• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.981-989
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• 2010

In this study, we introduce a new architecture of fuzzy inference system. In the fuzzy inference system, we use Fuzzy C-Means clustering algorithm to form the premise part of the rules. The membership functions standing in the premise part of fuzzy rules do not assume any explicit functional forms, but for any input the resulting activation levels of such radial basis functions directly depend upon the distance between data points by means of the Fuzzy C-Means clustering. As the consequent part of fuzzy rules of the fuzzy inference system (being the local model representing input output relation in the corresponding sub-space), four types of polynomial are considered, namely constant, linear, quadratic and modified quadratic. This offers a significant level of design flexibility as each rule could come with a different type of the local model in its consequence. Either the Least Square Estimator (LSE) or the weighted Least Square Estimator (WLSE)-based learning is exploited to estimate the coefficients of the consequent polynomial of fuzzy rules. In fuzzy modeling, complexity and interpretability (or simplicity) as well as accuracy of the obtained model are essential design criteria. The performance of the fuzzy inference system is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules(clusters) and the order of polynomial in the consequent part of the rules. Accordingly we can obtain preferred model structure through an adjustment of such parameters of the fuzzy inference system. Moreover the comparative experimental study between WLSE and LSE is analyzed according to the change of the number of clusters(rules) as well as polynomial type. The superiority of the proposed model is illustrated and also demonstrated with the use of Automobile Miles per Gallon(MPG), Boston housing called Machine Learning dataset, and Mackey-glass time series dataset.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.76-82
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• 2010

In this paper, we develop Linguistic Model (LM) based on information granules as a systematic approach to generating fuzzy if-then rules from a given input-output data. The LM introduced by Pedrycz is performed by fuzzy information granulation obtained from Context-based Fuzzy Clustering(CFC). This clustering estimates clusters by preserving the homogeneity of the clustered patterns associated with the input and output data. Although the effectiveness of LM has been demonstrated in the previous works, it needs to improve in the sense of performance. Therefore, we focus on the automatic generation of linguistic contexts, addition of bias term, and the transformed form of consequent parameter to improve both approximation and generalization capability of the conventional LM. The experimental results revealed that the improved LM yielded a better performance in comparison with LM and the conventional works for automobile MPG(miles per gallon) predication and Boston housing data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.6
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• pp.525-532
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• 2014

Since mine wastes were merely dumped in the mine waste dump, they have produced acid mine drainage (AMD). Therefore, main objective of this study was to evaluate the effect of coal combustion products (CCPs) on heavy metal stabilization and detoxification for mine wastes. Total six treatments for incubation test were conducted depending on mixing method (completely mixing and layered). Also, lysimeter experiment was conducted to examine efficiency of polyacrylamide (PAM) on reduction of mine wastes erosion. Result of incubation test showed that concentrations of soluble aluminium (Al) and iron (Fe) in leachate decreased compared to control. The lowest soluble Al and Fe in leachate was observed in 50% mixed treatment (14.2 and $1.03mg\;kg^{-1}$ for Al and Fe respectively) compared to control treatment (253.0 for Al and $52.6mg\;kg^{-1}$ for Fe). The pH of mine wastes (MW) and leachate increased compared to control after mixing with CCPs and ordered as control (MW 6.4, leachate 6.3) < 10% (MW 7.7, leachate 7.1) < 20% (MW 9.0, leachate 7.8) < 30% (MW 9.5, leachate 8.3) < 40% (MW 9.9, leachate 8.5) < 50% (MW 10.5, leachate 8.6). Application of PAM, both in liquid and granular type, dramatically decreased the suspended solid (SS) concentration of CCPs treatments. Reduction of SS loss was ordered as MW70CR30L ($24.4mg\;L^{-1}$) > MW70CR30LPL ($6.7mg\;L^{-1}$) > NT ($3.1mg\;L^{-1}$) > MW70CR30M ($1.6mg\;L^{-1}$) > MW70CR30MPL ($1.1mg\;L^{-1}$) > MW70CR30PGM ($0.7mg\;L^{-1}$) > MW70CR30LPG ($0.5mg\;L^{-1}$) > MW70CR30MPG ($0.4mg\;L^{-1}$). Overall, application of CCPs can be environmental friendly and cost-effective way to remediate coal mine wastes contaminated with heavy metals. In addition, use of PAM could help to prevent the erosion coal mine wastes in mine waste disposal area.

• Journal of the Microelectronics and Packaging Society
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• v.13 no.3 s.40
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• pp.9-12
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• 2006

We have designed and fabricated micro power generators by electroplating which is important in MEMS(micro electro mechanical system) technique. We have electroplated MEMS coils on the glass substrates and have chosen one of these coils for experiments. The thickness, width, and length of the coil are $7{\mu}m,\;20{\mu}m$, and 1.6 m, respectively. We have analyzed the structure of MEMS coil by SEM. We have made a vibrating system for reproducible results in measurement. With reciprocating a magnet on the surface of a fabricated winding coil, the micro power generator produce an alternating voltage. We have changed the vibrational frequency from 0.5 Hz to 8 Hz. The generated voltage was 106 mV at 3 Hz and 198 mV at 6 Hz. We aim at the micro power generator which can change vibration energy to useful electric energy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.29-38
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• 2011

In order to derive domain-independent heuristics from the specification of a planning problem, it is required to relax the given problem and then solve the relaxed one. In this paper, we present a new planning graph, Merged Planning Graph(MPG), and GD heuristics for solving contingent planning problems with both uncertainty about the initial state and non-deterministic action effects. The merged planning graph is an extended one to be applied to the contingent planning problems from the relaxed planning graph, which is a common means to get effective heuristics for solving the classical planning problems. In order to get heuristics for solving the contingent planning problems with sensing actions and non-deterministic actions, the new graph utilizes additionally the effect-merge relaxations of these actions as well as the traditional delete relaxations. Proceeding parallel to the forward expansion of the merged planning graph, the computation of GD heuristic excludes the unnecessary redundant cost from estimating the minimal reachability cost to achieve the overall set of goals by analyzing interdependencies among goals or subgoals. Therefore, GD heuristics have the advantage that they usually require less computation time than the overlap heuristics, but are more informative than the max and the additive heuristics. In this paper, we explain the experimental analysis to show the accuracy and the search efficiency of the GD heuristics.

• Korean Journal of Clinical Laboratory Science
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• v.52 no.4
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• pp.417-424
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• 2020

This study aimed to explore the effects of brain-derived neurotrophic factor (BDNF), produced by engineered immortalized mesenchymal stem cells (imMSC), on lower urinary tract symptoms (LUTS) in a rat model with neurogenic bladder (NB). Forty-eight Sprague-Dawley (SD) rats were randomly divided into the following groups: Sham control, LUTS, LUTS+imMSC (treated with immortalized MSC), and LUTS+BDNF-eMSC (treated with BDNF-expressing MSC) groups. LUTS was induced by a crush injury to the major pelvic ganglion (MPG). Bladder function was tested under anesthesia, and bladder tissue strips were collected thereafter for contractility test and western blot analysis. Western blot results showed that the expression of both Angiopoietin 1 (Ang 1) and platelet-derived growth factor (PDGF) increased with MSC injection. The effect of treatment with BDNF-eMSC on LUTS was also evaluated, and the results were found to be better than those with imMSC (P<0.05). BDNF-eMSC prevented fibrosis in the bladder tissue and significantly reduced caspase-3 levels. In conclusion, high expression of BDNF in vivo resulted in recovery of bladder function and contractility, along with the inhibition of apoptosis in a rat model.

• KSBB Journal
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• v.19 no.2
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• pp.93-97
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• 2004

We investigate the effect of enzyme pretreatment using protease, carbohydrase, and lipase on improvement of sludge treatment efficiency by measuring SCOD and TCOD. The enzyme-pretreatment increases SCOD of excess sludge. In addition, the amount of sludge reduction during digestion, in terms of SCOD and TCOD, are enhanced by enzyme-pretreatment. Among pretense, carbohydrase, and lipase, pretense showed the best enhancement of the sludge treatment efficiency. Sludge digestion followed by ozone and enzyme treatments showed more effective sludge treatment when compared with ozone treatment alone. Therefore, we expect that enzyme pretreatment can be used as a useful tool for enhancing the sludge treatment efficiency.