• Journal of the Korean Society of Safety
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• v.32 no.2
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• pp.46-51
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• 2017

This paper presents classification of domestic fire detector using response time index. Response time is measured using fire detector distributed in Korea, and the response time index is estimated. Plunge test prescribed by FM is conducted to measure response time of fire detector. The detector used to test is fixed temperature type(thermistor and bimetal type) and rate of rise temperature type(thermistor and pneumatic type). The nominal operation temperature of fixed temperature type detector is $70^{\circ}C$ and rate of rise temperature is $15^{\circ}C/min$. The fixed temperature type is measured 7 products, and the rate of rise temperature type is measured 5 products. The results show that in case of fixed temperature type(thermistor) is classified "Quick" or "Standard" and fixed temperature type(bimetal) is not classified. The rate of rise temperature type(thermistor) is classified "Fast" or "Ultra Fast" and the rate of rise temperature type(pneumatic) is classified "Very Fast" or "Ultra Fast". The pneumatic type shows more fast response than thermistor type. Also these results indicate the fixed temperature type(bimetal) is not suitable for early stage fire detection.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.379-384
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• 2003

In this paper, we introduce a change detection system that can extract and analyze change elements from high-resolution satellite imagery as well as low- or middle-resolution satellite imagery. The developed system provides not only 7 pixel-based methods that can be used to detect change from low- or middle-resolution satellite images but also a float window concept that can be used in manual change detection from high-resolution satellite images. This system enables fast process of the very large image, because it is constituted by OGC grid coverage components. Also new change detection algorithms can be easily added into this system if once they are made into grid coverage components.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.44-44
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• 2009

Fault detection is necessary for yield enhancement and cost reduction in semiconductor manufacturing. Sensory data acquired from the semiconductor processing tool is too large to analyze for the purpose of fault detection and classification(FDC). We studied the techniques of fault detection using statistical method. Multiple regression analysis smoothly detected faults and can be easy made a model. For real-time and fast computing time, the huge data was analyzed by each step. We also considered interaction and critical factors in tool parameters and process.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.2
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• pp.128-137
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• 1992

An automatic noise detection system is developed to sense abnormal noises in operating a microwave electronic range. A noise detection method is presented which accounts for the effects of backgound and dynamic noises of the range. A recursive formula used as a noise estimator is a special case of the discrete-time Kalman filter in stochastic processes. Noise levels were measured using a noise acquisition processor in a closed room free of background noise, and detected signals were processes using a microcomputer. The results obtaines showed that the fault detection system should be fast in response to the data acquired and should be high in accuracy and reliability.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.835-838
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• 2008

In this paper, we present automatic face detection and tracking which is robustness in rotation and translation. Detecting a face image, we used Haar-like feature, which is fast detect facial image. Also tracking, we applied Lucas-Kanade feature tracker and KLT algorithm, which has robustness for rotated facial image. In experiment result, we confirmed that face detection and tracking which is robustness in rotation and translation.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1513-1522
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• 2018

This paper proposes a new islanding detection method for inverter-based distributed generators by continuously injecting a negligible amount of 2nd order harmonic current. The proposed method adopts a proportional resonant (PR) controller for the output current control of the inverter, and a PR filter to extract the 2nd order harmonic voltage at the point of common coupling (PCC). The islanding state can be detected by measuring the magnitude ratio of the 2nd order harmonic voltage to the fundamental voltage at the PCC by injecting a 2nd order harmonic current with a 0.8% magnitude. The proposed method provides accurate and fast detection under grid voltage unbalance and load unbalance. The operation of the proposed method has been verified through simulations and experiments with a 5kW hardware set-up, considering the islanding test circuit suggested in UL1741.

• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.436-443
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• 2008

This paper presents a new method for building detection and reconstruction from aerial images. In our approach, we extract useful building location information from the generated disparity map to segment the interested objects and consequently reduce unnecessary line segments extracted in the low level feature extraction step. Hypothesis selection is carried out by using an undirected graph, in which close cycles represent complete rooftops hypotheses. We test the proposed method with the synthetic images generated from Avenches dataset of Ascona aerial images. The experiment result shows that the extracted 3D line segments of the reconstructed buildings have an average error of 1.69m and our method can be efficiently used for the task of building detection and reconstruction from aerial images.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.612-619
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• 2013

This paper proposes an early diagnosis technique for the stator-turn fault (STF) in an interior permanent magnet (IPM)-type brushless DC (BLDC) motor using the impedance parameter. We have analyzed the varying characteristics owing to the STF through various experiments and the finite element method (FEM). As a result, we have presented a simple method for fault detection. This technique can be applied without requiring a fast Fourier transform (FFT) and the calculation of the negative-sequence impedance. The fault detection system works on the basis of the comparison the measured impedance with the database impedance. The variations in the characteristics owing to the STF as well as the proposed technique have been verified through the simulation and experiment.

• Journal of Multimedia Information System
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• v.1 no.2
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• pp.119-126
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• 2014

We propose a bicycle detection system riding on people based on modified projected local binary pattern(PLBP) for vision based intelligent vehicles. Projection method has robustness for rotation invariant and reducing dimensionality for original image. The features of Local binary pattern(LBP) are fast to compute and simple to implement for object recognition and texture classification area. Moreover, We use uniform pattern to remove the noise. This paper suggests that modified LBP method and projection vector having different weighting values according to the local shape and area in the image. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. Our experimental results show that a bicycle and motorcycle riding on people detection system based on proposed PLBP features achieve higher detection accuracy rate than traditional features.