• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.237-245
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• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.229-239
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• 2008

Most algorithms for detecting incidents have been developed under the premise that congestion must happen whenever an incident occurs. For that reason, the performance of these algorithms could not be guaranteed in cases where congestion did not happen due to traffic operations with low flows despite the occurrence of an incident. The objective of this paper is to develop an automatic incident detection algorithm using a new diagram that can reliably detect the incident under various conditions of traffic operations including a low volume state. Compared with the McMaster Algorithm, the proposed algorithm in this paper was evaluated with three different cases in which the incidents occur in traffic operations with a low volume state, a relatively high volume state, and a recurrent congestion state. It is shown that the new algorithm has a capability to identify the flow characteristics of incidents for all the three cases and is much better than McMaster algorithm in terms of detection rate and false alarm rate.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.05a
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• pp.215-215
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• 2004

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.500-506
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• 2012

A method to model left ventricular assist device (LVAD) and detect suction occurrence for safe LVAD operation is presented. An axial flow blood pump as a LVAD has been used to assist patient with heart problems. While an axial flow blood pump, a kind of a non-pulsatile pump, has relative advantages of small size and efficiency compared to pulsatile devices, it has a difficulty in determining a safe pump operating condition. It can show different pump operating statuses such as a normal status and a suction status whether suction occurs in left ventricle or not. A fuzzy subtractive clustering method is used to determine a model of the axial flow blood pump with this pump operating characteristic and the developed pump model can provide blood flow estimates before and after suction occurrence in left ventricle. Also, a fuzzy subtractive clustering method is utilized to develop a suction detection model which can identify whether suction occurs in left ventricle or not.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5D
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• pp.623-631
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• 2011

In this study, a new method which can detect incidents in interrupted traffic flow was suggested. The applied method of detecting the incident is the Latin Square Analysis Method by using traffic traits. In the Latin Square Analysis, unlike other previously tried methods, the traffic situation was analyzed, this time considering the changes in traffic traits for each lane and for each time period. The data used in this study were the data observed in the actual field with fine weather. The traffic volumes, the vehicle speed and the occupancy rate were collected on the interrupted flow road. The data were collected in normal and incident situations. The incidents occurred on the second lane, the time of persistent incidents was set to 10 minutes. The Latin Square Analyses were performed using the collected data with the traffic volume, with the vehicle speed or with the occupancy rate. As a result in this study, in case of detecting the traffic situations with Latin Square Analysis, it will be more successful to apply traffic volume to detect the traffic situations than to apply other factors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1254-1260
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• 2009

This paper proposes history-aware random early detection (HRED), a modified version of RED, to lessen bandwidth monopoly by a few of stations employing multiple parallel TCP flows. Stations running peer-to-peer file sharing applications such as BitTorrent use multiple TCP flows. If those stations share a link with other stations with only a small number of TCP flows, the stations occupy most of link bandwidth leading to undesirable bandwidth monopoly. HRED like RED determines whether to drop incoming packets according to probability which changes based on queue length. However it adjusts the drop probability based on bandwidth occupying ratio of stations, thus able to impose harder drop penalty on monopoly stations. The results of simulations assuming various scenarios show that HRED is at least 60% more effective than RED in supporting the bandwidth fairness among stations and at least 4% in utilization.

• Journal of the Korea Society for Simulation
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• v.22 no.3
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• pp.35-41
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• 2013

This paper applies an active period based bottleneck detection method to flow shop manufacturing system with limited buffer size. Manufacturing systems are constrained by one or more bottlenecks which degrades the system throughput. Conventional bottleneck detection methods include the waiting time or queue length of production stations and their utilization. Due to the random events such as production time of items, machine failure and repair times, the systems may change over time, and subsequently bottlenecks shift from one station to another station. Active period of working station may cause other stations to wait for productions. Information when and where active periods occur helps to find bottlenecks in production systems. Based on these informations, we predict bottlenecks in applying AweSim simulation language. We compare the simulation results of conventional methods with those obtained from duration of active period method, and duration ratio method of both sole and shift bottleneck periods. Even though simulation results are from simple flow shop model, they are quite promising for predicting bottlenecks of production stations. We hope this study aids in decision making regarding the improving system production yield and allocation of available resources of system.

• Journal of the Korean Society of Visualization
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• v.20 no.3
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• pp.136-145
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• 2022

Later flow immunoassay (LFIA) is a protein analytical method based on immunoreaction. On the LFIA based protein analytical method, bioreceptor molecule plays a key role, and so a system that evaluates and manages the binding affinity of bioreceptor is needed to secure detection reliability. In this study, Lateral Flow Immunoassay based rapid Bioreceptor Screening Method (rBSM) is presented that provide a simple and quick evaluating method for the binding affinity to the target protein of the antibody as model bioreceptor. To verify this evaluation method, Virus-like particles (VLP) and anti-VLP antibodies are selected as a model norovirus, which is target protein, and the candidate bioreceptors respectively. Among the 5 different candidate antibodies, appropriate antibody could be sorted out within 30 minutes through rBSM. In addition, selected antibodies were applied to two representative LFIA based techniques, sandwich assay and competitive assay. Among these methods, sandwich assay showed more effective VLP detection method. Through applying selected antibodies and techniques to the commercialized mass production lines, an VLP detecting LFIA kit was developed with a detection limit of 10¹² copies/g of VLPs in real samples. Since this proposed method in this study could be easily transformable into other combinations with bioreceptors, it is expected that this technique would be applied to LFIA kit development system and bioreceptor quality management.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.367-369
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• 2001

멀티미디어 환경의 발전에 따라 동영상에 대한 효과적인 검색 및 관리와 CG와 일반 영상의 합성을 위하여 영상 내의 카메라 동작 요소 검출 기법이 필요하다. 본 논문에서는 sub-block당 투영 영상을 이용만 카메라 동작 요소 검출 방법을 제안한다. 제안한 방법은 sub-block당 평균값을 이용만 투영 영상상에서 각 sub-block 내에서의 x, y 방향 이동 성분을 구하여 이를 통한 Optical flow를 얻는다. 제안하는 방법은 기존의 block-matching을 통하여 optical flow를 얻는 방법보다 계산량의 감소와 계산 속도의 증가를 나타낸다. 실험 결과에서는 제안하는 방법에 의하여 얻은 optical flow를 보여주며 예측도의 증가를 보여준다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.1
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• pp.111-116
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• 2019

The fire detectors used in the engine rooms of ships offer only a slow response to emergencies because smoke or heat must reach detectors installed on ceilings, but the air flow in engine rooms can be very fluid depending on the use of equipment. In order to overcome these disadvantages, much research on video-based fire detection has been conducted in recent years. Video-based fire detection is effective for initial detection of fire because it is not affected by air flow and transmission speed is fast. In this paper, experiments were performed using images of smoke from a smoke generator in an engine room. Data generated using LBP and GLCM operators that extract the textural features of smoke was classified using SVM, which is a machine learning classifier. Even if smoke did not rise to the ceiling, where detectors were installed, smoke detection was confirmed using the image-based technique.