• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.48 no.10
• /
• pp.687-692
• /
• 1999

A new technique to measure low level capacitance variations of the gyroscope is proposed and verified by computer simulation. It is based on the new CV(capacitance-voltage) converter circuit biased by dc current source and the peak detector without low pass filter. The CV converter biased by dc current source provides good signal-to-noise ratio and this setup of the detection circuitry without low pass filter makes it possible to provide short settling time, that is, higher speed of measurement and wide operation range if only a few parameters are adjusted. The key parameters that affect the performance of the detection circuitry are illustrated and computer simulation results are presented. The demonstrated detection circuitry shows linear response from 10 fF to 130 fF at 10 kHz and shows good linearity.

• Journal of Information Processing Systems
• /
• v.13 no.3
• /
• pp.590-598
• /
• 2017

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multichannel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection.

• Journal of Korea Multimedia Society
• /
• v.21 no.5
• /
• pp.558-570
• /
• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.8
• /
• pp.914-916
• /
• 2016

The noise in a power signal degrades the detection rate of the power quality (PQ) event signals. We present a new wavelet de-noising technique for PQ event detection that employs the correlation-based thresholding instead of the wavelet-scale-based thresholding of existing schemes. The simulation results show that the proposed scheme is more robust to Gaussian and impulsive noisy conditions and has further improved detection ratio than existing schemes.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.307-313
• /
• 1999

This paper reviews and assesses the analogy between the human immune system and network intrusion detection systems. The promising results from a growing number of proposed computer immune models for intrusion detection motivate this work. The paper begins by briefly introducing existing intrusion detection systems (IDS's). A set of general requirements for network-based IDS's and the design goals to satisfy these requirements are identified by a careful examination of the literature. An overview of the human immune system is presented and its salient features that can contribute to the design of competent network-based IDS's are analysed. The analysis shows that the coordinated actions of several sophisticated mechanisms of the human immune system satisfy all the identified design goals. Consequently, the paper concludes that the design of a novel network-based IDS based on the human immune system is promising for future network-based IDS's

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.1
• /
• pp.40-44
• /
• 2005

Applying artificial intelligence, machine learning and data mining techniques to intrusion detection system are increasing. But most of researches are focused on improving the performance of classifier. These classifiers are performed by batch way and it is not proper method for realtime intrusion detection system. We propose an incremental feature extraction and classification technique for realtime intrusion detection system. Applying proposed system to KDD CUP 99 data, experimental result shows that it has similar capability compared to batch way intrusion detection system.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.307-313
• /
• 1999

This paper reviews and assesses the analogy between the human immune system and network intrusion detection systems. The promising results from a growing number of proposed computer immune models for intrusion detection motivate this work. The paper begins by briefly introducing existing intrusion detection systems (IDS's). A set of general requirements for network-based IDS's and the design goals to satisfy these requirements are identified by a careful examination of the literature. An overview of the human immune system is presented and its salient features that can contribute to the design of competent network-based IDS's are analysed. The analysis shows that the coordinated actions of several sophisticated mechanisms of the human immune system satisfy all the identified design goals. Consequently, the paper concludes that the design of a network-based IDS based on the human immune system is promising for future network-based IDS's

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.11
• /
• pp.4869-4873
• /
• 2016

Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.698-701
• /
• 2006

Remotely sensed data have been utilized efficiently for damage detection immediately after the natural disaster since they provide valuable information on land cover change due to spatial synchronization and multitemporal observation over large areas. Damage information obtained at an early stage is important for rapid emergency response and recovery works. Many useful techniques to analyze the characteristics of the pre- and post-event satellite images in large-scale damage detection have been successfully investigated for emergency management. Since high-resolution satellite images provide a wealth of information on damage occurred in urban areas, they are successfully utilized for damage detection in urban areas. In this research, a method to perform automated damage detection is proposed based on the differences of the textural characteristics in pre- and post- high resolution satellite images.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.7C
• /
• pp.672-679
• /
• 2006

Cognitive radio, which is designed to dynamically adapt its transmission to the environments, is believed to be one of the fundamental techniques for future spectrum utilization. As the first step of cognitive radio, spectrum sensing is treated as the most important technique, through which cognition is well explained. In this paper, we propose a spectral correlation based detection method for spectrum sensing. An unlicensed secondary user system operating in TV broadcast bands is taken as an example. Based on the cyclostationarity of communication signals, spectral correlation function is used to minimize the effect of random noise and interference. Energy measurement and peak detection based criteria are proposed. Simulation results show that the proposed detection method outperforms the energy detection and is more suitable for spectrum sensing in cognitive radios.