• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.20-29
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• 2021

In the Industry 4.0 era, artificial intelligence has attracted considerable interest for learning mass data to improve the accuracy of forecasting and classification. On the other hand, the current method of detecting anomalies relies on traditional statistical methods for a limited amount of data, making it difficult to detect accurate anomalies. Therefore, this paper proposes an artificial intelligence-based anomaly detection methodology to improve the prediction accuracy and identify new data patterns. In particular, data were collected and analyzed from the point of view that sensor data collected at vehicle idle could be used to detect abnormalities. To this end, a sensor was designed to determine the appropriate time length of the data entered into the forecast model, compare the results of idling data with the overall driving data utilization, and make optimal predictions through a combination of various sensor data. In addition, the predictive accuracy of artificial intelligence techniques was presented by comparing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) as the predictive methodologies. According to the analysis, using idle data, using 1.5 times of the data for the idling periods, and using CNN over LSTM showed better prediction results.

• Journal of KIBIM
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• v.10 no.4
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• pp.32-39
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• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.4
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• pp.311-320
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• 2021

A study is performed for the real time fault diagnosis during operation and health estimation relating to performance deterioration in a turbojet engine used for an unmanned air vehicle. For this study the real time dynamic model is derived from the transient thermodynamic gas path analysis. For real fault conditions which are manipulated for the simulation, the detection techniques are applied such as Kalman filter and probabilistic decision-making approach based on statistical hypothesis test. Thereby the effectiveness is verified by showing good fault detection and isolation performances. For the health estimation with measurement parameters, it shows using an assumed performance degradation that the method by adaptive Kalman filter is feasible in practice for a condition based diagnosis and maintenance.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.47-53
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• 2021

With increasing interest in the 4th industrial revolution, data-driven scientific methodologies have developed. However, there are limitations of data collection in the real estate field of research. In addition, as the public becomes more knowledgeable about the real estate market, the qualitative sentiment comes to play a bigger role in the real estate market. Therefore, we propose a method to collect quantitative data that reflects sentiment using text mining and k-means algorithms, rather than the existing source data, and to predict the direction of housing index through artificial neural network learning based on the collected data. Data from 2012 to 2019 is set as the training period and 2020 as the prediction period. It is expected that this study will contribute to the utilization of scientific methods such as artificial neural networks rather than the use of the classical methodology for real estate market participants in their decision making process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.25-29
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• 2021

The strip binary is a binary from which debug symbol information has been deleted, and therefore it is difficult to analyze the binary through techniques such as reverse engineering. Traditional binary analysis tools rely on debug symbolic information to analyze binaries, making it difficult to detect or analyze malicious code with features of these strip binaries. In order to solve this problem, the need for a technology capable of effectively extracting the information of the strip binary has emerged. In this paper, focusing on the fact that the byte code of the binary file is generated very differently depending on compiler version, optimazer level, etc. For effective compiler version extraction, the entire byte code is read and imaged as the target of the stripped binaries and this is applied to the convolution neural network. Finally, we achieve an accuracy of 93.5%, and we provide an opportunity to analyze stripped binary more effectively than before.

• The Research Journal of the Costume Culture
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• v.30 no.1
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• pp.66-87
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• 2022

This study explores the possibility of creating new experimental hanbok designs by accommodating the latest world fashion trends and the changing needs of consumers, in order to attempt to overcome the limitations of traditional Korean fashion design. To do so, We analyze works by contemporary Korean fashion designers to investigate current developments in Korean fashion design and to identify areas of improvement within hanbok design. The results show that most contemporary hanbok designs repeat stereotypes of traditional hanbok with minor modifications. So there arises a need to create new hanbok designs that are clearly distinct from traditional hanbok but also maintain its core features. To develop such designs, I apply the techniques of deconstruction fashion, which allow making experiments with form, composition, and materials use to realize new aesthetics. The use of CLO 3D fashion design software also proves to be very efficient for developing experimental designs. The study results make meaningful contributions to the development of virtual clothing and 3D fashion for hanbok, particularly as metaBUS, a cloud-based research synthesis platform, is rapidly gaining ground, and reality and virtual reality are increasingly mixed in everyday life. This attempt at 3D design of hanbok is expected to trigger more creative experimentation in hanbok design.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1531-1537
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• 2021

The quality of the game can be evaluated by its fun and functional stability. Among them, the quality area for fun is the essence of the game, so it is a very important factor to be considered from the point of view of a game designer. Usually, fun games with high originality and immersion are designed in the early stages of development, such as idea generation and concept setting. At this time, making it possible to verify the fun factor of the game has an important influence on the success of the game. In this study, the case of using PIT technique when teaching fun element design to students who are starting to study game design was introduced and the effect was analyzed. It is expected that the PIT technique will be used more actively in the game design education field for high-quality and fun game design.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.1-18
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• 2022

As digital technology converges into the e-commerce market across industries, online transactions have activated, and the use of online has increased. With the recent spread of infectious diseases such as COVID-19, this market flow is accelerating, and various product information can be provided to customers online. Providing a variety of information provides customers with various opportunities but causes difficulties in decision-making. The recommendation system can help customers to make a decision more effectively. However, the previous research on recommendation systems is limited to only quantitative data and does not reflect detailed factors of products and customers. In this study, we propose an intelligent recommendation system that quantifies the attributes of products and customers by applying text mining techniques to qualitative data based on online reviews and integrates the existing objective indicators of total star rating, sentiment, and emotion. The proposed integrated recommendation model showed superior performance to the overall rating-oriented recommendation model. It expects the new business value to be created through the recommendation result reflecting detailed factors of products and customers.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1677-1689
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• 2022

Air pollution is social issue that has long-term and short-term harmful effect on the health of animals, plants, and environments. Chimneys are the primary source of air pollutants that pollute the atmosphere, so their location and type must be detected and monitored. Power plants and industrial complexes where chimneys emit air pollutants, are much less accessible and have a large site, making direct monitoring cost-inefficient and time-inefficient. As a result, research on detecting chimneys using remote sensing data has recently been conducted. In this study, YOLOv5-based chimney detection model was generated using BUAA-FFPP60 open dataset create for power plants in Hebei Province, Tianjin, and Beijing, China. To improve the detection model's performance, data split and data augmentation techniques were used, and a training strategy was developed for optimal model generation. The model's performance was confirmed using various indicators such as precision and recall, and the model's performance was finally evaluated by comparing it to existing studies using the same dataset.

• Journal of The Korean Astronomical Society
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• v.55 no.4
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• pp.111-121
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• 2022

Based on the light an exoplanet blocks from its host star as it passes in front of it during a transit, the mid-transit time can be determined. Periodic variations in mid-transit times can indicate another planet's gravitational influence. We investigate 83 transits of TrES-1 b as observed from 6-inch telescopes in the MicroObservatory robotic telescope network. The EXOTIC data reduction pipeline is used to process these transits, fit transit models to light curves, and calculate transit midpoints. This paper details the methodology for analyzing transit timing variations (TTVs) and using transit measurements to maintain ephemerides. The application of Lomb-Scargle period analysis for studying the plausibility of TTVs is explained. The analysis of the resultant TTVs from 46 transits from MicroObservatory and 47 transits from archival data in the Exoplanet Transit Database indicated the possible existence of other planets affecting the orbit of TrES-1 and improved the precision of the ephemeris by one order of magnitude. We now estimate the ephemeris to be (2 455 489.66026 BJD_TDB ± 0.00044 d) + (3.0300689 ± 0.0000007) d × epoch. This analysis also demonstrates the role of small telescopes in making precise midtransit time measurements, which can be used to help maintain ephemerides and perform TTV analysis. The maintenance of ephemerides allows for an increased ability to optimize telescope time on large ground-based telescopes and space telescope missions.