• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.562-565
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• 2018

A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. However, the frequent appearance of fault data makes it difficult to extract correct information, thereby sending incorrect commands to actuators that can threaten human privacy and safety. For this reason, it is necessary to have a mechanism to detect fault data collected from sensors. In this paper, we present a trend-adaptive multi-scale principal component analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.233-243
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• 2020

The purpose of this study was to investigate the changes in the use of mobile phones due to COVID-19. The subjects of this study were those who lived in Jeju City and used their own mobile phone for more than 2 years, and were included in adult men and women aged 15 to 80 years old. The purpose of this study was explained and a questionnaire survey was conducted on 156 people who agreed. The survey period lasted from June 15 to July 4, 2020. As a result, the daily use time and function of the mobile phone, which were used more than before the occurrence of COVID-19, increased. This was a statistically significant trend (p<0.001) with increasing trend after COVID-19 in all age groups. In addition, in the mobile phone function, all age groups used more 'KakaoTalk' than 'call', but it was found that only the group with less than 1 hour of daily using time used the call function a lot.

• Journal of Navigation and Port Research
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• v.33 no.4
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• pp.277-282
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• 2009

This paper estimated the container volumes for the Incheon port with univariate time series. As best suited models Winters' additive model, ARIMA model,and Winters' additive model were selected by import-export, coastal, and transshipment volume respectively, based on the data of monthly volume by October 2008 since January 2001. This study supposed the import-export container volumes would be decreased by 14% against that in 2008 and would have been recovered to the increasing trend of the volumes beyond the fourth quarter of 2010. The future import-export and transshipment volumes showed the increasing trend beyond 2011, while the coastal volumes would be on the stagnation. The yearly container volumes were finally forecasted as 1,705, 2,432, and 3,341 thousand TEU in 2011, 2015, and 2020 respectively.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.101-115
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• 2005

We propose in this article a procedure for testing unit and fractional orders of integration, with the roots simultaneously occurring in the trend, the seasonal and the cyclical component of the time series. The tests have standard null and local limit distributions. However, finite sample critical values are computed, and several Monte Carlo experiments conducted across the paper show that the rejection frequencies against unit (and fractional) orders of integration are relatively high in all cases. The tests are applied to the UK consumption and income series, the results showing the importance of the roots corresponding to the trend and the seasonal components and, though the unit roots are found to be fairly suitable models, we show that fractional processes (including one for the cyclical component) may also be plausible alternatives in some cases.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.340-343
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• 1992

In this paper, To estimate monthly water distribution volume required optimization control of operating scheme & water distribution management for water transmission system in water supply, both Thomas-Fiering technique and Fourier series are compared and analyzed, respectively. Since water distribution volume is periodically repeated and has a linear fluctuation trend, parameters in each element are estimated through dividing into linear fluctuation trend component and periodical component. Finally, results of time-series analysis are proved to be more reasonable than that of Thomas-Fiering techniques by comparing simulation with observation data.

• Journal of Aerospace System Engineering
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• v.1 no.1
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• pp.60-66
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• 2007

The importance of software development in the integration of an avionics system is continuously increasing. And we can expect enlarged soft ware portion in the system integration for the more intelligent, reliable, and automated avionics system. For an avionics system software development the selection of a real-time operating system and internal avionics data bus protocol is very important from the viewpoint of the integration with the system hardware. In this paper, we present current trend of an avionics system software development including software development methodology, software development process, and international software process assessment improvement model.

• Journal of Electrochemical Science and Technology
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• v.8 no.2
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• pp.115-123
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• 2017

Electrochemical noise signals in many cases exhibit a DC drift that should be removed prior to further data analysis. Polynomial fitting and moving average removal method have been used to remove trends of electrochemical noise (EN) in time domain. The corrosion inhibition of synthesized schiff base N,N'-bis(3,5-dihydroxyacetophenone)-2,2-dimethylpropandiimine on API-5L-X70 steel in hydrochloric acid solutions were used to study the effects of drifts removal methods on noise resistance calculation. Also, electrochemical impedance spectroscopy (EIS) was used to study the corrosion inhibition property of the inhibitor. The results showed that for the calculation of $R_n$, both methods were effective in trend removal and the polynomial with m=4 and MAR with p=40 were in agreement.

• Tribology and Lubricants
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• v.13 no.2
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• pp.82-88
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• 1997

This paper describes an empirical equation which can be used to predict the engine condition of supersonic aircraft. The equation, which is derived from the trend analysis of JOAP data, represents the concentration of Fe particles in the engine oil. The result of the trend analysis shows that the concentration of Fe particles is a function of running time of engine oil. Meanwhile the slope of Fe concentration is a function of running time of engine. Threfore, the empirical equation was derived as $w=a(t_e).t_o+b$. However, the equation could not enough to diagnose the damaged part of engine quantitatively. To make up for the weak points of the equation, qualitative analysis was carried out. For that purpose wear debris were collected from the abnormal engine and analyzed by EDS to detect the damaged parts of engine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3533-3549
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• 2019

A big database utilizes the establishing network technology, and it became an emerging trend in the computing field. Therefore, there is a necessity for an optimal and effective data distribution approach to deal with this trend. This research presents the practical perspective of designing and implementing distributed database features. The proposed system has been establishing the satisfying, reliable, scalable, and standardized use of information. Furthermore, the proposed scheme reduces the vast and recurring efforts for designing an individual system for each university, as well as it is effectively participating in solving the course equivalence problem. The empirical finding in this study shows the superiority of the distributed system performance based on the average response time and the average waiting time than the centralized system. The system throughput also overcomes the centralized system because of data distribution and replication. Therefore, the analyzed data shows that the centralized system thrashes when the workload exceeds 60%, while the distributed system becomes thrashes after 81% workload.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.149-154
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• 2022

In recent years, the application of deep learning method to computer vision has shown to achieve great performances. Thus, many research projects have also applied deep learning technology to railroad defect detection. In this paper, we have reviewed the researches that applied computer vision based deep learning method to railroad defect detection and inspection, and have discussed the current trend and the direction of those researches. Many research projects were targeted to operate automatically without visual inspection of human and to work in real-time. Therefore, methods to speed up the computation were also investigated. The reduction of the number of learning parameters was considered important to improve computation efficiency. In addition to computation speed issue, the problem of annotation was also discussed in some research projects. To alleviate the problem of time consuming annotation, some kinds of automatic segmentation of the railroad defect or self-supervised methods have been suggested.