• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.279-285
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• 2020

The study on the temporal and spatial monitoring of passing vessels is important in terms of protection and management the marine ecosystem in the coastal area. In this paper, we propose the automatic detection technique of passing vessel by utilizing an artificial intelligence technology and broadband striation patterns which are characteristic of broadband noise radiated by passing vessel. Acoustic measurements to collect underwater noise spectrum images and ship navigation information were conducted in the southern region of Jeju Island in South Korea for 12 days (2016.07.15-07.26). And the convolution neural network model is optimized through learning and validation processes based on the collected images. The automatic detection performance of passing vessel is evaluated by precision (0.936), recall (0.830), average precision (0.824), and accuracy (0.949). In conclusion, the possibility of the automatic detection technique of passing vessel is confirmed by using an artificial intelligence technology, and a future study is proposed from the results of this study.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.11
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• pp.637-644
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• 2014

This paper investigates the detection performance of fault location using STDR(sequence time domain reflectometry) and SSTDR(spread spectrum time domain reflectometry) with various length and types of sequences, and then, proposes an improved detection technique by eliminating the injected signal in SSTDR. The detection error rates are compared and analyzed in power line channel model with various fault locations, fault types, and spreading sequences such as m-sequence, binary Barker sequence, and 4-phase Frank sequence. It is shown that the proposed technique is able to improve the detection performance obviously when the reflected signal is weak or the fault location is extremely close.

• Smart Structures and Systems
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• v.20 no.6
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• pp.683-696
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• 2017

In this study, the reliability of nonlinear ultrasonic modulation based fatigue crack detection is improved using a feature-level data fusion approach. When two ultrasonic inputs at two distinct frequencies are applied to a specimen with a fatigue crack, modulation components at the summation and difference of these two input frequencies appear. First, the spectral amplitudes of the modulation components and their spectral correlations are defined as individual features. Then, a 2D feature space is constructed by combining these two features, and the presence of a fatigue crack is identified in the feature space. The effectiveness of the proposed fatigue crack detection technique is experimentally validated through cyclic loading tests of aluminum plates, full-scale steel girders and a rotating shaft component. Subsequently, the improved reliability of the proposed technique is quantitatively investigated using receiver operating characteristic analysis. The uniqueness of this study lies in (1) improvement of nonlinear ultrasonic modulation based fatigue crack detection reliability using feature-level data fusion, (2) reference-free fatigue crack diagnosis without using the baseline data obtained from the intact condition of the structure, (3) application to full-scale steel girders and shaft component, and (4) quantitative investigation of the improved reliability using receiver operating characteristic analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.67-77
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• 2007

Virus throttling technique, one of many early worm detection techniques, detects Internet worm propagation by limiting connect requests within a certain ratio. The typical virus throttling controls the period of rate limiter autonomically by utilizing weighted average delay queue length to reduce connection delay time without hanving a large effect on worm detection time. In the existing virus throttling research, a minimum period of variable rate limiter is fired and a turning point which is a point that the period of rate limiter has been being decreased and starts to be increased is also fixed. However, these two performance factors have different effects on worm detection time and connection delay. In this paper, we analyze the effect of minimum period and turning point of variable rate limiter, and then propose an algorithm which determines values of performance factors by referencing current traffic pattern. Through deep experiments, it is verified that the proposed technique is more efficient in respect of reducing worm detection time and connection delay than the existing virus throttling which fixed the performance factors.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.324-332
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• 2010

The majority of event detection in real-time embedded sensor network systems is based on data fusion that uses noisy sensor data collected from complicated real-world environments. Current research has produced several excellent low-level mechanisms to collect sensor data and perform aggregation. However, solutions that enable these systems to provide real-time data processing using readings from heterogeneous sensors and subsequently detect complex events of interest in real-time fashion need further research. We are developing real-time event detection approaches which allow light-weight data fusion and do not require significant computing resources. Underlying the event detection framework is a collection of real-time monitoring and fusion mechanisms that are invoked upon the arrival of sensor data. The combination of these mechanisms and the framework has the potential to significantly improve the timeliness and reduce the resource requirements of embedded sensor networks. In addition to that, we discuss about a privacy that is foundation technique for trusted embedded sensor network system and explain anonymization technique to ensure privacy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.127-136
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• 2013

The islanding detection and prevention function in the important performance of the grid-connected inverter is the essential details which should be considered for worker's safety to maintain the system. This paper tries to suggest the active frequency shake islanding detection technique in the grid-connected PV generation system using sunlight. The existing active frequency drift method algorithm is described to suggest active frequency shake method algorithm. And, the algorithm which can reduce the non-detection zone, improve THD, and improve the detection speed in comparison with the existing algorithm is suggested. And we applied the proposed algorithm to the 3kW grid-connected photovoltaic inverter generation and verified the feasibility of the technique through the simulation and experiments.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.282-291
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• 1999

In order to construct the News Video Database System, cut detection technique is very important. In general, the color histogram, $\chi$2 histogram or Bin-to-Bin difference(B2B) techniques are mainly using for the scene partitioning. In this paper, we propose the efficient algorithm that is applied the weight in terms of NTSC standard to cut detection. This algorithm is able to reduce the time of acquiring and comparing histogram using by separate calculation of R, G, and B for the color histogram technique. And it also provide the efficient selection method fo threshold value by and use the news videos of KBS, MBC, SBS, CNN and NHK as experimental domains. By the result of experiment, we present the proposed algorithm is more efficient for cut detection than the previous methods, and that the basis for the automatic selection of threshold values.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.105-112
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• 1999

Image Processing Technique has been used as an efficient method to collect traffic information on the road such as vehicle counts, speed, queues, congestion and incidents. Most of the current methods which have been used to detect vehicles by the image processing are based on point processing, dealing with the local gray level of each pixel in the small window. However, these methods have some drawbacks. Firstly, detection is restricted by image quality. Secondly, they can not deal with occlusion and perspective projection problems, In this research, a new method which possibly deals with occlusion and perspective problems will be proposed. It extracts spatial information such as the position, the relationship of vehicles in 3-dimensional space, as well as vehicle detection in the image. The main algorithm used in this research is based on an extension of the Hough Transform. The Hough Transform which is proposed to estimates parameters of vertices and directed edges analytically on the Hough Space, is a valuable method for the 3-dimensional analysis of static scenes, motion detection and the estimation of viewing parameters.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1263-1268
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• 2018

In this paper, a video analysis is proposed to implement video surveillance system with deep learning object detection and probabilistic data association filter for tracking multiple objects, and suggests its implementation using GPU. The proposed video analysis technique involves object detection and object tracking sequentially. The deep learning network architecture uses ResNet for object detection and applies probabilistic data association filter for multiple objects tracking. The proposed video analysis technique can be used to detect intruders illegally trespassing any restricted area or to count the number of people entering a specified area. As a results of simulations and experiments, 48 channels of videos can be analyzed at a speed of about 27 fps and real-time video analysis is possible through RTSP protocol.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.661-674
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• 2021

Malware, such as a rootkit that modifies the kernel, can adversely affect the analyst's judgment, making the analysis difficult or impossible if a mechanism to evade memory analysis is added. Therefore, we plan to preemptively respond to malware such as rootkits that bypass detection through advanced kernel modulation in the future. To this end, the main structure used in the Windows kernel was analyzed from the attacker's point of view, and a method capable of modulating the kernel object was applied to modulate the memory dump file. The result of tampering is confirmed through experimentation that it cannot be detected by memory analysis tool widely used worldwide. Then, from the analyst's point of view, using the concept of tamper resistance, it is made in the form of software that can detect tampering and shows that it is possible to detect areas that are not detected by existing memory analysis tools. Through this study, it is judged that it is meaningful in that it preemptively attempted to modulate the kernel area and derived insights to enable precise analysis. However, there is a limitation in that the necessary detection rules need to be manually created in software implementation for precise analysis.