• IEMEK Journal of Embedded Systems and Applications
• /
• v.12 no.2
• /
• pp.113-120
• /
• 2017

In this paper, we present a night-time vehicle detection method using CNN (Convolutional Neural Network) classification. The camera based night-time vehicle detection plays an important role on various advanced driver assistance systems (ADAS) such as automatic head-lamp control system. The method consists mainly of thresholding, labeling and classification steps. The classification step is implemented by existing CIFAR-10 model CNN. Through the simulations tested on real road video, we show that CNN classification is a good alternative for night-time vehicle detection.

• Journal of Korea Multimedia Society
• /
• v.19 no.3
• /
• pp.633-641
• /
• 2016

Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.

• Journal of information and communication convergence engineering
• /
• v.11 no.3
• /
• pp.199-206
• /
• 2013

An emergency monitoring system for the elderly, which uses acceleration data measured with an accelerometer, angular velocity data measured with a gyroscope, and heart rate measured with an electrocardiogram, is proposed. The proposed fall detection algorithm uses multiple parameter combinations in which all parameters, calculated using tri-axial accelerations and bi-axial angular velocities, are above a certain threshold within a time period. Further, we propose an emergency detection algorithm that monitors the movements of the fallen elderly person, after a fall is detected. The results show that the proposed algorithms can distinguish various types of falls from activities of daily living with 100% sensitivity and 98.75% specificity. In addition, when falls are detected, the emergency detection rate is 100%. This suggests that the presented fall and emergency detection method provides an effective automatic fall detection and emergency alarm system. The proposed algorithms are simple enough to be implemented into an embedded system such as 8051-based microcontroller with 128 kbyte ROM.

• Computers and Concrete
• /
• v.32 no.6
• /
• pp.615-623
• /
• 2023

Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.3 no.1
• /
• pp.76-84
• /
• 2000

As first step of any acoustic defence system, a attacking target warning system needs to be extremely reliable. This means the system must ensure a high probability of target classification together with a very low false alarm rate. In this paper, a algorithms for underwater target automatic classification is available for use in the passive sonar will be presented. In first, we will describe the precise automatic extraction of frequency lines for the detection of acoustic signatures. Also, a neural network and fuzzy based algorithms for target classification will be described. Thus the performances of these algorithms are very good with a high probability of classification.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.19 no.8
• /
• pp.1787-1798
• /
• 1995

Monitoring and diagnosis of the operating machine mean evaluating the condition of a machine such as the detection of the defects and the prediction of the time to failure in the machine elements, while it is running. In this study, a technique of automatic diagnosis using probability concept is studied and the analyses of the pattern comparison are introduced. An expert system, which is able to analyze the automatic identification of the multiple defects in the ball bearings, is also developed. Finally, to confirm the effectiveness of the programmed algorithms, some tests were made with specimens of the ball bearings involving the multiple defects. The proposed system reasonably predicts the defects.

• Journal of the Korean Society for Precision Engineering
• /
• v.7 no.2
• /
• pp.85-94
• /
• 1990

In automatic metal cuttings, the chip control is one of the serious problems. So the automatic detection of chip forms is essential to the chip control in automatic metal cuttings. Cutting experiments were carried out under the variety of cutting conditions (cutting speed, feed, depth of cut and tool geometry) and with workpiece made of steel (S45C), and cutting forces were measured in-processing by using a piezoelectric type Tool Dynamometer. In this report, the frequency analysis of dynamic components, the upper frequency distributions, the ratio of RMS values, the numbers of null point and the probability density were calculated from the dynamic componeents of cutting forces filtered through various band pass filters. Experimental results showed that computer chip form monitoring system based on the cutting forces was designed and simulated and that 6 type of chip forms could be detected while in-process machining.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.5
• /
• pp.415-422
• /
• 2017

Finding influential factors from given clustering result is a typical data science problem. Genetic Algorithm based method is proposed to derive influential factors and its performance is compared with two conventional methods, Classification and Regression Tree (CART) and Chi-Squared Automatic Interaction Detection (CHAID), by using Dunn's index measure. To extract the influential factors of preference towards political parties in South Korea, the vote result of $18^{th}$ presidential election and 'Demographic', 'Health and Welfare', 'Economic' and 'Business' related data were used. Based on the analysis, reverse engineering was implemented. Implementation of reverse engineering based approach for influential factor analysis can provide new set of influential variables which can present new insight towards the data mining field.

• Proceedings of the KIEE Conference
• /
• 2003.11b
• /
• pp.199-202
• /
• 2003

An effective method is proposed for detecting, acquisition and tracking of a moving object using a disturbance map method in complex image sequences. A significant moving object is detected and tracked within the field of view by computing a modified disturbance map method between an Input image and a temporal average image. This method is very efficient in the serveillance application of digital CCTV and an automatic tracking camera. Experimental results using a real image sequence confirmed that the proposed method can effectively detect and track a significant moving object in complex image sequences.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.19 no.4
• /
• pp.18-23
• /
• 2011

Generally, the digital circuit card can be tested by automatic test equipment using LASAR(Logic Automated Stimulus and Response). This paper proposes the ROM data extracting algorithm which can test the digital circuit card that consists usually ROMs. We are implemented of the proposed fault detecting program by LabWindow/CVI 8.5 and the digital automatic test instrument with NI-VXI(National Instrument - Versa Bus Modular Europe eXtentions for Instrumentation) card. We also make an interface circuit board connecting the digital test instrument and the digital circuit card. It shows the good performance of getting the data from ROMs.