• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.1 s.24
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• pp.24-32
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• 2006

This paper is to analyze the effectiveness of multistatic sonar network based on detection performance. The multistatic sonar network is a distributed detection system that places a source and multi-receivers apart. So it needs a detection technique that relates to decision rule and optimization of sonar system to improve the detection performance. For this we propose a data fusion procedure using Bayesian decision and optimal sensor arrangement by optimizing a bistatic sonar. Also, to analyze the detection performance effectively, we propose the environmental model that simulates a propagation loss and target strength suitable for multistatic sonar networks in real surroundings. The effectiveness analysis on the multistatic sonar network confirms itself as a promising tool for effective allocation of detection resources in multistatic sonar system.

• ETRI Journal
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• v.41 no.5
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• pp.684-695
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• 2019

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.502-512
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• 2016

The wireless carrier sense multiple access with collision detection (WCSMA/CD) and carrier sense multiple access with collision resolution (CSMA/CR) protocols are considered representative distributed collision detection protocols for fully connected dense wireless local area networks. These protocols identify collisions through additional short-sensing within a collision detection (CD) period after the start of data transmission. In this study, we analyze their throughput numerically and show that the throughput has a trade-off that accords with the length of the CD period. Consequently, we obtain the optimal length of the CD period that maximizes the throughput as a closed-form solution. Analysis and simulation results show that the throughput of distributed collision detection protocols is considerably improved when the optimal CD period is allocated according to the number of stations and the length of the transmitted packet.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.52-60
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• 2024

APT (Advanced Persistent Threat) attack is a dangerous, targeted attack form with clear targets. APT attack campaigns have huge consequences. Therefore, the problem of researching and developing the APT attack detection solution is very urgent and necessary nowadays. On the other hand, no matter how advanced the APT attack, it has clear processes and lifecycles. Taking advantage of this point, security experts recommend that could develop APT attack detection solutions for each of their life cycles and processes. In APT attacks, hackers often use phishing techniques to perform attacks and steal data. If this attack and phishing phase is detected, the entire APT attack campaign will be crash. Therefore, it is necessary to research and deploy technology and solutions that could detect early the APT attack when it is in the stages of attacking and stealing data. This paper proposes an APT attack detection framework based on the Network traffic analysis technique using open-source tools and deep learning models. This research focuses on analyzing Network traffic into different components, then finds ways to extract abnormal behaviors on those components, and finally uses deep learning algorithms to classify Network traffic based on the extracted abnormal behaviors. The abnormal behavior analysis process is presented in detail in section III.A of the paper. The APT attack detection method based on Network traffic is presented in section III.B of this paper. Finally, the experimental process of the proposal is performed in section IV of the paper.

• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.177-188
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• 1987

Detection of left ventricular boundary for the functional analysis of LV(left ventricle) is obtained using automatic boundary detection algorithm based on dynamic program ming method. This scheme reduces the edge searching time and ensures connective edge detection, since it does not require general edge operator, edge thresholding and linking process of other edge detection methods. The left ventricular diastolic volume and systolic volume were computed after this automatic boundary detection, and these volume data were applied to analyze LV ejection fraction.

• Analytical Science and Technology
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• v.28 no.2
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• pp.117-124
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• 2015

Genetically modified (GM) papaya line 55-1, which is resistant to PRSV infection, has been marketed globally. Prompt and sensitive protocols for specific detections are essential for the traceability of this line. Here, an event- and construct-specific real-time polymerase chain reaction (RT-PCR) method was established to detect 55-1. Qualitative detection was possible for fresh papaya fruit up to dilutions of 0.005% and 0.01% for the homozygous SunUp and heterozygous Rainbow cultivars, respectively, in non-GM papaya. The method was applied in the qualitative detection of 55-1 in eight types of commercially processed papaya products. Additionally, papaya products were monitored to distinguish GM papaya using the P35S and T-nos RT-PCR detection methods. As expected, detection capacity was improved via modified sample preparation and the established RT-PCR detection method. Taking these results together, it can be suggested that a suitable method for the extraction and purification of DNA from processed papaya products was established for the detection of GM papaya.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.593-607
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• 2022

To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.276-276
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• 2013

Here we report an integrated microdevice consisting of an efficient passive mixer, a magnetic separation chamber, and a capillary electrophoretic microchannel in which DNA barcode assay, target pathogen separation, and barcode DNA capillary electrophoretic analysis were performed sequentially within 30 min for multiplex pathogen detection at the single-cell level. The intestine-shaped serpentine 3D micromixer provides a high mixing rate to generate magnetic particle-pathogenic bacteria-DNA barcode labelled AuNP complexes quantitatively. After magnetic separation and purification of those complexes, the barcode DNA strands were released and analyzed by the microfluidic capillary electrophoresis within 5 min. The size of the barcode DNA strand was controlled depending on the target bacteria (Staphylococcus aureus, Escherichia coli O157:H7, and Salmonella typhimurium), and the different elution time of the barcode DNA peak in the electropherogram allows us to recognize the target pathogen with ease in the monoplex as well as in the multiplex analysis. In addition, the quantity of the DNA barcode strand (~104) per AuNP is enough to be observed in the laser-induced confocal fluorescence detector, thereby making single-cell analysis possible. This novel integrated microdevice enables us to perform rapid, sensitive, and multiplex pathogen detection with sample-in-answer-out capability to be applied for biosafety testing, environmental screening, and clinical trials.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.38-43
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• 1994

In milling process, monitoring and diagosis system is very importent to accomplish factory automation. In this study, to drvelope on-line tool breakage detection system in face milling operation, analysis and experiment were performed. The tool breakage detection experiment was performed in machining center and the effectiveness of the detection tool breakage detection alorithm and the usage of feed drive current as a detection signal were verified.

• Steel and Composite Structures
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• v.50 no.1
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• pp.89-105
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• 2024

Multichannel analysis of surface waves (MASW) method has exhibited broad application prospects in the nondestructive detection of interfacial debonding in steel-concrete composite structures (SCCS). However, due to the structural diversity of SCCS and the high stealthiness of interfacial debonding defects, the feasibility of MASW method needs to be investigated in depth. In this study, synthetic parametric study on MASW nondestructive debonding detection for SCCSs is performed. The aim is to quantitatively analyze influential factors with respect to structural composition of SCCS and MASW measurement mode. First, stress wave composition and propagation process in SCCS are studied utilizing 2D numerical simulation. For structural composition in SCCS, the thickness variation of steel plate, concrete core, and debonding defects are discussed. To determine the most appropriate sensor arrangement for MASW measurement, the effects of spacing and number of observation points, along with distances between excitation points, nearest boundary, as well as the first observation point, are analyzed individually. The influence of signal type and frequency of transient excitation on dispersion figures from forwarding analysis is studied to determine the most suitable excitation signal. The findings from this study can provide important theoretical guidance for MASW-based interfacial debonding detection for SCCS. Furthermore, they can be instrumental in optimizing both the sensor layout design and signal choice for experimental validation.