• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.1
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• pp.51-57
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• 2010

This study was to analyze the difference in pilot error by using the Signal Detection Theory. The task was to detect the targeted aircraft(signal) which is different shape from many other aircraft(noise). From the two experiments, we differentiated the task difficulty followed by change in noise stimuli. Experiment 1 was to search the signal stimuli(fighter plane) while the noise stimuli(cargo plane) were increasing. The results from the Experiment 1 showed the tendency to decrease the hit rate by increasing the number of noise stimuli. However, the false alarm rate was not increased. The sensitivity(d') showed quite high. In Experiment 2, a disturbance stimulus(helicopter) was added to noise stimuli. The result was generally similar to those of Experiment 1. However, the hit rate was lower than that of Experiment 1.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.1-7
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• 2003

This paper proposes a method to detect misfired cylinders by the exhaust pressure ascent rate. The misfire is generated by faults of electric system or faults of fuel delivery system. It is one of the abnormal combustions. Therefore, it increases the unburned hydrocarbon and the carbon monoxide and affects a bad influence to the 3-way catalyst. The misfire causes to decrease the power of the engine and increase the consumption of the fuel. Early detection and correction of the misfired cylinders can prevent these unusual phenomena. The misfired cylinders can be detected by the comparison of exhaust pressure ascent rate during each cycle. The exhaust pressure ascent rate is defined as pressure rise per time. Our experimental results showed that the proposed method is effective in the detection of the misfired cylinders on a gasoline engine regardless loads and revolutions of the engine.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.745-755
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• 2023

The wire rope is an indispensable production machinery in coal mines. It is the main force-bearing equipment of the underground traction system. Accurate detection of wire rope defects and positions exerts an exceedingly crucial role in safe production. The existing defect detection solutions exhibit some deficiencies pertaining to the flexibility, accuracy and real-time performance of wire rope defect detection. To solve the aforementioned problems, this study utilizes the camera to sample the wire rope before the well entry, and proposes an object based on YOLOv5. The surface small-defect detection model realizes the accurate detection of small defects outside the wire rope. The transfer learning method is also introduced to enhance the model accuracy of small sample training. Herein, the enhanced YOLOv5 algorithm effectively enhances the accuracy of target detection and solves the defect detection problem of wire rope utilized in mine, and somewhat avoids accidents occasioned by wire rope damage. After a large number of experiments, it is revealed that in the task of wire rope defect detection, the average correctness rate and the average accuracy rate of the model are significantly enhanced with those before the modification, and that the detection speed can be maintained at a real-time level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2651-2673
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• 2019

In order to detect Denial of Service (DoS) attacks, victim-side detection methods are used popularly such as static threshold-based method and machine learning-based method. However, as DoS attacking methods become more sophisticated, these methods reveal some natural disadvantages such as the late detection and the difficulty of tracing back attackers. Recently, in order to mitigate these drawbacks, source-side DoS detection methods have been researched. But, the source-side DoS detection methods have limitations if the volume of attack traffic is relatively very small and it is blended into legitimate traffic. Especially, with the subtle attack traffic, DoS detection methods may suffer from high false positive, considering legitimate traffic as attack traffic. In this paper, we propose an effective source-side DoS detection method with traffic seasonality aware adaptive threshold. The threshold of detecting DoS attack is adjusted adaptively to the fluctuated legitimate traffic in order to detect subtle attack traffic. Moreover, by understanding the seasonality of legitimate traffic, the threshold can be updated more carefully even though subtle attack happens and it helps to achieve low false positive. The extensive evaluation with the real traffic logs presents that the proposed method achieves very high detection rate over 90% with low false positive rate down to 5%.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.119-124
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• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.1
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• pp.18-25
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• 2016

As more people have cars, the threat of traffic accidents is posed on men and women of all ages. The main culprit of traffic accidents is driving while intoxicated or drowsy. The method to recognize and prevent the cause of traffic accidents is to use lane detection. In this study, a total of 4,000 frames (day image: 2,900 frames, night image: 1,100 frames) were used to test lane detection. According to the test, in the case of day image, when the threshold of Sobel edge detection technique was detected with second-order differential equation, there was the highest candidate lane detection rate which was 86.1%. In the threshold of Canny edge detection technique, the highest detection rate of 88.0% was found at Low=50, and High=300. In the case of night image, the threshold of Sobel edge detection technique, when horizontal calculation and vertical calculation had second-order differential equation, and when horizontal-vertical calculation had 1.5th-order differential equation, there was the highest detection rate which was 83.1%. In the threshold of Canny edge detection technique, the highest detection rate of 89.9% was found at Low=50, and High=300.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.761-769
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• 2002

Due to the large scale application of software systems, software reliability plays an important role in software developments. In this paper, a software reliability growth model (SRGM) is proposed. The testing time on the right is truncated in this model. The instantaneous failure rate, mean-value function, error detection rate, reliability of the software, estimation of parameters and the simple applications of this model are discussed .

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.4
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• pp.553-558
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• 1998

We present in this paper the ture union bound of the performance of chip level detection for coded DS/CDMA system operating in Rician fading channels such as LEO satellite mobile radio where the maximum doppler frequency is very high. The main objective of this paper is to calculate the exact doe union bound of BER performance of different performance of different quadrature detectors and to find a optimum spreading factor as a function of fade rate. The rationale of using multiple chip detection is to reduce the effective fade rate or variation. We considered chip level differential detection, chip level maximum likelihood sequence estimation, noncoherent detection and coherent detection with perfect channel state information as a reference.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.