• Korean Journal of Child Studies
• /
• v.27 no.2
• /
• pp.1-14
• /
• 2006

This study examined the spontaneous utterances of 20 1-2 year old Korean-Chinese children in Yanji, China by videotaping their spontaneous natural speech during interaction with his/her caregiver for 30 minutes on 4 separate occasions. Utterances were transcribed and coded by nominative and accusative case markers; then analyzed by grammatical functions. Results showed that the order and pattern of the acquisition of the case marking system of Korean-Chinese bilinguals differed from that of Korean monolinguals. Bilinguals used the accusative marker -lul earlier than the nominative marker-ka and overextended the accusative marker more often than the nominative marker. These results are consistent with those of Gathercole (in press) who found that bilingual children differed from monolinguals in mastering grammar.

• English Language & Literature Teaching
• /
• v.12 no.4
• /
• pp.31-68
• /
• 2006

The present research investigates the acquisition of the English locative alternation by Korean EFL learners, which poses a learnability paradox, taking Pinker's framework of learnability theory as its basis. It addresses two questions (1) how lexical knowledge is represented initially and at different levels of interlanguage development and (2) what kinds of difficulty Korean learners find in the acquisition of English locative verbs and their constructions. Three groups of learners at different proficiency levels with a control group of English native speakers are examined by two instruments: elicited production task and grammaticality judgment task. According to different levels of proficiency, the learners exhibit gradual sensitivity to a change-of-state meaning and obtain complete perception of the meanings of locative verbs (manner-of-motion and change-of-state) and their constructions. Overgeneralization errors are observed in their performance. The errors are due to misinterpretations of particular lexical items in conjunction with the universal linking rules. More fundamental cause of difficulty is accounted for by partial use of learning mechanisms, caused by insufficient L2 input.

• Proceedings of the KIEE Conference
• /
• 2001.07a
• /
• pp.416-418
• /
• 2001

This Paper presents the 2-step load cycle of daily load curve for representative load pattern of power distribution transformer. We decide the representative load pattern of distribution transformer in domestic using the pattern classification algorithm. The K-mean method is used for the pattern classification algorithm. The acquisition equipment of field load data is utilized for 96-sample distribution transformers and the field data is used in the construction of the database for the creation of daily load pattern.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.5
• /
• pp.83-93
• /
• 1998

Three dimensional data acquisition system based on the structured light is developed in this work. The system is composed of a CCD camera, slide projector, and various image processing programs. Calibration procedures and several image processing steps which are necessary to get the rnage data are described. A new grid labeling technique and a grid pattern are devised to improve the accuracy of th eobtained data. Preliminary experimental result shows that the developed system may be used as a simple and cheap 3D data acquisition system. Severla suggestions are included for further research.

• Proceedings of the KIEE Conference
• /
• 2001.05a
• /
• pp.89-91
• /
• 2001

This paper presents the result of pattern classification of load demand for distribution transformer in domestic. The field data of load demand is measured using the load acquisition device and the measurement data is used for the database system for load management of distribution transformed. For the pattern classification, the load data and the customer information data are also used. The K-MEAN method is used for the pattern classification algorithm. The result of pattern classification is used for the 2-step format of load demand curve.

• International Journal of Precision Engineering and Manufacturing
• /
• v.5 no.3
• /
• pp.11-18
• /
• 2004

In this paper, the classification of artificial defects in semiconductor packages is studied by using pattern recognition technology. For this purpose, the pattern recognition algorithm includes the user made MATLAB code. And preprocess is made of the image process and self-organizing map, which is the input of the back-propagation neural network and the dimensionality reduction method, The image process steps are data acquisition, equalization, binary and edge detection. Image process and self-organizing map are compared to the preprocess method. Also the pattern recognition technology is applied to classify two kinds of defects in semiconductor packages: cracks and delaminations.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2004.04a
• /
• pp.291-298
• /
• 2004

Effective 3 dimensional modelling is to be essential work for design of construction, mechanic and industrial part. Especially, it makes possible for reverse design. It need rapidity, accuracy, reality. Data acquisition method for modelling are contact 3dimensional measurement system, LASER scanner, Pattern scanner, and digital photogrammetry. This study introduce to 3 dimensional modelling methods and analysis of these method. We tried to 3D modelling of automobile part using OPTO-Top pattern scanner which system have rapidity and accuracy, and compared effectiveness of each method. The 3D display web environment was made.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.12 no.2
• /
• pp.65-70
• /
• 2003

In this study, researchers developed the estimative algorithm for artificial defect 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 algerian was provided the recognition rates of 75.7% (for Crack) and 83.4% (for Delamination) and 87.2 % (for Normal).

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2005.07a
• /
• pp.221-222
• /
• 2005

In this paper, application of NN (Neural Network) as a method of pattern discrimination of PD(partial discharge) which occurs at the stator coil of traction motor was studied. For PD data acquisition, three defective models are manufactured such as internal discharge model, slot discharge model and surface discharge model. PD data for recognition were acquired from PD detector and DAQ board which is able to analysis the PD signal and perform the pattern discrimination. Statistical distributions and parameters are calculated to discriminate PD sources. And also these statistical distribution parameters are applied to classify PD sources by BP and has good recognition rate on the discharge sources.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.345-350
• /
• 2002

In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method fur entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.