• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.29-34
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• 2015

Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.561-570
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• 2018

Artificial Intelligence is one of the technologies that are being discussed in the Fourth Industrial Revolution, attracting the attention from companies around the world and this technology is being applied to various industries such as education, finance, automobile, etc. AI integrated ChatBot is a system designed to respond to user questions according to defined response rules. This system is gradually expanded from simple inquiry responses for intelligent virtual assistant service, weather, traffic, schedule, etc. to service provisions through user pattern analysis, to solidify its position as a life-style service. As a result, research on AI integrated ChatBot platform has become necessary. Therefore, this study suggests the design and implementation of an intelligent chatbot service platform for small businesses.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.4
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• pp.422-429
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• 2002

The identification of ultrasonic flaw signals is a truly difficult task in the angle beam testing of welded joints due to non-relevant signals from the geometric reflectors such as weld roots and counter bores. This paper describes a new approach called "technique for identification of flaw signal using deconvolution(TIFD)" in order to identify the flaw signals in such a problematic situation. The concept of similarity function based on the deconvolution is introduced in the proposed approach. The "reference" signals from both flaws and geometric reflectors and test signals are acquired and normalized. The similarity functions are obtained by deconvolution of test signals with reference signals. The flaw signals could be identified by the patterns of similarity function. The initiative results show great potential of TIFD to distinguish notch comer signals from the geometric reflections.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.135-147
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• 2001

This study proposes bankruptcy prediction model using fuzzy neural networks. Neural networks offer preeminent learning ability but they are often confronted with the inconsistent and unpredictable performance for noisy financial data. The existence of continuous data and large amounts of records may pose a challenging task to explicit concepts extraction from the raw data due to the huge data space determined by continuous input variables. The attempt to solve this problem is to transform each input variable in a way which may make it easier fur neural network to develop a predictive relationship. One of the methods selected for this is to map each continuous input variable to a series of overlapping fuzzy sets. Appropriately transforming each of the inputs into overlapping fuzzy membership sets provides an isomorphic mapping of the data to properly constructed membership values, and as such, no information is lost. In addition, it is easier far neural network to identify and model high-order interactions when the data is transformed in this way. Experimental results show that fuzzy neural network outperforms conventional neural network for the prediction of corporate bankruptcy.

• Journal of Sensor Science and Technology
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• v.7 no.6
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• pp.418-425
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• 1998

Stereo cameras are the most widely used sensing systems for automated machines including robots to interact with their three-dimensional(3D) working environments. The position of a target point in the 3D world coordinates can be measured by the use of stereo cameras and the camera calibration is an important preliminary step for the task. Existing camera calibration techniques can be classified into two large categories - linear and nonlinear techniques. While linear techniques are simple but somewhat inaccurate, the nonlinear ones require a modeling process to compensate for the lens distortion and a rather complicated procedure to solve the nonlinear equations. In this paper, a method employing a neural network for the calibration problem is described for tackling the problems arisen when existing techniques are applied and the results are reported. Particularly, it is shown experimentally that by utilizing the function approximation capability of multi-layer neural networks trained by the back-propagation(BP) algorithm to learn the error pattern of a linear technique, the measurement accuracy can be simply and efficiently increased.

• Science of Emotion and Sensibility
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• v.11 no.3
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• pp.427-439
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• 2008

In order to effectively communicate the fragrances of commercial perfumes to consumers, it is important to apply congruent colors to their bottles and packaging. This research investigated the cross-modal associations between colors and fragrances through two experiments. In the first experiment, bottle colors of more than 200 popular perfumes in the market were analyzed. Distinguishable color design patterns of the bottles were revealed in accordance with their fragrance types. The second experiment expanded the use of color-odor matching task to a test population of Korean participants. Participants selected colors evoked by fragrances of three test perfumes in a blind setting. These three perfumes had characteristic hues and their associated hues were similar with the real colors of their bottles. In addition, there were significant variations in color tone across fragrance notes, viz. the top notes, middle notes, and base notes. The results of the two experiments suggest the existence of robust cross-modal associations between particular colors and fragrances in commercial perfumery.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.675-686
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• 2005

This paper proposes a new method of mathematically modeling and computer simulating television ghosts wherein television signals that have undergone multipath fading are generated without using approximations by considering the attenuation, time delay, phase, and timing jitter between consecutive frames. Conventional methods used polynomial interpolation or complex arithmetic to take into account the ghost phase, but our method uses only real arithmetic by employing the Hilbert transform and also reduces the computation time using the FFT (fast Fourier transform) algorithm. Furthermore, it is also possible to observe the transmit waveforms in both RF and IF ranges. Various ghost patterns generated in software provide for essential data required for the development of ghost canceling algorithms, and are deemed to be very useful in analyzing the constituent blocks of the transmitter and receiver chain in television broadcasting. The development of ghost cancelers needs to be preceded by the task of mathematically modeling ghosts and their extensive computer simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.1
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• pp.14-22
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• 2001

Off-line handwritten numeral recognition is a very difficult task and hard to achieve high recognition results using a single feature and a single classifier, since handwritten numerals contain many pattern variations which mostly depend upon individual writing styles. In this paper, we propose handwritten numeral recognition system using hybrid features and combined classifier. To improve recognition rate, we select mutually helpful features -directional features, crossing point feature and mesh features- and make throe new hybrid feature sets by using these features. These hybrid feature sets hold the local and global characteristics of input numeral images. And we implement combined classifier by combining three neural network classifiers to achieve high recognition rate, where fuzzy integral is used for multiple network fusion. In order to verify the performance of the proposed recognition system, experiments with the unconstrained handwritten numeral database of Concordia University, Canada were performed. As a result, our method has produced 97.85% of the recognition rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.30-37
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• 2011

This paper proposes a language set of hand motions for enhancing the performance of vision-based hand signal recognizer. Based on the statistical analysis of the angular tendency of hand movements in sign language and the hand motions in practical use, we construct four motion primitives as building blocks for basic hand motions. By combining these motion primitives, we design a discernable 'fundamental hand motion set' toward increasing the hand signal recognition. To demonstrate the validity of proposed designing method, we develop a 'fundamental hand motion set' recognizer based on hidden Markov model (HMM). The recognition system showed 99.01% recognition rate on the proposed language set. This result validates that the proposed language set enhances discernaility among the hand motions such that the performance of hand signal recognizer is improved.