• ETRI Journal
• /
• 제15권2호
• /
• pp.1-9
• /
• 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.

• 제어로봇시스템학회논문지
• /
• 제18권4호
• /
• pp.321-327
• /
• 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.

• 한국시뮬레이션학회논문지
• /
• 제31권4호
• /
• pp.11-22
• /
• 2022

단거리와 달리 장거리 수중 음향 통신에서는 전파 손실을 최소화하기 위해 저주파 신호와 심해 음파 채널을 사용한다. 이 경우 대역 확산과 같은 은밀 통신 기법을 이용하더라도 통신 사실을 숨기기 어려우며, 통신 신호가 탐지 신호처럼 작용하므로 감청기에 수중 운동체의 존재가 노출될 수 있다. 수중 운동체의 경우 은밀성 유지가 매우 중요하므로, 감청기가 통신 신호를 이용하여 아군 수중 운동체를 탐지할 가능성을 반드시 고려해야 한다. 본 논문에서는 수중 운동체의 피탐지 성능 분석을 위한 장거리 수중 음향 통신 환경을 모델링하고, 피탐지 성능 분석을 위한 관심영역 설정 방법과 평가 척도를 제안하였다. 전산 모의 실험을 통해 파라미터를 산출하고, 관심영역에서 피탐지 확률 분석 및 피탐지 성능 분석을 수행하였다. 분석 결과는 제안된 수중 운동체의 피탐지 성능 분석 방법이 장거리 수중 통신 장비의 운용에 있어 중요한 역할을 할 수 있음을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권8호
• /
• pp.3865-3883
• /
• 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
• /
• 제11권4호
• /
• pp.76-85
• /
• 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
• /
• 제26권2호
• /
• pp.268-272
• /
• 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
• /
• 제47권4호
• /
• pp.39-47
• /
• 2010

본 논문은 결정 궤환(Decision Feedback) 기반 차동 위상 검출 방식인 DF-DPD와 DPD-RGPR의 성능이 차동 복호(Differential Decoding)를 갖는 동기 검출 (Coherent Detection) 방식의 성능에 근접한다는 것을 수치적으로 증명한다. M-ary DPSK에 대한 기존 차장 위삼 검출 빙식은 수신기 구조를 간단하게 만들지만, 참조 위상으로 활용되는 이전 심볼에서의 잡음 성분으로 인해 열화된 수신 성능을 갖는다. 기존 차동 검출 방식의 수신 성능을 향상시키기 위해 DF-DPD, DPD-RGPR 등을 포함하는 다중 심볼 차동 검출 방식들이 제시되었다. 하지만, 이러한 방식들의 검출 성능에 대한 분석 및 비교에 대한 연구는 거의 진행되지 않았다. 그러므로, 본 논문에서는 DF-DPD와 DPD-RGPR 등의 결정 궤환 기반 차동 위상 검출 방식들의 성능을 수치적으로 분석한다. 수치적 분석 결파, 결정 궤환을 갖는 차동 위상 검출 방식들은 차동 복호를 갖는 동기 검출의 성능에 근접할 수 있으며 향상된 성능을 갖는 비동기 검출 (Noncoherent Detection)에 활용될 수 있음을 볼 수 있었다. 하드웨어 복잡도를 고려하면, 검출 길이가 증가함에 따라 복잡도가 증가하는 구조에 기반한 DF-DPD 방식보다 반복적으로 갱신되는 참조 위상을 사용하는 검출 방식에 기반한 DPD-RGPR 방식이 구현에 더욱 효과적임을 알 수 있었다.

• 전자공학회논문지A
• /
• 제29A권7호
• /
• pp.1-9
• /
• 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
• /
• 제41권5호
• /
• pp.684-695
• /
• 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.

• 한국군사과학기술학회지
• /
• 제26권3호
• /
• pp.234-245
• /
• 2023

The Arena Fragmentation Test(AFT) is designed to analyze warhead performance by measuring fragmentation data. In order to evaluate the results of the AFT, a set of AFT images are captured by high-speed cameras. To detect objects in the AFT image set, ResNet-50 based Faster R-CNN is used as a detection model. However, because of the low resolution of the AFT image set, a detection model has shown low performance. To enhance the performance of the detection model, Super-resolution(SR) methods are used to increase the AFT image set resolution. To this end, The Bicubic method and three SR models: ZSSR, EDSR, and SwinIR are used. The use of SR images results in an increase in the performance of the detection model. While the increase in the number of pixels representing a fragment flame in the AFT images improves the Recall performance of the detection model, the number of pixels representing noise also increases, leading to a slight decreases in Precision performance. Consequently, the F1 score is increased by up to 9 %, demonstrating the effectiveness of SR in enhancing the performance of the detection model.