• Journal of Korea Society of Digital Industry and Information Management
• /
• v.16 no.2
• /
• pp.45-52
• /
• 2020

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

• 한국정보통신설비학회:학술대회논문집
• /
• 2009.08a
• /
• pp.127-130
• /
• 2009

We propose a method for detecting DDoS (Distributed Denial of Service) traffic in real-time inside the backbone network. For this purpose, we borrow the concepts of MACD (Moving Average Convergence Divergence) and RoC (Rate of Change), which are used for technical analysis in the stock market Due to the fact that the method is based on a quantitative, rather than a heuristic, detection level, DDoS traffic can be detected with greater accuracy (by reducing the false alarm ratio). Through simulation results, we show how the detection level is determined and demonstrate how much the accuracy of detection is enhanced.

• Proceedings of the IEEK Conference
• /
• 2002.07b
• /
• pp.928-931
• /
• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• Proceedings of the IEEK Conference
• /
• 2003.07a
• /
• pp.242-245
• /
• 2003

This paper introduces a new algorithm of pulse generation and detection for UWB communication system. The existing UWB systems using Gaussian pulse have some difficulties to cope with bandwidth limitation and frequency transition. Moreover. the system sensitivity to channel noise has made the processes of acquisition and tacking difficult. in this paper, we introduce a new pulse generation method which is able to control the bandwidth and center frequency applying modulation method. thus could improve the detection performance of receiving algorithm. Also, we made a system to search maximum perk by applying the proposed algorithm and consequently could guarantee the correct detection. By the result of simulation, when accumulate 10 times at every 2dB band shifting from 0 to 18dB on AWGN channel, we could confirm the proposed method has 97.4% PDR(Pulse Detection Rate) and 1.868% FAR(False Alarm Rate) performance at 4dB SNR and 15% transmission power threshold level.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.171-171
• /
• 2000

We have developed m innovative surface inspection system for automated quality control for steel products in POSCO. We had ever installed the various kinds of surface inspection systems, such as a linear CCD and a laser typed surface inspection systems at cold rolled strips production lines. But, these systems cannot fulfill the sufficient detection and classification rate, and real time processing performance. In order to increase detection and classification rate, we have used the Dark, Bright and Transition Field illumination and area type CCD camera, and fur the real time image processing, parallel computing has been used. In this paper, we introduced the automatic surface inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms and its performance obtained at the production line.

• Speech Sciences
• /
• v.8 no.4
• /
• pp.59-72
• /
• 2001

In this paper, we experimented with speaker change detection that uses a statistical method for NOD (News On Demand) service. A specified speaker's change can find out content of each data in speech if analysed because it means change of data contents in news data. Speaker change detection acts as preprocessor that divide input speech by speaker. This is an important preprocessor phase for speaker tracking. We detected speaker change using GLR(generalized likelihood ratio) distance base division and BIC (Bayesian information criterion) base division among matrix method. An experiment verified speaker change point using BIC base division after divide by speaker unit using GLR distance base method first. In the experimental result, FAR (False Alarm Rate) was 63.29 in high noise environment and FAR was 54.28 in low noise environment in MDR (Missed Detection Rate) 15% neighborhood.

• International Journal of Computer Science & Network Security
• /
• v.22 no.12
• /
• pp.1-12
• /
• 2022

Besides unexpected growth perceived by IoT's, the variety and volume of threats have increased tremendously, making it a necessity to introduce intrusion detections systems for prevention and detection of such threats. But Intrusion Detection and Prevention System (IDPS) inside the IoT network yet introduces some unique challenges due to their unique characteristics, such as privacy inference, performance, and detection rate and their frequency in the dynamic networks. Our research is focused on the privacy inferences of existing intrusion prevention and detection system approaches. We also tackle the problem of providing unified a solution to implement the open-source IDPS in the IoT architecture for assessing the performance of IDS by calculating; usage consumption and detection rate. The proposed scheme is considered to help implement the human health monitoring system in IoT networks

• Journal of Digital Convergence
• /
• v.14 no.10
• /
• pp.271-276
• /
• 2016

In this paper, we propose robust face and eye detection algorithm under changing environmental condition such as lighting and pose variations. Generally, the eye detection process is performed followed by face detection and variations in pose and lighting affects the detection performance. Therefore, we have explored face detection based on Modified Census Transform algorithm. The eye has dominant features in face area and is sensitive to lighting condition and eye glasses, etc. To address these issues, we propose a robust eye detection method based on Gabor transformation and Features from Accelerated Segment Test algorithms. Proposed algorithm presents 27.4ms in detection speed with 98.4% correct detection rate, and 36.3ms face detection speed with 96.4% correct detection rate for eye detection performance.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.8
• /
• pp.577-581
• /
• 2009

Recently, various new services based on ubiquitous computing and networking have been developed. In this paper, we contrive Adaptive PIR(Pyroelectric Infrared Radiation) Detection Algorithm (APIDA), a PIR-sensor based digital signal processing algorithm, that detects the movement of an invading object by the recognition of heat change in the detection area, since the object like person or car emits heat(i.e., infrared radition), We devised APIDA as a highly reliable signal processing algorithm that increases the successful detection rate and decreases the false alarm rate in the intruding object detection. According to performance evaluation experiment, APIDA shows the successful detection rate of 90% and low false alarm in the plain area.

• Women's Health Nursing
• /
• v.16 no.2
• /
• pp.126-136
• /
• 2010

Purpose: This study was done to evaluate factors affecting active early detection behaviors of breast cancer and performance rate of breast self examination (BSE), physical examination and mammography. Methods: The participants were 264 women from an outpatient breast clinic of a university hospital and materials were collected from March 2007 to February 2008 using a structured questionnaire. The data were analyzed using $x^2$ test, logistic analysis. Results: The rate for BSE was 58.3%, for physical examination, 55.3% and for mammography experience, 63.4%. Women with all of these active early detection behaviors accounted for 31.8% of the participants. Various factors such as age, income, marital status, and menopause showed increased significant performance rate. The explanation power of logistic model was 48.5%, and was significant for age, income and health belief. Factors related to high performance rate were being over 40 years of age, high income and high health belief score. Conclusion: Active early detection behaviors were not high in spite of marked increases in breast cancer incidence. Encouragement for women practicing early detection behavior is important, but there is also a need to develop interest and support for the low performance group. More sustained education and public relations are needed to further improve active early detection behavior.