• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.2
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• pp.142-150
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• 2016

This paper developed new manufacture process technology of slim type flexible flat lighting product used lower power white LED. Flexible flat lighting is applied to letter sign lighting, traffic lighting, interior wall lighting, flat lighting, aquarium back lighting, wreath light etc. Main manufacture process technology were developed drawing software for electronics circuit, inkjet electronic circuit pattern and inkjet white ink coating. For pattern printing it was utilized for piezoelectonic inkjet head printing technology. Also high vacuum pressure laminating technology was waterproofing for LED flat lighting protection. Hence, form process technology we were manufactured for flexible flat lighting product of the power 12 W, input voltage 48 V and plane size $480{\times}480mm$. It acquired a these validity from experiment results.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1103-1106
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• 2008

For the current display process, the innovative micro pattern fabrication process using semiconductor process should be developed, which requires the expensive equipment, the limited process environment and the expensive optic-sensitive material. The effort of process innovation during past several years ends up the limit of cost reduction. The existing ink jet technologies such as a thermal bubble ink jet printing and a piezo ink jet printing are required to shorten the nozzle diameter in order to apply to the micro pattern fabrication. In this paper, as one way to cope these problems the micro pattern equipment based on the electrostatic ink jet has been developed and carried out some experiments.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.6
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• pp.686-692
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• 2012

Solid free-form fabrication (SFF) technology was developed to fabricate three-dimensional (3D) scaffolds for tissue engineering (TE) applications. In this study, we developed a polymer deposition system (PDS) and created 3D microstructures using a bioresorbable polycaprolactone (PCL) polymer. Fabrication of 3D scaffolds by PDS requires a combination of several devices, including a heating system, dispenser, and motion controller. The system can process a polymer with extremely high precision by using a 200 ${\mu}m$ nozzle. Based on scanning electron microscope (SEM) images, both the line width and the piled line height were fine and uniform. Several 3D micro-structures, including the ANU pattern (a pattern named after Andong National University), $45^{\circ}$ pattern square, frame, cylindrical, triangular, cross-shaped, and hexagon, have been fabricated using the polymer deposition system.

• Nuclear Engineering and Technology
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• v.36 no.1
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• pp.12-23
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• 2004

In the present study a new measurement technique has been developed, which uses an ultrasonic transmission signal in order to identify the vertical two phase flow pattern. The ultrasonic measurement system developed in the present study not only provides the information required for the identification of vertical two phase flow patterns but also makes real time identification possible. Various vertical two phase flow patterns such as bubbly, slug, churn, annular flow etc. have been accurately identified with the present ultrasonic measurement system under atmospheric condition. In addition, the present test apparatus can practically simulate the ultrasonic propagation characteristics under high temperature and high pressure systems. Therefore, it is expected that the present ultrasonic flow pattern identification technique could be applicable to the vertical two phase flow systems under high temperature and high pressure conditions.

• The Journal of Korean Medicine
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• v.41 no.3
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• pp.1-8
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• 2020

Objectives: This paper aims to investigate the Donguibogam-based pattern diagnosis by applying natural language processing and machine learning. Methods: A database has been constructed by gathering symptoms and pattern diagnosis from Donguibogam. The symptom sentences were tokenized with nouns, verbs, and adjectives with natural language processing tool. To apply symptom sentences into machine learning, Word2Vec model has been established for converting words into numeric vectors. Using the pair of symptom's vector and pattern diagnosis, a pattern prediction model has been trained through Logistic Regression. Results: The Word2Vec model's maximum performance was obtained by optimizing Word2Vec's primary parameters -the number of iterations, the vector's dimensions, and window size. The obtained pattern diagnosis regression model showed 75% (chance level 16.7%) accuracy for the prediction of Six-Qi pattern diagnosis. Conclusions: In this study, we developed pattern diagnosis prediction model based on the symptom and pattern diagnosis from Donguibogam. The prediction accuracy could be increased by the collection of data through future expansions of oriental medicine classics.

• Smart Structures and Systems
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• v.6 no.1
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• pp.39-56
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• 2010

Structural Control relies, with a great deal, on the ability of the control algorithm to identify the current state of the system, at any given point in time. When such algorithms are designed to perform in a smart manner, several smart technologies/devices are called upon to perform tasks that involve pattern recognition and control. Smart pattern recognition is proposed to replace/enhance traditional state identification techniques, which require the extensive manipulation of intricate mathematical equations. Smart pattern recognition techniques attempt to emulate the behavior of the human brain when performing abstract pattern identification. Since these techniques are largely heuristic in nature, it is reasonable to ensure their reliability under real life situations. In this paper, a neural network pattern recognition scheme is explored. The pattern identification of three structural systems is considered. The first is a single bay three-story frame. Both the second and the third models are variations on benchmark problems, previously published for control strategy evaluation purposes. A Neural Network was developed and trained to identify the deformed shape of structural systems under earthquake excitation. The network was trained, for each individual model system, then tested under the effect of a different set of earthquake records. The proposed smart pattern identification scheme is considered an integral component of a Smart Structural System. The Reliability assessment of such component represents an important stage in the evaluation of an overall reliability measure of Smart Structural Systems. Several studies are currently underway aiming at the identification of a reliability measure for such smart pattern recognition technique.

• ETRI Journal
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• v.35 no.6
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• pp.1011-1020
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• 2013

In this paper, we present a method for obtaining a high-quality 3D depth. The advantages of active pattern projection and passive stereo matching are combined and a system is established. A diffractive optical element (DOE) is developed to project the active pattern. Cross guidance (CG) and auto guidance (AG) are proposed to perform the passive stereo matching in a stereo image in which a DOE pattern is projected. When obtaining the image, the CG emits a DOE pattern periodically and consecutively receives the original and pattern images. In addition, stereo matching is performed using these images. The AG projects the DOE pattern continuously. It conducts cost aggregation, and the image is restored through the process of removing the pattern from the pattern image. The ground truth is generated to estimate the optimal parameter among various stereo matching algorithms. Using the ground truth, the optimal parameter is estimated and the cost computation and aggregation algorithm are selected. The depth is calculated and bad-pixel errors make up 4.45% of the non-occlusion area.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.1
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• pp.91-102
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• 2001

The purpose of this study is to suggest a individually fitted slacks pattern in order to help ourselves prepared for the days of the order-based production like the EC in the apparel industry. A pattern was designed to make the personal fit improved based on the present study and the analysis of the measurements and the body shapes. The main characteristics of this newly designed slacks pattern are as follows. To improve fitting, the pattern requires more detailed data such as front and back waist arc, front and back hip arc, hip depth, the gradient of hip. And for personal fitted pattern, knee girth was established in the proportion to the hip girth and the crotch curve was adjusted to the measurements after drawing and the side seam curve was changed according to the front hip type and the ease in waist and hip was established by the percentage. Slacks pattern was developed into an automatic drafting program. Through the sensory evaluation for the appearance and moving fitness, newly designed slacks pattern was turned out to be improved in personal fit espacially for the body type out of the average.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.505-510
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• 2000

Vehicle performances such as fuel consumption and catalyst-out emissions are affected by a driving pattern, which is defined as a driving cycle with the grade in this study. We developed an algorithm to recognize a current driving pattern by using a neural network. And this algorithm can be used in adapting the driving control strategy to the recognized driving pattern. First, we classified the general driving patterns into 6 representative driving patterns, which are composed of 3 urban driving patterns, 2 suburban driving patterns and 1 expressway driving pattern. A total of 24 parameters such as average cycle velocity, positive acceleration kinetic energy, relative duration spent at stop, average acceleration and average grade are chosen to characterize the driving patterns. Second, we used a neural network (especially the Hamming network) to decide which representative driving pattern is closest to the current driving pattern by comparing the inner products between them. And before calculating inner product, each element of the current and representative driving patterns is transformed into 1 and -1 array as to 4 levels. In the end, we simulated the driving pattern recognition algorithm in a temporary pattern composed of 6 representative driving patterns and, verified the reliable recognition performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.11
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• pp.1201-1206
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• 2014

This paper presents the development of an event-based operator model of a wheel loader for virtual V-pattern working. The objective of this study is to analyze the performance and dynamic behavior of the wheel loader for a typical V-pattern. The proposed typical V-pattern working is divided into four stages. The developed operator model is based on eight events, and the operator's inputs are occurred sequentially by event. A 3D dynamic simulation model of the wheel loader is developed and used to analyze the dynamic behavior during working, and the simulation results are compared with the experimental data of V-pattern working. The proposed 3D dynamic simulation model and operator model are constructed using MATLAB/Simulink. The proposed operator model for V-pattern working is expected to enable evaluation of the working performance and dynamic behavior of the wheel loader.