• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1996.11a
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• pp.316-319
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• 1996

In this study, characteristics of ultrasonic transducer fabricated with porous piezoelectric resonator are investigated, 3-D underwater object recognition using the self-made ultrasonic transducer and SOFM(Self-Organizing Feature Map) neural network are presented. The self-made transducer was satisfied the required condition of ultrasonic transducer in water, and the recognition rates for the training data and the testing data were 100 and 95.3% respectively. The experimental results have shown that the ultrasonic transducer fabricated with porous piezoelectric resonator could be applied for sonar system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.91-96
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• 2005

In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

• The Journal of the Acoustical Society of Korea
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• v.18 no.3
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• pp.62-67
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• 1999

In this paper, we investigates a reliable model of the Predictive Recurrent Neural Network for the speech recognition. Predictive Neural Networks are modeled by syllable units. For the given input syllable, then a model which gives the minimum prediction error is taken as the recognition result. The Predictive Neural Network which has the structure of recurrent network was composed to give the dynamic feature of the speech pattern into the network. We have compared with the recognition ability of the Recurrent Network proposed by Elman and Jordan. ETRI's SAMDORI has been used for the speech DB. In order to find a reliable model of neural networks, the changes of two recognition rates were compared one another in conditions of: (1) changing prediction order and the number of hidden units: and (2) accumulating previous values with self-loop coefficient in its context. The result shows that the optimum prediction order, the number of hidden units, and self-loop coefficient have differently responded according to the structure of neural network used. However, in general, the Jordan's recurrent network shows relatively higher recognition rate than Elman's. The effects of recognition rate on the self-loop coefficient were variable according to the structures of neural network and their values.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.346-349
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• 2011

In this paper, we design a perceptually invisible dot pattern layout and its recognition scheme, and we apply the recognition scheme into a smart self learning aid for interactive learning aid. To increase maximum information capacity and also increase robustness to the noises, we design a ECC (error correcting code) based dot pattern with directional vector indicator. To make a smart self-learning aid, we embed the micro dot pattern (20 information bit + 15 ECC bits + 9 layout information bit) using K ink (CMYK) and extract the dot pattern using IR (infrared) LED and IR filter based camera, which is embedded in the smart pen. The reason we use K ink is that K ink is a carbon based ink in nature, and carbon is easily recognized with IR even without light. After acquiring IR camera images for the dot patterns, we perform layout adjustment using the 9 layout information bit, and extract 20 information bits from 35 data bits which is composed of 20 information bits and 15 ECC bits. To embed and extract information bits, we use topology based dot pattern recognition scheme which is robust to geometric distortion which is very usual in camera based recognition scheme. Topology based pattern recognition traces next information bit symbols using topological distance measurement from the pivot information bit. We implemented and experimented with sample patterns, and it shows that we can achieve almost 99% recognition for our embedding patterns.

• Journal of Families and Better Life
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• v.24 no.2 s.80
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• pp.61-71
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• 2006

The purpose of this study was to investigate the relationships between parental childrearing attitude and parental childrearing behavior and the effect of parental childrearing attitude and parental childrearing behavior on children's self-efficacy. The subjects included 293 children from the 4th grade to the 6th grade in two elementary schools in Seoul and Incheon. The results were as follows: First, the subjects recognized the difference between parental childrearing attitude and parental childrearing behavior; Second, the children's self-efficacy varied depending upon the style of parental childrearing attitude and the level of recognition of parental childrearing attitude by the children; Third, the children's self-efficacy varied depending upon the style of parental childrearing behavior and the level of recognition of parental childrearing behavior by the children. Several suggestions were made concerning future parental childrearing attitude and parental childrearing behavior.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.483-489
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• 2020

This paper proposes acoustic model training using self-attention for low-resource speech recognition. In low-resource speech recognition, it is difficult for acoustic model to distinguish certain phones. For example, plosive /d/ and /t/, plosive /g/ and /k/ and affricate /z/ and /ch/. In acoustic model training, the self-attention generates attention weights from the deep neural network model. In this study, these weights handle the similar pronunciation error for low-resource speech recognition. When the proposed method was applied to Time Delay Neural Network-Output gate Projected Gated Recurrent Unit (TNDD-OPGRU)-based acoustic model, the proposed model showed a 5.98 % word error rate. It shows absolute improvement of 0.74 % compared with TDNN-OPGRU model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2129-2137
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• 2015

Lots of research and development works have been actively focused on the self-driving vehicles, locally and globally. In order to implement the self-driving vehicles, lots of fundamental core technologies need to be successfully developed and, specially, it is noted that traffic lights detection and recognition system is an essential part of the computer vision technologies in the self-driving vehicles. Up to nowadays, most conventional algorithm for detecting and recognizing traffic lights are mainly based on the color signal analysis, but these approaches have limits on the performance improvements that can be achieved due to the color signal noises and environmental situations. In order to overcome the performance limits, this paper introduces the morphological analysis for the traffic lights recognition. That is, by considering the color component analysis and the shape analysis such as rectangles and circles simultaneously, the efficiency of the traffic lights recognitions can be greatly increased. Through several simulations, it is shown that the proposed method can highly improve the recognition rate as well as the mis-recognition rate.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.100-105
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• 2021

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.

• Journal of the Korea Society of Computer and Information
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• v.22 no.9
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• pp.125-132
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• 2017

The purpose of this study was to examine the factors influencing career preparation behavior based on the perception of college students from the perspective of social cognitive career theory and to examine the effect of career barriers and career decision self - efficacy on career preparation behavior And career - decision self - efficacy. The results of the study are as follows. First, career barriers perceived by college students showed a significant positive correlation with career decision self - efficacy and career preparation behavior(-), and career decision efficacy showed a statistically significant correlation with career preparation behavior(+). Second, as a result of linear regression analysis to examine the effect of career barriers on career preparation behavior, lack of self - clarification, lack of job information, and lack of recognition of need were subordinate factors of career barriers. Third, as a result of linear regression analysis to examine the effect of career decision - making self - efficacy on career preparation behavior, goal setting and job information, which are sub - factors of career decision self - efficacy, were analyzed. Fourth, mediating effects of career decision self - efficacy on career barriers and career preparation behavior were analyzed by hierarchical regression analysis. The results of this study confirm that the level of career barrier, which is an important factor in career preparation behavior of college students, should be lowered and career decision self - efficacy should be increased.

• Analytical Science and Technology
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• v.18 no.6
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• pp.460-468
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• 2005

The biotin-avidin complex, as a model recognition system, has been constructed through N-hydroxysuccinimide(NHS) reaction on a variety of substrates such as a smooth Au film, electrochemically roughened Au electrode and chemically modified mica. Stepwise self-assembled monolayers (SAMs) of biotin-avidin system were characterized by surface-enhanced resonance Raman scattering (SERRS) spectroscopy, atomic force microscopy (AFM) and surface plasmon resonance (SPR). A strong SERRS signal of rhodamine tags labeled in avidin from the SAMs on a roughened gold electrode indicated the successful complex formation of stepwise biotin-avidin recognition system. AFM images showed the circular shaped avidin aggregates (hexamer) with ca. $60{\AA}$ thick on the substrate, corresponding to one layer of avidin. The surface coverage and concentration of avidin molecules were estimated to be 90% and $7.5{\times}10^{-12}mol/cm^2$, respectively. SPR technique allowed one to monitor the surface reaction of the specific recognition with high sensitivity and precision.