• Journal of Hydrospace Technology
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• v.2 no.2
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• pp.24-35
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• 1996

This paper describes parameter identification techniques formulated for the estimation of maneuvering coefficients of a submerged body. The first part of this paper is concerned with the identifiability of the system parameters. The relationship between a stochastic linear time-invariant system and the equivalent dynamic system is investigated. The second is concerned with the development of the numerically stable identification technique. Two identification techniques are tested; one is the ma7mum likelihood (ML) methods using the Holder & Mead simplex search method and using the modified Newton-Raphson method, and the other is the modified extended Kalman filter (MEKF) method with a square-root algorithm, which can improve the numerical accuracy of the extended Kalman filter. As a results, it is said that the equations of motion for a submerged body have higher probability to generate simultaneous drift phenomenon compared to general state equations and only the ML method using the Holder & Mead simplex search method and the MEKF method with a square-root algorithm gives acceptable results.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.3
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• pp.9-14
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• 2009

Recently, a rate-2, $2{\times}2$ space-time code with simple ML decoding has been designed. Though the simple ML decoding algorithm does reduce the ML decoding complexity, there is still need for improvement. In this paper, we propose an efficient decoding algorithm for the rate-2, $2{\times}2$ space-time code using interference cancellation techniques with performance virtually identical to that of ML decoding. Also, the decoding complexity of the proposed algorithm is significantly reduced compared to the conventional simple ML decoding, especially for large modulation orders.

• The Journal of the Korean Society for Microbiology
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• v.22 no.4
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• pp.435-443
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• 1987

A total of 30 strains of Candida albicans were examined for susceptibility to amphotericin B and ketoconazole using Sabouraud's dextrose broth, Kimmig broth and Supplemented yeast nitrogen base broth media. Furthermore, the growth curve and colony forming units were checked for use of stationary-phase cells and 2-hour incubation cells in the absence of atifungal agents. The viable counts were determined periodically during incubation by standard plate count techniques. The minimum inhibitory concentrations of amphotericin B for use of stationary phase cells were as follows: SDB, $0.09{\sim}0.97mcg/ml$(0.39mcg/ml); Kimmig broth, $0.19{\sim}0.39mcg/ml$(0.42 mcg/ml) and SYNB, $0.19{\sim}0.39mcg/ml$mcg/ml(0.23mcg/ml). In ketoconazole, MICs were value SDB, $3.12{\sim}25.0mcg/ml$(12.5mcg/ml); Kimmig broth, $12.5{\sim}25.0mcg/ml$ (22.5mcg/ml) and SYNB, $3.12{\sim}12.5mcg/ml$(6.71mcg/ml). The MICs of amphotericin B(0.2mcg/ml cone.) for use of 2-hour incubation cells in absence of AMB were, SDB, $0.04{\sim}0.39mcg/ml$(0.11mcg/ml); Kimmig broth, $0.09{\sim}0.39mcg/ml$(0.18mcg/ml) and SYNB, $0.09{\sim}0.19mcg/ml$(0.14mcg/ml) and in KTZ, the value of MICs were SDB, $3.12{\sim}25.0mcg/ml$(12.22mcg/ml); Kimmig broth, $0.78{\sim}25.0mcg/ml$(11.01mcg/ml) and SYNB, $1.56{\sim}12.5mcg/ml$(3.90mcg/ml). The two-log reductions in CFU per milliliter observed when 2 hour preincubation cells were treated with 0.2mcg/concentrations of AMB and 25.0mcg/ml of KTZ. However, AMB treated cells were restored to growth activity, it suggested that the AMB has no active antifungal activity.

• Computers and Concrete
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• v.33 no.3
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• pp.285-299
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• 2024

Recently, many engineering computations have realized their digital transformation to Machine Learning (ML)-based systems. Predicting the behavior of a structure, which is mainly computed with structural analysis software, is an essential step before construction for efficient structural analysis. Especially in the seismic-based design procedure of the structures, predicting the lateral load capacity of reinforced concrete (RC) columns is a vital factor. In this study, a novel ML-based model is proposed to predict the maximum lateral load capacity of RC columns under varying axial loads or cyclic loadings. The proposed model is generated with a Deep Neural Network (DNN) and compared with traditional ML techniques as well as a popular commercial structural analysis software. In the design and test phases of the proposed model, 319 columns with rectangular and square cross-sections are incorporated. In this study, 33 parameters are used to predict the maximum lateral load capacity of each RC column. While some traditional ML techniques perform better prediction than the compared commercial software, the proposed DNN model provides the best prediction results within the analysis. The experimental results reveal the fact that the performance of the proposed DNN model can definitely be used for other engineering purposes as well.

• The Mathematical Education
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• v.47 no.1
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• pp.75-89
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• 2008

Internet has been widely spread out and the need of convenient mathematical expression in the internet has been in need. In this paper, we discvss how MathML and JAVA have been developed for mathematical expressions in the internet. And we introduce our JAVA applet tools that adapted the object oriented programming techniques of the MathML and JAVA. Most of our JAVA toots have been developed for linear algebra course whose method can be applied to other subject as well. Then we discuss how we use those tools and output from the use of them in our class.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• Journal of the Korean Chemical Society
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• v.21 no.5
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• pp.372-378
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• 1977

The following new techniques have been developed: (A); To a 500ml of sample water, it was adjusted pH 10 with ammonia-anmonium citrate, added 10ml of 1${\%}$ sodium diethyldithiocarbamate and extracted three times with 10ml of CHCl3. The extract was shaken with 10ml of 0.05N $HCl-4{\times}10^{-4}M\;HgCl_2$. The aqueous solution was added 2ml of 2N KCl and washed two times with 10ml of pure $CHCl_3$, and then recorded square wave polarograms. (B); To a 500ml of sample water adjusted pH 10 with ammonia-ammonium citrate, it was added 2ml of 1${\%}$ 8-hydroxyquinoline and extracted three times with 10ml $CHCl_3$. The separated $CHCl_3$ phase was shaken with 10ml of 0.2 N HCl. The aqueous solution was recorded polarograms directly. These methods can be used for determination of the ppb order of metal in water with an error of ${\pm}10{\%}$. The method (B) can not be used for the determination of zinc on account of the free 8-hydroxyquinoline.

• ETRI Journal
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• v.40 no.2
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• pp.218-226
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• 2018

Multiuser detection (MUD) and channel estimation techniques in space-division multiple-access aided orthogonal frequency-division multiplexing systems recently has received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean-squared error (MMSE) MUD exhibits poor performance, although it achieves lower computational complexity. With almost the same complexity, an MMSE with successive interference cancellation (SIC) scheme achieves a better bit error rate performance than a linear MMSE multiuser detector. In this paper, hybrid ML-MMSE with SIC adaptive multiuser detection based on the joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method achieves good performance close to the optimal ML performance at low SNR values and a low computational complexity at high SNR values.

• Korean Journal of Animal Reproduction
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• v.12 no.2
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• pp.91-96
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• 1988

A study was conducted to improve breeding efficiency of dairy and beef cows on Cheju Island. Milk progesterone and plasma progesterone profiles were determined by using radioimmunoassay techniques during the oestrus cycle, onset on oestrus and during the gestation period. Progesterone connections in milk were very low during the oestrus with a means value of 2.4ng/ml and 4.6ng/ml at the onset of oestrus but gradually declined at 4 to 9 hr after oestrus. In the cyclic cow, milk progesterone concentraitons in the early part of the cycle, ranged from 3 to 5ng/ml and increased from 6.0 to 11.6ng/ml on day 13 to 21 of cycle. Milk progesterone concentrations of pregnancy cows ranged from 5.0 to 40ng/ml during the gestation and grom basal line to 3.5ng/ml for nonpregnant cows. Plasma progesterone concentrations of beef during pregnancy period ranged form 3.0 to 33ng/ml and basal levels ranged from 0 to less than 2.5ng/ml for non-pregnant beef cows.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.5
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• pp.305-310
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• 2019

The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.