• Journal of Sericultural and Entomological Science
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• v.13 no.2
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• pp.99-107
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• 1971

In the suboesophageal ganglion of Bombyx mori, the diapause regulator producing cells which may give an information to the diapause factor cells were found by means of electron microscopy. The diapause regulator producing cells had larger granules (2000 to 5000 A$^{\circ}$ in diameter) than did the diapause factor cells which were partially surrounded by the formers. Highly electron-dense material of lysosome in the diapause regulator producing cells was observed in the diapause-egg producer but such lysosomes were not in the non-diapause egg-producer. It was found that many cytoplasmic granules fuse with lysosome, and smaller granules come out of lysosomes. Some implications of the diapause factor cell and the diapause regulator producing cell were discussed.

• The Plant Pathology Journal
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• v.33 no.1
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• pp.53-65
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• 2017

Barley yellow dwarf virus (BYDV) belongs to Luteovirus and is limited only at phloem related tissues. An open reading frame (ORF) 4 of BYDV codes for the movement protein (MP) of BYDV gating plasmodesmata (PD) to facilitate virus movement. Like other Luteoviruses, ORF 4 of BYDV is embedded in the ORF3 but expressed from the different reading frame in leaky scanning manner. Although MP is a very important protein for systemic infection of BYDV, there was a little information. In this study, MP was characterized in terms of subcellular localization and programmed cell death (PCD). Gene of MP or its mutant (ΔMP) was expressed by Agroinfiltration method. MP was clearly localized at the nucleus and the PD, but ΔMP which was deleted distal N-terminus of MP showed no localization to PD exhibited the different target with original MP. In addition to PD localization, MP appeared associated with small granules in cytoplasm whereas ΔMP did not. MP associated with PD and small granules induced PCD, but ΔMP showed no association with PD and small granules did not exhibit PCD. Based on this study, the distal N-terminal region within MP is seemingly responsible for the localization of PD and the induction small granules and PCD induction. These results suggest that subcellular localization of BYDV MP may modulate the PCD in Nicotiana benthamiana.

• Journal of Pharmaceutical Investigation
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• v.36 no.1
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• pp.39-44
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• 2006

Tamsulosin or a salt thereof such as its hydrochloride salt has been known to have an adrenaline ${\alpha}$ receptor blocking action for urethra and prostate areas. It has been widely used as a drug which lowers the prostate pressure and improves urinary disturbance accompanied by prostate-grand enlargement, thus for the treatment of prostatic hyperplasia. To avoid dose-dependent side effects of tamsulosin upon oral administration, the development of sustained-release delivery system is essentially required, that can maintain therapeutic drug levels for a longer period of time. The aim of this study was therefore to formulate sustained-release tamsulosin granules and assess their formulation variables. We designed entric coated sustained-release tamsulosin granules for this purpose. Nano-pores in the outer controlled release membrane were needed in order to obtain initial tamsulosin release even in an acidic environment such as gastric region. In our sustained release osmotic granule system, hydroxypropylmethylcellulose in a drug-containing layer was used as a rate controller. The drug-containing granules were coated with hydroxypropylmethylcellulose phthalate (HPMCP) and Eudragit, along with glycerol triacetate as an aqueous nano-pore former. The release of tamsulosin depended heavily on the type of Eudragit such as RS, RL, NE 30D, used in the formulation of controlled release layer. These results obtained clearly suggest that the sustained-release oral delivery system for tamsulosin could be designed with satisfying drug release profile approved by the Korean Food and Drug Administration.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.101-107
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• 2008

We introduce an optimization of fuzzy set-fuzzy systems based on IG (Information Granules). The proposed fuzzy model implements system structure and parameter identification by means of IG and GAs. The concept of information granulation was coped with to enhance the abilities of structural optimization of the fuzzy model. Granulation of information realized with C-Means clustering helps determine the initial parameters of the fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. The initial parameters are adjusted effectively with the help of the GAs and the standard least square method. To optimally identify the structure and the parameters of the fuzzy model we exploit GAs with successive tuning method to simultaneously search the structure and the parameters within one individual. We also consider the variant generation-based evolution to adjust the rate of identification of the structure and the parameters in successive tuning method. The proposed model is evaluated with the performance of the conventional fuzzy model.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.3-5
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• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Animal Systematics, Evolution and Diversity
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• v.22 no.2
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• pp.209-215
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• 2006

Two rare ciliates from soil under the oak tree and mosses in the top of mountain in Korea were identified as Gonostomum algicola Gellert, 1942 and G. gonostomoida (Hemberger, 1985), respectively. There is little information on their morphological features, therefore their detailed redescriptions are needed. The description was based on the observation of living and protargol impregnated specimens, and biometric analysis. Their diagnostic characteristics are as follows. Gonostomum algicola; $88-113\times30-40{\mu}m$ in vivo, colourless cortical granules, 20-31 adoral membranelles, two fronto-terminal cirri, five fronto-ventral cirri, no mid-ventral cirri, two transverse cirri, two to three micronuceli. Gonostomum gonostomoida; $60-121\times21-40{\mu}m$ in vivo, no cortical granules, 27-34 adoral membranelles, no fronto-terminal cirri, two fronto-ventral cirral rows with each row bearing three cirri, midventral cirral row with 11-14 cirri, two to three transverse cirri, one to six micronuceli. So far, total three species within the genus Gonostomum have been recorded from Korea by the present study.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.207-209
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• 2007

In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1320-1326
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• 2010

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.81-86
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informally speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality Granulation of information with the aid of Hard C-Means (HCM) clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method (LSM). An aggregate objective function with a weighting factor is also used in order to achieve a balance between performance of the fuzzy model. The proposed model is evaluated with using a numerical example and is contrasted with the performance of conventional fuzzy models in the literature.