• Journal of Sensor Science and Technology
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• v.27 no.4
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• pp.269-274
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• 2018

High-sensitivity signal-to-noise ratio (SNR) microphones are essentially required for a broad range of automatic speech recognition applications. Piezoelectric microphones have several advantages compared to conventional capacitor microphones including high stiffness and high SNR. In this study, we designed a new piezoelectric membrane structure by using the finite elements method (FEM) and an optimization technique to improve the sensitivity of the transducer, which has a high-quality AlN piezoelectric thin film. The simulation demonstrated that the sensitivity critically depends on the inner radius of the top electrode, the outer radius of the membrane, and the thickness of the piezoelectric film in the microphone. The optimized piezoelectric transducer structure showed a much higher sensitivity than that of the conventional piezoelectric transducer structure. This study provides a visible path to realize micro-scale high-sensitivity piezoelectric microphones that have a simple manufacturing process, wide range of frequency and low DC bias voltage.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.139-146
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• 2008

Masked fake face detection using ordinary visible images is a formidable task when the mask is accurately made with special makeup. Considering recent advances in special makeup technology, a reliable solution to detect masked fake faces is essential to the development of a complete face recognition system. This research proposes a method for masked fake face detection that exploits reflectance disparity due to object material and its surface color. First, we have shown that measuring of albedo can be simplified to radiance measurement when a practical face recognition system is deployed under the user-cooperative environment. This enables us to obtain albedo just by grey values in the image captured. Second, we have found that 850nm infrared light is effective to discriminate between facial skin and mask material using reflectance disparity. On the other hand, 650nm visible light is known to be suitable for distinguishing different facial skin colors between ethnic groups. We use a 2D vector consisting of radiance measurements under 850nm and 659nm illumination as a feature vector. Facial skin and mask material show linearly separable distributions in the feature space. By employing FIB, we have achieved 97.8% accuracy in fake face detection. Our method is applicable to faces of different skin colors, and can be easily implemented into commercial face recognition systems.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.7
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• pp.209-214
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• 2012

RFID(Radio Frequency IDentification) is an emerging technology which brings enormous productivity benefits in applications where objects have to be identified automatically. But despite of RFID's advantage, it is not easy to realize the RFID technology in business world. The failure to read RFID tags is the most urgent problem that should be solved for RFID application. Specially, in metal and liquid material, recognition rate of RFID tag is lower than others. Though some special tags for metal and liquid have been invented, it has not prevalent in business world on account of high price. In this paper, styrofoam pad is suggested to improve recognition rate of RFID tag for metal pipe which is used in fish cultivating industry. We makes experiment using Taguchi method and analyze the effects on styrofoam thickness, attachment location of tag, and angle of antenna.

• Korean Journal of Human Ecology
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• v.16 no.6
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• pp.1083-1095
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• 2007

This study had been done to investigate that early childhood teacher's recognition and demand on children's language education and 20 early childhood teachers were interviewed individually who work at state-owned/ public-owned/ private-owned kindergardens residing G, I, and K cities in Jeollabuk-do. First, the purpose of language education was recognized on the formations of essence, concept, expertise, technique and attitude toward language. Second, the contents of language education must be selected by children's experience that they encounter in ordinary life based on oral language and written language. Besides, early childhood teachers strongly felt the necessity of new contents of language education, although they thought of insufficiency of their knowledge on the issue. Third, the method of language education was mainly accomplished by teaching material and objects. Besides, they were aware of looking for new organized teaching methods and also concerned of the importances of teacher's attitude and group formation method. Fourth, the evaluation of language education must be acquired by desirable evaluation method that was based on the recognition of children's unrealistic language capabilities, even though they had recognized the difficulty to do because of knowledge insufficiency. They also showed the tendency of negligence on the evaluation of language education. Fifth, the required facts for early childhood teachers on language education were development and supply of teaching materials, demand on teacher's education and appropriate evaluation method, and cognitive changes on language education by public toward the written language.

• Korea Journal of Hospital Management
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• v.23 no.2
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• pp.54-66
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• 2018

Purpose: This study was to figure out relationships of perceived Technology Readiness Index(TRI), usefulness, acceptance intension, and the recognition of substitute employment of medical personnel on the artificial intelligence (AI) and internet of things (IoT) among main technologies. Methodology: To achieve the purpose, this study utilized structured survey tools to conduct a questionnaire survey of nursing, administrative and medical technology professionals at six university hospitals in Korea metropolitan area. A PLS(Partial Least Square) Path analysis was utilized To analyze the material. Findings: In the relation with the technology readiness and perceived usefulness, it had a positive influence to the perceived usefulness when the optimism and innovativeness were higher and the discomfort was lower. In the relation with the technology readiness and acceptance intension, it showed a positive influence when the innovativeness was higher and the discomfort was lower. In the relation with the perceived usefulness and acceptance intension, it had a positive influence to the acceptance intension when the perceived usefulness was higher. In the relation with the acceptance intension and the recognition of substitute employment, it showed a positive influence to the recognition of substitute employment when the acceptance intension was higher. Practical Implications: Judging based on the above study results and reference reviews, it confirmed that it is necessary to prepare in the level of hospital organization in the $4^{th}$ Industrial Revolution. They should increase the efficiency of human resources through the technological factors or changes of employment types for the additional demands of human resources to handle increasing medical demands or induce to secure necessary abilities which are changing at the right time by performing the $4^{th}$ Industrial Revolution related re-training continuously to develop the value of existing human resources.

• Fashion & Textile Research Journal
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• v.4 no.4
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• pp.369-375
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• 2002

The present study attempts to examine the degree of recognition of the Personal Colors by the age of the woman, and to analyze the influence of the recognition on the factors for choosing specific colors for hair coloring and facial make-up and the consequent satisfaction. The data will be used as a basic material for research and marketing in the field of color consulting in the beauty industry. Collected data were statistically processed using the SPSS WIN program. Depending on the nature of the contents to be analyzed, either the percentage calculation or the Chi-square analysis or the ANOVA was carried out. The findings of the study are as follows; The overall recognition of the Personal Colors was generally low in terms of the knowledge, information and experiences. While the degree of recognition was the highest in teenagers, the necessity of diagnosing the Personal Colors was most deeply perceived by the women in their 30s. One of the factors for choosing a specific color for hair coloring was their favorite color for the teenagers, and the Personal Color or the advice of the professional for the women in their 30s. Meanwhile, the highest factor for those in their 20s was the colors in vogue. For the facial color make-up as well, this sensitivity to popular colors was also highest in the twenty-something women. The color choice in consideration of favorite colors and the Personal Colors was the most prominent in the teenagers. The tendency of utilizing the advice of sales people or the professionals was the highest in the women in their 30s. In the survey of satisfaction with the chosen colors for hair coloring and make-up, it was found that satisfaction was the highest in the cases of choosing the Personal Colors in all the age groups, while it was the lowest for the choice of popular colors.

• Steel and Composite Structures
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• v.21 no.6
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.60-69
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• 2015

Labor attendance management has traditionally been carried out by writing a table for checking an attendance of labor, which requires a lot of time and effort. As electronic devices with additions such as barcodes, Quick Response codes, and Radio Frequency Identification(RFID) have been developed, however, automated labor attendance management systems have appeared. Recently, various types of labor recognition devices converged with biometrics (fingerprint, vein, face recognition, etc.) have been released. However, although these devices can be used to check attendance automatically, there is insufficient guidance when it comes to selecting the appropriate labor attendance management system for construction sites. Therefore, this study proposed a decision framework to determine which labor attendance management system would be suitable for a construction site and to select the labor recognition device. This study investigated different labor recognition devices, focusing on how they worked, and tested the performance of devices and their usability for construction labor attendance management. The test results showed that RFID is most suitable when verifying the attendance of many laborers over a short period of time. The devices for hand vein and fingerprint recognition did not function when there was a foreign material such as cement or paint on the laborer's hand, except for a deformed finger. Reflecting these test results, this study suggested a framework for selecting a labor attendance system and recognition device; this is expected to contribute to the development of more efficient labor management systems.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.391-401
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• 2022

As deep learning-based object detection and recognition research have been developed recently, the scope of application to industry and real life is expanding. But deep learning-based systems in the construction system are still much less studied. Calculating materials in the construction system is still manual, so it is a reality that transactions of wrong volumn calculation are generated due to a lot of time required and difficulty in accurate accumulation. A fast and accurate automatic drawing recognition system is required to solve this problem. Therefore, we propose an AI-based automatic drawing recognition accumulation system that detects and recognizes steel materials in construction drawings. To accurately detect steel materials in construction drawings, we propose data augmentation techniques and spatial attention modules for improving small object detection performance based on YOLOv4. The detected steel material area is recognized by text, and the number of steel materials is integrated based on the predicted characters. Experimental results show that the proposed method increases the accuracy and precision by 1.8% and 16%, respectively, compared with the conventional YOLOv4. As for the proposed method, Precision performance was 0.938. The recall was 1. Average Precision AP_0.5 was 99.4% and AP_0.5:0.95 was 67%. Accuracy for character recognition obtained 99.9.% by configuring and learning a suitable dataset that contains fonts used in construction drawings compared to the 75.6% using the existing dataset. The average time required per image was 0.013 seconds in the detection, 0.65 seconds in character recognition, and 0.16 seconds in the accumulation, resulting in 0.84 seconds.

• Proceedings of the Korean Fiber Society Conference
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• 2002.04a
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• pp.305-308
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• 2002

선택적인 이온센서의 개발은 환경적, 생리학적, 의학적 중요성에 의해 높은 관심을 받고 있다. 지금까지 전기 화학적인 검출, 형광검출, 그리고 다른 광학적인 방법들에 근거한 매우 다양한 이온센서들이 연구되어 왔다. 금속이온들의 선택적인 결합은 음이온들이나 중성물질 보다 상당히 쉽게 결합하기 때문에, 금속이온 인식을 위한 형광 화학센서들의 개발이 일찍부터 이루어졌다. (중략)