• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.73-76
• /
• 2013

This paper proposes a tongue and its direction recognition algorithm which compares and estimates pixel distribution in the mouth area. As the size of smart phones grows, facial gesture control technology for a smart phone is required. Firstly, the nose area is detected and the mouth area is detected based on the ratio of the nose to mouth. After detecting the mouth area, it is divided by a pattern of grid and the distribution of pixels having the similar color to the tongue is tested for each segment. The recognition rate was nearly 80% in the experiments performed with five researchers among our laboratory members.

• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1499-1502
• /
• 2002

A method for acquiring an environmental map by integrating distance data obtained by sonars of a moving robot with web interface is proposed. Sonar data contains outliers in some cases such as ultrasonic beam is projected onto a corner of an object. Therefore, the influence of the outliers should be reduced by detecting outliers. In our method, the outliers are detected by two ways: (i) a method considering geometrical .elation among the observed surface and the projected ultrasonic beau, and (ii) a method considering consistency with data obtained by other sonars. By measurement by the sonar, the distance from the sonar to the obstacle is obtained. Assuming the two dimensional space we can know that the inside of the sector, whose renter coincide with the sonar and whose radius is equal to the obtained distance, is the free area, and a part of the arc of this sector is the obstacle area. The generation of the environmental map is done by integrating the free area and the obstacle area obtained by each measurement by the sonars. Before the integration, the outliers detection is done by two ways mentioned above. Experimental results show that obtained maps obtained by our methods with outliers defection are much better than those by a method without outliers detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.447-450
• /
• 2013

This paper proposes the way to prevent drowsy driving by recognizing drivers eyes and yawn using a front camera. The method uses the Viola-Jones algorithm to detect eyes area and mouth area from detection face region. In the eyes area, it uses the Hough transform to recognize eye circle in order to distinguish drowsy driving. In the mouth area, it determines whether for the driver to yawn through a sub-window testing by applying a HSV-filter and detecting skin color of the tongue. The test result shows that the recognition rate of yawn reaches up to 90%. It is expected that the method introduced in this paper might contribute to reduce the number of drowsy driving accidents.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.8
• /
• pp.4699-4704
• /
• 2013

Background: MicroRNAs have been demonstrated to play important roles in the development and progression of colorectal cancer. Several studies utilizing microRNAs as diagnostic biomarkers for colorectal cancer (CRC) have been reported. The aim of this meta-analysis was to comprehensively and quantitatively summarize the diagnostic value of microRNAs for detecting colorectal cancer. Methods: We searched PubMed, Embase and Cochrane Library for published studies that used microRNAs as biomarkers for the diagnosis of colorectal cancer. Summary estimates for sensitivity, specificity and other measures of accuracy of microRNAs in the diagnosis of colorectal cancer were calculated using the bivariate random effects model. A summary receiver operating characteristic (SROC) curve was also generated to summarize the overall effectiveness of the test. Result: Thirteen studies from twelve published articles met the inclusion criteria and were included. The overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odd ratio of microRNAs for the diagnosis of colorectal cancer were 0.81 (95%CI: 0.79-0.84), 0.78 (95%CI: 0.75-0.82), 4.14 (95%CI: 2.90-5.92), 0.24 (95%CI: 0.19-0.30), and 19.2 (95%CI: 11.7-31.5), respectively. The area under the SROC curve was 0.89. Conclusions: The current evidence suggests that the microRNAs test might not be used alone as a screening tool for CRC. Combining microRNAs testing with other conventional tests such as FOBT may improve the diagnostic accuracy for detecting CRC.

• Journal of IKEEE
• /
• v.24 no.3
• /
• pp.783-790
• /
• 2020

In this paper, the human detecting radar system for indoor security applications is proposed, and its FPGA-based implementation results are presented. In order to minimize the complexity and memory requirements of the computation, the top half of the spectrogram was used to extract features, excluding the feature extraction techniques that require complex computation, feature extraction techniques were proposed considering classification performance and complexity. In addition, memory requirements were minimized by designing a pipeline structure without storing the entire spectrogram. Experiments on human, dog and robot cleaners were conducted for classification, and 96.2% accuracy performance was confirmed. The proposed system was implemented using Verilog-HDL, and we confirmed that a low-area design using 1140 logics and 6.5 Kb of memory was possible.

• Journal of the Korea Safety Management & Science
• /
• v.17 no.2
• /
• pp.129-137
• /
• 2015

This study is in regard to the gas detection system and gas detection method utilizing smart phone. This study includes; 1) the sensor module attached to the smart phone to detect and measure flammable gas or toxic gas; and 2) gas detection APP which is installed inside the smart phone and recognizes the user information and location information automatically by reading RFID tag indicating the user or the location to detect gas through the contact area where RFID and blue tooth reader is installed inside of the above mentioned smart phone, and then measures the combustible gas or toxic gas by operating above mentioned sensor module and obtains the data thus measured, and above mentioned smart phone is characterized by its transmission of the above mentioned user information, location information and measured data which are obtained by above mentioned gas detecting APP to operation server via communication network. With this, reliability for the location detecting gas by the user, the result of the measurement, etc. can be secured. Furthermore, this provides the effect of preventing artificial manipulation at the time of input which is associated with the identification of the user to be measured by utilizing removable sensor module and application or the mistake resulted from wrong input by the user. In addition, by transmitting the measured data from the sensor module carrying out gas detection to operation server, this provides the effect of making it possible to process the data thus collected to a specialized data for combustible gas or toxic gas.

• Journal of IKEEE
• /
• v.25 no.3
• /
• pp.430-436
• /
• 2021

The non-contact body temperature measurement system is one of the key factors, which is manage febrile diseases in mass facilities using optical and thermal imaging cameras. Conventional systems can only be used for simple body temperature measurement in the face area, because it is used only a deep learning-based face detection algorithm. So, there is a limit to detecting abnormal symptoms of the infants and young children, who have difficulty expressing their opinions. This paper proposes an improved facial expression recognition algorithm for detecting abnormal symptoms in infants and young children. The proposed method uses an object detection model to detect infants and young children in an image, then It acquires the coordinates of the eyes, nose, and mouth, which are key elements of facial expression recognition. Finally, facial expression recognition is performed by applying a selective sharpening filter based on the obtained coordinates. According to the experimental results, the proposed algorithm improved by 2.52%, 1.12%, and 2.29%, respectively, for the three expressions of neutral, happy, and sad in the UTK dataset.

• Journal of the Architectural Institute of Korea Planning & Design
• /
• v.36 no.5
• /
• pp.93-103
• /
• 2020

The purpose of this study is to suggest a supervisory way to improve the efficiency of Building Supervision using Deep Learning, especially object detecting technology. Since the establishment of the Building Supervision system in Korea, it has been changed and improved many times systematically, but it is hard to find any improvement in terms of implementing methods. Therefore, the Supervision is until now the area where a lot of money, time and manpower are needed. This might give a room for superficial, formal and documentary supervision that could lead to faulty construction. This study suggests a way of Building Supervision which is more automatic and effective so that it can lead to save the time, effort and money. And the way is to detect the hoop-bars of a column and count the number of it automatically. For this study, we made a hoop-bar detecting network by transfor learnning of YOLOv2 network through MATLAB. Among many training experiments, relatively most accurate network was selected, and this network was able to detect rebar placement in building site pictures with the accuracy of 92.85% for similar images to those used in trainings, and 90% or more for new images at specific distance. It was also able to count the number of hoop-bars. The result showed the possibility of automatic Building Supervision and its efficiency improvement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.6 no.2
• /
• pp.42-52
• /
• 1988

The main object of this study is to prove the effectiveness of Landsat data in detecting the stressed areas in forest by extracting these areas. And also to choose the effective bands for this type of survey and to reduce the effect of shadow in forest to improve the accuracy of classification are the other objects. In this study Landsat-5 TM data is used and image processing techniques such as spatial filtering and ratio are taken to reduce the effect of shadow and to improve the classification accuracy. As a result following conclusions are obtained. First, Landsat TM data is useful to detect the stressed areas in forest. Second, when detecting the stressed area, band 4 and 5 are the most effective. Third, spatial filtering and ratio are useful to reudce the effect of shadow and improve the classification accuracy. Especially, ratio has great effect on improving the classification accuracy between forest and other areas.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.36 no.5
• /
• pp.384-390
• /
• 2016

It is important to have a pipeline leak-detection system that determines the presence of a leak and quickly identifies its location. Current leak detection methods use a acoustic emission sensors, microphone arrays, and camera images. Recently, many researchers have been focusing on using cameras for detecting leaks. The advantage of this method is that it can survey a wide area and monitor a pipeline over a long distance. However, conventional methods using camera monitoring are unable to target an exact leak location. In this paper, we propose a method of detecting leak locations using leak-detection results combined with multi-frame analysis. The proposed method is verified by experiment.