• ETRI Journal
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• v.41 no.4
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• pp.437-451
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• 2019

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph-based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state-of-the-art methods in terms of accuracy and work well with various data types.

• Journal of Communications and Networks
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• v.6 no.1
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• pp.35-37
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• 2004

A new power and bandwidth efficient modem technique-Correlative Encoded FSK (CEFSK) is proposed. CEFSK has a spectral efficiency comparable to Gaussian filtered FSK (GFSK), and it achieves 0.7db Eb/N0 improvement at bit error rate (BER) of 1 * 10 -4 over GFSK in an additive white Gaussian noise (AWGN) channel and 3.0dB improvement in a Rayleigh fading channel

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3789-3808
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• 2017

This paper studies a multi-antenna wireless relay network in the presence of a jammer. In this network, the source node transmits signals to the destination node through a multi-antenna relay node which adopts the amplify-and-forward scheme, and the jammer attempts to inject additive signals on all antennas of the relay node. With the linear beamforming scheme at the relay node, this network can be modeled as an equivalent Gaussian arbitrarily varying channel (GAVC). Based on this observation, we deduce the mathematical closed-forms of the capacities for two special cases and the suboptimal achievable rate for the general case, respectively. To reduce complexity, we further propose an optimal structure of the beamforming matrix. In addition, we present a second order cone programming (SOCP)-based algorithm to efficiently compute the optimal beamforming matrix so as to maximize the transmission rate between the source and the destination when the perfect channel state information (CSI) is available. Our numerical simulations show significant improvements of our propose scheme over other baseline ones.

• Journal of Digital Convergence
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• v.16 no.6
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• pp.151-161
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• 2018

Income is a vital aspect of economic life. Knowing what their income will help people create budgets that allow them to pay for their living expenses. Income data is used by banks, stores, and service companies for marketing purposes and for retaining loyal customers; it is a crucial demographic element used at a wide variety of customer touch points. Therefore, it is essential to be able to make income predictions for existing and potential customers. This paper aims to predict employment earnings or income based on history, and uses machine learning techniques such as SVMs (Support Vector Machines), Gaussian, decision tree and DCNNs (Deep Convolutional Neural Networks) for predicting employment earnings. The results show that the DCNN method provides optimum results with 88% compared to other machine learning techniques used in this paper. Improvement of the data length such PCA has the potential to provide more optimum result.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.1
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• pp.55-62
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• 2009

In this paper, we propose a method on the training sequence based on channel estimation issues for relay networks that employ amplify-and-forward(AF) transmission scheme. In $^{[1]}$ and $^{[2]}$, we have to point out that jointly estimating the channel from source to relay and from relay to destination suffers from many drawbacks in fast fading case because the estimation of previous pilots is not suitable for current channel. In this paper, we consider a new joint estimation of overall channel impulse response(CIR) using one OFDM signal without pilots. Using the maximum likelihood(ML) function, we derive a channel estimator by taking the frequency domain of transmitted signal as Gaussian and averaging the ML function over the resulting Gaussian distribution. Simulation results show that our proposed channel estimator performs a fraction of 1dB compared with $^{[1]}$ in high SNR region.

• Journal of Communications and Networks
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• v.5 no.4
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• pp.365-373
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• 2003

This paper investigates the performance of M-ary pulse position modulation (PPM) multiuser ultra-wideband (UWB) communication systems in terms of symbol error rate (SER) over fading and additive white Gaussian noise (AWGN) channel. Based on Gaussian approximation for the multiple access interference, an expression for the signal-to-noise ratio (SNR) is derived for the UWB system. This expression is used to derive exact SER expressions for coherent UWB receivers. The effect of pulse selection on the SER of multiuser UWB system is studied. In addition to rectangular pulse, the 2nd derivative Gaussian waveform and Rayleigh pulses were considered. We show that the system capacity and/or SER performance can be significantly increased by using the monocycle pulse in fading channels.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.442-445
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• 1997

We present a hybrid self-tuning method of fuzzy inference systems with hyper elliptic Gaussian membership functions using genetic algorithm(GA) and back-propagation algorithm. The proposed self-tuning method has two phases : one is the coarse tuning process based on GA and the other is the fine tuning process based on back-propagation. But the parameters which is obtained by a GA are near optimal solutions. In order to solve the problem in GA applications, it uses a back-propagation algorithm, which is one of learning algorithms in neural networks, to finely tune the parameters obtained by a GA. We provide Box-Jenkins time series to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1404-1407
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• 1996

A quality monitoring system in butt welding process is proposed to estimate weld pool sizes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to prove the integrity of the weld quality. The monitoring variables used are the surface temperatures measured at three points on the top surface of the weldment. The temperature profile is assumed that it has a gaussian distribution in vertical direction of torch movement and verify this assumption through temperature analysis. A neural network estimator is designed to estimate weld pool size from temperature informations. The experimental results show that the proposed neural network estimator which used gaussian distribution as temperature information can estimate the weld pool sizes accurately than used three point temperatures as temperature information. Considering the change of gap size in butt welding, the experiment were performed on various gap size.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.1-10
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• 2007

This paper proposes a multimodal generalized Gaussian distribution (MGGD) to effectively model the varying statistical properties of the extrinsic information. A subsidiary maximum likelihood decoding (MLD) algorithm is subsequently developed to dynamically select the most suitable MGGD parameters to be used in the component maximum a posteriori (MAP) decoders at each decoding iteration to derive the more reliable metrics performance enhancement. Simulation results show that, for a wide range of block lengths, the proposed approach can enhance the overall turbo decoding performance for both parallel and serially concatenated codes in additive white Gaussian noise (AWGN), Rician, and Rayleigh fading channels.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.97-98
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• 2006

Fusion of decisions from multiple distributed sensor nodes is studied in this work. Based on the canonical parallel fusion model, we derive the optimal likelihood ratio based fusion rule with the assumptions of the generalized Gaussian noise model and the arbitrary fading channel. This optimal fusion rule, however, requires the complete knowledge of the channels and the detection performance of local sensor nodes. To mitigate these requirements and to provide near optimum performance, we derive suboptimum fusion rules by using high and low signal-to-noise ratio (SNR) approximations to the optimal fusion rule. Performance evaluation is conducted through simulations.