• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.

• Journal of Microbiology and Biotechnology
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• v.14 no.3
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• pp.639-642
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• 2004

The pH as a control parameter for oxidation-reduction potential (ORP) was investigated through the denitrification by Ochrobactrum anthropi SY509 under non-growing condition. The optimal pH of nitrate reductase was 7.0, and the minimal ORP level was －250 mV for the denitrification under aerobic condition. In the case of anaerobic condition, the optimal pHs of nitrate and nitrite reductase were shifted to 10.0 and 9.0, respectively, and the minimal ORP levels of nitrate and nitrite reductase were decreased to －370 mV and －340mV, respectively. In the case of alkaline pH and anaerobic condition, the denitrification efficiency of nitrate was increased up to about 2-fold over that of neutral pH and anaerobic condition. Therefore, the combined control of pH and ORP in the anaerobic condition is shown to be an important parameter in the biological denitrification process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.11
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• pp.1202-1208
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• 2009

An inverse finite element (FE) model parameter estimation algorithm can be used to characterize mechanical properties of biological tissues. Using this algorithm, we can consider the influence of material nonlinearity, contact mechanics, complex boundary conditions, and geometrical constraints in the modeling. In this study, biomechanical experiments on macro and micro samples are conducted and characterized with the developed algorithm. Macro scale experiments were performed to measure the force response of porcine livers against mechanical loadings using one-dimensional indentation device. The force response of the human liver cancer cells was also measured by the atomic force microscope (AFM). The mechanical behavior of porcine livers (macro) and human liver cancer cells (micro) were characterized with the algorithm via hyperelastic and linear viscoelastic models. The developed models are suitable for computing accurate reaction force on tools and deformation of biomechanical tissues.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.93-100
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• 1988

The Auto Regressive Parameter Estimation and Pattern Classification of EKG Signal for Automatic Diagnosis. This paper presents the results from pattern discriminant analysis of an AR (auto regressive) model parameter group, which represents the HRV (heart rate variability) that is being considered as time series data. HRV data was extracted using the correct R-point of the EKG wave that was A/D converted from the I/O port both by hardware and software functions. Data number (N) and optimal (P), which were used for analysis, were determined by using Burg's maximum entropy method and Akaike's Information Criteria test. The representative values were extracted from the distribution of the results. In turn, these values were used as the index for determining the range o( pattern discriminant analysis. By carrying out pattern discriminant analysis, the performance of clustering was checked, creating the text pattern, where the clustering was optimum. The analysis results showed first that the HRV data were considered sufficient to ensure the stationarity of the data; next, that the patern discrimimant analysis was able to discriminate even though the optimal order of each syndrome was dissimilar.

• ALGAE
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• v.32 no.4
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• pp.325-336
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• 2017

Intertidal community recovery and resilience were investigated with quantitative and qualitative perspectives as a function of disturbance timing. The study was conducted in a lower intertidal rock bed of the southern coast of South Korea. Six replicates of artificial disturbance of a $50cm{\times}50cm$ area were made by clearing all visible organisms on the rocky substrate in four seasons. Each of the seasonally cleared plots was monitored until the percent cover data reached the control plot level. There was a significant difference among disturbance timing during the recovery process in terms of speed and community components. After disturbances occurred, Ulva pertusa selectively preoccupied empty spaces quickly (in 2-4 months) and strongly (50-90%) in all plots except for the summer plots where non-Ulva species dominated throughout the recovery period. U. pertusa acted as a very important biological variable that determined the quantitative and qualitative recovery capability of a community. The qualitative recovery of communities was rapid in summer plots where U. pertusa did not recruit and the community recovery rate was the lowest in winter plots where U. pertusa was highly recruited with a long duration of distribution. In this study, U. pertusa was a pioneer species while being a dominant species and acted as a clearly negative element in the process of qualitative recovery after disturbance. However, the negative effect of U. pertusa did not occur in summer plots, indicating that disturbance timing should be considered as a parameter in understanding intertidal community resilience in temperate regions with four distinct seasons.

• Bulletin of the Korean Chemical Society
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• v.33 no.3
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• pp.963-970
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• 2012

We review novel chemical kinetics proposed for quantitative description of fluctuations in reaction times and in the number of product molecules in a heterogeneous biological system, and discuss quantitative interpretation of randomness parameter data in enzymatic turnover times of ${\beta}$-galactosidase. We discuss generalization of renewal theory for description of chemical fluctuation in product level in a multistep biopolymer reaction occurring in a dynamically heterogeneous environment. New stochastic simulation results are presented for the chemical fluctuation of a dynamically heterogeneous reaction system, which clearly show the effects of the initial state distribution on the chemical fluctuation. Our stochastic simulation results are found to be in good agreement with predictions of the analytic results obtained from the generalized master equation.

• Journal of Microbiology and Biotechnology
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• v.11 no.2
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• pp.229-233
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• 2001

Trichoderma reesei Rut C-30 produced high levels of ${\beta}$-glucosidase, endo-${\beta}$-glucosidase, endo-${\beta}$-1,4-glucanase, and exo-${\beta}$-1,4-glucanase. In pilot-scale production (50-1 fermentor), productivity and yield of CMCase (carborymethyl cellulose) and FPase (filter paper activity) were 273 U/ml and 35 U/ml, and 162 FPU/l.h and 437 FPU/g, respectively. The fed-batch techniques were used to improve enzyme activities with constant cell concentration. The acidity was an important parameter and controlled at pH 3.9 and 5.0 by automatic addition of ammonium hydroxide. Cellulase powder was prepared by ammonium sulfate precipitation and its CMCase and FPase activities were 3,631 U/g and 407 U/g, respectively.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.05
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• pp.64-67
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• 1992

To improve signal-to-noise ratio in sodium image, short echo time (2-3 ms) and long data acquisition (10-20 ms) protocols are used. Sodium in biological specimens demonstrates a bi-exponential decay of transverse magnetization and the fast decaying component of the sodium signal results in the reconstruction of images which are blurred significantly. The spatially-dependent nature of the blurs are due mainly to the presence of short local transverse relaxation values (0.7-3 ms) of sodium in tissue. We present an algorithm that corrects for object-dependent blurs due to fast-decaying T2 and improves the computational behavior of the algorithm by incorporating a relaxation parameter into the iterative process.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.370-372
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• 2005

Inconveniences which might arise in transmitting measured biological data based on cable protocols generally are recognized critical points in tele-monitoring environment and also restrict the mobility of the user. a. Especially, activity monitoring which is importantly recognized as a core parameter in ubiquitous healthcare arena and weight management, pervasive and wireless measuring technology is most needed. In this paper, we would like to suggest lower power, miniaturized communication system in order to solve the above problems. The suggested system is powered by small coin-size battery. Also, The suggested system is compared with a blue-tooth module which is generally available in the commercial market. Even though, the suggested system didn't have higher transmission rate, its low power consumption make the suggested system would be feasible in ubiquitous monitoring of biological signals in ubiquitous healthcare arena.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.157-160
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• 1995

Neural networks potentially offer a general framework for modeling and control of nonlinear systems. The conventional neural network models are a parody of biological neural structures, and have very slow learning. In order to emulate some, dynamic functions, such as learning and adaption, and to better reflect the dynamics of biological neurons, M.M.Gupta and D.H.Rao have developed a 'dynamic neural model'(DNU). Proposed neural unit model is to introduce some dynamics to the neuron transfer function, such that the neuron activity depends on internal states. Numerical examples are presented for a model system. Those case studies showed that the proposed DNU is so useful in practical sense.