• Journal of Microbiology and Biotechnology
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• v.30 no.4
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• pp.533-539
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• 2020

A quantitative real-time polymerase chain reaction (QPCR) was applied to estimate biokinetic coefficients of Clostridium cadaveris and Clostridium sporogenes, which utilize protein as carbon source. Experimental data on changes in peptone concentration and 16S rRNA gene copy numbers of C. cadaveris and C. sporogenes were fitted to model. The fourth-order Runge-Kutta approximation with non-linear least squares analysis was employed to solve the ordinary differential equations to estimate biokinetic coefficients. The maximum specific growth rate (μ_max), half-saturation concentration (K_s), growth yield (Y), and decay coefficient (K_d) of C. cadaveris and C.sporogenes were 0.73 ± 0.05 and 1.35 ± 0.32 h^-1, 6.07 ± 1.52 and 5.67 ± 1.53 g/l, 2.25 ± 0.75 × 10¹⁰ and 7.92 ± 3.71 × 10⁹ copies/g, 0.002 ± 0.003 and 0.002 ± 0.001 h^-1, respectively. The theoretical specific growth rate of C. sporogenes always exceeded that of C. cadaveris at peptone concentration higher than 3.62 g/l. When the influent peptone concentration was 5.0 g/l, the concentration of C.cadaveris gradually decreased to the steady value of 2.9 × 10¹⁰ copies/ml at 4 h Hydraulic retention time (HRT), which indicates a 67.1% reduction of the initial population, but the wash out occurred at HRTs of 1.9 and 3.2 h. The 16S rRNA gene copy numbers of C. sporogenes gradually decreased to steady values ranging from 1.1 × 10¹⁰ to 2.9 × 10¹⁰ copies/ml. C. sporogenes species was predicted to wash out at an HRT of 1.6 h.

• Geophysics and Geophysical Exploration
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• v.4 no.2
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• pp.25-33
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• 2001

Loop-loop electromagnetic (EM) survey in frequency domain has been carried out in order to provide basic solution to geotechnical applications. Source and receiver configuration may be horizontal co-planar (HCP) and/or vertical co-planar (VCP). Three quadrature components of mutual impedance ratio for each configuration are used to construct the subsurface image. For the purpose of obtaining the model response and validating the reasonable performance of the inversion, we obtained each responses of two-layered and three-layered earth models and two-dimensional (2-D) isolated anomalous body. The response of 2-D isolated anomalous body has been calculated using extended Born approximation for the solution of 2.5-D integral equation describing EM scattering problem. As a result of the least-squares inversion with variable Lagrangian multiplier, we could construct more resolvable image from HCP data than VCP data. Furthermore, joint inversion of HCP and VCP data made better stability and resolution of the inversion. Resistivity values, however, did not exactly match the true ones. Loop-loop EM field data was obtained with EM34-3XL system manufactured by Geonics Ltd. (Canada). Electrical resistivity survey was conducted on the same line for the comparison in advance. Since the constructed image from loop-loop EM data by 2-D inversion algorithm showed almost similar resistivity distribution to that from electrical resistivity one, we expect the developed 2.5-D loop-loop EM inversion program can be applied for the reconnaissance site survey.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.3 s.345
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• pp.49-58
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• 2006

In this paper, a curve-fitting channel estimation method is proposed for orthogonal frequency division multiplexing (OFDM) system in a time-varying frequency-selective fading channel. The method can greatly improve channel state information (CSI) estimation accuracy by performing smoothing and interpolation through consecutive curve-fitting processes in both time domain and frequency domain. It first evaluates least-squares (LS) estimates using pilot symbols and then the estimates are approximated to a polynomial with proper degree in the LS error sense, starting from one preferred domain in which pilots we densely distributed. Smoothing, interpolation, and prediction are performed subsequently to obtain CSI estimates for data transmission. The channel estimation processes are completed by smoothing and interpolating CSI estimates in the other domain once again using the channel estimates obtained in one domain. The performance of proposed method is influenced heavily on the time variation and frequency selectivity of channel and pilot arrangement. Hence, a proper degree of polynomial and an optimum approximation interval according to various system and channel conditions are required for curve-fitting. From extensive simulation results in various channel environments, we see that the proposed method performs better than the conventional methods including the optimal Wiener filtering method, in terms of the mean square error (MSE) and bit error rate (BER).

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• Journal of the Korean Institute of Gas
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• v.24 no.2
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• pp.44-49
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• 2020

In this paper, a measuring circuit is designed through analyzing manufacture specification of the sensor based on MOS. And the best input-output polynomial are induced that really gas sensors are used in gas safety management industrial fields. Response characteristics of a MOS gas sensor is analysed by through sensor's output voltages are measured after standard gases with six kinds of concentrations are manufactured and are injected to the sensor. A lookup table is created by relations of sensor's output voltages by injecting gases with other concentrations. Because data of the formed lookup table are equal interval, a polynomial can be induced of method of approximation function. So the 5th polynomial of input-output for a sensor is defined, coefficients are calculated by using least squares method, and the 5th polynomial is completed for representing characteristics of the sensor. If the proposed polynomial is applied to gas leak detectors, an inverse transformation of polynomial and programing of array codes are recreated. In this research, polynomial is implemented with array types that intervals of values of a lookup table are one-fifth sampled and interpolated. The performance of proposed 5th calibration equation is verified that errors are reduced than a linear expression when tests are performed by measurement of concentrations against injection of standard gases.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.6
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• pp.583-590
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• 2016

This paper presents a dynamic crack propagation algorithm with Rayleigh damping effect based on the MLS(Moving Least Squares) Difference Method. Dynamic equilibrium equation and constitutive equation are derived by considering Rayliegh damping and governing equations are discretized by the MLS derivative approximation; the proportional damping, which has not been properly treated in the conventional strong formulations, was implemented in both the equilibrium equation and constitutive equation. Dynamic equilibrium equation including time relevant terms is integrated by the Central Difference Method and the discrete equations are simplified by lagging the velocity one step behind. A geometrical feature of crack is modeled by imposing the traction-free condition onto the nodes placed at crack surfaces and the effect of movement and addition of the nodes at every time step due to crack growth is appropriately reflected on the construction of total system. The robustness of the proposed numerical algorithm was proved by simulating single and multiple crack growth problems and the effect of proportional damping on the dynamic crack propagation analysis was effectively demonstrated.