• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.153-162
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• 2009

This paper suggests the novel light-weight signal processing algorithm for wireless sensor network applications which needs low computing complexity and power consumption. Exponential average method (EA) is utilized by real time, to process the magnetometer signal which is analyzed to understand the own physical characteristic in time domain. EA provides the robustness about noise, magnetic drift by temperature and interference, furthermore, causes low memory consumption and computing complexity for embedded processor. Hence, optimal parameter of proposal algorithm is extracted by statistical analysis. Using general and precision magnetometer, detection probability over 90% is obtained which restricted by 5% false alarm rate in simulation and using own developed magnetometer H/W, detection probability over 60~70% is obtained under 1~5% false alarm rate in simulation and experiment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.12
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• pp.68-76
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• 2015

We examine the standalone lightning warning system (LWS) and its warning performances for three years. This system acquires and analyzes the data of cloud-to-ground strike (CG), intra-cloud discharge (IC) and electrostatic field (EF) to produce prior warnings with respect to the impending arrival of CG in the area of concern (AOC). The warnings in this system are carried out based on the fixed two areas method. To evaluate warning performances, we analyzed the statistics of warnings with probability of detection (POD) and false alarm ratio (FAR). Based on the previous study, we revised the trigger and clear conditions of lightning warning for improving the performances of the system. As a result of this revision, POD increased from 0.18 to 0.44 and FAR decreased from 0.96 to 0.78 during the summer of 2014. However, the LWS was not possible to trigger effective alerts (EA) because there was no effective lead time (LT) for the fixed two areas method. Problems related to the low detection efficiency of IC and the use of EF data for warnings still decreased POD and increased FAR. Hence, we proposed the development method of a new LWS (NLWS) that would be composed of integrated weather data, the flexible two areas and the user software in order to trigger EA and improve warning performances.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.661-669
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• 2019

High frequency active sonar is appropriate for detecting small targets such as a diver in coast environment. In case of using high frequency active sonar in shallow coastal environment, a false alarm rate is high due to clutters caused by marine biological noise, ship noise, wake, etc. In this paper, we propose an algorithm for target detection which is robust against clutter in active sonar system in shallow coastal environment. The proposed algorithm increases the rate of reduction clutter using calculation of statistical characteristics of signal and a clustering method. The algorithm is evaluated and analysed with sea trial data, as a result, that shows the rate of reducing rate of clutter of 96 % and over.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.38 no.4
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• pp.1-10
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• 2001

The 3-step cell search has been considered for fast acquisition of the scrambling code unique to a cell in the W -CDMA system. In this paper, the performance of the cell search scheme is analyzed in Rayleigh fading channels. And the system parameters for cell search scheme and the design parameters for the receivers are examined. The probabilities of detection, miss and false alarm for each step are derived in closed forms based on the statistics of CDMA noncoherent demodulator output. Through the analysis, the effect of threshold setting and post detection integration for each step is investigated, and the optimal values of the power allocation for the synchronization channels are also considered. The number of post-detection integrations for each step is a design parameter for the receiver, and the optimum values may depend on not only the power allocation for each channel related to the cell search, but the false alarm penalty time. It is shown that optimal values could be determined through the analysis. Also, the cumulative probability distribution of the average cell search time is obtained.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.75-81
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• 2004

Signature based intrusion detection system (IDS), having stored rules for detecting intrusions at the library, judges whether new inputs are intrusion or not by matching them with the new inputs. However their policy has two restrictions generally. First, when they couldn`t make rules against new intrusions, false negative (FN) errors may are taken place. Second, when they made a lot of rules for maintaining diversification, the amount of resources grows larger proportional to their amount. In this paper, we propose the learning algorithm which can evolve the competent of anomaly detectors having the ability to detect anomalous attacks by genetic algorithm. The anomaly detectors are the population be composed of by following the negative selection procedure of the biological immune system. To show the effectiveness of proposed system, we apply the learning algorithm to the artificial network environment, which is a computer security system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.8-16
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• 2010

This paper presents a new small target detection method using the cross product of the temporal pixels based on the temporal profile in infrared (IR) image sequences. The temporal characteristics of small targets and the various backgrounds are different. A new algorithm classifies target pixels and the background pixels through the hypothesis testing using the cross product of pixels on the temporal profile and predicts the temporal backgrounds based on the results. The small targets are detected by subtracting the predicted temporal background profile from the original temporal profile. For the performance comparison between the proposed algorithm and the conventional algorithms, the receiver operating characteristics (ROC) curves is computed in experiment. Experimental results show that the proposed algorithm has better discrimination and a lower false alarm rate than the conventional methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.839-850
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• 2014

As the social and financial damages caused by APT attack such as 3.20 cyber terror are increased, the technical solution against APT attack is required. It is, however, difficult to protect APT attack with existing security equipments because the attack use a zero-day malware persistingly. In this paper, we propose a host based anomaly detection method to overcome the limitation of the conventional signature-based intrusion detection system. First, we defined 39 features to identify between normal and abnormal behavior, and then collected 8.7 million feature data set that are occurred during running both malware and normal executable file. Further, each process is represented as 83-dimensional vector that profiles the frequency of appearance of features. the vector also includes the frequency of features generated in the child processes of each process. Therefore, it is possible to represent the whole behavior information of the process while the process is running. In the experimental results which is applying C4.5 decision tree algorithm, we have confirmed 2.0% and 5.8% for the false positive and the false negative, respectively.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.33-37
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• 2014

This paper propose a method to sort flame regions and non-flame regions in a color image based on temperature Characteristics of flame. The traditional algorithms simply detect flame regions those are colored between yellow and red and there are lot of false detection in this method. But the colors of real flame are fallen between white and red and flame color variation over the flame. In this paper, it reduce false detection by separating colors according to temperature Characteristics of flame. The proposed method firstly finds a color model to express the temperature Characteristics of fire and then the color model is non-linearly quantized based on color values and analyzed using histogram and finally detect the candidate flame regions. The proposed method has 71.8% of matching rate and if it is compared with non-matching rate of traditional algorithms, the non-matching rate is improved by 27 times than others.

• Journal of the Korea Society of Computer and Information
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• v.6 no.3
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• pp.78-83
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• 2001

In this paper, we are considered Nakagami-m fading, which can model variable multipath mobile radio communication channel, in DS-CDMA system. System modeling using nakagami -m fading is suited for urban mobile communication channel with multipath. We used adaptive serial search PN code acquisition scheme and derived the detection probability($P_D$) and false alarm probability($P_FA$) which have influence on code acquisition time, over Nakagami-m fading. Detection probability($P_D$) and false alarm probability($P_FA$) are detection variable to decide PN code acquisition time and should use to calculate mean and variance. of acquisition time. From computer simulation, we analyzed mean and variance about PN code acquisition of fading channel. Then we can apply it to the H/W design of mobile communication.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1653-1661
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• 2022

In this study, the classification of cold water and normal water based on Geo-Kompsat 2A images was performed. Daily mean surface temperature products provided by the National Meteorological Satellite Center (NMSC) were used, and convolution neural network (CNN) deep learning technique was applied as a classification algorithm. From 2019 to 2022, the cold water occurrence data provided by the National Institute of Fisheries Science (NIFS) were used as the cold water class. As a result of learning, the probability of detection was 82.5% and the false alarm ratio was 54.4%. Through misclassification analysis, it was confirmed that cloud area should be considered and accurate learning data should be considered in the future.