• Journal of the Korean Geophysical Society
• /
• v.8 no.1
• /
• pp.7-14
• /
• 2005

Early detection in real-time response of slope movements ensures tremendous saving of lives and repair costs from catastrophic disaster. Therefore, it is essential to constantly monitor the performance and integrity of slope-stabilizing structures such as Rock bolt, Nail and Pile during or after installation. We developed a novel monitoring system using Fiber Bragg Grating (FBG) sensor. It's advantages are highly sensitivity, small dimension and electro-magnetic immunity. capability of multiplexing, system integrity, remote sensing - these serve real-time health monitoring of the structures. Real-time strain measurement by the signal processing program is shown graphically and it gives a warning sound when the monitored strain state exceeds a given threshold level so that any sign of abnormal disturbance on the spot can be easily perceived.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.538-540
• /
• 2021

In this paper, we proposed a smart target detection system that detects and recognizes a designated target to provide relative motion information when performing a target detection mission of a drone. The proposed system focused on developing an algorithm that can secure adequate accuracy (i.e. mAP, IoU) and high real-time at the same time. The proposed system showed an accuracy of close to 1.0 after 100k learning of the Google Inception V2 deep learning model, and the inference speed was about 60-80[Hz] when using a high-performance laptop based on the real-time performance Nvidia GTX 2070 Max-Q. The proposed smart target detection system will be operated like a drone and will be helpful in successfully performing surveillance and reconnaissance missions by automatically recognizing the target using computer image processing and following the target.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.8
• /
• pp.3884-3910
• /
• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.25 no.2
• /
• pp.96-102
• /
• 2011

There are many problems with the fire detection devices being used in currently, because it is difficult to find location of the source of fire and determine where devices are working or not. In this paper, we proposed fire detection and rescue system using wireless sensor network that can be real-time monitoring and determine safe exit. Fire detection and rescue system based on ubiquitous sensor network can know exactly source of fire and help determine rescue tactics using sensing data from wireless sensor nodes. Transmitted wirelessly in real-time thermal sensor and gas sensor information to analyze the GUI to monitor the status information output to the screen by use of a system implemented in everyday life, looked at the possibility.

• The KIPS Transactions:PartC
• /
• v.9C no.3
• /
• pp.359-366
• /
• 2002

Nowadays, the hacking techniques are developed increasingly with wide use of internet. The recent type of scanning attack is appeared in against with multiple target systems on the large scaled domain rather than single network of an organization. The development of scan detection management system which can detect and analyze scan activities is necessary to prevent effectively those attacking at the central system. The scan detection management system is useful for effective utilization of various detection information that received from scan detection agents. Real time scan detection management system that can do the integrated analysis of high lever more that having suitable construction in environment of large scale network is developed.

• Proceedings of the KIEE Conference
• /
• 1997.07a
• /
• pp.346-348
• /
• 1997

This paper presents microprocessor-based real-time speed and position detection by inductive radio loop in new transportation system, such as magnetically levitated train system, rubber tyred train, and linear-motor car. The constant elapsed time method is used in this study for high accurate detection over a wide speed range. And for reliability and safety of the system, it is duplicated and data-bus level comparison is performed by fail-safe comparator.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.336-338
• /
• 2006

One of the important topics concerning the safety of electrical and electronic system is the reliability of the wiring system. The Time-Frequency Domain Reflectometry(TFDR) is a state-of-the-art system for detection and estimation of the fault on a wiring/cable. The purpose of this paper is to implement a Labview based TFDR Real Time system though the instruments of PCI extensions for Instrumentation(PXI). The TFDR Real Time system consists of the five parts: Reference signal design, signal generation, signal acquisition, algorithm execution, results diplay part. In the signal generation and acquisition parts we adopt the Arbitrary Waveform Generator(AWG) and Digital Storage Oscilloscope(DSO) PXI modules which offer commonality, compatibility and easy integration at low cost. And execution of the PXI modules not only is controlled by the Labview programing but also the total system process is executed by the Labview application software.

• The Journal of Korea Robotics Society
• /
• v.17 no.3
• /
• pp.273-281
• /
• 2022

In this paper, we propose a robot sound recognition system that detects various sound events. The proposed system is designed to detect various sound events in real-time by using a microphone on a robot. To get real-time performance, we use a VGG11 model which includes several convolutional neural networks with real-time normalization scheme. The VGG11 model is trained on augmented DB through 24 kinds of various environments (12 reverberation times and 2 signal to noise ratios). Additionally, based on random forest algorithm, a decision logic is also designed to generate event signals for robot applications. This logic can be used for specific classes of acoustic events with better performance than just using outputs of network model. With some experimental results, the performance of proposed sound recognition system is shown on real-time device for robots.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.2C
• /
• pp.181-187
• /
• 2012

Millimeter wave (MMW) readily penetrates fabrics, thus it can be used to detect objects concealed under clothing. A passive MMW imaging system can operate as a stand-off type sensor that scans people in both indoors and outdoors. However, because of the diffraction limit and low signal level, the imaging system often suffers from low image quality. Therefore, suitable statistical analysis and computational processing would be required for automatic analysis of the images. In this paper, a real-time concealed object detection is addressed by means of the multi-level segmentation. The histogram of the image is modeled with a Gaussian mixture distribution, and hidden object areas are segmented by a multi-level scheme involving $k$-means, the expectation-maximization algorithm, and a decision rule. The complete algorithm has been implemented in C++ environments on a standard computer for a real-time process. Experimental and simulation results confirm that the implemented system can achieve the real-time detection of concealed objects.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.3
• /
• pp.523-530
• /
• 2024

Online, such as web pages and digital documents, have the ability to search for specific words or specific phrases that users want to search in real time. Printed materials such as printed books and reference books often have difficulty finding specific words or specific phrases in real time. This paper describes the development of a deep learning model for detecting text and a real-time character detection system using OCR for recognizing text. This study proposes a method of detecting text using the EAST model, a method of recognizing the detected text using EasyOCR, and a method of expressing the recognized text as a bounding box by comparing a specific word or specific phrase that the user wants to search for. Through this system, users expect to find specific words or phrases they want to search in real time in print, such as books and reference books, and find necessary information easily and quickly.