• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.8-19
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• 2016

Potholes are caused by the presence of water in the underlying soil structure, which weakens the road pavement by expansion and contraction of water at freezing and thawing temperatures. Recently, automatic pothole detection systems have been studied, such as vibration-based methods and laser scanning methods. However, the vibration-based methods have low detection accuracy and limited detection area. Moreover, the costs for laser scanning-based methods are significantly high. Thus, in this paper, we propose a new pothole detection system using a commercial black-box camera. Normally, the computing power of a commercial black-box camera is limited. Thus, the pothole detection algorithm should be designed to work with the embedded computing environment of a black-box camera. The designed pothole detection algorithm has been tested by implementing in a black-box camera. The experimental results are analyzed with specific evaluation metrics, such as sensitivity and precision. Our studies confirm that the proposed pothole detection system can be utilized to gather pothole information in real-time.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.69-76
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• 2020

In this paper, the problem of determining the optimal time to market of software was studied using error correction time, an indicator of error correction difficulty. In particular, it was intended to modify the assumption that error detection time and correction time are independent in the software reliability growth model considering the existing error correction time, and to establish a general framework model that expresses the correlation between error detection time and correction time to determine when the software will be released. The results showed that it was important from an economic perspective to detect errors that took time to correct early in the test. It was concluded that it was very important to analyze the correlation between error detection time and error correction time in determining when to release the optimal software.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.44-47
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• 2006

One of the critical issues in a sensor network concerns the detection of changes in data streams. Recently presented change detection schemes primarily use a sliding window model to detect changes. In such a model, a distance function is used to compare two sliding windows. Therefore, the performance of the change detection scheme is greatly influenced by the distance function. With regard to sensor nodes, however, energy consumption constitutes a critical design concern because the change detection scheme is implemented in a sensor node, which is a small battery-powered device. In this paper, we present a comparative study of various distance functions in terms of execution time, energy consumption, and detecting accuracy through simulation of speech signal data. The simulation result demonstrates that the Euclidean distance function has the highest performance while consuming a low amount of power. We believe our work is the first attempt to undertake a comparative study of distance functions in terms of execution time, energy consumption, and accuracy detection.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1106-1116
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• 2011

LINS (Laser Inertial Navigation System) consists of RLG (Ring Laser Gyroscopes)/accelerometers and provides real-time navigation information to the target system. Therefore it is very important to make a decision in the real time whether LINS is in the normal operation or not. That is called a fault detection method. In this paper, we propose the fault detection method of LINS based on the overlapping model. We also show that the fault detection probability is increased through overlapping the hardware model and the software model. It is verified through the long-term operation and RAM (Reliability Availability Maintainability) analysis of LINS that the fault detection method proposed in this paper is able to detect about 97% of probable system failures.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.1
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• pp.75-84
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• 2008

A new automatic small target detection and tracking algorithm for the real-time IR surveillance system is presented. The automatic target detection and tracking algorithm of the real-time systems, requires low complexity and robust tracking performance in the cluttered environment. Linear-array and parallel-scan IR systems usually suffer from severe scan noise caused by the detector non-uniformity. After the spatial filtering and thresholding, this scan noise still remains as high amplitude clutter which degrades the target detection rate and tracking performance. In this paper, we propose a new feature which consists of area and validity information of a measurement. By adopting this feature to the measurements selection and track confirmation, we can increase the target detection rate and reduce both the track loss rate and false track rate. From the experimental results, we can validate the feasibility of the proposed method in the noisy IR images.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.29-31
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• 2005

Detection methods for atrial tachycardia and fibrillation on the time axis have the advantages of light operational load and are easy to apply to various applications. Despite these advantages, arrhythmia detection algorithm on the time axis cannot stand much noise such as motion artifacts, moreover the peak detection algorithm has high complexity. In this paper, we use a spectrum analysis method for the detection of atrial tachycardia and fibrillation. By applying spectrum analysis and digital filtering on obtained electrogram signals, we can diagnose heart arrhythmia without using peak detection algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3638-3654
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• 2014

Cognitive Radio (CR) users need to sense the environment or channel at regular time interval for sharing the spectrum band of the primary users (PUs). Once find the spectrum idle, CR users start their transmission through it. Even while transmitting, they need to continue the sensing process so that they can leave the spectrum immediately whenever find a PU wanting to use the band. Therefore, detecting PUs is one of the main functions of cognitive radio before transmission and higher the detection probability ensures better protection to the primary users. However, it is not possible to attain a high detection probability (or a low miss detection probability) and low false alarm probability simultaneously as there is a tradeoff between false alarm probability ($P_{fa}$) and the probability of detection ($P_d$). In this paper, the author has provided a comprehensive study on different sensing techniques and discussed their advantages and disadvantages. Moreover, it is expected that, with this article, readers can have a through understanding of sensing techniques in CR and the current research trends in this area.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.142-145
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• 2006

Detection methods for atrial tachycardia and atrial fibrillation on the time axis have the advantages of light operational load and are easy to apply to various applications. Despite these advantages, arrhythmia detection algorithm on the time axis cannot stand much noise such as motion artifacts, moreover the peak detection algorithm has high complexity. In this paper, we use a spectrum analysis method for the detection of atrial tachycardia and atrial fibrillation. By applying spectrum analysis and digital filtering on obtained electrogram signals, we can diagnose heart arrhythmia without using peak detection algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.9
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• pp.1358-1361
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• 2012

This paper presents a study on the analysis for activation time and threshold value of heat detection sensor using HRR(Heat Release Rate). And it is represented to quantity of heat to activate heat detection sensor. The experiment is conducted to measure activation time and HRR of fire detection sensor burning alcohol and n-heptane. In order to burn the alcohol and n-heptane using $43.5cm(L){\times}43.5cm(W){\times}5cm(D)$ and $33cm(L){\times}33cm(L){\times}5cm(D)$ steel pan and the quantity of alcohol and n-heptane are 2.5 L and 650 g, respectively. The results show that peak HRR are in case of alcohol 66.13 kW and in case of n-heptane 151.64 kW, respectively. Total heat releases of heat detection sensor are in case of alcohol approximately 20.7 MJ and in case of n-heptane approximately 18 MJ, respectively.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1057-1068
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• 2005

This paper proposes a real-time face detection system which detects multiple faces from low resolution video such as web-camera video. First, It finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next, it generates reduced feature vector for each face region candidate by using principle component analysis. Finally, it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine) based binary classification. According to experiment results, the proposed method achieves real-time face detection from low resolution video. Also, it reduces the false detection rate than existing methods by using PCA and SVM based face classification step.