• The KIPS Transactions:PartB
• /
• v.13B no.3 s.106
• /
• pp.345-352
• /
• 2006

The practical English-Korean machine translation system should be able to translate long sentences quickly and accurately. The intra-sentence segmentation method has been proposed and contributed to speeding up the syntactic analysis. This paper proposes the syntactic category prediction method using decision trees for getting accurate parsing results. In parsing with segmentation, the segment is separately parsed and combined to generate the sentence structure. The syntactic category prediction would facilitate to select more accurate analysis structures after the partial parsing. Thus, we could improve the parsing accuracy by the prediction. We construct features for predicting syntactic categories from the parsed corpus of Wall Street Journal and generate decision trees. In the experiments, we show the performance comparisons with the predictions by human-built rules, trigram probability and neural networks. Also, we present how much the category prediction would contribute to improving the translation quality.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1256-1256
• /
• 2001

The physico-chemical and texture characteristics of meat determine the nutritional, technological and sensory quality. However, the analysis of meat quality requires expensive, laborious and time consuming analytical methods. The objective of this study was to evaluate NIR spectroscopy using transmittance for determining the moisture, fat, protein and total pigment content, the water holding capacity (WHC) and the toughness of beef meat. A total of 318 spectra were recorded from ground beef samples by a Feed Analyzer 1265 of Infratec. The samples were obtained from the Longissimus muscle of the 10$^{th}$ rib of yearling bulls, ground with an electrical chopper, vacuum packaged, aged during 7 days and frozen at -24$^{\circ}C$ until the analyses were done. Moisture content was measured by oven drying at 10$0^{\circ}C$, fat content was determined by Soxhlet extraction and protein content was estimated from nitrogen content using the Kjeldahl analysis. The total pigment content was determined by the method of Hornsey and the WHC using the method of filter paper press. The instrumental evaluation of texture (maximum load WB, maximum stress MS and toughness) was conducted in an Instron equipment with a Warner-Bratzler shearing device. This analysis was performed on a chop of 3.5 cm obtained from the longissimus of the 8$^{th}$ rib, aged during 7 days, kept frozen at -24$^{\circ}C$ and cooked before the analysis. Near infrared spectra were recorded as log 1/T (T=transmittance) at 2 nm intervals from 850 to 1050 nm using a Feed Analyzer 1265 of Infratec. Calibrations were performed with the WinISI software (vs. 1.02) using the MPLS method. To examine the effect of scatter correction o. derivation of spectra on the calibration performance, calibrations were calculated with the crude spectra or pretreated with different mathematical treatments (inverse MSC, SNVD) and/or second derivative operation. For chemical composition, the use of the scatter corrections improved the calibration statistics, in terms of lower SECV and higher $r^2$. In most of the variables, the use of the 2$^{nd}$ derivative improved the predictions, mainly when combined with the SNVD treatment. However, for predicting the texture traits, the best estimation was obtained from the crude spectrum. These results showed that the equations obtained for predicting moisture, fat and total pigments were very accurate, with $r^2$ being higher that 0.9. However, the prediction of the texture traits (WB, MS, toughness) from ground meat was poor.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.37 no.4
• /
• pp.350-357
• /
• 2017

Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, $R_{cv}^2$, ranged from 0.76 to 0.97); the exception to this result was crude ash ($R_{cv}^2=0.49$ and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy ($R_{cv}^2$ 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.3B
• /
• pp.221-230
• /
• 2009

In Korea, many water intake plants are easily affected by effluents of sewage treatment plants because sewage treatment plants are usually located upstream or nearby the plants of the same riverine area. Furthermore, the inflow of harmful contaminants owing to pollutant spills or transportation accidents of vehicles using the roads and bridges intersecting the river causes significant impact on the management of water intake plants. Paldang lake, the main water intake plants in Korea, is especially exposed to various water pollution accidents, because the drainage basin area is significantly large compared to the water surface area of the lake. Therefore it is necessary to predict the possible pollutant spill in advance and consider measurements in case of water pollution. In this study, water quality prediction was performed in Paldang Lake in Korea durig the dry season using two-dimensional numerical models. In order to represent the cases of pollutant accidents, the difference of pollutant transport patterns with varying injection points was analyzed. Numerical simulations for hydrodynamics of water flow and water quality predictions were performed using RMA-2 and RAM4 respectively. As a result of simulation, the difference of pollutant transport with the injection points was analyzed. As a countermeasure against the pollutant accident, the augmentation of the flow rate is proposed. In comparison with the present state, the rapid dilution and flushing effects on the pollutant cloud could be expected with increase of flow rate. Thus, increase of flow rate can be used for operation of water intake plants in case of pollutant spill accidents.

• Geomechanics and Engineering
• /
• v.35 no.5
• /
• pp.539-554
• /
• 2023

This article is dedicated to the pursuit of establishing a robust empirical relationship that allows for the estimation of in-situ modulus of deformations (E_m and G_m) within sedimentary rock slope masses through the utilization of Q_slope values. To achieve this significant objective, an expansive and thorough methodology is employed, encompassing a comprehensive field survey, meticulous sample collection, and rigorous laboratory testing. The study sources a total of 26 specimens from five distinct locations within the South Pars (known as Assalouyeh) region, ensuring a representative dataset for robust correlations. The results of this extensive analysis reveal compelling empirical connections between E_m, geomechanical characteristics of the rock mass, and the calculated Q_slope values. Specifically, these relationships are expressed as follows: E_m = 2.859 Q_slope + 4.628 (R² = 0.554), and G_m = 1.856 Q_slope + 3.008 (R² = 0.524). Moreover, the study unravels intriguing insights into the interplay between in-situ deformation moduli and the widely utilized Rock Mass Rating (RMR) computations, leading to the formulation of equations that facilitate predictions: RMR = 18.12 E_m0.460 (R² = 0.798) and RMR = 22.09 G_m0.460 (R² = 0.766). Beyond these correlations, the study delves into the intricate relationship between RMR and Rock Quality Designation (RQD) with Qslope values. The findings elucidate the following relationships: RMR = 34.05e^0.33Qslope (R² = 0.712) and RQD = 31.42e^0.549Qslope (R² = 0.902). Furthermore, leveraging the insights garnered from this comprehensive analysis, the study offers an empirically derived support system tailored to the distinct characteristics of discontinuous rock slopes, grounded firmly within the framework of the Q_slope methodology. This holistic approach contributes significantly to advancing the understanding of sedimentary rock slope stability and provides valuable tools for informed engineering decisions.

• Journal of Korean Society of Environmental Engineers
• /
• v.31 no.5
• /
• pp.358-363
• /
• 2009

The HSPF (Hydrological Simulation Program-Fortran) model was applied to the Kyoungan stream watershed to analyze effects of watershed management plans on receiving water qualities. Utilizing BASINS 3.1 GIS program, the Kyoungan stream watershed was divided into 57 sub-basins and model input parameters were obtained, from DEM (Digital Elevation Model), land use type, stream map, and wastewater treatment facilities, etc.. The hydrologic module of the model was validated based on the measured meteorological data and stream flow data. Then the model was calibrated and verified against the field measurements of water qualities, including temperature, DO, BOD, $NO_3-N$, $NH_3-N$, Org-N, TN and TP. In most cases, there were reasonable agreements between measurements and predictions. The validated model was used to analyze the water quality improvements in the main stream of Kyoungan stream according to the watershed management plans in sub-basins, which are three different scenarios: water quality improvement in tributaries through watershed management activities, expansion and up-grade of wastewater treatment plants, and application of first and second scenarios together. It was concluded that expansion and upgrade of wastewater treatment plants would be more effective than watershed management activities. In order to improve water qualities to the satisfactory level, both watershed management and point source control must be required in the Kyoungan stream.

• Journal of the Korean Crystal Growth and Crystal Technology
• /
• v.22 no.3
• /
• pp.115-121
• /
• 2012

Sapphire single crystals have been highlighted for epitaxial gallium nitride films in high-power laser and light emitting diode (LED) industries. Among the many crystal growth methods, the Kyropoulos process is an excellent commercial method for growing larger, high-optical-quality sapphire crystals with fewer defects. Because the properties and growth behavior of sapphire crystals are influenced largely by the temperature distribution and convection of molten sapphire during the manufacturing process, accurate predictions of the thermal fields and melt flow behavior are essential to design and optimize the Kyropoulos crystal growth process. In this study, computational fluid dynamic simulations were performed to examine the effects of the crucible geometry aspect ratio on melt convection during Kyropoulos sapphire crystal growth. The results through the evolution of various growth parameters on the temperature and velocity fields and convexity of the crystallization interface based on finite volume element simulations show that lower aspect ratio of the crucible geometry can be helpful for the quality of sapphire single crystal.

• Journal of the Korea Concrete Institute
• /
• v.24 no.2
• /
• pp.137-145
• /
• 2012

Strength model for blasted furnace slag mortar blended with sodium was investigated in this study. The main parameters of AAS (alkali activated slag) mortar were dosage of alkali activator, water to binder ratio (W/B), and aggregate to binder ratio (A/B). For evaluating the property related to the dosage of alkali activator, sodium carbonate ($Na_2CO_3$) of 4~8% was added to 4% dosage of sodium hydroxide (NaOH). W/B and A/B was varied 0.45~0.60 and 2.05~2.85, respectively. An alkali quality coefficient combining the amounts of main compositions of source materials and sodium oxide ($Na_2O$) in sodium hydroxide and sodium carbonate is proposed to assess the compressive strength of alkali activated mortars. Test results clearly showed that the compressive strength development of alkali-activated mortars were significantly dependent on the proposed alkali quality coefficient. Compressive strength development of AAS mortars were also estimated using the formula specified in the previous study, which was calibrated using the collected database. Predictions from the simplified equations showed good agreements with the test results.

• Journal of Intelligence and Information Systems
• /
• v.29 no.4
• /
• pp.325-345
• /
• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• The KIPS Transactions:PartD
• /
• v.12D no.6 s.102
• /
• pp.869-874
• /
• 2005

In the recent years there has been an increase of service competition in the activity of product selling, especially in the extension of warranty coverage and qualify. The variables in connection with the service competition are not crisp, and required the expertise of the production line. It thus becomes all the more necessary to use subtler tools as decision supports. These problems are typical not only of product companies but also of financial organizations, credit institutions, insurance, which need predictions of credibility for firms or persons in which they have any kind of interest. A suitable approach for minimizing the risk is to use a knowledge-based system. Most often expert systems are not standalone programs, but are embedded into a larger application. The aim of this paper is to discuss an approach for developing an embedded fuzzy expert system with respect to the product selling policy, especially to present the decision system of automobile selling activity around the extension of warranty coverage and quality. We use the MATLAB tools which integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Also, we present the API functions embedding into the existing application.