• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.269-286
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• 2018

Signal detection in symmetric alpha-stable ($S{\alpha}S$) distributed noise is a challenging problem. This paper proposes a detector based on a decreasing exponential function (DEF). The DEF detector can effectively suppress the impulsive noise and achieve good performance in the presence of $S{\alpha}S$ noise. The analytical expressions of the detection and false alarm probabilities of the DEF detector are derived, and the parameter optimization for the detector is discussed. A performance analysis shows that the DEF detector has much lower computational complexity than the Gaussian kernelized energy detector (GKED), and it performs better than the latter in $S{\alpha}S$ noise with small characteristic exponent values. In addition, the DEF detector outperforms the fractional lower order moment (FLOM)-based detector in $S{\alpha}S$ noise for most characteristic exponent values with the same order of magnitude of computational complexity.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.580-583
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• 2003

This paper presents the motion detection algorithm that can run robustly about recusive motion. The existing motion detection algorithm that uses difference image is robustly in some degree brightness or noise, but it frequently causes false alarms to temporal clutter, at the repetitive motion within a certain area. We developed a motion detection algorithm using mean absoulte error(MAE) which calculates the set of Moving regions and performs block matching. The experimental results revealed that our approach is superior to existing methodologies to handling various temporal clutter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.959-981
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• 2017

Existing Android malware detection approaches mostly have concentrated on superficial features such as requested or used permissions, which can't reflect the essential differences between benign apps and malware. In this paper, we propose a quantitative calculation model of application risks based on the key observation that the essential differences between benign apps and malware actually lie in the way how permissions are used, or rather the way how their corresponding permission methods are used. Specifically, we employ a fine-grained analysis on Android application risks. We firstly classify application risks into five specific categories and then introduce comprehensive risk, which is computed based on the former five, to describe the overall risk of an application. Given that users' risk preference and risk-bearing ability are naturally fuzzy, we design and implement a fuzzy logic system to calculate the comprehensive risk. On the basis of the quantitative calculation model, we propose a risk classification based approach for Android malware detection. The experiments show that our approach can achieve high accuracy with a low false positive rate using the RandomForest algorithm.

• ETRI Journal
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• v.39 no.5
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• pp.621-631
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• 2017

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.3
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• pp.123-129
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• 1985

This paper describes the determination of threshold value in order to determine the presence of absence of weak signal with SNR of 0 dB in 100kHz bandwidth. As a detection method, it has been used a recent LOC structure fitting for detecting weak signal in stead of a conventional method like Neyman-Peason crtical criterion. The signal for detection is the OOK modulation signal used in data and morse code transmission. The non-Gaussian noise similar to Laplacian type has been chosen in transmission path. As a result of experiment, comparing probability of detection by one critical point with that by two critical points with fixing as arbitrary false alarm probability, we have found that method has been shown to be better than the conventional method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.380-388
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• 2004

This study proposed an improved spike detection algorithm which automatically detects suitable spike threshold on the amplitude of surface electromyography(SEMG) signal during isometric contraction. The EMG data from the low back muscles was obtained in six channels and the proposed signal processing algorithm is compared with the median frequency and Gabriel's spike parameter. As a result, the reliability of spike parameter was inferior to the median frequency. This fact indicates that a spike parameter is inadequate for analysis of multi-channel EMG signal. Because of uncertainty of fixed spike threshold, the improved spike detection algorithm was proposed. It automatically detects suitable spike threshold depending on the amplitude of the EMG signal, and the proposed algorithm was able to detect optimal threshold based on mCFAR(modified Constant False Alarm Rate) in the every EMG channel. In conclusion, from the reliability points of view, neither median frequency nor existing spike detection algorithm was superior to the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.4
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• pp.350-357
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• 2015

Many countries have been developing and operating an underwater surveillance system in order to protect their oceanic environment from infiltrating hostile marine forces which intend to lay mines, conduct reconnaissance and destroy friendly ships anchored at the harbor. One of the most efficient methods to detect unidentified submarine approaching harbor is sensing variation of magnetism of target by magnetic sensors. This measurement system has an advantage of high possibility of detection and low probability of false alarm, compared to acoustic sensors, although it has relatively decreased detection range. The contents of this paper mainly cover the analysis of possible effectiveness of magnetic sensors. First of all, environmental characteristics of surveillance area and magnetic information of simulated targets has been analyzed. Subsequently, a signal processing method of separating target from geomagnetic field and methods of estimating target location has been proposed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.39-44
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• 2011

Automatic fire alarm system is set up to automatically detect fire on buildings. Because of economic reasons, P-type receiver and a conventional type fire detector is normally used for automatic fire alarm system in Korea. Because early detection of fire is regarded as important, the need of finding technique of fire location increases. This paper is studied a method to improve a reliability and add a function of fire location detection on a conventional type fire detector and P-type receiver. Fire location is detected by a method that controller attached on the receiver and the detector is read with a time lag. A reliability of fire detection alarm system is improved with a method that false fire alarm is able to decrease using two different principle detector together. This paper is studied for basic data of improvement of low-cost addressable automatic fire alarm system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.1932-1950
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• 2018

The paper proposes a code-clone detection method that gives the highest possible precision and recall, without giving much attention to efficiency and scalability. The goal is to automatically create a reliable reference corpus that can be used as a basis for evaluating the precision and recall of clone detection tools. The algorithm takes an abstract-syntax-tree representation of source code and thoroughly examines every possible pair of all duplicate tree patterns in the tree, while avoiding unnecessary and duplicated comparisons wherever possible. The largest possible duplicate patterns are then collected in the set of pattern clusters that are used to identify code clones. The method is implemented and evaluated for a standard set of open-source Java applications. The experimental result shows very high precision and recall. False-negative clones missed by our method are all non-contiguous clones. Finally, the concept of neighbor patterns, which can be used to improve recall by detecting non-contiguous clones and intertwined clones, is proposed.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.120-124
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• 2010

This paper presents a method that can automatically detect variety of degradations (i.e., scratches and blotches) in old film archive. The proposed method consists of candidate detection and verification. Degradations are first identified by finding the local extreme of a frame in spatiotemporal domains, thereby using edge detector and SROD detector. Then, to remove some false alarms occurred in the first stages, the verification is performed using the texture and shape properties of scratches and blotches. The textural properties of scratches and blotches are learned using neural networks (NNs) and their shapes are represented using morphological filters. The experiments were performed on several old films, then the results demonstrated the effectiveness of the proposed method, where it has a precision of 81% and a recall of 79%.