• Journal of applied mathematics & informatics
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• v.41 no.5
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• pp.947-961
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• 2023

This paper summarizes some research findings that show how the differential transform method (DTM) is used to resolve the Holling-Tanner model. To confirm the application, effectiveness, and correctness of the approach, a comparison between the differential transform method (DTM) and the Adomian decomposition method (ADM) is carried out, and an accurate solution representation in truncated series is discovered. The approximate solution obtain using both techniques and comparison demonstrates same outcome which remains a preferred numerical method for resolving a system of nonlinear differential equations.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.6
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• pp.758-769
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• 2004

The trends and seasonalities of most time series have a large variability. The result of the Singular Spectrum Analysis(SSA) processing is a decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, forecasting by the SSA method should be applied to time series governed (may be approximately) by linear recurrent formulae(LRF). This study examined forecasting ability of SSA-LRF model. These methods are applied to daily water demand data. These models indicate that most cases have good ability of forecasting to some extent by considering statistical and visual assessment, in particular forecasting validity shows good results during 15 days.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.35-41
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• 2014

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-case manner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in a systematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remaining time-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of setting control chart limits for characteristic data with periodic components in addition to ARIMA components.

• The Korean Journal of Ecology
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• v.18 no.2
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• pp.219-230
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• 1995

Although many studies of nutrient cycling in forest ecosystems have reported that clearcutting creates increased organic matter decomposition and nitrogen (N) mineralization in soils, little is known about the change of these factors following various levels of canopy removal. A series of experimental plots with four levels of canopy cover, i.e., clearcut, 25％, 75％, and uncut, was established in northern red oak (Quercus rubra L.) stands in northern Lover Michigan, U.S.A. I examined decomposition of cellulose filter papers and N mineralization using an in situ soil incubation technique in the top 15cm of mineral soil during the second growing season (1992, May-October) following stand manipulation. Mass loss from cellulose filter papers was more rapid in the canopy removal treatments than in the uncut treatment. similarly, net N mineralization was significantly greater in the canopy removal treatments than in the uncut treatment. There was no significant difference in net N mineralization rates among the three levels of canopy removal. Net N mineralization for the growing season was 58 kg/ha for the clearcut, 54 kg/ha for the 25％ canopy cover, 51 kg/ha for the 75％ canopy cover, and 22 kg/ha for the uncut treatment. These results indicated that even only small amounts of canopy removal (leaving 75％ canopy cover) let to substantial increases of cellulose decomposition and the amount of available soil nitrogen.

• Korean Journal of Acupuncture
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• v.20 no.3
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• pp.49-60
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• 2003

The aim of this study was to examine the effects of electroacupuncture(EA) at the PC6 (Naegwan) on normal humans using KarhunenLoeve decomposition method. Electroencephalogram(EEG) is a multi-scaled signal consisting of several components of time series with different dominant frequency ranges and different origins. EEG KarhunenLoeve decomposition method exibit site-specific and state-related differences in specific frequency bands. In this study, KarhunenLoeve decomposition method was used as a measure(D2) of complexity. 30 channel EEG study was carried out in 10 subjects (10 males; $age=21.4{\pm}0.5$ years). Results : We found that the average values and standard deviations of D2 at FP1, FP2, FTC1, FTC2, TT1, TT2, T4, TCP1, P3, P4, T6, OZ channel (p<0.05) were higher than during the acupuncture treatment, and the average values and standard deviations of D2 at F3, F8 channels(p<0.05) were lowered than during the acupuncture treatment. However, the comparison with that before and after the treatment shows no significant differences in all channels.

• Environmental Engineering Research
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• v.22 no.2
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• pp.193-202
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• 2017

The study estimated the relationship between carbon dioxide, crop and livestock production index in Ghana: Estimating the long-run elasticities and variance decomposition by employing a time series data spanning from 1960-2013 using both fit regression and ARDL models. There was evidence of a long-run equilibrium relationship between carbon dioxide emissions, crop production index and livestock production index. Evidence from the study shows that a 1% increase in crop production index will increase carbon dioxide emissions by 0.52%, while a 1% increase in livestock production index will increase carbon dioxide emissions by 0.81% in the long-run. There was evidence of a bidirectional causality between a crop production index and carbon dioxide emissions and a unidirectional causality exists from livestock production index to carbon dioxide emissions. Evidence from the variance decomposition shows that 37% of future fluctuations in carbon dioxide emissions are due to shocks in the crop production index while 18% of future fluctuations in carbon dioxide emissions are due to shocks in the livestock production index. Efforts towards reducing pre-production, production, transportation, processing and post-harvest losses are essential to reducing food wastage which affects Ghana's carbon footprint.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.789-802
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• 2021

Synthetic Aperture Radar (SAR) at C-band is an ideal remote sensing system for crop monitoring owing to its short wavelength, which interacts with the upper parts of the crop canopy. This study evaluated the potential of dual polarimetric Sentinel-1 at C-band for monitoring rice phenology. Rice phenological variations occur in a short period. Hence, the short revisit time of Sentinel-1 SAR system can facilitate the tracking of short-term temporal morphological variations in rice crop growth. The sensitivity of SAR backscattering coefficients, backscattering ratio, and polarimetric decomposition parameters on rice phenological stages were investigated through a time-series analysis of 33 Sentinel-1 Single Look Complex images collected from 10th April to 25th October 2020 in Gimhae, South Korea. Based on the observed temporal variations in SAR parameters, we could identify and distinguish the phenological stages of the Gimhae rice growth cycle. The backscattering coefficient in VH polarisation and polarimetric decomposition parameters showed high sensitivity to rice growth. However, amongst SAR parameters estimated in this study, the VH backscattering coefficient realistically identifies all phenological stages, and its temporal variation patterns are preserved in both Sentinel-1A (S1A) and Sentinel-1B (S1B). Polarimetric decomposition parameters exhibited some offsets in successive acquisitions from S1A and S1B. Further studies with data collected from various incidence angles are crucial to determine the impact of different incidence angles on polarimetric decomposition parameters in rice paddy fields.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.53-76
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• 2020

The synchronous phasor measurement algorithm is the core content of the phasor measurement unit. This manuscript proposes a dynamic synchronous phasor measurement algorithm based on compressed sensing theory. First, a dynamic signal model based on the Taylor series was established. The dynamic power signal was preprocessed using a least mean square error adaptive filter to eliminate interference from noise and harmonic components. A Chirplet overcomplete dictionary was then designed to realize a sparse representation. A reduction of the signal dimension was next achieved using a Gaussian observation matrix. Finally, the improved orthogonal matching pursuit algorithm was used to realize the sparse decomposition of the signal to be detected, the amplitude and phase of the original power signal were estimated according to the best matching atomic parameters, and the total vector error index was used for an error evaluation. Chroma 61511 was used for the output of various signals, the simulation results of which show that the proposed algorithm cannot only effectively filter out interference signals, it also achieves a better dynamic response performance and stability compared with a traditional DFT algorithm and the improved DFT synchronous phasor measurement algorithm, and the phasor measurement accuracy of the signal is greatly improved. In practical applications, the hardware costs of the system can be further reduced.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2158-2167
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• 2018

This paper analyzed the magnetic field produced by the cylindrical stator coils of permanent magnet spherical motor (PMSM). The elliptic equations about the vector magnetic potential were given. Given that the eddy current effects are neglected, the magnet field of the PMSM is regarded as irrotational field, which can be calculated by scalar magnetic potential. The current density of cylindrical stator coil was proposed based on the definition of current density. The expression of current density of stator coil was obtained according to the double Fourier series decomposition and spherical harmonic functions. Then the magnetic flux density for scalar magnetic potential was derived. Further, the influence of different parameters on radial flux density was also analyzed. Finally, the results by the analytical method in this paper were validated by finite element analysis (FEA).