• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.241-244
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• 2014

Wireless Sensor Network(WSN) has been attracting lots of interest in recent years for smart surveillance systems. WSN-based surveillance systems need to figure out the occurrence or existence of events or objects and to find out where the events have occurred or the objects are present. In our surveillance system, it is needed to give an alarm only when the detected object is human (not pets or rodents) for reducing false alarms and improving the system reliability. In this paper, we propose a height prediction mechanism to determine if the detected object is human using Heron's formula. Finally, we verify the performance of our proposed mechanism through various experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5249-5267
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• 2016

Cognitive radio is an efficient technique to improve spectrum efficiency and relieve the pressure of spectrum resources. In this paper, we investigate the spectrum sensing period in cooperative relay cognitive radio networks; analyze the relationship between the available capacity and the signal-to-noise ratio of the received signal of second users, the target probability of detection and the active probability of primary users. Finally, we derive the closed form expression of the optimal spectrum sensing period in terms of maximum throughput. We simulate the probability of false alarm and available capacity of cognitive radio networks and compare optimal spectrum sensing period scheme with fixed sensing period one in these performance. Simulation results show that the optimal sensing period makes the cognitive networks achieve the higher throughput and better spectrum sensing performance than the fixed sensing period does. Cooperative relay cognitive radio networks with optimal spectrum sensing period can achieve the high capacity and steady probability of false alarm in different target probability of detection. It provides a valuable reference for choosing the optimal spectrum sensing period in cooperative relay cognitive radio networks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.9-15
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• 2018

This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, 'False alarm' and 'Miss' in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a 'False alarm' error. In addition, this study found that people's dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users' opinion in almost real time, which is impossible through survey or interview.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.229-237
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• 2017

In this paper we propose an auto tonal detection method which exploits short term stationary when targets located in a detection beam area and then additional methods are proposed in order to reduce the computational complexity of the proposed method. The proposed method is adaptive to input signals and robust against interference caused by multiple targets because it compares an expected value of input signals with a threshold value which are estimated from a single beam while signals are keep stationary. The performances of the proposed methods are evaluated using by simulated data and acquired data from real ocean. The proposed method has shown better performance than conventional CFAR (Constant False Alarm Rate) methods.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.394-400
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• 2002

In digital speech communications, the quality of service can be increased by speech coding scheme that is adaptive to the error rate of voice packet transmission. However, current communication protocol in cellular and internet communications does not provide the function that transmits the channel error information. To solute this problem, in this paper, new method for real-time transmission of channel error information is proposed, where channel error information is embedded in voice packet. The proposed method utilizes the pulse positions of codevector in ACELP speech codec, which results in little degradation in speech quality and low false alarm rate. The simulations with various speech data show that the proposed method meets the requirement in speech quality, detection rate, and false alarm rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.444-452
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• 2012

The detection performances, in this paper, are derived according to combination of the multiband GNSS signals in broadband jamming environments. The detection probabilities depending on the false alarm probabilities are derived and presented via Monte-Carlo simulation under the assumption as follows: the GNSS signals are perfectly orthogonal and simultaneously received by the receiver using non-coherent correlation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.9 no.1
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• pp.43-51
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• 1998

In this paper, the performance about the CDMA inital synchronization under the Rician fading channel, which is actively studied as a CAI for IMT2000(FPLMTS) is analyzed. Through the performance analysis with the double dwell serial search code acquisition, the minimum mean initial synchronization acquisition time vs signal detection threshold value and first dwell duration time respectively with parameters of false alarm probability, detection probability and test PN chips is presented and the results show the mean initial synchronization acquisition time is increased with lower slope than Rayleigh fading as the threshold value of the initial synchronization acquisition decision is increased.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.141-146
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• 2018

The Ground Moving Target Indication(GMTI) technique can detect the moving targets on land using its Doppler returns. Also, the GMTI system can work in night regardless of the weather condition because it is an active sensor that uses the electromagnetic waves as its source. In order to develop the GMTI system, Constant False Alarm Rate(CFAR) threshold optimization is important because the main performances like detection probability, false alarm rate and Minimum Detectable Velocity(MDV) are related deeply with CFAR threshold. These key variables are used to calculate CFAR threshold and then trade-off between the variables is performed. In this paper, CFAR threshold optimization procedures are introduced, and the optimization results are demonstrated.

• Journal of Satellite, Information and Communications
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• v.5 no.1
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• pp.27-31
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• 2010

Recently, spectrum requirements are rapidly increasing in accordance with wireless communication development. For this reason, FCC(Federal communications commission) is considering cognitive radio system to increase spectral efficiency. In this paper, we present the performance analysis of signal detection by using RS(Reference signal) for LTE environments. Especially, we analyze the performance of detection probability in case of downlink LTE system. In the simulation, we generate OFDMA signal format which is specified in the LTE system. We assume additive white Gausssian noise channel environment. We estimate the performance by setting the threshold value of 5 % and 10 % based on CFAR(Constant false alarm rate) and false alarm rate, respectively. Finally, we discuss a future study plan on the applicability of CR to the LTE system.