• Journal of the Korean Institute of Intelligent Systems
• /
• v.5 no.4
• /
• pp.41-55
• /
• 1995

In this paper, an optimal idenfication method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together wlth optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzzy-neural networks(FNNs) are tuned automatically using improved modified complex method and modified learning algorithm. For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activateti sluge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• International Journal of Control, Automation, and Systems
• /
• v.2 no.4
• /
• pp.423-434
• /
• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• Journal of Environmental Health Sciences
• /
• v.39 no.5
• /
• pp.391-407
• /
• 2013

Micropollutants emerge in surface water through untreated discharge from sewage and wastewater treatment plants (STPs and WWTPs). Most micropollutants resist the conventional systems in place at water treatment plants (WTPs) and survive the production of tap water. In particular, pharmaceuticals and endocrine disruptors (ECDs) are micropollutants frequently detected in drinking water. In this review, we summarized the distribution of micropollutants at WTPs and also scrutinized the effectiveness and mechanisms for their removal at each stage of drinking water production. Micropollutants demonstrated clear concentrations in the final effluents of WTPs. Although chronic exposure to micropollutants in drinking water has unclear adverse effects on humans, peer reviews have argued that continuous accumulation in water environments and inappropriate removal at WTPs has the potential to eventually affect human health. Among the available removal mechanisms for micropollutants at WTPs, coagulation alone is unlikely to eliminate the pollutants, but ionized compounds can be adsorbed to natural particles (e.g. clay and colloidal particles) and metal salts in coagulants. Hydrophobicities of micropollutants are a critical factor in adsorption removal using activated carbon. Disinfection can reduce contaminants through oxidation by disinfectants (e.g. ozone, chlorine and ultraviolet light), but unidentified toxic byproducts may result from such treatments. Overall, the persistence of micropollutants in a treatment system is based on the physico-chemical properties of chemicals and the operating conditions of the processes involved. Therefore, monitoring of WTPs and effective elimination process studies for pharmaceuticals and ECDs are required to control micropollutant contamination of drinking water.

• Journal of Microbiology and Biotechnology
• /
• v.27 no.1
• /
• pp.141-148
• /
• 2017

The oxidation ditch (OD) is one of the most widely used processes for treating municipal wastewater. However, the microbial communities in the OD systems have not been well characterized, and little information about the shift of bacterial community during the startup process of the OD systems is available. In this study, we investigated the bacterial community changes during the startup period (over 100 days) of a full-scale OD. The results showed that the bacterial community dramatically changed during the startup period. Similar to the activated sludge samples in other studies, Proteobacteria (accounting for 26.3%-48.4%) was the most dominant bacterial phylum in the OD system, but its relative abundance declined nearly 40% during the startup process. It was also found that Planctomycetes proliferated greatly (from 4.79% to 13.5%) and finally replaced Bacteroidetes as the second abundant phylum in the OD system. Specifically, some bacteria affiliated with genus Flavobacterium exhibited remarkable decreasing trends, whereas bacterial species belonging to the OD1 candidate division and Saprospiraceae family were found to increase during the startup process. Despite of the bacterial community shift, the organic matter, nitrogen, and phosphorus in the effluent were always in low concentrations, suggesting the functional redundancy of the bacterial community. Moreover, by comparing with the bacterial community in other municipal wastewater treatment bioreactors, some potentially novel bacterial species were found to be present in the OD system. Collectively, this study improved our understandings of the bacterial community structure and microbial ecology during the startup of a full-scale wastewater treatment bioreactor.

• Parasites, Hosts and Diseases
• /
• v.48 no.3
• /
• pp.219-224
• /
• 2010

This study has been conducted to estimate the occurrence of Cryptosporidium oocysts in water supplies in the Metropolitan area of Seoul, South Korea, for 10 years from 2000 to 2009. Water samples were collected quarterly at 6 intakes in the Han River and its largest stream and 6 conventional Water Treatment Plants (WTPs) serving drinking water for 10 million people of Seoul. Cryptosporidium oocysts were found in 22.5% of intake water samples and arithmetic mean was 0.65 oocysts/10 L (range 0-22 oocysts/10 L). Although the annual mean of oocyst number was as low as 0.04-1.90 oocysts/10 L, 3 peaks in 2004 and 2007 were observed and the pollution level was a little higher in winter. The lowest density was observed at Paldang intake and the pollution level increased at Kuui and Jayang intakes. At the end of the largest stream, oocysts were found in 70% of collected samples (mean 5.71 oocysts/10 L) and it seemed that its joining the Han River resulted in the increase at Kuui intake and downstream. Oocyst removal by physical process exceeded 2.0-2.3 log and then all finished water samples collected at 6 WTPs were negative for Cryptosporidium in each 100 L sample for 10 years. These results suggested that domestic wastewater from the urban region could be a source of Cryptosporidium pollution and separating sewage systems adjacent to the intakes could be meaningful for some intakes having weakness related to parasitological water quality.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.50 no.7
• /
• pp.339-349
• /
• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Journal of Korea Water Resources Association
• /
• v.37 no.3
• /
• pp.195-206
• /
• 2004

One of the main factors which reduces the efficiency of a sewage treatment plant is the Inflow/Infiltration(Ⅰ/Ⅰ) in the sewer First we must calculate the quantity of Ⅰ/Ⅰ via the investigation of each sewer to establish the reduction plan of Ⅰ/Ⅰ. However, in Korea, we apply the results of a surveyed sample to the entire study area to establish the reduction plan of Ⅰ/Ⅰ. This methodology just considers the total Ⅰ/Ⅰ for the entire study area but it does not consider the quantity of Ⅰ/Ⅰ for the individual sewer systems. Therefore, we may need the model to consider the Ⅰ/Ⅰ in the individual sewer systems and we develop the model to calculate the Ⅰ/Ⅰ that happen in urban sewer systems. We estimate the Ⅰ/Ⅰ of individual systems by the developed model and the estimated Ⅰ/Ⅰ are utilized as the basic data for the establishment of Ⅰ/Ⅰ reduction plan. The observed Ⅰ/Ⅰ for the entire study area is distributed into the individual sewer systems according to their defect states. Here, the weights of defect elements are calculated using AHP(Analytic Hierarchy Process) and we perform the uncertainty analysis for considering the errors using MCS(Monte Carlo Simulation).

• Clean Technology
• /
• v.9 no.3
• /
• pp.133-144
• /
• 2003

In this study, Insoluble catalyst electrode for oxide systems were manufactured, by using of them, carried out experiments on electrolytic treatment of dyeing wastewater containing persistent organic compounds, and then made a comparative study of the efficiency of treatment for environmental pollutants and whether each of them is valuable of not as an electrode for soluble electrode(Fe, Al) and insoluble electrode(SUS, R.C.E; Replaced Catalyst Electrode) which were used in the electrolytic system. Besides, it was investigated the conditions for electrolytic treatment to find the maximum efficiency of electrolytic treatment. As the result of this study, by using of insoluble catalyst electrode for oxide can solved the stability of electrode that is one of the greatest problems in order to put to practical use of electrolysis process in the treatment of the sewage and wastewater and the result runs as follows; 1. The durability of insoluble catalyst electrode(R.C.E) can be verified the most favorable when the molar ratio of $RuO_2-SnO_2-IrO_2-TiO_2$(4 compounds system) is 70/20/5/5. 2. The efficiency of treatment was obtained a more than 90% goodness for CODMn and also a good results for T-N removal in the experimental conditions of the distance of electrode 5 mm, time of electrolysis 60 minutes, permissible voltage 10V, processing capacity $0.5{\ell}$.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.5 no.4
• /
• pp.31-44
• /
• 1985

The application of anaerobic attached microbial films in the expanded bed process has recently been examined at high temperatures ($55^{\circ}C$) and with particulate matter. Extrapolation of the kinetics suggested that waste activated sludge (WAS) could be efficiently digested at hydraulic retention times as short as six hours in the expanded bed process. This would represent a 99 percent digester reactor volume reduction and would introduce interesting solids management alternatives if such a high rate process were developed. This paper presents a summary of a 1.5 year study of the feasibility of such a process. Three continuously fed $55^{\circ}C$ laboratory reactor systems were used to define the kinetics and the site of reactions-control completely mixed reactors were compared to the expanded beds (AAFEB) with and without a hydrolysis unit preceding the attached film unit. Well defined laboratory-generated WAS was compared to actual WAS from a domestic sewage treatment facility. Sixty percent of the biodegradable organics were converted in an AAFEB at a 15-hour hydraulic retention time without hydrolysis, whereas greater than 95 perccent of the biodegradable organics were stabilized in a two-stage system consisting of a 3-day HRT hydrolysis reactor followed by a 15-hour HRT AAFEB. The limitations of this high rate process and its potential application are discussed.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.35-52
• /
• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.