• Environmental Engineering Research
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• v.25 no.4
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• pp.515-521
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• 2020

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1($\frac{{\mu}g}{l}$), however, the four-layer NNs proved superior in terms of R² and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.

• Journal of Korea Multimedia Society
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• v.22 no.3
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• pp.335-348
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• 2019

Cell segmentation and counting represent one of the most important tasks required in order to provide an exhaustive understanding of biological images. Conventional features suffer the lack of spatial consistency by causing the joining of the cells and, thus, complicating the cell counting task. We propose, in this work, a cascade of networks that take as inputs different versions of the original image. After constructing a Gaussian pyramid representation of the microscopy data, the inputs of different size and spatial resolution are given to a cascade of deep convolutional autoencoders whose task is to reconstruct the segmentation mask. The coarse masks obtained from the different networks are summed up in order to provide the final mask. The principal and main contribution of this work is to propose a novel method for the cell counting. Unlike the majority of the methods that use the obtained segmentation mask as the prior information for counting, we propose to utilize the hidden latent representations, often called the high-level features, as the inputs of a neural network based regressor. While the segmentation part of our method performs as good as the conventional deep learning methods, the proposed cell counting approach outperforms the state-of-the-art methods.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.231-239
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• 2001

This paper proposes a region segmenting method for the Pap-smear image. The proposed method uses a pixel classifier based on neural network, which consists of four stages : preprocessing, feature extraction, region segmentation and postprocessing. In the preprocessing stage, brightness value is normalized by histogram stretching. In the feature extraction stage, total 36 features are extracted from $3{\times}3$ or $5{\times}5$ window. In the region segmentation stage, each pixel which is associated with 36 features, is classified into 3 groups : nucleus, cytoplasm and background. The backpropagation network is used for classification. In the postprocessing stage, the pixel, which have been rejected by the above classifier, are re-classified by the relaxation algorithm. It has been shown experimentally that the proposed method finds the nucleus region accurately and it can find the cytoplasm region too.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.107-110
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• 2000

In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.415-420
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• 2005

Mathematical modeling and simulation of biochemical reaction networks gained a lot of attention recently since it can provide valuable insights into the interrelationships and interactions of genes, proteins and metabolites in a reaction network. A number of attempts have been made for modeling and storing biochemical reaction networks without their dynamical properties but unfortunately storing and efficiently querying of the dynamic (mathematical) models are not yet studied extensively. In this paper, we present a novel nested relational data schema to store a pathway with its dynamic properties. We then show how to make the mapping between this dynamic pathway schema with the corresponding static pathway representation.

• Genomics & Informatics
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• v.1 no.2
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• pp.75-79
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• 2003

Every protein can be characterized by either a distinct motif or a combination of motifs. Nevertheless, little is known about the relationships among (more than two) the motifs. Some of the proteins in the world are share motifs for evolutional or other biological benefits - they can save energy, time and resource for controlling and managing a variety of proteins. In some cases of motifs, the tendency is quite common and they can act the 'hub' motif of a network of the motif associations. The hubs are structurally and functionally important in themselves and also important in disease-related mutations. They will be highly resistant mutation to conserve their functions. But, in case of the a rare mutation, mutations on the position of hub can more easily cause fatal diseases.

• BMB Reports
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• v.45 no.4
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• pp.213-220
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• 2012

Viruses have evolved to manipulate the host cell machinery for virus propagation, in part by interfering with the host cellular signaling network. Molecular studies of individual pathways have uncovered many viral host-protein targets; however, it is difficult to predict how viral perturbations will affect the signaling network as a whole. Systems biology approaches rely on multivariate, context-dependent measurements and computational analysis to elucidate how viral infection alters host cell signaling at a network level. Here we describe recent advances in systems analyses of signaling networks in both viral and non-viral biological contexts. These approaches have the potential to uncover virus- mediated changes to host signaling networks, suggest new therapeutic strategies, and assess how cell-to-cell variability affects host responses to infection. We argue that systems approaches will both improve understanding of how individual virus-host protein interactions fit into the progression of viral pathogenesis and help to identify novel therapeutic targets.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.615-618
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• 2000

A number of studies have recently been made on hardware for a biological neuron f3r application with information processing functions of neural networks. We have been trying to produce hardware from the viewpoint that development of a new hardware neuron model is one of the important problems in the study of neural networks. In this paper, we first discuss the circuit structure of a pulse-type hardware neuron model with the enhancement-mode MOSFETs (E-MOSFETs). And we construct a pulse-type hardware neuron model using I-MOSFETs. As a result, it is shown that our proposed new model can exhibit firing phenomena even if the power supply voltage becomes less than 1.5[V]. So it is verified that our model is profitable for IC.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.281-285
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• 1996

Pulsatile swelling behaviors and their application to drug delivery system were studied by using interpenetrating polymer networks(IPN) hydrogels constructed with poly(vinyl alcohol) and poly(acrylic acid). The PVA/PAAc IPNs hydrogels were symthesized by UV irradiation tallowed by repetitive freezing and thawing method. These hydrogels showed pH and temperature sensitive swelling behaviors. From the release experiment, the release amount of model drug incorporated into these hydrogels showed pulsatile patterns. Permeability coefficients obtained by various solutes differed in response to changes of permeation conditions.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.775-784
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• 2002

Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.