• The Journal of Bigdata
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• v.7 no.2
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• pp.217-224
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• 2022

Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Therefore, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'A'. According to the simulation results, it was confirmed that Bidirectional-LSTM(Bi-LSTM) compared to LSTM(Long Short-Term Memory) takes more simulation time about more than 50%, but the prediction accuracy of non-periodic time series data such as clothing product sales data is the same.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.1-7
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• 2016

As new information and communication technologies evolve, security threats are also becoming increasingly intelligent and advanced. In this paper, we analyze the time series data continuously entered through a series of periods from the network device or lightweight IoT (Internet of Things) devices by using the statistical technique and propose a system to detect abnormal behaviors of the device or abnormality based on the analysis results. The proposed system performs the first level abnormal detection by using previously entered data set, thereafter performs the second level anomaly detection according to the trust bound configured by using stored time series data based on time attribute or group attribute. Multi-level analysis is able to improve reliability and to reduce false positives as well through a variety of decision data set.

• Journal of Korean Society of Transportation
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• v.16 no.4
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• pp.153-166
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• 1998

본 연구에서는 도시 가로망에서의 구간 통행시간을 예측하기 위하여 time-frequency 분석의 일종인 웨이브렛변환과 RBF신경망 모형을 이용한 예측모형을 개발하였다. 웨이브렛 변환을 이용한 시계열 자료 분석을 통해서 통행시간에 내재되어 있는 다양한 패턴의 특징을 추출함으로써 오전/오후의 첨두현상, 신호교차로의 현시주기 등 주기적으로 발생되는 요인들에 의해서 통행시간 시계열 자료의 패턴에 나타나는 규칙성을 분석해 내었다. 분석된 패턴정보에 대한 규명은 카오스 이론을 근간으로한 시간지연좌표를 이용하여 시계열 자료의 규칙성을 시각적으로 판별하여 예측모형 구축에 활용하도록 하였다. 또, RBF신경망을 이용하여 예측범위의 공간적/시간적 확대에 따른 모형 구축에 소요되는 시간을 최소화하도록 하였으며, 시내버스 노선의 정류장간 운행시간 예측을 통해서 기존 연구에서 제기되었던 현실세계의 단순화, 다단계 예측시 정확성 등의 문제를 해결하였다. 예측실험결과 웨이브렛 변환을 데이터의 전처리 과정에 삽입하여 링크 통행시간의 패턴정보 예측에 활용할 경우, 기존의 예측모형에 비해서 훨씬 정확한 예측이 가능한 것으로 나타났으며, RBF 신경망은 짧은 학습시간에도 불구하고 역전파 신경망보다 우수한 예측력을 갖고 있는 것으로 밝혀졌다.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.389-399
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• 2013

This article introduces Gaussian process regression and shows its application with time-course microarray gene expression data. Gene screening for yeast cell cycle microarray expression data is accomplished with a ratio of log marginal likelihood that uses Gaussian process regression with a squared exponential covariance kernel function. Gaussian process regression fitting with each gene is done and shown with the nine top ranking genes. With the screened data the Gaussian model-based clustering is done and its silhouette values are calculated for cluster validity.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.127-135
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• 2010

The validation of a simulation model is a key to demonstrate that the simulation model is reliable. However, among various validation methods have been introduced, it is very poor to research the specific techniques for the time series data. Therefore, this paper suggests the methodology to verify the simulation using the time series data by Wavelet Transform, Power Spectrum and Coherence. This method performs 2 steps as followed. Firstly, we get spectrum using the Wavelet transform available for non-periodic signal separation. Secondly, we compare 2 patterns of output data from simulation model and actual system by Coherence Analysis. As a result of comparing it with other validation techniques, the suggested way can judge simulation model accuracy more clearly. By this way, we can make it possible to perform the simulation validation test under various situations using detailed sectional validation method, which has been impossible using a single statistics for the whole model.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1001-1019
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• 2017

The signal processing is a field of the electrical engineering but it is very much related with the time series analysis. Thesedays the commercial softwares are widely used by the reseachers. We have attempted to make a guide for using the R-package in the digital signal processing. It would be good to read the materials in each section first and to follow the plots in the section 8 and to run the attached R-codes. The article consists of (1) Fourier transform and Fourier inverse transform, (2) spectral analysis (3) parametric and non-parametric estimation for the period (4) filter design. Simple theoretical explanations are provided and R implementations are added.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.489-490
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• 2008

본 고에서는 체내 심실신호를 농하여 신경학적 분석 및 다형성의 측면에서 심실세동이 일어나는 것을 예측하는 분석 알고리즘을 설계하였다. 신경학적 측면에서는 시계열 신호의 Peak to Peak Interval을 예측법과 0.15Hz를 기준으로 HRV 신호의 AR Burg 모델링을 통하여 고주파성과 저주파성을 나누어 교감신경과 부교감신경의 활동성 통한 신경학적 예측법을 제시하였으며 또한 체내 심실신호의 비선형적 특성을 고려한 Fractal Dimension을 생성시킴으로서 주기성의 특성과 다형성 통한 예측법을 제시하였다. 체내 심전도를 기반으로 Simulation 하였으며 각 분석별 조합을 통하여 최적의 예측 구조를 찾고자 하였다. 의학적 의미가 있는 민감도와 특이도를 판별하였으며 예측을 위한 수행시간을 실험하였다. 이를 통하여 자율신경 활성도와 다형성 판별을 조합한 방법이 심실세동 예측을 위한 민감도의 측면에서 가장 우수함을 나타내었고 시뮬레이션을 위만 시뮬레이터(Simulator) UI(User Interface)를 제시하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.763-773
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• 2017

In recent years, personal information leakage and technology leakage accidents are frequently occurring. According to the survey, the most important part of this spill is the 'insider' within the organization, and the leakage of technology by insiders is considered to be an increasingly important issue because it causes huge damage to the organization. In this paper, we try to learn the normal behavior of employees using machine learning to prevent insider threats, and to investigate how to detect abnormal behavior. Experiments on the detection of abnormal behavior by implementing an Autoencoder composed of Recurrent Neural Network suitable for learning time series data among the neural network models were conducted and the validity of this method was verified.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1541-1551
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• 2023

Radiometric calibration is a fundamental step in ocean color remote sensing since the step to derive solar radiance spectrum in visible to near-infrared wavelengths from the sensor-observed electromagnetic signals. Generally, satellite sensor suffers from degradation over the mission period, which results in biases/uncertainties in radiometric calibration and the final ocean products such as water-leaving radiance, chlorophyll-a concentration, and colored dissolved organic matter. Therefore, the importance of radiometric calibration for the continuity of ocean color satellites has been emphasized internationally. This study introduces an approach to improve the radiometric calibration algorithm for the visible bands of the Geostationary Ocean Color Imager-II (GOCI-II) satellite with a focus on stability. Solar Diffuser (SD) measurements were employed as an on-orbit radiometric calibration reference, to obtain the continuous monitoring of absolute gain values. Time series analysis of GOCI-II absolute gains revealed seasonal variations depending on the azimuth angle, as well as long-term trends by possible sensor degradation effects. To resolve the complexities in gain variability, an azimuth angle correction model was developed to eliminate seasonal periodicity, and a sensor degradation correction model was applied to estimate nonlinear trends in the absolute gain parameters. The results demonstrate the effects of the azimuth angle correction and sensor degradation correction model on the spectrum of Top of Atmosphere (TOA) radiance, confirming the capability for improving the long-term stability of GOCI-II data.