• Journal of Energy Engineering
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• v.23 no.2
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• pp.49-56
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• 2014

It is important to keep stable effluent water quality and minimize operation cost in biological wastewater treatment plant. However, the optimal operation is difficult because of the change of influent flow rate and concentrations, the nonlinear dynamics of microbiology growth rate and other environmental factors. Therefore, many wastewater treatment plants are operated for much more redundant oxygen or chemical dosing than the necessary. In this study, the optimal control scheme for dissolved oxygen (DO) is suggested to prevent over-aeration and the reduction of the electric cost in plant operation while maintaining the dissolved oxygen (DO) concentration for the metabolism of microorganisms in oxic reactor. For optimal control, The oxygen uptake rate (OUR) is realtime measured for the identification of influent characterization and the identification of microorganisms' oxygen requirement in oxic reactor. Optimal DO seT-Point needed for the microorganism is suggested based on real time measurement of oxygen uptake of microorganism and the control of air blower. Therefore, both stable effluent quality and minimization of electric cost are satisfied with a suggested optimal setpoint decision system by providing the necessary oxygen supply requirement to the microorganisms coping with the variations of influent loading.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.3 no.1
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• pp.86-93
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• 2003

Background : The SCL began screening of newborns and high risk group blood spots with tandem mass spectrometry (MS/MS) in April 2001. Our goal was to determine approximate prevalence of metabolic disorders, optimization of decision criteria for estimation of preventive effect with early diagnosis. This report describes the ongoing effort to identify more than 30 metabolic disorders by MS/MS in South Korea. Methods : Blood spot was collected from day 2 to 30 (mostly from day 2 to 10) after birth for newborn. Blood spot of high risk group was from the pediatric patients in NICU, developmental delay, mental retardation, strong family history of metabolic disorders. One punch (3.2 mm ID) of dried blood spots was extracted with $150{\mu}L$ of methanol containing isotopically labelled amino acids (AA) and acylcarnitines (AC) internal standards. Butanolic HCl was added and incubated at $65^{\circ}C$ for 15 min. The butylated extract was introduced into the inlet of MS/MS. Neutral loss of m/z 102 and parent ion mode of m/z 85 were set for the analyses of AA and AC, respectively. Diagnosis was confirmed by repeating acylcarnitine profile, urine organic acid and plasma amino acid analysis, direct enzyme assay, or molecular testing. Results : Approximately 31,000 neonates and children were screened and the estimated prevalence (newborn/high risk group), sensitivity, specificity and recall rate amounted to 1:2384/1:2066, 96.55%, 99.98%, and 0.73%, respectively. Confirmed 28 (0.09%) multiple metabolic disorders (newborn/high risk) were as follows; 13 amino acid disorders [classical PKU (3/4), BH4 deficient-hyperphenylalaninemia (0/1), Citrullinemia (1/0), Homocystinuria (0/2), Hypermethioninemia (0/1), Tyrosinemia (1/0)], 8 organic acidurias [Propionic aciduria (2/1), Methylmalonic aciduria (0/1), Isovaleric aciduria (1/1), 3-methylcrotonylglycineuria (1/0), Glutaric aciduria type1 (1/0)], 7 fatty acid oxidation disorders [LCHAD def. (2/2), Mitochondrial TFP def. (0/1), VLCAD def. (1/0), LC3KT def. (0/1). Conclnsion : The relatively normal development of 10 patients with metabolic disorders among newborns (except for the expired) demonstrates the usefulness of newborn screening by MS/MS for early diagnosis and medical intervention. However, close coordination between the MS/MS screening laboratory and the metabolic clinic/biochmical geneticists is needed to determine proper decision of screening parameters, confirmation diagnosis, follow-up scheme and additional tests.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.1-6
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• 2017

Recent researches on radio frequency energy harvesting networks(RF-EHNs) with limited energy resource like battery have been focusing on the development of a new scheme that can effectively extend the whole lifetime of a network to semipermanent. In order for considerable increase both in the amount of energy obtained from radio frequency energy harvesting and its charging effectiveness, it is very important to design a network that supports energy harvesting and data transfer simultaneously with the full consideration of various characteristics affecting the performance of a RF-EHN. In this paper, we proposes an interference-based charging aware routing protocol(ICARP) that utilizes interference information and charging time to maximize the amount of energy harvesting and to minimize the end-to-end delay from a source to the given destination node. To accomplish the research objectives, this paper gives a design of ICARP adopting new network metrics such as interference information and charging time to minimize end-to-end delay in energy harvesting wireless networks. The proposed method enables a RF-EHN to reduce the number of packet losses and retransmissions significantly for better energy consumption. Finally, simulation results show that the network performance in the aspects of packet transmission rate and end-to-end delay has enhanced with the comparison of existing routing protocols.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.399-401
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• 2022

A definitive diagnosis to classify the breast malignancy status may be achieved by microscopic analysis using surgical open biopsy. However, this procedure requires experts in the specializing of histopathological image analysis directing to time-consuming and high cost. To overcome these issues, deep learning is considered practically efficient to categorize breast cancer into benign and malignant from histopathological images in order to assist pathologists. This study presents a pre-trained convolutional neural network model architecture with a 100% fine-tuning scheme and Adagrad optimizer to classify the breast cancer histopathological images into benign and malignant using a 40× magnification BreaKHis dataset. The pre-trained architecture was constructed using the InceptionResNetV2 model to generate a modified InceptionResNetV2 by substituting the last layer with dense and dropout layers. The results by demonstrating training loss of 0.25%, training accuracy of 99.96%, validation loss of 3.10%, validation accuracy of 99.41%, test loss of 8.46%, and test accuracy of 98.75% indicated that the modified InceptionResNetV2 model is reliable to predict the breast malignancy type from histopathological images. Future works are necessary to focus on k-fold cross-validation, optimizer, model, hyperparameter optimization, and classification on 100×, 200×, and 400× magnification.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2255-2265
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• 2013

This study presents the width function-based Clark model. To this end, rescaled width function with distinction between hillslope and channel velocity is used as time-area curve and then it is routed through linear storage within the framework of not finite difference scheme used in original Clark model but analytical expression of linear storage routing. There are three parameters focused in this study: storage coefficient, hillslope velocity and channel velocity. SCE-UA, one of the popular global optimization methods, is applied to estimate them. The shapes of resulting IUHs from this study are evaluated in terms of the three statistical moments of hydrologic response functions: mean, variance and the third moment about the center of IUH. The correlation coefficients to the three statistical moments simulated in this study against these of observed hydrographs were estimated at 0.995 for the mean, 0.993 for the variance and 0.983 for the third moment about the center of IUH. The shape of resulting IUHs from this study give rise to satisfactory simulation results in terms of the mean and variance. But the third moment about the center of IUH tend to be overestimated. Clark model proposed in this study is superior to the one only taking into account mean and variance of IUH with respect to skewness, peak discharge and peak time of runoff hydrograph. From this result it is confirmed that the method suggested in this study is useful tool to reflect the heterogeneity of drainage path and hydrodynamic parameters. The variation of statistical moments of IUH are mainly influenced by storage coefficient and in turn the effect of channel velocity is greater than the one of hillslope velocity. Therefore storage coefficient and channel velocity are the crucial factors in shaping the form of IUH and should be considered carefully to apply Clark model proposed in this study.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.6
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• pp.307-317
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• 2002

In this paper we propose a new mesh reconstruction scheme that produces a displaced subdivision surface directly from unorganized points. The displaced subdivision surface is a new mesh representation that defines a detailed mesh with a displacement map over a smooth domain surface, but original displaced subdivision surface algorithm needs an explicit polygonal mesh since it is not a mesh reconstruction algorithm but a mesh conversion (remeshing) algorithm. The main idea of our approach is that we sample surface detail from unorganized points without any topological information. For this, we predict a virtual triangular face from unorganized points for each sampling ray from a parameteric domain surface. Direct displaced subdivision surface reconstruction from unorganized points has much importance since the output of this algorithm has several important properties: It has compact mesh representation since most vertices can be represented by only a scalar value. Underlying structure of it is piecewise regular so it ran be easily transformed into a multiresolution mesh. Smoothness after mesh deformation is automatically preserved. We avoid time-consuming global energy optimization by employing the input data dependant mesh smoothing, so we can get a good quality displaced subdivision surface quickly.