• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권9호
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• pp.2312-2325
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• 2013

In this paper, we propose a preprocessing method that it is to correct the distortion of text area in Korean signboard images as a preprocessing step to improve character recognition. Distorted perspective in recognizing of Korean signboard text may cause of the low recognition rate. The proposed method consists of four main steps and eight sub-steps: main step consists of potential vertical components detection, vertical components detection, text-boundary estimation and distortion correction. First, potential vertical line components detection consists of four steps, including edge detection for each connected component, pixel distance normalization in the edge, dominant-point detection in the edge and removal of horizontal components. Second, vertical line components detection is composed of removal of diagonal components and extraction of vertical line components. Third, the outline estimation step is composed of the left and right boundary line detection. Finally, distortion of the text image is corrected by bilinear transformation based on the estimated outline. We compared the changes in recognition rates of OCR before and after applying the proposed algorithm. The recognition rate of the distortion corrected signboard images is 29.63% and 21.9% higher at the character and the text unit than those of the original images.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.24-31
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• 2023

The solid-state drive (SSD) possesses higher input and output speeds, more resistance to physical shock, and lower latency compared with regular hard disks; hence, it is an increasingly popular storage device. However, tiny components on an internal printed circuit board (PCB) hinder the manual detection of malfunctioning components. With the rapid development of artificial intelligence technologies, automatic detection of components through convolutional neural networks (CNN) can provide a sound solution for this area. This study proposes applying the YOLOv5 model to SSD PCB component detection, which is the first step in detecting defective components. It achieves pioneering state-of-the-art results on the SSD PCB dataset. Contrast experiments are conducted with YOLOX, a neck-and-neck model with YOLOv5; evidently, YOLOv5 obtains an mAP@0.5 of 99.0%, essentially outperforming YOLOX. These experiments prove that the YOLOv5 model is effective for tiny object detection and can be used to study the second step of detecting defective components in the future.

• 한국컴퓨터정보학회논문지
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• 제23권1호
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• pp.25-32
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• 2018

In this paper, we propose an edge detection algorithm for auto focus of infrared camera. We designed and implemented the edge detection of infrared image by using a spatial filter on FPGA. The infrared camera should be designed to minimize the image processing time and usage of hardware resource because these days surveillance systems should have the fast response and be low size, weight and power. we applied the $3{\times}3$ mask filter which has an advantage of minimizing the usage of memory and the propagation delay to process filtering. When we applied Laplacian filter to extract contour data from an image, not only edge components but also noise components of the image were extracted by the filter. These noise components make it difficult to determine the focus state. Also a bad pixel of infrared detector causes a problem in detecting the edge components. So we propose an adaptive edge detection filter that is a method to extract only edge components except noise components of an image by analyzing a variance of pixel data in $3{\times}3$ memory area. And we can detect the bad pixel and replace it with neighboring normal pixel value when we store a pixel in $3{\times}3$ memory area for filtering calculation. The experimental result proves that the proposed method is effective to implement the edge detection for auto focus in infrared camera.

• Journal of electromagnetic engineering and science
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• 제12권1호
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• pp.70-76
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• 2012

We compare the detection probabilities of the time-reversal(TR) detection system and the conventional radar system. The target is assumed to be hidden inside a random medium such as a forest. We propose a TR detection system based on the SAR(Synthetic Aperture Radar) algorithm. Unlike the conventional SAR images, the proposed TR-SAR system has an interesting property. Specifically, the target-related signal components due to the time-reversal refocusing characteristics, as well as some of clutter-related signal components are concentrated at the time-reversal reference point. The remaining clutter-related signal components are scattered around that reference point. In this paper, we model the random media as a collection of point scatterers to avoid unnecessary complexities. We calculate the detection probability of the TR radar system based on the proposed simple random media model.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2000년도 제31회 춘계학술대회
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• pp.68-75
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• 2000

The development of flexible automation in the manufacturing industry is concerned with production activities performed by unmanned machining system. A major topic relevant to metal-cutting operations is monitoring tool wear, which affects process efficiency and product quality, and implementing automatic tool replacements. In this paper, the measurement of the cutting force components has been found to provide a method for an in-process detection of tool wear. Cutting force components are divided into static and dynamic components in this paper, and the static components of cutting force have been used to detect flank wear. To eliminate the influence of variations in cutting conditions, tools, and workpiece materials, the force modeling is performed for various cutting conditions. The normalized force disparities are defined in this paper, and the relationships between normalized disparity and flank wear are established. Finally, Artificial neural network is used to learn these relationships and detect tool wear. According to the proposed method, the static force components could provide the effective means to detect flank wear for varying cutting conditions in turning operation.

• International Journal of Contents
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• 제9권3호
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• pp.1-9
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• 2013

In this paper, we propose a fast and accurate system for detecting pedestrians from a static image. Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian detection systems but extracting HOG is expensive due to its high dimensional vector. It will cause long processing time and large memory consumption in case of making a pedestrian detection system on high resolution image or video. In order to deal with this problem, we use Principal Components Analysis (PCA) technique to reduce the dimensionality of HOG. The output of PCA will be input for a linear SVM classifier for learning and testing. The experiment results showed that our proposed method reduces processing time but still maintains the similar detection rate. We got twenty five times faster than original HOG feature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3946-3965
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• 2018

Network anomaly detection in Software Defined Networking, especially the detection of DDoS attack, has been given great attention in recent years. It is convenient to build the Traffic Matrix from a global view in SDN. However, the monitoring and management of high-volume feature-rich traffic in large networks brings significant challenges. In this paper, we propose a moving window Principal Components Analysis based anomaly detection and mitigation approach to map data onto a low-dimensional subspace and keep monitoring the network state in real-time. Once the anomaly is detected, the controller will install the defense flow table rules onto the corresponding data plane switches to mitigate the attack. Furthermore, we evaluate our approach with experiments. The Receiver Operating Characteristic curves show that our approach performs well in both detection probability and false alarm probability compared with the entropy-based approach. In addition, the mitigation effect is impressive that our approach can prevent most of the attacking traffic. At last, we evaluate the overhead of the system, including the detection delay and utilization of CPU, which is not excessive. Our anomaly detection approach is lightweight and effective.

• 전기전자학회논문지
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• 제20권1호
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• pp.82-88
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• 2016

영상 인식에서 널리 이용되는 PPHT(Progressive Probability Hough Transform)는 직선을 정확하게 인식하는 우수한 알고리즘이나 원본 영상이 선명하지 않거나 복잡하여 잡음 성분이 많은 경우 인식률이 감소하는 문제가 있다. 이러한 문제를 해결하기 위해 잡음에 강하고 손상된 가장자리 패턴을 복구하며 직선을 인식하는 개선된 PPHT 방식을 제안한다. 제안하는 알고리즘은 픽셀 단위로 직선을 추적하고 검증하여 선분을 검출하는 방식으로 잡음의 영향을 최소화하고 손상된 가장자리 패턴을 일정 범위 내에서 복구하여 인식률을 증가시켰다. 제안한 알고리즘을 차선 인식에 적용하여 직선의 오인식률을 30% 이상 감소시키고 선분 인식률이 15%까지 증가함을 확인하였다.

• 전력전자학회논문지
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• 제26권4호
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• pp.271-276
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• 2021

Arc fault detector based on multilevel DWT with analysis of high-frequency noise components over 100 kHz is proposed in this study to improve the performance in detecting serial arcs and distinguishing them from inverter noise in PV systems. PV inverters generally operate at a frequency range of 20-50 kHz for switching operation and maximum power tracking control, and the effect of these frequency components on the signal for arc detection leads to negative arc detection. High-speed ADC and multilevel DWT are used in this study to analyze frequency components above 100 kHz. Such high frequency components are less influenced by inverter noise and utilized to detect as well as separate DC series arc from inverter noise. Arc detectors identify the input current of PV inverters using a Rogowski coil. The sensed signal is filtered, amplified, and used in 800kSPS ADC and DWT analysis and arc occurrence determination in DSP. An arc detection simulation facility in UL1699B was constructed and AFD tests the proposed detector were conducted to verify the performance of arc detection and performance of distinction of the negative arc. The satisfactory performance of the arc detector meets the standard of arc detection and extinguishing time of UL1699B with an arc detection time of approximately 0.11 seconds.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.68.4-68
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• 2002

$\textbullet$ This report presents an implementation a set of reusable software components which use of fault-tolerance embedded controller for railway signalling systems. These components can be used in real-time applications without application reprogramming. $\textbullet$ This library runs under VxWorks operating system and is oriented on real-time embedded systems. The library includes fault detection, fault containment, checkpointing and recovery components. $\textbullet$ The library enables to support high-speed response to fault occurrence in application software. Garbage collector together with VxWorks Watchdog provides both dead tasks detection and useless resources removing to avoid an overflow. Control flow...