• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.7
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• pp.1543-1549
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• 1997

In this paper, rank-based nonparmetric detection of composite signals in additive noise is considered. Based on signs and ranks of observations, the locally optimum detector is deived for weak-signal detection under any specified noise probability density funhction. This detector has similarities to the locally optimum detector for comjposite signals in additive noise. The asymptotic performance of this nonparametric detector is shown to be as good as that of the locally optimum detector.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.359-375
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• 2017

We developed fog detection algorithm (KNU_FDA) based on the optical and textural properties of fog using satellite (COMS) and ground observation data. The optical properties are dual channel difference (DCD: BT3.7 - BT11) and albedo, and the textural properties are normalized local standard deviation of IR1 and visible channels. Temperature difference between air temperature and BT11 is applied to discriminate the fog from other clouds. Fog detection is performed according to the solar zenith angle of pixel because of the different availability of satellite data: day, night and dawn/dusk. Post-processing is also performed to increase the probability of detection (POD), in particular, at the edge of main fog area. The fog probability is calculated by the weighted sum of threshold tests. The initial threshold and weighting values are optimized using sensitivity tests for the varying threshold values using receiver operating characteristic analysis. The validation results with ground visibility data for the validation cases showed that the performance of KNU_FDA show relatively consistent detection skills but it clearly depends on the fog types and time of day. The average POD and FAR (False Alarm Ratio) for the training and validation cases are ranged from 0.76 to 0.90 and from 0.41 to 0.63, respectively. In general, the performance is relatively good for the fog without high cloud and strong fog but that is significantly decreased for the weak fog. In order to improve the detection skills and stability, optimization of threshold and weighting values are needed through the various training cases.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.73-81
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• 2012

This paper proposes an ETWAD(Encapsulation and Tunneling Wormhole Attack Detection) design based on positional information and hop count on Ad-Hoc Network. The ETWAD technique is designed for generating GAK(Group Authentication Key) to ascertain the node ID and group key within Ad-hoc Network and authenticating a member of Ad-hoc Network by appending it to RREQ and RREP. In addition, A GeoWAD algorithm detecting Encapsulation and Tunneling Wormhole Attack by using a hop count about the number of Hops within RREP message and a critical value about the distance between a source node S and a destination node D is also presented in ETWAD technique. Therefore, as this paper is estimated as the average probability of Wormhole Attack detection 91%and average FPR 4.4%, it improves the reliability and probability of Wormhole Attack Detection.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.120-128
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• 2012

In this paper, we propose a modified iterative pre-whitening projection statistics (MIPPS) scheme for improving multi-target detection performance in non-homogeneous clutter environments. As a non-homogeneity detection (NHD) technique of space-time adaptive processing algorithm for airborne radar, the MIPPS scheme improves the average detection probability of weak target when multiple targets with different reflection signal intensities are located in close range. Numerical results show that the conventional NHD schemes suffers from the masking effect by strong targets and clutters and the proposed MIPPS scheme outperforms the conventional schemes with respect to the average detection probability of the weak target at low signal-to-clutter ratio.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.21-28
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• 2015

In this paper, we propose a method of glasses detection in facial image. we develop a detection method of glasses with a weighted sum of the results that detected by facial element detection and glasses frame candidate region. Component of the face detection method detects the glasses, by defining the detection probability of the glasses according to the detection of a face component. Method using the candidate region of the glasses frame detects the glasses, by defining feature of the glasses frame in the candidate region. finally, The results of the combined weight of both methods are obtained. The proposed method in this paper is expected to increase security system's recognition on facial accessories by raising detection performance of glasses or sunglasses for using ATM.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.11
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• pp.82-88
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• 2013

It is important to give lightning warning prior to a cloud-to-ground (CG) discharge within an Area of Concern (AOC) because most of lightning damage and victim are usually occurred by the first lightning in the AOC. The aim of this study is to find the optimal operation conditions of the automated lightning warning systems in order to make the best use of the available data. In this paper, the test-operated results of the automated lightning alert and risk management system (ALARM) based on detections of CG discharge and eletrostatic field and optimized at probability of lightning have been described. It was possible to obtain the following warning performance parameters: probability of detection (POD), false alarm ratio (FAR), probability of lightning (POL) and failure-to-warn rate (FTW). The data obtained from trial operation for 5months were not sufficient but the first analysis of domestic lightning warning was carried out. We have observed that the evaluated statistical results through trial operation depend on the various factors such as analysis methods and criteria, topographical conditions, etc. Also we suggest some methods for improvement of POL and POD including the finding of the optimal electric field threshold level to be used, based on the high values of FAR and FTW found in this work.

• Journal of Advanced Navigation Technology
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• v.17 no.5
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• pp.506-515
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• 2013

In cognitive radio networks, several research works typically address the framework which consists of a spectrum sensing period and a data transmission period. When the frame period is short, there is the problem that the throughput of secondary users decrease. In this paper, aggregated data slot structure is considered to increase the throughput of secondary users. Chapman-Kolmogorov equation is used for the modeling of the transmission probability of primary users and formulation of an optimization problem to maximize the throughput of secondary users. Solution of the optimization problem results in the optimal spectrum sensing time, the length of data slot and the number of data slots governed by a spectrum sensing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.518-520
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• 2014

Radar Cross Section(RCS) is very important factor to detect target by radar. Even if the same target, RCS value is significantly different according to the direction facing the radar. Therefore, it is advantageous to place the radar, where RCS is larger to increase the probability of detecting a target with a radar. North Korean ballistic missiles are major threat to our security, ballistic missiles should be detected early and traced for ballistic missile defense. In this paper, it is analyzed that ballistic missile's RCS characteristics and trajectory and proposed a way of radar deployment to improve the detection probability of ballistic missile.

• Journal of Integrative Natural Science
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• v.13 no.4
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• pp.147-151
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• 2020

In this paper, we propose a method to detect concept drift by applying Convolutional Neural Network (CNN) in a data stream environment. Since the conventional method compares only the final output value of the CNN and detects it as a concept drift if there is a difference, there is a problem in that the actual input value of the data stream reacts sensitively even if there is no significant difference and is incorrectly detected as a concept drift. Therefore, in this paper, in order to reduce such errors, not only the output value of CNN but also the probability vector are used. First, the data entered into the data stream is patterned to learn from the neural network model, and the difference between the output value and probability vector of the current data and the historical data of these learned neural network models is compared to detect the concept drift. The proposed method confirmed that only CNN output values could be used to reduce detection errors compared to how concept drift were detected.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.4
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• pp.39-46
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• 1994

In this paper, the idea of using a multiple symbol obervation interval to improve error probability performance is applied to differential detection of MTCM(multiple trellis code modulation) with ${\Pi}$/4 shift QPSK, 8DPSK and 16DPSK. We propose two types of sliding multiple symbol differential detection algorithm, type 1 and type 2. The two types of sliding detection scheme are examined and compared with conventional(symbol-by-symbol) detection and bolck detection with these modulation formats in an additive white Gaussian noise(AWGN) using the Monte Carlo simulation. We show that the amount of improvement over conventional and block detection depends on the number of phases and the number of additional symbol intervals added to the observation. Computer simulagtion results are presented for 2,4,8 states in AWGN channel.