• Journal of Institute of Control, Robotics and Systems
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• v.1 no.1
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• pp.38-42
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• 1995

This paper presents a target scoring method using shock wave signals, which are generated from the supersonic speed of a projectile. The shock wave is detected from three acoustic sensors located in the target plane and the difference of the delay times are measured. The target coordinates are calculated from the effective propagation of velocity (EPV) and the delay times of the shock wave; and the EPV is from the projectile velocity and the delay time. With a comparison between the measurement result and the known coordinates, the accuracy and the usefulness of the proposed scheme is validated.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.629-634
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• 2009

In real-time system application, the feature extraction and scoring algorithm for classification of the passive sonar target has the following problems: it requires an accurate and efficient feature extraction method because it is very difficult to distinguish the features of the propeller shaft rate (PSR) and the blade rate (BR) from the frequency spectrum in real-time, it requires a robust and effective feature scoring method because the classification database (DB) composed of extracted features is noised and incomplete, and further, it requires an easy design procedure in terms of structures and parameters. To solve these problems, an intelligent feature extraction and scoring algorithm using the evolution strategy (ES) and the fuzzy theory is proposed here. To verify the performance of the proposed algorithm, a passive sonar target classification is performed in real-time. Simulation results show that the proposed algorithm effectively solves sonar classification problems in real-time.

• MALSORI
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• no.66
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• pp.41-59
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• 2008

Knowledge in linguistic rhythm of the target language plays a major role in foreign language proficiency. This study attempts to discover valid rhythm features that can be utilized in automatic assessment of non-native English pronunciation. Eight previously proposed and two novel rhythm metrics are investigated with 360 English read speech tokens obtained from 27 Korean learners and 9 native speakers. It is found that some of the speech-rate normalized interval measures and above-word level metrics are effective enough to be further applied for automatic scoring as they are significantly correlated with speakers' proficiency levels. It is also shown that metrics need to be dynamically selected depending upon the structure of target sentences. Results from a preliminary auto-scoring experiment through a Multi Regression analysis suggest that appropriate control of unexpected input utterances is also desirable for better performance.

• Journal of Integrative Natural Science
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• v.3 no.1
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• pp.49-53
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• 2010

Molecular docking is a critical event which mostly forms Van der waals complex in molecular recognition. Since the majority of developed drugs are small molecules, docking them into proteins has been a prime concern in drug discovery community. Since the binding pose space is too vast to cover completely, many search algorithms such as genetic algorithm, Monte Carlo, simulated annealing, distance geometry have been developed. Proper evaluation of the quality of binding is an essential problem. Scoring functions derived from force fields handle the ligand binding prediction with the use of potential energies and sometimes in combination with solvation and entropy contributions. Knowledge-based scoring functions are based on atom pair potentials derived from structural databases. Forces and potentials are collected from known protein-ligand complexes to get a score for their binding affinities (e.g. PME). Empirical scoring functions are derived from training sets of protein-ligand complexes with determined affinity data. Because non of any single scoring function performs generally better than others, some other approaches have been tried. Although numerous scoring functions have been developed to locate the correct binding poses, it still remains a major hurdle to derive an accurate scoring function for general targets. Recently, consensus scoring functions and target specific scoring functions have been studied to overcome the current limitations.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.85-94
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• 2013

As the financial market becomes larger, the loss increases due to the failure of the credit risk managements from the poor management of the customer information or poor decision-making. Thus, the credit risk management also becomes more important and it is essential to develop a credit scoring model, which is a fundamental tool used to minimize the credit risk. Credit scoring models have been studied and developed only for binary target variables. In this paper, we consider other types of target variables such as ordinal multinomial data or longitudinal binary data and suggest credit scoring models. We then apply our developed models to real data and random data, and investigate their performance through Kolmogorov-Smirnov statistic.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.1
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• pp.99-105
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• 2012

Track-before-detect techniques based on dynamic programming have provided solutions for detecting targets from a sequence of images. In its application to airborne threat detection, dynamic programming solutions should take into account the distinguishable properties of objects in a collision course. This paper describes the development of a new track scoring function that accumulates scores for airborne targets in Bayesian framework. Numerical results show that the proposed scoring function has slightly better detection capabilities.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.1-16
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• 2009

Recently, most companies are speeding up the movement of modeling and strategically applying IDI (Integration Database Information). This is due to the fact that CRM (Customer Relationship Management) representing communication and relation maintenance with customers, has been raised one of the most important issues for companies. From this point of view, this study is to develop the scoring campaign system connect-ing customer scoring model to marketing layer through data mining techniques which are the core factor for CRM. This developed system makes users easily choose the target customers as well as easily obtain customer scoring results under GUI circumstances which helps users easily apply as a result.

• Journal of Integrative Natural Science
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• v.5 no.4
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• pp.216-219
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• 2012

Structure-based drug design possibly benefit from in silico methods that precisely predict the binding affinity of small molecules to target macromolecules. There are many limitations arise from the difficulty of predicting the binding affinity of a small molecule to a biological target with the current scoring functions. There is thus a strong interest in novel methodologies based on MD simulations that claim predictions of greater accuracy than current scoring functions, helpful for a regular use designed for drug discovery in the pharmaceutical industry. Herein, we report a short review on free energy calculations using MMPBSA method a useful method in structure based drug discovery.

• BMB Reports
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• v.43 no.5
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• pp.319-324
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• 2010

Experimental bioinformatics data obtained from an E. coli cell-based eukaryotic protein purification experiment were analyzed in order to identify any bottleneck as well as the factors affecting the target purification. All targets were expressed as His-tagged maltose-binding protein (MBP) fusion constructs and were initially purified by immobilized metal affinity chromatography (IMAC). The targets were subsequently separated from the His-tagged MBP through TEV protease cleavage followed by a second IMAC isolation. Of the 743 total purification trials, 342 yielded more than 3 mg of target proteins for structural studies. The major reason for failure of target purification was poor TEV proteolysis. The overall success rate for target purification decreased linearly as cysteine content or isoelectric point (pI) of the target increased. This pattern of pI versus overall success rate strongly suggests that pI should be incorporated into target scoring criteria with a threshold value.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.6
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• pp.690-699
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• 2014

Jet engine modulation(JEM) is a technique used to identify the jet engine type from the radar target signature modulated by periodic rotation of the jet engine mounted on the aircraft target. As a new approach of JEM, this paper proposes an automatic algorithm for extracting the jet engine information. First, the rotation period of the jet engine is yielded from auto-correlation of the JEM signal preprocessed by complex empirical mode decomposition(CEMD). Then, the final blade number is estimated by introducing the DM(Divisor-Multiplier) rule and the 'Scoring' concept into JEM spectral analysis. Application results of the simulated and measured JEM signals demonstrated that the proposed algorithm is effective in accurate and automatic extraction of the jet engine information.