• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.3 no.2
• /
• pp.1-15
• /
• 1983

This paper has concentrated on estimating the possibility and mathematical analysis for the application of BFB to the treatment of nightsoil with low dilution rate. The experiment for the study of this purpose was conducted by continuous type reactor at $20^{\circ}C$, varying F/M ratio from 0.12 to 0.37 and dilution ratio from 2 to 10, and in it provided matted reticulated polypropylene sheets for the solid supports. The obtained results showed that the application of BFB to the treatment of nightsoil would be more effective than any other biological treatment process. Also, it has observed that the optimum dilution ratio was about 5 times and the optimum HRT was about 17 hours, and then it was estimated that the reactor volume and the quantity of weak water could be reduced to the extent of 70 percent and 80 percent. The experimental results of BFB could be analysed by the mathematical models applied to complete mixing activated sludge process. The substrate removal rates which were obtained by McKinney's($K_m$) and EcKenfelder's($K_e$) equation was 1.784/hr and $2.0{\times}10l/mg{\cdot}day$, and substrate was removed very rapidly compared to those of conventional type biological treatment processes. The biomass yield coefficient($a_5$), the endogeneous respiration rate(b), the synthesis oxygen demand rate($a{_5}^{\prime}$), and the endogeneous respiration oxygen demand rate(b') were 0.349, 0.0237/day, 0.495 and 0.0336, respectively.

• Journal of Elementary Mathematics Education in Korea
• /
• v.10 no.1
• /
• pp.67-87
• /
• 2006

This study is to recognize a problem in the practice of performance assessment in elementary school, and to find out some suggestive points for improvement of teaching·learning method in elementary mathematics through assessment by reducing time restriction according to assessment through the practice of self-assessment, peer-assessment and observation, and then by reflecting the results of assessment on teaching learning plan. For that, the questions of study set up are as follows ; 1. How should self-assessment and peer-assessment be applied to in elementary mathematics assessment? 2. How should the time for 'let's play an interesting game' be managed for assessment of elementary mathematics? 3. How should the results of assessment be reflected on the Process of teaching and learning of mathematics? To solve these problems, a researcher of this thesis performed self-assessment, peer-assessment on 40 students of second grade under her charge as a class teacher, and applied observation in the time management process for 'let's play an interesting game' for a semester. self-assessment was made by mathematics journal, self-assessment chart, peer-assessment was by the analysis of conversation record among students in the situations of assessment, and observation was by observation of activity when playing with the results data of play analyzed. the concrete methods of application as follows ; First, mathematics journal was applied $1{\sim}2$ times by each unit with reconstruction into the level of second grade on the basis of the preceding-study models. Second, peer-assessment was applied to the unit-assessment time and the play-activities time by the method of recording·analyzing the contents of conversation among students in the process of assessment. Third, mathematical attitude & dispositions of students making use of the self- assessment table were examined referring to the teaching learning plan. Fourth, the time management for 'let's play an interesting game' was made through the prior recognition of play method and the joyful play-activities by use of the play-plate. Assessment depended on analysis of play-activities results of students making use of an observation form. Fifth, the results of self-assessment, peer-assessment, and observation were analyzed, and then they were made use of as self-observation data, of teacher her/his self, or teaching·learning improvement data. Students' self-assessment datum (mathematics diary, self-assessment sheets, conversation contents in the process of assessment) and observation materials (check lists, Play-activity result materials, conversation contents in the process of play) obtained in the process of application was analyzed as follows ; 1. From the practice of self-assessment in form of mathematics journal, I could obtain not only datum showing how much students was understanding the learning aims by unit time and to any degree they reached but also information about their response to learning datum and favorable type of learning. 2. Assessment by self-assessment chart was useful in planning the mathematics teaching learning process because it helps ascertain mathematical attitude & dispositions of students. 3. Through the application of peer-assessment, students had the opportunity of communicating with other students looking back on his/her explaining process, and teachers could obtain basic materials for assessment of students. 4, In case of time management for 'let's play an interesting game', there was natural extension of play made through time-security by prior looking into the method of play-activity, and then, for a remained time, by making children play a new game. 5, I could easily record the activities of students by use of the observation. form, and make use of it as basic data for descriptive assessment. 6, Each kinds of data obtained from the results of assessment was helpful for securing self-observation materials in the process of teaching learning and for their betterment in mathematics subject. However, because they were in the second grade of elementary school and there was an individual difference, some students could not make use of mathematics diary or self-assessment form properly. In case of these students, assessment data would be obtained through interview or observation. And for effective operation of play, its purpose & method and matters that demand special attention when play-acting should be clearly guided. Also, when applying an effective play in addition to play activities in textbook, to lessons, interesting mathematics lessons could be guided.

• Journal of Food Hygiene and Safety
• /
• v.32 no.3
• /
• pp.206-210
• /
• 2017

This study was performed to develope mathematical models for predicting growth kinetics of Staphylococcus aureus in the processed meat product, pyeonyuk. Growth patterns of S. aureus in pyeonyuk were determined at the storage temperatures of 4, 10, 20, and $37^{\circ}C$ respectively. The number of S. aureus in pyeonyuk increased at all the storage temperatures. The maximum specific growth rate (${\mu}_{max}$) and lag phase duration (LPD) values were calculated by Baranyi model. The ${\mu}_{max}$ values went up, while the LPD values decreased as the storage temperature increased from $4^{\circ}C$ to $37^{\circ}C$. Square root model and polynomial model were used to develop the secondary models for ${\mu}_{max}$ and LPD, respectively. Root Mean Square Error (RMSE) was used to evaluate the developed model and the fitness was determind to be 0.42. Therefore the developed predictive model was useful to predict the growth of S. aureus in pyeonyuk and it will help to prevent food-born disease by expanding for microbial sanitary management guide.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.1 no.1
• /
• pp.53-68
• /
• 1981

Most clays under sustained load exhibit time-dependent deformation because of creep movement of soil particles and many investigators have attempted to relate their findings to the creep behavior of natural ground and to the long-term stability of slopes. Since the creep behavior of clays may assume a variety of forms depending on such factors as soil plasticity, activity and water content, it is difficult and complicated to analyse the creep behavior of clays. Rheological models composed of linear springs in combination with linear or nonlinear dashpots and sliders, are generally used for the mathematical description of the time-dependent behavior of soils. Most rheological models, however, have been proposed to simulate the behavior of secondary compression for saturated clays and few definitive data exist that can evaluate the behavior of non-saturated clays under the action of sustained stress. The clays change gradually from a solid state through plastic state to a liquid state with increasing water content, therefore, the rheological models also change. On the other hand, creep is time-dependent, and also the effect of thixotropy is time-function. Consequently, there may be certain correlations between creep behavior and the effects of thixotropy in compacted clays. In addition, the states of clay depend on water content and hence the height of the specimen under drained conditions. Futhermore, based on present and past studies, because immediate elastic deformation occurs instantly after the pressure increment without time-delayed behavior, the factor representing immediate elastic deformations in the rheological model is necessary. The investigation described in this paper, based on rheological model, is designed to identify the immediate elastic deformations and the effects of thixotropy and height of clay specimens with varing water content and stress level on creep deformations. For these purposes, the uniaxial drain-type creep tests were performed. Test results and data for three compacted clays have shown that a linear top spring is needed to account for immediate elastic deformations in the rheological model, and at lower water content below the visco-plastic limit, the effects of thixotropy and height of clay specimens can be represented by the proposed rheological model not considering the effects. Therefore, the rheological model does not necessitate the other factors representing these effects. On the other hand, at water content higher than the visco-plastic limit, although the state behavior of clays is visco-plastic or viscous flow at the beginning of the test, the state behavior, in the case of the lower height sample, does not represent the same behavior during the process of the test, because of rapid drainage. In these cases, the rheological model does not coincide with the model in the case of the higher specimens.

• Journal of Intelligence and Information Systems
• /
• v.18 no.2
• /
• pp.29-45
• /
• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Global Scholars of Marketing Science
• /
• v.19 no.1
• /
• pp.37-46
• /
• 2009

The Census Bureau of the Department of Commerce announced in May 2008 that U.S. retail e-commerce sales for 2006 reached $ 107 billion, up from $ 87 billion in 2005 - an increase of 22 percent. From 2001 to 2006, retail e-sales increased at an average annual growth rate of 25.4 percent. The explosive growth of E-Commerce has caused profound changes in marketing channel relationships and structures in many industries. Despite the great potential implications for both academicians and practitioners, there still exists a great deal of uncertainty about the impact of the Internet channel introduction on distribution channel management. The purpose of this study is to investigate how the ownership of the new Internet channel affects the existing channel members and consumers. To explore the above research questions, this study conducts well-controlled mathematical experiments to isolate the impact of the Internet channel by comparing before and after the Internet channel entry. The model consists of a monopolist manufacturer selling its product through a channel system including one independent physical store before the entry of an Internet store. The addition of the Internet store to this channel system results in a mixed channel comprised of two different types of channels. The new Internet store can be launched by the independent physical store such as Bestbuy. In this case, the physical retailer coordinates the two types of stores to maximize the joint profits from the two stores. The Internet store also can be introduced by an independent Internet retailer such as Amazon. In this case, a retail level competition occurs between the two types of stores. Although the manufacturer sells only one product, consumers view each product-outlet pair as a unique offering. Thus, the introduction of the Internet channel provides two product offerings for consumers. The channel structures analyzed in this study are illustrated in Fig.1. It is assumed that the manufacturer plays as a Stackelberg leader maximizing its own profits with the foresight of the independent retailer's optimal responses as typically assumed in previous analytical channel studies. As a Stackelberg follower, the independent physical retailer or independent Internet retailer maximizes its own profits, conditional on the manufacturer's wholesale price. The price competition between two the independent retailers is assumed to be a Bertrand Nash game. For simplicity, the marginal cost is set at zero, as typically assumed in this type of study. In order to explore the research questions above, this study develops a game theoretic model that possesses the following three key characteristics. First, the model explicitly captures the fact that an Internet channel and a physical store exist in two independent dimensions (one in physical space and the other in cyber space). This enables this model to demonstrate that the effect of adding an Internet store is different from that of adding another physical store. Second, the model reflects the fact that consumers are heterogeneous in their preferences for using a physical store and for using an Internet channel. Third, the model captures the vertical strategic interactions between an upstream manufacturer and a downstream retailer, making it possible to analyze the channel structure issues discussed in this paper. Although numerous previous models capture this vertical dimension of marketing channels, none simultaneously incorporates the three characteristics reflected in this model. The analysis results are summarized in Table 1. When the new Internet channel is introduced by the existing physical retailer and the retailer coordinates both types of stores to maximize the joint profits from the both stores, retail prices increase due to a combination of the coordination of the retail prices and the wider market coverage. The quantity sold does not significantly increase despite the wider market coverage, because the excessively high retail prices alleviate the market coverage effect to a degree. Interestingly, the coordinated total retail profits are lower than the combined retail profits of two competing independent retailers. This implies that when a physical retailer opens an Internet channel, the retailers could be better off managing the two channels separately rather than coordinating them, unless they have the foresight of the manufacturer's pricing behavior. It is also found that the introduction of an Internet channel affects the power balance of the channel. The retail competition is strong when an independent Internet store joins a channel with an independent physical retailer. This implies that each retailer in this structure has weak channel power. Due to intense retail competition, the manufacturer uses its channel power to increase its wholesale price to extract more profits from the total channel profit. However, the retailers cannot increase retail prices accordingly because of the intense retail level competition, leading to lower channel power. In this case, consumer welfare increases due to the wider market coverage and lower retail prices caused by the retail competition. The model employed for this study is not designed to capture all the characteristics of the Internet channel. The theoretical model in this study can also be applied for any stores that are not geographically constrained such as TV home shopping or catalog sales via mail. The reasons the model in this study is names as "Internet" are as follows: first, the most representative example of the stores that are not geographically constrained is the Internet. Second, catalog sales usually determine the target markets using the pre-specified mailing lists. In this aspect, the model used in this study is closer to the Internet than catalog sales. However, it would be a desirable future research direction to mathematically and theoretically distinguish the core differences among the stores that are not geographically constrained. The model is simplified by a set of assumptions to obtain mathematical traceability. First, this study assumes the price is the only strategic tool for competition. In the real world, however, various marketing variables can be used for competition. Therefore, a more realistic model can be designed if a model incorporates other various marketing variables such as service levels or operation costs. Second, this study assumes the market with one monopoly manufacturer. Therefore, the results from this study should be carefully interpreted considering this limitation. Future research could extend this limitation by introducing manufacturer level competition. Finally, some of the results are drawn from the assumption that the monopoly manufacturer is the Stackelberg leader. Although this is a standard assumption among game theoretic studies of this kind, we could gain deeper understanding and generalize our findings beyond this assumption if the model is analyzed by different game rules.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.147-168
• /
• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Korean Society of Disaster and Security
• /
• v.9 no.2
• /
• pp.63-75
• /
• 2016

For safe water supply, residual chlorine has to be maintained in tap-water above a certain level from drinking water treatment plants to the final tap-water end-point. However, according to the current literature, approximately 30-60% of residual chlorine is being lost during the whole water supply pathways. The losses of residual chlorine may have been attributed to the current tendency for water supply managers to reduce chlorine dosage in drinking water treatment plants, aqueous phase decomposition of residual chlorine in supply pipes, accelerated chlorine decomposition at a high temperature during summer, leakage or losses of residual chlorine from old water supply pipes, and disappearances of residual chlorine in water storage tanks. Because of these, it is difficult to rule out the possibility that residual chlorine concentrations become lower than a regulatory level. In addition, it is concerned that the regulatory satisfaction of residual chlorine in water storage tanks can not always be guaranteed by using the current design method in which only storage capacity and/or hydraulic retention time are simply used as design factors, without considering other physico-chemical processes involved in chlorine disappearances in water storage tank. To circumvent the limitations of the current design method, mathematical models for aqueous chlorine decomposition, sorption of chlorine into wall surface, and mass-transfer into air-phase via evaporation were selected from literature, and residual chlorine reduction behavior in water storage tanks was numerically simulated. The model simulation revealed that the major factors influencing residual chlorine disappearances in water storage tanks are the water quality (organic pollutant concentration) of tap-water entering into a storage tank, the hydraulic dispersion developed by inflow of tap-water into a water storage tank, and sorption capacity onto the wall of a water storage tank. The findings from his work provide useful information in developing novel design and technology for minimizing residual chlorine disappearances in water storage tanks.

• Journal of Aquaculture
• /
• v.6 no.1
• /
• pp.13-27
• /
• 1993

In order to determine the protein requirements of the Korean rockfish Sebastes schlegeli six isocaloric diets containing crude protein level from 20\%\;to\;60\%$ were fed to two groups of fish, small and large size, with the initial average body weight of 8 g and 220 g respectively. White fish meal was used as a sole protein source. Daily weight gain, daily protein retention. daily energy retention, feed efficiency, protein retention efficiency and energy retention efficiency were significantly affected by the dietary protein content (p< 0.05). The growth parameters (that is, daily weight gain, daily protein retention and daily energy retention) increased up to $44\%$ protein level with no additional response above this point. The protein requirements were determined from daily weight gain using two different mathematical models. Second order polynomial regression analysis showed that maximum daily weight gain occurred at $56.7\%\;and\;50.6\%$ protein levels for the small size group and the large size group, respectively. However the protein requirements, determined by the broken line model, appeared to be about $40\%$ for both groups. Nutrient utilization also suggested that the protein requirements of both groups were close to $40\%$. When daily protein intake was considered, daily protein requirements per 100g of fish, estimated by the broken line model, were 0.99g and 0.35g for the small and large size groups respectively. Based on these results, a $40\%$ dietary crude protein level could be recommended for the optimum growth and efficient nutrient utilization of the Korean rockfish weighing between 8g and 300g.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.4 no.1
• /
• pp.11-26
• /
• 1984

The aim of this study is to establish a synthetical optimal method that simultaneously analyze and design reinforced concrete flat slab-column structures involving multi-constraints and multi-design variables. The variables adopted in this mathematical models consist of design variables including sectional sizes and steel areas of frames, and analysis variable of the ratio of bending moment redistribution. The cost function is taken as the objective function in the formulation of optimal problems. A number of constraint equations, involving the ultimate limit state and the serviceability limit state, is derived in accordance with BSI CP110 requirements on the basis of limit state design theory. Both objective function and constraint equations derived from design variables and an analysis variable generally become high degree nonlinear problems. Using SLP as an analytical method of nonlinear optimal problems, an optimal algorithm is developed so as to analyze and design the structures considered in this study. The developed algorithm is directly applied to a few reinforced concrete flat slab-column structures to assure the validity of it and the possibility of optimization From the research it is found that the algorithm developed in this study is applicable to the optimization of reinforced concrete flat slab column structures and it converges to a optimal solution with 4 to 6 iterations regardless of initial variables. The result shows that an economical design can be possible when compared with conventional designs. It is also found that considering the ratio of bending moment redistribution as a variable is reasonable. It has a great effect on the composition of optimal sections and the economy of structures.