• Proceedings of the Korea Society of Poultry Science Conference
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• 2003.11a
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• pp.131-132
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• 2003

Common finch is a kind of ornamental birds and could not distinguish male and female birds. For sex discrimination of Common finch, we investigated 400 birds of Common finch using the anatomical differences of Pars intermandibularis and traditional methods that currently used at the farms. Both methods have the sex determination accuracy of 48.82 and 46.0 ％ that the anatomical difference method seems little higher results than the traditional methods. More researches is needed for improving the sex determination accuracy.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1063-1071
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• 2023

Unmanned aerial vehicles (UAVs, drones) are becoming increasingly useful in a variety of fields. Advances in UAV and camera technology have made it possible to equip them with ultra-high resolution sensors and capture images at low altitudes, which has improved the reliability and classification accuracy of object identification on the ground. The distinctive contribution of this study is the derivation of sensor-specific performance metrics (GRD/GSD), which shows that as the GSD increases with altitude, the GRD value also increases. In this study, we identified the characteristics of various onboard sensors and analysed the image quality (discrimination resolution) of aerial photography results using UAVs, and calculated the shooting conditions to obtain the discrimination resolution required for reading ground objects.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.518-525
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• 2012

In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.235-244
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• 2004

This study focuses on the assessment for proposed algorithm to discriminate cloudy pixels from snowy pixels through use of visible, near infrared, and short wave infrared channel data in VEGETATION-1 sensor embarked on SPOT-4 satellite. Traditional threshold algorithms for cloud and snow masks did not show very good accuracy. Instead of these independent masking procedures, K-Means clustering scheme is employed for cloud/snow discrimination in this study. The pixels used in clustering were selected through an integration of two threshold algorithms, which group ensemble the snow and cloud pixels. This may give a opportunity to simplify the clustering procedure and to improve the accuracy as compared with full image clustering. This paper also compared the results with threshold methods of snow cover and clouds, and assesses discrimination capability in VEGETATION channels. The quality of the cloud and snow mask even more improved when present algorithm is implemented. The discrimination errors were considerably reduced by 19.4% and 9.7% for cloud mask and snow mask as compared with traditional methods, respectively.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.723-738
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• 2020

In this paper, we study a method to discriminate between trend stationary and difference stationary processes. Since a crucial ingredient of this discrimination is to determine the existence of unit root, we can use a unit root testing strategy. So, we introduce a discrimination based on unit root testing and propose the method using the adaptive lasso. Our Monte Carlo simulation experiments show that the adaptive lasso improves the discrimination accuracy when the process is trend stationary, but has lower accuracy than unit root strategy where the process is difference stationary.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.61-70
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• 2017

In This Paper, the Classification Speed of Vehicle Headlight Modules is Improved by a CPU-based Parallel Processing Using OpenMP. Also, a Classification Method of Headlight Modules which Extracts their Features after Revising their Alignment is Proposed. To Analyze the Performance of the Proposed Method, the Discrimination Accuracy and the Processing Speed were Compared with the Method Using Gray Image and the Method Using Line Detection. As the Results of the Analysis, in the Discrimination Accuracy, the Proposed Method and the Line Detection Method Showed good Performance, but the Proposed Method Showed Better Performance than the Line Detection Method by the Processing Speed. Also, the Gray-based Method was the Best in Processing Speed, but the Proposed Method is Better than the Gray-based Method in the Discrimination Accuracy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.419-422
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• 2003

To improve the recognition accuracy of a developed artificial odor discrimination system for three mixture fragrance recognition, Fuzzy Similarity based Self-Organized Network inspired by Immune Algorithm (F-SONIA) is proposed. Minimum, average, and maximum values of fragrance data acquisitions are used to form triangular fuzzy numbers. Then the fuzzy similarity treasure is used to define the relationship between fragrance inputs and connection strengths of hidden units. The fuzzy similarity is defined as the maximum value of the intersection region between triangular fuzzy set of input vectors and the connection strengths of hidden units. In experiments, performances of the proposed method is compared with the conventional Self-Organized Network inspired by Immune Algorithm (SONIA), and the Fuzzy Learning Vector Quantization (FLVQ). Experiments show that F-SONIA improves recognition accuracy of SONIA by 3-9％. Comparing to the previously developed artificial odor discrimination system that used FLVQ as pattern classifier, the recognition accuracy is increased by 14-25％.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.19-33
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• 2001

Link weight analysis approach is suggested as a heuristic for selection of input nodes in artificial neural network for bankruptcy prediction. That is to analyze each input node\\\\`s link weight-absolute value of link weight between an input node and a hidden node in a well-trained neural network model. Prediction accuracy of three methods in this approach, -weak-linked-neurons elimination method, strong-linked-neurons selection method and integrated link weight model-is compared with that of decision tree and multivariate discrimination analysis. In result, the methods suggested in this study show higher accuracy than decision tree and multivariate discrimination analysis. Especially an integrated model has much higher accuracy than any individual models.

• MALSORI
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• no.64
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• pp.89-103
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• 2007

In this paper, we introduce mel-cepstrum modulation energy (MCME) for a feature to discriminate speech and music data. MCME is a mel-cepstrum domain extension of modulation energy (ME). MCME is extracted on the time trajectory of Mel-frequency cepstral coefficients, while ME is based on the spectrum. As cepstral coefficients are mutually uncorrelated, we expect the MCME to perform better than the ME. To find out the best modulation frequency for MCME, we perform experiments with 4 Hz to 20 Hz modulation frequency. To show effectiveness of the proposed feature, MCME, we compare the discrimination accuracy with the results obtained from the ME and the cepstral flux.

• Speech Sciences
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• v.13 no.3
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• pp.7-18
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• 2006

Diagnosis of pathological voice is one of the important issues in biomedical applications of speech technology. This study focuses on the discrimination of voice disorder using HMM (Hidden Markov Model) for automatic detection between normal voice and vocal fold disorder voice. This is a non-intrusive, non-expensive and fully automated method using only a speech sample of the subject. Speech data from normal people and patients were collected. Mel-frequency filter cepstral coefficients (MFCCs) were modeled by HMM classifier. Different states (3 states, 5 states and 7 states), 3 mixtures and left to right HMMs were formed. This method gives an accuracy of 93.8% for train data and 91.7% for test data in the discrimination of normal and vocal fold disorder voice for sustained /a/.