• Journal of Korean Society of Environmental Engineers
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• v.32 no.1
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• pp.97-104
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• 2010

At present, the biological nutrient removal (BNR) process for removal of nitrogen and phosphorus is being constructing to keep pace with the reinforced standard of effluent quality and the traditional activated sludge process of preexistence is being promoting to retrofit. At the most case of retrofitting, processes are subjected to be under consideration as alternative BNR process for retrofitting. However, process evaluation methods are restricted to compare only treatment efficiency. Therefore, when BNR process apply, process evaluation was needed various method for treatment efficiency as well as sludge production and aeration cost, and all. In this study, the evaluation method of alternative process was suggested for the case for retrofitting S wastewater treatment plant which has been operated the standard activated sludge process. Three BNR processes for evaluation of proper alternatative process were selected and evaluated with suggested method. The selected $A^2$/O, VIP and DNR processes were evaluated using the mathematical model which is time and cost effective as well as gathered objective evaluation criteria. The evaluation between 5 individual criteria was possible including sludge production and energy efficiency as well as treatment performance. The objective final decision method for selection of optimal process was established through the fuzzy inference.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.87-95
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• 1996

A new design scheme for the fuzzy pattern matching classifier (FPMC) is proposed. in conventional design of FPMC, there are no exact information about the membership function of which shape and number critically affect the performance of classifier. So far, a trial and error or heuristic method is used to find membership functions for the input patterns. But each of them have limits in its application to the various types of pattern recognition problem. In this paper, a new method to find the appropriate shape and number of membership functions for the input patterns which minimize classification error is proposed using genetic algorithms(GAs). Genetic algorithms belong to a class of stochastic algorithms based on biological models of evolution. They have been applied to many function optimization problems and shown to find optimal or near optimal solutions. In this paper, GAs are used to find the appropriate shape and number of membership functions based on fitness function which is inversely proportional to classification error. The strings in GAs determine the membership functions and recognition results using these membership functions affect reproduction of next generation in GAs. The proposed design scheme is applied to the several patterns such as tire tread patterns and handwritten alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.439-447
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• 2011

It is a main concern for sustainable development in water resources management to evaluate adaptation capability of water resources structures under the future climate conditions. This study introduced the Fuzzy Inference System (FIS) to represent the change of release and storage of reservoirs in the Han River basin corresponding to various inflows. Defining the adaptation capability of reservoirs as the change of maximum and/or minimum of storage corresponding to the change of inflow, the study showed that Gangdong Dam has the worst adaptation capability on the variation of inflow, while Soyanggang Dam has the best capability. This study also constructed an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the more accurate and efficient simulation of the adaptation capability of the Soyanggang Dam. Nine Inflow scenarios were generated using historical data from frequency analysis and synthetic data from two general circulation models with different climate change scenarios. The ANFIS showed significantly different consequences of the release and reservoir storage upon inflow scenarios of Soyanggang Dam, whilst it provides stable reservoir operations despite the variability of rainfall pattern.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.405-414
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• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.857-871
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• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.75-88
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• 2024

It would be advantageous to predict acreage and yield of crops in major grain-exporting countries, which would improve decisions on policy making and grain trade in Korea. A climate suitability model can be used to assess crop acreage and yield in a region where the availability of observation data is limited for the use of process-based crop models. The objective of this study was to determine the climate suitability index of wheat by province in Ukraine, which would allow for the spatial assessment of acreage and yield for the given crop. In the present study, the official data of wheat acreage and yield were collected from the State Statistics Service of Ukraine. The EarthStat data, which is a data product derived from satellite data and official crop reports, were also gathered for the comparison with the map of climate suitability index. The Fuzzy Union model was used to create the climate suitability maps under the historical climate conditions for the period from 1970 to 2000. These maps were compared against actual acreage and yield by province. It was found that the EarthStat data for acreage and yield of wheat differed from the corresponding official data in several provinces. On the other hand, the climate suitability index obtained using the Fuzzy Union model explained the variation in acreage and yield at a reasonable degree. For example, the correlation coefficient between the climate suitability index and yield was 0.647. Our results suggested that the climate suitability index could be used to indicate the spatial distribution of acreage and yield within a region of interest.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.189-199
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• 1998

This study presents a FZOGP(fuzzified zero-one goal programming) model and a DHP (Delphic Hierarchy Process) that can be used to help information systems(IS) managers decides which IS projects should be selected. Delphic method is conducted prior to AHP so that not only can the objectives to be considered in analysis be determined, but the opinions of all decision makers can also be incorporated in problem formulation. While the DHP provides an ideal ranking process for the selection of IS Projects, it does not consider real constraints that exists in decision making process. Then this study intends to show how the DHP can be used to establish a priority structure for use within a FZOGP model. The advantages of FZOGP model are as follows: the imprecise aspiration level for each objective can be considered in FZOGP model. And, the common features between the new FZOGP and the GP models are that the objective functions in both models are minimized and the structure of their formulations are the same.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.739-744
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• 2002

Recently we have discovered that sediments should be separated from lithosphere, and soil should be separated from biosphere, both sediment and soil will be mixed sediments-soil-sphere (Seso-sphere), which is using particulate mechanics to be solved. Erosion and sediment both are moving by particulate matter with water or wind. But ancient sediments will be erosion same to soil. Nowadays, real soil has already reduced much more. Many places have only remained sediments that have ploughed artificial farming layer. Thus it means sediments-soil-sphere. This paper discusses sediments-soil-sphere erosion modeling. In fact sediments-soil-sphere erosion is including water erosion, wind erosion, melt-water erosion, gravitational water erosion, and mixed erosion. We have established geographical remote sensing information modeling (RSIM) for different erosion that was using remote sensing digital images with geographical ground truth water stations and meteorological observatories data by remote sensing digital images processing and geographical information system (GIS). All of those RSIM will be a geographical multidimensional gray non-linear equation using mathematics equation (non-dimension analysis) and mathematics statistics. The mixed erosion equation is more complex that is a geographical polynomial gray non-linear equation that must use time-space fuzzy condition equations to be solved. RSIM is digital image modeling that has separated physical factors and geographical parameters. There are a lot of geographical analogous criterions that are non-dimensional factor groups. The geographical RSIM could be automatic to change them analogous criterions to be fixed difference scale maps. For example, if smaller scale maps (1:1000 000) that then will be one or two analogous criterions and if larger scale map (1:10 000) that then will be four or five analogous criterions. And the geographical parameters that are including coefficient and indexes will change too with images. The geographical RSIM has higher precision more than mathematics modeling even mathematical equation or mathematical statistics modeling.

• Journal of Korea Trade
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• v.24 no.8
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• pp.81-100
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• 2020

Purpose - Based on complexity theory, this study develops a configurational model to predict the profitability of Halal cosmetics firms in the Indonesian and Malaysian markets. The proposed research model involves two level configurations-industry context and selling strategies-to predict high and low scores of a firm's profitability. The industry context configuration model comprises industry stability, product homogeneity, price sensitivity, and switching cost. Selling strategies include customer-focused, competitor-focused, and margin-focused approaches. Design/methodology - This is the first empirical study that calculates causal models using a combination of industry context and selling strategy factors to predict profitability. Data obtained from the marketing managers of cosmetics firms are used to test the proposed configurational model using fuzzy-set qualitative comparative analysis (fsQCA). It contributes to the current knowledge of business marketing by identifying the factors necessary to achieve profitability using analysis of condition (ANC). Findings - The results revealed that unique and distinct models explain the conditions for high and low profitability in the Indonesian and Malaysian halal cosmetic markets. While customer-focused selling strategy is necessary to attain a higher profit in both the markets, margin-focused selling strategy appears to be an essential factor only in Malaysia. Complexity of the interactions of selling strategies with industry factors and differences between across two study markets confirmed that complexity theory can support the research configurational model. The theoretical and practical implications are also illustrated. Originality/value - Despite the rapid growth of the global halal industry, there is little knowledge about the halal cosmetic market. This study contributes to the current literature of the halal market by performing a set of asymmetric analytical approaches using a complex theoretical model. It also deepens our understating of how the Korean firms can approach the Muslim consumer's needs to generate more beneficial turnover/revenue.