• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.61-67
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• 2017

The detection of oil spills using radar image has been studied extensively. However, most of the proposed techniques have been focused on improving detection accuracy through the advancement of algorithms. In this study, research has been conducted to improve the accuracy of oil spill detection by improving the quality of radar images, which are used as input data to detect oil spills. Thresholding algorithms were used to measure the image improvement both before and after processing. The overall accuracy increased by approximately 16%, the producer accuracy increased by 40%, and the user accuracy increased by 1.5%. The kappa coefficient also increased significantly, from 0.48 to 0.92.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.9
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• pp.872-880
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• 2004

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this study, an air handling unit fault test apparatus was built and fault diagnosis algorithms were applied to diagnose various faults of an air handling unit. Test results showed the good diagnosis for applied faults. Therefore, these algorithms may be effectively used to develope the real time fault detection and diagnosis system for the air handling unit.

• Proceedings of the KSRS Conference
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• v.2
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• pp.574-577
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• 2006

In this paper, we developed a forest fire detection algorithm which uses a regression function between NDVI and land surface temperature. Previous detection algorithms use the land surface temperature as a main factor to discriminate fire pixels from non-fire pixels. These algorithms assume that the surface temperatures of non-fire pixels are intrinsically analogous and obey Gaussian normal distribution, regardless of land surface types and conditions. And the temperature thresholds for detecting fire pixels are derived from the statistical distribution of non-fire pixels’ temperature using heuristic methods. This assumption makes the temperature distribution of non-fire pixels very diverse and sometimes slightly overlapped with that of fire pixel. So, sometimes there occur omission errors in the cases of small fires. To ease such problem somewhat, we separated non-fire pixels into each land cover type by clustering algorithm and calculated the residuals between the temperature of a pixel under examination whether fire pixel or not and estimated temperature of the pixel using the linear regression between surface temperature and NDVI. As a result, this algorithm could modify the temperature threshold considering land types and conditions and showed improved detection accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1680-1686
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• 2015

Edge detection is an essential preprocessing for most image processing application, and there are several existing detection methods such as Sobel, Roberts, Laplacian, LoG(Laplacian of Gaussian) operators, etc. Those existing edge detection methods have not given satisfactory results since they do not offer enough pixel brightness change in low light level environment. Therefore, in this study new algorithms using brightness transfer function in the preprocessing and for edge detection applying standard deviation and average-weighted local masks are proposed. In addition, the performance of proposed algorithms was evaluated in comparison with the existing edge detection methods such as Sobel, Roberts, Prewitt, Laplacian, LoG operators.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.428-442
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• 2022

A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficient scheduling and management of transport routes. In this study, we introduce a lightweight head detection network using deep learning applicable to an embedded system. Currently, object detection algorithms using deep learning have been found to be successful. However, these algorithms essentially need a graphics processing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network using certain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using the proposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK 710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiency of the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system, we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances of exiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite the fact that only the CPU was used.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.98-102
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• 2023

This paper presents a study on how augmenting semi-synthetic image data improves the performance of human detection algorithms. In the field of object detection, securing a high-quality data set plays the most important role in training deep learning algorithms. Recently, the acquisition of real image data has become time consuming and expensive; therefore, research using synthesized data has been conducted. Synthetic data haves the advantage of being able to generate a vast amount of data and accurately label it. However, the utility of synthetic data in human detection has not yet been demonstrated. Therefore, we use You Only Look Once (YOLO), the object detection algorithm most commonly used, to experimentally analyze the effect of synthetic data augmentation on human detection performance. As a result of training YOLO using the Penn-Fudan dataset, it was shown that the YOLO network model trained on a dataset augmented with synthetic data provided high-performance results in terms of the Precision-Recall Curve and F1-Confidence Curve.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2199-2200
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• 2006

Reliable frequency estimation is important for active power control, load shedding and generator protection. Thereby, frequency estimation is researched and some algorithms is proposed. This paper analyzed strength and weakness of each algorithms through comparative analysis of frequency estimation. Used algorithms are Zero Crossing detection, Discrete Fourier Transformation, Least Error Squares.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.567-568
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• 2006

Reliable frequency estimation is important for active power control, load shedding and generator protection. Thereby, frequency estimation is researched and some algorithms is proposed. This paper analyzed strength and weakness of each algorithms through comparative analysis of frequency estimation. Used algorithms are Zero Crossing detection, Discrete Fourier Transformation, Least Error Squares.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.1233-1234
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• 2006

Reliable frequency estimation is important for active power control, load shedding and generator protection. Thereby, frequency estimation is researched and some algorithms is proposed. This paper analyzed strength and weakness of each algorithms through comparative analysis of frequency estimation. Used algorithms are Zero Crossing detection, Discrete Fourier Transformation, Least Error Squares.