective.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.3
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• pp.72-83
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• 2017

GPUs can provide high throughput with latency hiding by executing many warps in parallel. MSHR(Miss Status Holding Registers) for L1 data cache tracks cache miss requests until required data is serviced from lower level memory. In recent GPUs, excessive requests for cache resources cause underutilization problem of GPU resources due to cache resource reservation fails. In this paper, we propose a new warp scheduling technique to reduce stall cycles under MSHR resource shortage. Cache miss rates for each warp is predicted based on the observation that each warp shows similar cache miss rates for long period. The warps showing low miss rates or computation-intensive warps are given high priority to be issued when MSHR is full status. Our proposal improves GPU performance by utilizing cache resource more efficiently based on cache miss rate prediction and monitoring the MSHR entries. According to our experimental results, reservation fail cycles can be reduced by 25.7% and IPC is increased by 6.2% with the proposed scheduling technique compared to loose round robin scheduler.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2D
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• pp.311-318
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• 2009

Interactive terrain visualization is an important research area with applications in GIS, games, virtual reality, scientific visualization and flight simulators, besides having military use. This is a complex and challenging problem considering that some applications require precise visualizations of huge data sets at real-time rates. In general, the size of data sets makes rendering at real-time difficult since the terrain data cannot fit entirely in memory. In this paper, we suggest the effective Real-time LOD(level-of-detail) algorithm for displaying the huge terrain data and processing mass geometry. We used a hierarchy structure with $4{\times}4$ and $2{\times}2$ tiles for real-time rendering of mass volume DEM which acquired from Digital map, LiDAR, DTM and DSM. Moreover, texture mapping is performed to visualize realistically while displaying height data of normalized Giga Byte level with user oriented terrain information and creating hill shade map using height data to hierarchy tile structure of file type. Large volume of terrain data was transformed to LOD data for real time visualization. This paper show the new LOD algorithm for seamless visualization, high quality, minimize the data loss and maximize the frame speed.

• Journal of The Korean Association For Science Education
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• v.26 no.4
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• pp.475-491
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• 2006

In physics education, the research on students' conceptions has developed in the discussion on the nature and the difficulty of conceptual change. Recently, mental models have been a theoretical background in concrete arguments on "how students' conceptions are constructed or created." Mental models that integrate information in the presented problem and individual knowledge in their long-term memory have important information about not only expressed ideas but also in the thinking process behind the expressed ideas. The purpose of this study is to investigate the forming process and the characteristics of high school student's mental models about circular motion, and how they were changed by instruction. We used the think-aloud method based on the instrument for identifying student's mental models about circular motion, pretest of physics concept, mind map and interview for investigating student's characteristics. The results of the study showed that instructions based on the mental model theory facilitated scientific expressed model, but several factors that affected forming mental models like epistemological belief didn't change scientifically after 3 lessons.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.293-299
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• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.

• Journal of the Korean Chemical Society
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• v.66 no.4
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• pp.305-322
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• 2022

In this study, a practice-based teacher education program was developed and applied to improve the TPACK of pre-service chemistry teachers. Also the program effect and obstacles were confirmed by measuring the development of TPACK. The participants of this study were 20 pre-service chemistry teachers of 3rd grade and 2 pre-service chemistry teachers of 4th grade who took chemistry education courses at K University located in Chungcheongbuk Province. The developed teacher education program consisted of four stages: preparation, rehearsal, practice, and reflection. The feedbacks from researchers and colleagues pre-service teachers were provided in preparation, rehearsal, and reflection stages. As a result of the study, the program of this study did not show an educational effect in the "constructive learning activities" of preservice teachers, but it was found to have an educational effect in "problem solving". In other words, in "constructive learning activity", most pre-service teachers were at 0 level before and after the program. The pre-service teachers designed the class to unilaterally provide technology to simply use it as a tool to explain subject content or revise misconceptions, and learners can passively acquire knowledge. However, in the case of "problem solving", the pre-service teachers who were at level 0 before the educational program changed to level 1. Before the program, the pre-service teachers designed classes to solve problems by memory without using technology, but after the program they planned classes that provides opportunities to approach and solve various problems through the technology presented by the teacher. However, there were not many pre-service teachers corresponding to level 2, which constitutes voluntary learning in which learners use technology to solve various problems while selecting and variously manipulating technology. In addition, as obstacles to the TPACK development of pre-service chemistry teachers, there were external factors such as lack of classroom support environment for TPACK implementation, lack of time for education planning, and inadequate technology competency. And there were internal factors such as perspectives of traditional education and negative attitude toward technology. In particular, the proportion of pre-service teachers who preceived inappropriate technical competency as an external obstacles of TPACK development was high. Therefore, it was necessary to develop an education program corresponding to type 2 or type 3 that enables TPACK development through TK for pre-service teachers.