• Annual Conference of KIPS
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• 2018.10a
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• pp.362-364
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• 2018

Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.4
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• pp.301-309
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• 2021

Pulse count method is the classical reaction wheel speed detection method. In this study, we represent the pulse count method as mathematical equations. Instead of rotation speed, we model the reaction wheel rotation through rotation angle during sampling periods. We verified the effectiveness of the proposed model by comparing the pulse counts variation and averaging method effects from the model and previous research results. Then, we add tacho pulse non-uniformity to this verified model, and examine the errors of pulse count method. We express the measurement error increasement due to non-uniformity as mathematical equations, and also shows the requirement of moving average numbers to offset the measurement errors.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.7
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• pp.1128-1138
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• 2000

This paper presents two different approaches for channel estimation of IMT-2000 pilot symbol-assisted W-CDMA reverse link over Rayleigh fading channels of one and two paths. By obtaining BER performance through computer simulations, the proposed algorithms of 2-point second-order interpolation and IDD BWMA are compared with the performance of existing interpolation and adaptive algorithms. The BER performance of the proposed algorithms is superior to WMSA, linear and second-order Gaussian interpolation, LMS, and RLS algorithm in fast fading channels. In particular, the BER performance of the IDD BWMA algorithm is nearly insensitive for Doppler frequency within simulation range $E_b/N_0$ = 28 dB. The two proposed algorithms also have relatively simple structure and similar processing delay in comparison to the existing algorithms. Therefore, these algorithms are more suitable for high-speed mobile communication environments.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.82-95
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• 2019

In this study, we analyzed the beam time series of ocean reverberation which was conducted in the eastsouthern region of East Sea, Korea during the August, 2015. The reverberation data was gathered by moving research vessel towing LFM (Linear Frequency Modulation) source and triplet receiver array. After signal processing, we analyzed the variation of ocean reverberation level according to the seafloor bathymetry, source/receiver depth and sound speed profile. In addition, we used the normalized data by using cell averaging algorithm and identified the statistical characteristics of seafloor scatterer by using moment estimation method and estimated shape parameter. Also, we analyzed the coincidence of data with Rayleigh and K-distribution probability by Kolmogorov-Smirnov test. The results show that there is range dependency of reverberation according to the bathymetry and also that the time delay and the intensity level change depend on the depths of source and receiver. In addition, we observed that statistical characteristics of similar Rayleigh probability distribution in the ocean reverberation.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.113-120
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• 2018

In this study, we proposed a monitoring system for identifying and handling faulty sensing stream data on manufacturing equipments where low-cost sensors can be safely used. Low cost sensors will lessen the cost of implementing distributed monitoring system, but suffer from sensor noises and inaccurate sensed data. Therefore, a distributed monitoring system with low cost sensors should identify faulty signal data as either of sensor fault or machine fault, and filter out faulty signals from sensing fault. To this end, we adopted a fourier transform based diagnostic approach mixed with a weighed moving averaging method, in order to identify faulty signals. We measured how effective our approach is and found out our approach can filter out one-third faulty signals from our experimental environment. In addition, we attached wireless communication modules to reduce sensor and network installation cost. To handle massive sensor data efficiently, we employed unstructured data format with NoSQL based database.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.811-824
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• 2005

The ADCP is an instrument based on Doppler effect, which measures discharge of a river in a short time while crossing it. In this study we aim to make a comparison of the discharge results from a moving-vessel ADCP with those measured by velocity-area method at the same cross-section, and to investigate the characteristics of velocity and discharge data using ADCP. Bathymetry measured by ADCP almost coincides with that by direct depth measurements. Because velocity data from ADCP are essentially instantaneous, individual velocity profiles obtained by ADCP are rather different from time-averaged velocity profiles. But spatially averaged velocity profiles of the individual ADCP data near the comparable verticals have similar vertical velocity pattern with the time-averaged ones. The average velocity profile from repeatedly crossed data is also similar with the time-averaged one. In case of the velocity distribution, individual and spatially averaged data for the sub-width of mid-section method Have good agreement with those by velocity-area method. Discharge data determined by averaging several ADCP measurement transects have $0.1\%{\~}9.3\%$ of difference with those from velocity-area method, and as the number of measurement increases, the relative difference to the velocity-area method decreases.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.435-446
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• 2014

Various drought indices developed from previous studies can not consider the inherent uncertainty of drought because they assess droughts using a pre-defined threshold. In this study, to consider inherent uncertainty embedded in monthly streamflow data, Hidden Markov Model (HMM) based drought index (HMDI) was proposed and then probabilistic assessment of hydrologic drought was performed using HMDI instead of using pre-defined threshold. Using monthly streamflow data (1966~2009) of Pyeongchang river and Upper Namhan river provided by Water Management Information System (WAMIS), applying the HMM after moving-averaging the data with 3, 6, 12 month windows, this study calculated the posterior probability of hidden state that becomes the HMDI. For verifying the method, this study compared the HMDI and Standardized Streamflow Index (SSI) which is one of drought indices using a pre-defined threshold. When using the SSI, only one value can be used as a criterion to determine the drought severity. However, the HMDI can classify the drought condition considering inherent uncertainty in observations and show the probability of each drought condition at a particular point in time. In addition, the comparison results based on actual drought events occurred near the basin indicated that the HMDI outperformed the SSI to represent the drought events.

• Journal of Climate Change Research
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• v.4 no.4
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• pp.359-369
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• 2013

In this paper, correlations between driving parameters and $CO_2$ of light-duty vehicles have been analyzed. Three test vehicles equipped with PEMS (Portable Emission Measurement System) have been driven in real-road in urban areas of Seoul. Averaged vehicle speed, RPA(Relative Positive Acceleration) and stop ratio have been selected as main driving parameters. The analysis have been conducted in interrupted and uninterrupted road types. Averaged values in various driving conditions have been calculated with distance based moving averaging window method. The multiple linear regression method have been applied to account for correlation between driving parameters and $CO_2$ emissions. This approach has shown statistically that $CO_2$ emission per distance (g/km) have tendencies to be increased as decreased averaged vehicle speed and increased RPA and stop ratio. Compared with uninterrupted traffic, interrupted traffic have shown the lower vehicle speed and the higher RPA and stop ratio. These characteristics of driving parameters in interrupted traffic should cause the higher $CO_2$ emission per distance.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.359-366
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• 2022

While the Forward Freight Agreement (FFA) has emerged as an effective hedging tool since early 1990, the basis risk and cash flow distortions have been addressed as obstacles to the active use of FFAs. This research analyses the basis risk of FFAs and provides a feasible suggestion to reduce it. Basis risk is divided into timing basis, route basis, size basis, and low liquidity basis. The timing basis is defined as the difference between the physical hire, fixed on the specific contract date and the FFA settlement price, calculated by averaging spot rates for a certain period. Timing basis is considered the worst in eroding the effectiveness of FFAs. This paper suggests a change of hire payment criterion from contract date to 15-day moving average, as a means of mitigating the basis risk, and analyzed the effectiveness through historical simulation. The result revealed that the change is effective in mitigating the timing basis. This study delivers a meaningful implication to shipping practice in that the change of hire payment criterion mitigates the basis risk and eventually activates the use of FFAs in the future.

• Geophysics and Geophysical Exploration
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• v.10 no.3
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• pp.183-190
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• 2007

The site effects of seismic stations were evaluated by conducting a simultaneous inversion of the stochastic point-source ground-motion model (STGM model; Boore, 2003) parameters based on the accumulated dataset of horizontal shear-wave Fourier spectra. A model parameter $K_0$ and frequency-dependent site amplification function A(f) were used to express the site effects. Once after a H/V ratio of the Fourier spectra was used as an initial estimate of A(f) for the inversion, the final A(f) which is considered to be the result of combined effect of the crustal amplification and loca lsite effects was calculated by averaging the log residuals at the site from the inversion and adding the mean log residual to the H/V ratio. The seismic stations were classified into five classes according to $logA_{1-10}^{max}$(f), the maximum level of the site amplification function in the range of 1 Hz < f < 10 Hz, i.e., A: $logA_{1-10}^{max}$(f) < 0.2, B: 0.2 $\leq$ $logA_{1-10}^{max}$(f) < 0.4, C: 0.4 $\leq$ $logA_{1-10}^{max}$(f) < 0.6, D: 0.6 $\leq$ $logA_{1-10}^{max}$(f) < 0.8, E: 0.8 $\leq$ $logA_{1-10}^{max}$(f). Implication of the classified result was supported by observing a shift of the dominant frequency of average A(f) for each classified stations as the class changes. Change of site classes after moving seismic stations to a better site condition was successfully described by the result of the station classification. In addition, the observed PGA (Peak Ground Acceleration)-values for two recent moderate earthquakes were well classified according to the proposed station classes.