• 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.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.258-269
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• 2020

This paper proposes Snapping Shrimp Noise (SSN) detection and corrupted Orthogonal Frequency Division Multiplexing (OFDM) reconstruction methods to increase Bit Error Rate (BER) performance when OFDM transmitted signal is corrupted by impulsive SSNs in underwater acoustic communications. The proposed detection method utilizes multilayer wavelet packet decomposition for detecting impulsive and irregularly concentrated and SSN energy in specific frequency bands of SSN, and the proposed reconstruction scheme uses iterative decision directed-subcarrier reconstruction to recover corrupted OFDM signals using multiple carrier characteristics. Computer simulations were executed to show receiver operating characteristics curve for the detection performance and BER for the reconstruction. The practical ocean experiment of SAVEX 15 demonstrated that the proposed method exhibits a better detection performance compared with conventional detection method and improves BER by 250% and 1230% for uncoded and coded data, respectively, compared with the conventional reconstruction scheme.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.4
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• pp.36-47
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• 1992

In this paper, a set of TM(template matching) mask using the AM(associative mapping) technique was generated and the edge detection algorithm for a SAR image was proposed. And also, the performance of the proposed edge detection algorithm was tested with the conventional edge detection techniques. The proposed edge detection algorithm created an edge image which was more accurate and clear than the conventional edge detection techniques and the performance of the proposed detection technique was not deteriorated for low intensity area in the image because the uncertainly thresholded value genetated by the conventional detection methods was requested. Also, the number of masks and the detection time were reduced by adjusting resolution of edge detection and the consideration for the threshold value extracting the edge was very intuitive.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.538-547
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• 2015

The goal of this study is to develop an algorithm to propose optimal deployment of detection sensor nodes in the target area, based on a performance surface, which represents detection performance of active and passive acoustic sonar systems. The performance surface of the active detection system is calculated from the azimuthal average of maximum detection ranges, which is estimated with a transmission loss and a reverberation level predicted using ray-based theories. The performance surface of the passive system is calculated using the transmission loss model based on a parabolic equation. The optimization of deployment configurations is then performed by a hybrid method of a virtual force algorithm and a particle swarm optimization. Finally, the effectiveness of deployment configurations is analyzed and discussed with the simulation results obtained using the algorithm proposed in this paper.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.3
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• pp.328-336
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• 2017

The goal of this study is simulation of optimal search pattern of ships based on performance surface which are reflected underwater environmental. The process is as follows. First, temporal and spatial environmental database are extracted in complex environment and input hull mounted SONAR system parameters. The environmental database and SONAR system parameters are substituted to SONAR equations, and calculate signal excess, detection probability, detection range. And then, the performance surface, which can be used to provide operational insight of SONAR detection performance, are pictorialized. Finally, optimal search pattern of ships are simulated using genetic algorithm based on performance surface. And then, we certify optimal search pattern in various ways.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.698-714
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• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.6A
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• pp.586-592
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• 2006

In this paper, a novel detection method called the joint multiple frequency cell (JMFC) detection is addressed for spread spectrum code acquisition in the presence of residual Doppler frequency offset (RDFO). When the RDFO exists, the correlation peak used for detection during the acquisition process is split into several lower neighboring peaks, resulting in severe degradation in the detection performance, and consequently, in the overall acquisition performance. In the JMFC detection, a decision variable for detection is formed by combining several consecutive correlator outputs, so that the reduction in the correlation value due to the RDFO can be alleviated. Numerical results show that the proposed scheme can offer better detection performance over the conventional scheme based on the cell-by-cell detection.

• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.77-107
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• 1995

This paper documents the development of a fuzzy logic based incident detection model for urban diamond interchanges. Research in incident detection for intersections and arterials is at a very initial stage. Existing algorithms are still far from being robust in dealing with the difficulties related with data availability and the multi-dimensional nature of the incident detection problem. The purpose of this study is to develop a new real-time incident detection model for urban diamond interchanges. The development of the algorithm is based on fuzzy logic. The incident detection model developed through this research is capable of detecting lane¬blocking incidents when their effects are manifested by certain patterns of deterioration in traffic conditions and, thereby, adjustments in signal control strategies are required. The model overcomes the boundary condition problem inherent in conventional threshold-based concepts. The model captures system-wide incident effects utilizing multiple measures for more accurate and reliable detection, and serves as a component module of a real-time traffic adaptive diamond interchange control system. The model is designed to be readily scalable and expandable for larger systems of arterial streets. The prototype incident detection model was applied to an actual diamond interchange to investigate its performance. A simulation study was performed to evaluate the model's performance in terms of detection rate, false alarm rate, and mean time to detect. The model's performance was encouraging, and the fuzzy logic based approach to incident detection is promising.