• Journal of The Korean Association of Information Education
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• v.23 no.2
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• pp.107-116
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• 2019

The purpose of this study is to develop a Python sorting algorithm training program based on execution time as an educational method for enhancing the logical thinking power of elementary students and then to verify the effect. The education program was developed based on the results of the pre-demand analysis conducted on 100 elementary school teachers. In order to verify the effectiveness of the developed educational program, I teached 25 students of the volunteer sample of the elementary school education donation program conducted at ${\bigcirc}{\bigcirc}$ University conducted 42 hours, 7 days. The results of the pre-test and post-test were analyzed using the 'Group Assessment of Logical Thinking(GALT)' developed by the Korea Educational Development Institute. The results showed that the Python sorting algorithm training centered on execution time was effective in improving the logical thinking ability of elementary school students.

• The Journal of the Acoustical Society of Korea
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• v.43 no.5
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• pp.502-510
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• 2024

In this paper, we proposed the weakly-supervised deep learning algorithm for active sonar target recognition based on pseudo labeling using Conventional Recurrent Neural Network (CRNN) model widely used for acoustic signal processing because it can effectively utilize small and unbalanced active sonar data. Active sonar simulation data assuming two different SNRs and clutter environments were used in the training and testing process, and spectrogram obtained by applying Short Time Fourier Transform (STFT) to the simulation data was used as a feature factor for algorithm training. The algorithm proposed in this paper was evaluated based on the target and nontarget F1-score using test data independent of training data. As a result, it was confirmed that the CRNN model showed significant performance not only in typical acoustic signal processing but also active sonar target recognition. Also, pseudo-labeling helps to improve the performance of the active sonar target recognition algorithm used the CRNN model.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.44-50
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• 2011

We propose a multi-class AdaBoost algorithm for en efficient classification of multi-class data in this paper. Traditional AdaBoost algorithm is basically a binary classifier and it has limitations when applied to multi-class data problems even though multi-class versions are available. In order to overcome the problems on the AdaBoost algorithm for multi-class classification problems, we devise an AdaBoost architecture with a training algorithm that utilizes multi-class classifiers for its weak classifiers instead of series of binary classifiers. Experiments on a image classification problem using collected Caltech Image Database are preformed. The results show that the proposed AdaBoost architecture can reduce its training time while maintaining its classification accuracy competitive when compared to Adaboost.M2.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1588-1593
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• 2008

This paper describes a real-time isometric pinch force prediction algorithm from surface electromyogram (sEMG) using multilayer perceptron (MLP) for human robot interactive applications. The activities of seven muscles which are observable from surface electrodes and also related to the movements of the thumb and index finger joints were recorded during pinch force experiments. For the successful implementation of the real-time prediction algorithm, an off-line analysis was performed using the recorded activities. Four muscles were selected for the force prediction by using the Fisher linear discriminant analysis among seven muscles, and the four muscle activities provided effective information for mapping sEMG to the pinch force. The MLP structure was designed to make training efficient and to avoid both under- and over-fitting problems. The pinch force prediction algorithm was tested on five volunteers and the results were evaluated using two criteria: normalized root mean squared error (NRMSE) and correlation (CORR). The training time for the subjects was only 2 min 29 sec, but the prediction results were successful with NRMSE = 0.112 ${\pm}$ 0.082 and CORR = 0.932 ${\pm}$ 0.058. These results imply that the proposed algorithm is useful to measure the produced pinch force without force sensors in real-time. The possible applications include controlling bionic finger robot systems to overcome finger paralysis or amputation.

• The Journal of Korea Robotics Society
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• v.4 no.3
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• pp.185-191
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• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• Korean Journal of Geomatics
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• v.5 no.1
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• pp.27-34
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• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1139-1145
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• 2014

User recognition technology, which identifies or verifies a certain individual is absolutely essential under robotic environments for intelligent services. The conventional face recognition algorithm using single distance face image as training images has a problem that face recognition rate decreases as distance increases. The face recognition algorithm using face images by actual distance as training images shows good performance but this has a problem that it requires user cooperation. This paper proposes the LDA-based long distance face recognition method which uses multiple distance face images from a zoom camera for training face images. The proposed face recognition technique generated better performance by average 7.8% than the technique using the existing single distance face image as training. Compared with the technique that used face images by distance as training, the performance fell average 8.0%. However, the proposed method has a strength that it spends less time and requires less cooperation to users when taking face images.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.201-204
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• 1998

This paper study on blind adaptive interference suppression algorithm without training sequence to solve Near-Far problem due to multi access interference. And the performance of each algorithm in the presence of the multipath fading channels over DS-CDMA is evaluated. Simulation results showed that Modified LMS-CMA algorithm has a higher capacity than MOE in SIR/SNR.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.10
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• pp.3135-3141
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• 1996

In order to develop an efficient algorithm for S-to-M mapping in CMCA, characteristics of CMCA mappings is studied and conceptual mapping procedure is physically described. Then, careful observations on the mapping procedure and experience reveal a simple algorithm of the S-to-M mapping. The algorithm is described and compared with other procedures for S-to-M mapping. It is found very efficient in terms of computational operations and processing time.