• ETRI Journal
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• v.15 no.2
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• pp.1-9
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• 1993

Nitzberg has analyzed the detection performance of the clutter map constant false alarm rate (CFAR) detector using single pulse. In this paper, we extend the detection analysis to the clutter map CFAR detector that employs M-pulse noncoherent integration. Detection and false alarm probabilities for Swerling target models are derived. The analytical results show that the larger the number of integrated pulses M, the higher the detection probability. On the other hand, the analytical results for Swerling target models show that the detection performance of the completely decorrelated target signal is better than that of the completely correlated target.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.321-327
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• 2012

This paper presents a comparative analysis on three different types of performance indices of obstacle detection for an overlapped ultrasonic sensor ring. Due to beam overlap, the entire sensing zone of each ultrasonic sensor can be divided into three smaller sensing subzones, which leads to significant reduction of positional uncertainty in obstacle detection. First, the positional uncertainty in obstacle detection is expressed in terms of the area of a sensing subzone, and type 1 performance index is then defined as the area ratio of side and center sensing subzones. Second, based on the area of a sensing subzone, type 2 performance index is defined taking into account the size of the entire range of obstacle detection as well as the degree of the positional uncertainty in obstacle detection. Third, the positional uncertainty in obstacle detection is now expressed in terms of the length of the uncertainty arc spanning a sensing subzone, and type 3 performance index is then defined as the average value of the uncertainty arc lengths over the entire range of obstacle detection. Fourth, using a commercial low directivity ultrasonic sensor, the changes of three different performance indices depending on the parameter of an overlapped ultrasonic sensor ring are examined and compared.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2230-2252
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• 2024

Advances in deep learning technology have enabled the generation of more realistic deepfakes, which not only endanger individuals' identities but also exploit vulnerabilities in face recognition systems. The majority of existing deepfake detection methods have primarily focused on RGB-based analysis, offering unreliable performance in terms of detection accuracy and time. To address the issue, a grayscale-based deepfake detection method has recently been proposed. This method significantly reduces detection time while providing comparable accuracy to RGB-based methods. However, despite its significant effectiveness, the "key components" that directly affect the performance of grayscale-based deepfake detection have not been systematically analyzed. In this paper, we target three key components: RGB-to-grayscale conversion method, brightness level in grayscale, and resolution level in grayscale. To analyze their impacts on the performance of grayscale-based deepfake detection, we conducted comprehensive evaluations, including component-wise analysis and comparative analysis using real-world datasets. For each key component, we quantitatively analyzed its characteristics' performance and identified differences between them. Moreover, we successfully verified the effectiveness of an optimal combination of the key components by comparing it with existing deepfake detection methods.

• Journal of the Korea Society for Simulation
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• v.31 no.4
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• pp.11-22
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• 2022

Unlike a short-range, a long-range underwater acoustic communication(UWAC) uses low frequency signal and deep sound channel to minimize propagation loss. In this case, even though communication signals are modulated using a covert transmission technique such as spread spectrum, it is hard to conceal the existence of the signals. The unconcealed communication signal can be utilized as active sonar signal by enemy and presence of underwater vehicles may be exposed to the interceptor. Since it is very important to maintain stealthiness for underwater vehicles, the detection probability of friendly underwater vehicles should be considered when interceptor utilizes our long-range UWAC signal. In this paper, we modeled a long-range UWAC environment for analyzing the detection performance of underwater vehicles and proposed the region of interest(ROI) setup method and the measurement of detection performance. By computer simulations, we yielded parameters, analyzed the detection probability and the detection performance in ROI. The analysis results showed that the proposed detection performance analysis method for underwater vehicles could play an important role in the operation of long-range UWAC equipment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3865-3883
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• 2016

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.76-85
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• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• Bulletin of the Korean Chemical Society
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• v.26 no.2
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• pp.268-272
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• 2005

Different detection characteristics of fluorescence immunochromatography method and high performance liquid chromatography (HPLC) method for the analysis of cyanobacterial toxins were studied. In particular, low and high limits of detection, detection time and reproducibility and detectable microcystin species were compared when fluorescence immunochromatography method and high performance liquid chromatography method were applied for the detection of microcystin (MC), a cyclic peptide toxin of the freshwater cyanobacterium Microcystis aeruginosa. A Fluorescence immunochromatography assay system has the unique advantages of short detection time and low detection limit, and high performance liquid chromatography detection method has the strong advantage of individual quantifications of several species of microcystins.

• 전자공학회논문지 IE
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• v.47 no.4
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• pp.39-47
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• 2010

This paper proposes a numerical analysis to prove that the performance of the differential phase detections (DPDs) with the decision feedback, such as the decision feedback DPD (DF-DPD) and the DPD with recursively generated phase reference (DPD-RGPR), approach the performance of the coherent detection with differential decoding. The conventional differential phase detection for M-ary DPSK can make the receiver architecture simple, while it can make the bit-error rate (BER) performance poor because of the previous noisy phase as a reference phase. To improve the BER performance of the conventional differential detection, multiple symbol differential detection methods, including DF-DPD and DPD-RGPR, have been proposed. However, the studies on the analysis and on the comparison of these methods have been little performed. Then, this paper mathematically intends to analyze and compare the performance of the DPDs with the decision feedback. The analysis results show that the DPDs with the decision feedback can have the performance equal to that of the coherent detection with differential decoding and be available for the noncoherent detection in the improved performance. Considering the hardware complexity, the DPD RGPR with the simple detection process by using the recursively generated phase reference can be more simply implemented than the DF-DPD based on the architecture whose complexity increases according to the increasing detection length.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.7
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• pp.1-9
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• 1992

In this thesis, the trellis detection scheme is proposed to improve the error performance of the noncoherent detection in the TFM system. Trellis detection takes advantage of the trellis property of TFM-encoded signals. The trellis property is created by giving correlations among adjacent TFM-encoded signals at the transmitter. The performance of the trellis detection scheme is analyzed by means of the Bernoulli trials with the average symbol error probability, and is compared to that of the bit-by-bit detection scheme. As a result,when the SNR is below 20 dB in the Rayleigh fading and AWGN channel, the trellis detection is inferior to the bit-by-bit detections. But when SNR is above 20 dB, the trellis detection is superior to the bit-by-bit detection, and its performance enhancement is better as the SNR increases.

• 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.