• KSBB Journal
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• v.16 no.5
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• pp.452-458
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• 2001

Concentrations of ammonia in biological processes were controlled by PID controllers and also neural network based controllers (NN controllers). A flow injection analysis system has been to on-line monitor the concentrations of ammonia in a bioreactor. The effect of the analysis error and the residence time of samples on the control performance were studied. The optimal neural network structure was investigated by using computer simulation and found to be a 3(input layer)-2(hidden layer)-1(output layer). The NN controller is often time consuming, but it has advantage over the PID controller in sensitivity. The 3-2-1 NN controller has been applied to control the ammonia concentrations in a simulated bioprocess and also a real cultivation process of yeast. The good control performance showed that the 3-2-1 NN controller based on the FIA system can be used to control the concentration of substrates in biological processes very well.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.5
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• pp.1008-1013
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• 2009

In this paper, we proposed the biological signal (body heat, pulse, breathe rate, and blood pressure) analysis system using wireless sensor. In order to analyze, we designed a back-propagation neural network system using expert group system. The proposed system is consist of hardware patt such as UStar-2400 ISP and Wireless sensor and software part such as Knowledge Base module, Inference Engine module and User Interface module which is inserted in Host PC. To improve the accuracy of the system, we implement a FEC (Forward Error Correction) block. For conducting simulation, we chose 100 data sets from Knowledge Base module to train the neural network. As a result, we obtained about 95% accuracy using 128 data sets from Knowledge Base module and acquired about 85% accuracy which experiments 13 students using wireless sensor.

• Molecules and Cells
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• v.27 no.3
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• pp.271-277
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• 2009

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

• Korean Journal of Exercise Nutrition
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• v.25 no.3
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• pp.28-35
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• 2021

[Purpose] As Panax ginseng C. A. Meyer (ginseng) exhibits various physiological activities and is associated with exercise, we investigated the potential active components of ginseng and related target genes through network pharmacological analysis. Additionally, we analyzed the association between ginseng-related genes, such as the G-protein-coupled receptors (GPCRs), and improved exercise capacity. [Methods] Active compounds in ginseng and the related target genes were searched in the Traditional Chinese Medicine Database and Analysis Platform (TCMSP). Gene ontology functional analysis was performed to identify biological processes related to the collected genes, and a compound-target network was visualized using Cytoscape 3.7.2. [Results] A total of 21 ginseng active compounds were detected, and 110 targets regulated by 17 active substances were identified. We found that the active compound protein was involved in the biological process of adrenergic receptor activity in 80%, G-protein-coupled neurotransmitter in 10%, and leucocyte adhesion to arteries in 10%. Additionally, the biological response centered on adrenergic receptor activity showed a close relationship with G protein through the beta-1 adrenergic receptor gene reactivity. [Conclusion] According to bioavailability analysis, ginseng comprises 21 active compounds. Furthermore, we investigated the ginseng-stimulated gene activation using ontology analysis. GPCR, a gene upregulated by ginseng, is positively correlated to exercise. Therefore, if a study on this factor is conducted, it will provide useful basic data for improving exercise performance and health.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.157-166
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• 1995

The Electroencephalogram (EEG) and evoked potential (EP) t;ave widely been used for study of brain functions. The EEG and EP signals acquired from multi-channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG). Since these artifact-affected EEG signals degrade EEG mapping, the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. In this paper a neural-network based classification is proposed to replace or to reduce human expert's efforts and time. From experiments, the neural-network based classification performs as good as human experts : variation of decisions between the neural network and human expert appears even smaller than that between human experts.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.228-231
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• 1997

In this paper, we designed an ART(Adaptive Resonance Theory) network based on LPC(Linear Prediction Coefficient) for classification of PVB (Premature Ventricular Beat: PVC, LBBB, RBBB). The procedure of proposed system consists of the error calculation, feature generation and processing of the ART network. The error is calculated after processing by linear prediction algorithm and the features of ART network or classification are obtained from the binary ata determined by threshold method. In conclusion, ART network has good performance in classification of PVB.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.205-206
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• 1998

In this paper, we present the detection algorithm of interictal epileptic waveform using LVQ network and wavelet transform. First wavelet coefficients is used to represent the characteristics of a single channel EEG wave, and make a number of neural network input node smaller. Then, three-layer neural network employing LVQ network is trained and tested using parameters obtained from the first stage. This study showed that preprocessed EEG data can be successfully used to train ANNs to detect epileptogenic discharges with a high success.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.11
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• pp.522-531
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• 2001

Recently conventional artificial intelligence(AI) approaches have been employed to build action selectors for the autonomous mobile robot(AMR). However, in these approaches, the decision making process to choose an action from multiple competence modules is still an open question. Many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we attempt to construct an action selector for an AMR based on the artificial immune network and internet. The information from vision sensors is used for antibody. We propose a learning method for artificial immune network using evolutionary algorithm to produce antibody automatically. The internet environment for an AMR action selector shows the usefulness of the proposed learning artificial immune network application.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.330-335
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• 2003

This paper deals with the network interface of research and observation instruments in the oceanographic research vessel with an establishment of related database for measured information. The system is implemented to integrated communication network system which allows to effective survey by using real time observation and GUI(Graphic User Interface). The system also consists of the LAN systems and serial interface to link chemical, physical, biological and environmental relations. And, other network service and vessel data service for data communication between vessel and earth station such as INMARSAT-B, WWW service, BBS, E-Mail etc., are needed for integrated communication network system.

• Journal of Navigation and Port Research
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• v.31 no.3 s.119
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• pp.263-269
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• 2007

The outbreaks of red tide were sporadic in the South Sea until 1994, but have become frequent and widespread in whole coastal waters of the South Sea and East Sea since 1995 For monitoring of red tide, many kinds of techniques such as remote sensing, GIS and fuzzy model system have been developed and applied. The purpose of this paper is to develop red tide monitoring system for collection of red tide data and biological-oceanography parameters using wireless sensor network. The wireless sensor network has been noticed as a core technology in order to realize ubiquitous computing. In this paper, we design red tide database using wireless sensor network and suggest red tide monitoring software and web-service for user and biological-oceanographer.