Soil hydraulic properties such as hydraulic conductivity or water retention which are costly to measure can be indirectly generated by soil pedotransfer function (PTF) using easily obtainable soil data. The field soil structure description which is routinely recorded could also be used in PTF as an input to reduce the uncertainty. The purposes of this study were to use qualitative morphological soil structure descriptions and soil structural index into PTF and to evaluate their contribution in the prediction of soil hydraulic properties. We transformed categorical morphological descriptions of soil structure into quantitative values using categorical principal component analysis (CATPCA). This approach was tested with a large data set from the US National Pedon Characterization database with the aid of a categorical regression tree analysis. Six different PTFs were used to predict the saturated hydraulic conductivity and those results were averaged to quantify the uncertainty. Quantified morphological description was successively used in multiple linear regression approach to predict the averaged ensemble saturated conductivity. The selected stepwise regression model with only the transformed morphological variables and structural index as predictors predicted the $K_{sat}$ with $r^2$ = 0.48 (p = 0.018), indicating the feasibility of CATPCA approach. In a regression tree analysis, soil structure index and soil texture turned out to be important factors in the prediction of the hydraulic properties. Among structural descriptions size class turned out to be an important grouping parameter in the regression tree. Bulk density, clay content, W33 and structural index explained clusters selected by a two step clustering technique, implying the morphologically described soil structural features are closely related to soil physical as well as hydraulic properties. Although this study provided relatively new method which related soil structure description to soil structure index, the same approach should be tested using a datasets containing the actual measurement of hydraulic properties. More insight on the predictive power of soil structure index to estimate hydraulic properties would be achieved by considering measured the saturated hydraulic conductivity and the soil water retention.
Potential maximum soil moisture retention (S) is a dominant parameter in the Soil Conservation Service (SCS; now called the USDA Natural Resources Conservation Service (NRCS)) runoff Curve Number (CN) method commonly used in hydrologic modeling for event-based flood forecasting (SCS, 1985). Physically, S represents the depth [L] soil could store water through infiltration. The depth of soil moisture retention will vary depending on infiltration from previous rainfall events; an adjustment is usually made using a factor for Antecedent Moisture Conditions (AMCs). Application of the method for continuous simulation of multiple storms has typically involved updating the AMC and S. However, these studies have focused on a time step where S is allowed to vary at daily or longer time scales. While useful for hydrologic events that span multiple days, this temporal resolution is too coarse for short-term applications such as flash flood events. In this study, an approach for deriving a time-variable potential maximum soil moisture retention curve (S-curve) at hourly time-scales is presented. The methodology is applied to the Napa River basin, California. Rainfall events from 2011 to 2012 are used for estimating the event-based S. As a result, we derive an S-curve which is classified into three sections depending on the recovery rate of S for soil moisture conditions ranging from 1) dry, 2) transitional from dry to wet, and 3) wet. The first section is described as gradually increasing recovering S (0.97 mm/hr or 23.28 mm/day), the second section is described as steeply recovering S (2.11 mm/hr or 50.64 mm/day) and the third section is described as gradually decreasing recovery (0.34 mm/hr or 8.16 mm/day). Using the S-curve, we can estimate the hourly change of soil moisture content according to the time duration after rainfall cessation, which is then used to estimate direct runoff for a continuous simulation for flood forecasting.
The design requirements of modern satellites vary depending on the purpose of operation. Like conventional medium and large-scale satellites, small satellites which operate on low orbit may also serve military purposes. As a result, there is increased demand for high-resolution photos and videos and multi-target observation becomes important. The most important design parameter for multi-target observation is the satellites' maneuverability. For increased maneuverability, the miniaturization is required to increase the stiffness of the satellite as this decreases the mass moment of inertia of the satellite. In the case of a solar panel having relatively low stiffness compared to the satellites' body, vibrations are generated when the attitude maneuver is performed, which greatly influences the image acquisition. For verification of such vibrational characteristics, the satellites is modeled as a reduced model, and experimental zig for simulating attitude maneuver is introduced. A rigidity simulator for simulating the stiffness of the satellite is also proposed. Additionally, the objective of the experimental method is to simulate the maneuvering angle of the satellite based on the winding length of the wire using a step motor, and to experimentally verify the vibration characteristics of the satellite body and the solar panel generated during the maneuvering test.
In this study, anthraquinone-2,7-disulfonic acid (2,7-AQDS) is used as negative active material and Tiron is used as positive active material for aqueous redox flow battery (RFB). In previous results that used the 2,7-AQDS and Tiron, sulfuric acid ($H_2SO_4$) was a supporting electrolyte. However, in this study, ammonium chloride ($NH_4Cl$) is suggested as the electrolyte for the first time. By changing the supporting electrolyte from $H_2SO_4$ to $NH_4Cl$, the cell voltage of RFB is improved from 0.76 V to 1.01 V. To investigate the effect of $NH_4Cl$ supporting electrolyte of the performance of RFB, the full-cell tests of RFB using 2,7-AQDS and Tiron that are dissolved in $NH_4Cl$ supporting electrolyte are carried out, while cut-off voltage range is a main parameter to determine their performance. When the cut-off voltage range is 0.2~1.6 V, the hydrogen evolution occurs during charging step. To address the side reaction effect, the cut-off voltage range is changed to 0.2~1.2 V. When the revised cut-off voltage range is used and the current density of $40mA/cm^2$ is applied, hydrogen evolution is not observed and the optimal RFB shows the charge efficiency of 99% and discharge capacity of 3.3 Ah/L at 10cycle.
Kim, Gwang-Jun;Jang, Hyok;Suk, Kyung-Hyu;Na, Sang-Dong
Journal of the Korea Institute of Information Security & Cryptology
/
v.9
no.4
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pp.11-22
/
1999
Least-mean-square(LMS) adaptive filters have proven to be extremely useful in a number of signal processing tasks. However LMS adaptive filter suffer from a slow rate of convergence for a given steady-state mean square error as compared to the behavior of recursive least squares adaptive filter. In this paper an efficient signal interference control technique is introduced to improve the convergence speed of LMS algorithm with tap weighted vectors updating which were controled by reusing data which was abandoned data in the Adaptive transversal filter in the scheme with data recycling buffers. The computer simulation show that the character of convergence and the value of MSE of proposed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of LMS algorithm.
In this paper, parameter characteristics such as pH effect, isotherm, kinetic and thermodynamic parameters and competitive adsorption of dyes including malachite green (MG), direct red 81 (DR 81) and thioflavin S (TS), which have different functional groups, being adsorbed onto activated carbon were investigated. Langmuir, Freundlich and Temkin isotherm models were employed to find the adsorption mechanism. Effectiveness of adsorption treatment of three dyes by activated carbon were confirmed by the Langmuir dimensionless separation factor. The mechanism was found to be a physical adsorption which can be verified through the adsorption heat calculated by Temkin equation. The adsorption kinetics followed the pseudo second order and the rate limiting step was intra-particle diffusion. The positive enthalpy and entropy changes showed an endothermic reaction and increased disorder via adsorption at the S-L interface, respectively. For each dye molecule, negative Gibbs free energy increased with the temperature, which means that the process is spontaneous. In the binary component system, it was found that the same functional groups of the dye could interfere with the mutual adsorption, and different functional groups did not significantly affect the adsorption. In the ternary component system, the adsorption for MG lowered a bit, likely to be disturbed by the other dyes meanwhile DR 81 and TS were to be positively affected by the presence of MG, thus resulting in much higher adsorption.
This paper presents an idea of ScanSAR image formation. For image formation of ScanSAR that utilizes the burst mode for raw signal acquisition, most conventional single burst methods essentially require a step of azimuth stitching which contributes to radiometric and phase distortions to some extent. Time-domain cross correlation could replace SPECAN which is most popularly used for ScanSAR processing. The core idea of the proposed method is that it is possible to relieve the necessity of azimuth stitching by an extension of Doppler bandwidth of the reference function to the burst cycle period. Performance of the proposed method was evaluated by applying it to the raw signals acquired by a spaceborne SAR system, and results satisfied all image quality requirements including 3 dB width, peak-to-sidelobe ratio (PSLR), compression ratio,speckle noise, etc. Image quality of ScanSAR is inferior to that of Stripmap in all aspects. However, it is also possible to improve the quality of ScanSAR image competitive to that of Stripmap if focused on a certain parameter while reduced qualities of other parameters. Thus, it is necessary for a ScanSAR processor to offer a great degree of flexibility complying with different requirements for different applications and techniques.
This study was conducted to explore whether problem-solving ability mediates the effect of self-directed learning ability on interest level in a distance learning environment. The subjects of this study were 64 students in the Department of Christian Education and Early Childhood Education at A University located in Gyeonggi-do. For this study, data were collected through questionnaires on interest level, self-directed learning ability, and problem-solving ability. Collected data are analyzed using SPSS Macro and bootstrapping method. As a result, self-directed learning ability for interest level has a positive effect on .651 level. The influence of self-directed learning ability in step 2, which includes problem-solving ability, which is a parameter. It was lowered to .543 level. And the effect of problem-solving ability was found to be at .360 level. As a result, it can be judged that problem-solving ability mediates the effect of self-directed learning ability on interest level. The research results suggest that the level of interest can be improved by supporting problem-solving skills in a distance learning environment.
In text-to-speech systems, the conversion of text into prosodic parameters is necessarily composed of three steps. These are the placement of prosodic boundaries. the determination of segmental durations, and the specification of fundamental frequency contours. Prosodic boundaries. as the most important and basic parameter. affect the estimation of durations and fundamental frequency. Break prediction is an important step in text-to-speech systems as break indices (BIs) have a great influence on how to correctly represent prosodic phrase boundaries, However. an accurate prediction is difficult since BIs are often chosen according to the meaning of a sentence or the reading style of the speaker. In Japanese, the prediction of an accentual phrase boundary (APB) and major phrase boundary (MPB) is particularly difficult. Thus, this paper presents a method to complement the prediction errors of an APB and MPB. First, we define a subtle BI in which it is difficult to decide between an APB and MPB clearly as a variable break (VB), and an explicit BI as a fixed break (FB). The VB is chosen using the classification and regression tree, and multiple prosodic targets in relation to the pith and duration are then generated. Finally. unit-selection is conducted using multiple prosodic targets. In the MOS test result. the original speech scored a 4,99. while proposed method scored a 4.25 and conventional method scored a 4.01. The experimental results show that the proposed method improves the naturalness of synthesized speech.
Park, Do-Hyung;Chung, Jaekwon;Chung, Yeo Jin;Lee, Dongwon
Journal of Intelligence and Information Systems
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v.20
no.4
/
pp.1-23
/
2014
Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.
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