• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권6호
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• pp.1042-1062
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• 2010

Cooperative spectrum sensing (CSS) is an effective technology for alleviating the unreliability of local spectrum sensing due to fading/shadowing effects. Unlike most existing solutions, this paper considers the use of CSS technology in decentralized networks where a fusion center is not available. In such a decentralized network, some attackers may sneak into the ranks of cooperative users. On the basis of recent advances in bio-inspired consensus algorithms, an attack-proof, decentralized CSS scheme is proposed in which all secondary users can maintain cooperative sensing by exchanging information locally instead of requiring centralized control or data fusion. Users no longer need any prior knowledge of the network. To counter three potential categories of spectrum sensing data falsification (SSDF) attacks, some anti-attack strategies are applied to the iterative process of information exchange. This enables most authentic users to exclude potentially malicious users from their neighborhood. As represented by simulation results, the proposed scheme can generally ensure that most authentic users reach a consensus within the given number of iterations, and it also demonstrates much better robustness against different SSDF attacks than several existing schemes.

• Journal of Communications and Networks
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• 제18권2호
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• pp.173-181
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• 2016

In this paper, we address a new robust optimization problem in a multiuser multiple-input single-output broadcasting system with simultaneous wireless information and power transmission, where a multi-antenna base station (BS) sends energy and information simultaneously to multiple users equipped with a single antenna. Assuming that perfect channel-state information (CSI) for all channels is not available at the BS, the uncertainty of the CSI is modeled by an Euclidean ball-shaped uncertainty set. To optimally design transmit beamforming weights and receive power splitting, an average total transmit power minimization problem is investigated subject to the individual harvested power constraint and the received signal-to-interference-plus-noise ratio constraint at each user. Due to the channel uncertainty, the original problem becomes a homogeneous quadratically constrained quadratic problem, which is NP-hard. The original design problem is reformulated to a relaxed semidefinite program, and then two different approaches based on convex programming are proposed, which can be solved efficiently by the interior point algorithm. Numerical results are provided to validate the robustness of the proposed algorithms.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2005년도 학술대회 논문집
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.1248-1250
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• 2004

This paper is proposed adaptive fuzzy-neural network controller(NFC) for speed control of surface permanent magnet synchronous motor(SPMSM) drive. The design of this algorithm based on NFC that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive NFC is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 응용통계연구
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• 제20권3호
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• pp.561-571
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• 2007

국소선형회귀모형의 추정량은 좋은 특성을 가지고 있는 추정량으로서 가장 흔히 사용되는 비모수적 회귀모형의 추정량이라고 하겠다. 이러한 국소선형 추정량이 자료가 희박한 구간에서는 심하게 왜곡된 추정결과를 보이는 문제가 있으며, Hall과 Turlach(1997)이 제안한 선형보간법이 이러한 문제에 대한 매우 효과적인 해결방안이라는 것은 잘 알려진 사실이다. 그러나 Hall과 Turlach가 제안한 선형보간법이 이상값에 매우 취약하다는 사실은 아직 지적된 적이 없는 문제이다. 이 논문에서는 이상값의 영향력을 감소시킬 수 있는 수정된 선형보간법에 의한 유사자료의 생성방법을 제안하고, 그 특성을 모의실험을 통하여 기존의 방법과 비교하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5819-5840
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• 2018

Considering the topology of hierarchical tree structure, each cluster in WSNs is faced with various attacks launched by malicious nodes, which include network eavesdropping, channel interference and data tampering. The existing intrusion detection algorithm does not take into consideration the resource constraints of cluster heads and sensor nodes. Due to application requirements, sensor nodes in WSNs are deployed with approximately uncorrelated security weights. In our study, a novel and versatile intrusion detection system (IDS) for the optimal defense strategy is primarily introduced. Given the flexibility that wireless communication provides, it is unreasonable to expect malicious nodes will demonstrate a fixed behavior over time. Instead, malicious nodes can dynamically update the attack strategy in response to the IDS in each game stage. Thus, a multi-stage intrusion detection game (MIDG) based on Bayesian rules is proposed. In order to formulate the solution of MIDG, an in-depth analysis on the Bayesian equilibrium is performed iteratively. Depending on the MIDG theoretical analysis, the optimal behaviors of rational attackers and defenders are derived and calculated accurately. The numerical experimental results validate the effectiveness and robustness of the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.771-791
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• 2022

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.

• 지능정보연구
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• 제21권1호
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• pp.29-45
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• 2015

고객반응 예측모형은 마케팅 프로모션을 제공할 목표고객을 효과적으로 선정할 수 있도록 하여 프로모션의 효과를 극대화 할 수 있도록 해준다. 오늘날과 같은 빅데이터 환경에서는 데이터 마이닝 기법을 적용하여 고객반응 예측모형을 구축하고 있으며 본 연구에서는 사례기반추론 기반의 고객반응 예측모형을 제시하였다. 일반적으로 사례기반추론 기반의 예측모형은 타 인공지능기법에 비해 성과가 낮다고 알려져 있으나 입력변수의 중요도에 따라 가중치를 상이하게 적용함으로써 예측성과를 향상시킬 수 있다. 본 연구에서는 프로모션에 대한 고객의 반응여부에 영향을 미치는 중요도에 따라 입력변수의 가중치를 산출하여 적용하였으며 동일한 가중치를 적용한 예측모형과의 성과를 비교하였다. 목욕세제 판매데이터를 사용하여 고객반응 예측모형을 개발하고 로짓모형의 계수를 적용하여 입력변수의 중요도에 따라 가중치를 산출하였다. 실증분석 결과 각 변수의 중요도에 기반하여 가중치를 적용한 예측모형이 동일한 가중치를 적용한 예측모형보다 높은 예측성과를 보여주었다. 또한 고객 반응예측 모형과 같이 실생활의 분류문제에서는 두 범주에 속하는 데이터의 수가 현격한 차이를 보이는 불균형 데이터가 대부분이다. 이러한 데이터의 불균형 문제는 기계학습 알고리즘의 성능을 저하시키는 요인으로 작용하며 본 연구에서 제안한 Weighted CBR이 불균형 환경에서도 안정적으로 적용할 수 있는지 검증하였다. 전체데이터에서 100개의 데이터를 무작위로 추출한 불균형 환경에서 100번 반복하여 예측성과를 비교해 본 결과 본 연구에서 제안한 Weighted CBR은 불균형 환경에서도 일관된 우수한 성과를 보여주었다.

• 한국통신학회논문지
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• 제37권6C호
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• pp.502-511
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• 2012

GNSS의 중요성이 점점 증가하면서, 독자적인 위성항법시스템의 구축에 대한 필요성이 제기되고 있다. 위성항법 시스템 구축 시 위성 신호 설계는 반드시 필요한 과정이며, 이를 위한 요구조건 규정이 필수적이다. 본 논문에서는 위성 항법 설계 요소에 대한 수신 성능 분석을 수행하며, 이를 이용한 신호 설계 방안에 대해 제시한다. 먼저 설계 요소에 따른 후보군 정의 후, 성능 평가 지표에 따라 신호 후보의 수신 성능을 분석한다. 이때 다양한 적용 분야에서의 신호 성능을 판단하기 위하여 성능 평가 지표가 갖는 가중치를 정의하였으며, 정규화된 성능 평가 지표와 가중치간의 연산을 통해 최종적으로 성능 비교값을 도출하였다. 위성 항법 신호 설계 요소로 코드, 변조 기법, 반송파를 고려하였으며, 성능을 평가하기 위한 평가 지표로는 상관폭, DLL 및 PLL 열잡음 지터, 주파수 대역폭, 사이드로브 피크율을 정의하였다. 또한 적용 분야로는 측위 성능, 잡음에 대한 강인성, 대역 효율성을 고려하였다. 제안한 설계 방안 적용 시 소프트웨어 기반의 시뮬레이터를 이용하여 성능 분석을 수행하였으며, 최종적으로 성능 분석 결과로부터 신호 후보의 성능을 객관적으로 판단하고 비교하였다.