• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.603-605
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• 2023

최근 SNS 의 발달로 인해 자신의 감정을 빠르고 효과적으로 전달할 수 있는 이모지의 중요성이 커지고 있다. 하지만 이모지를 수동으로 생성하기 위해서 시간과 비용이 많이 들고 자신의 감정에 맞는 이모지를 찾아야 하며 해당 이모지가 없을 수 있다. 기존 DCGAN 을 활용한 이모지 자동 생성연구에서는 부족한 데이터셋으로 인해 G(Generator)와 D(Discriminator)가 동등하게 학습하지 못해서 두 모델 간 성능 차이가 발생한다. D 가 G 보다 최적해에 빠르게 수렴하여 G 가 학습이 되지 않아 낮은 품질의 이모지를 생성하는 불안정 문제가 발생한다. 이 문제를 해결하기 위해 본 논문에서는 Latent vector 분포를 데이터셋에 맞게 조정하여 적은 데이터로 G 에서 안정적으로 학습할 수 있게 하는 G 구조와 다양한 이모지 생성을 위한 Latent vector 평균 조정 기법을 제안한다. 비교 실험 결과 불안정 문제를 개선하였고 FID 와 IS 수치를 통해 성능 개선 효과를 검증했다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권6호
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• pp.1418-1432
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• 2013

This paper studies the problem of secure information exchange between two sources via multiple relays in the presence of an eavesdropper. To this end, we propose a relay beamforming scheme, i.e., relay beamforming with artificial noise (RBwA), where the relay beamforming vector and the artificial noise vector are jointly designed to maintain the received signal-to-interference-ratio (SINR) at the two sources over a predefined Quality of Service (QoS) threshold while limiting the received SINR at the eavesdropper under a predefined secure threshold. For comparison, the relay beamforming without artificial noise (RBoA) is also considered. We formulate two optimization problems for the two schemes, where our goal is to seek the optimal beamforming vector to minimize the total power consumed by relay nodes such that the secrecy of the information exchange between the two sources can be protected. Since both optimization problems are nonconvex, we solve them by semidefinite program (SDP) relaxation theory. Simulation results show that, via beamforming design, physical layer secrecy of two-way relay networks can be greatly improved and our proposed RBwA outperforms the RBoA in terms of both low power consumption and low infeasibility rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• ETRI Journal
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• 제36권1호
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• pp.62-68
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• 2014

This paper presents a novel user selection method based on the signal-to-interference-plus-noise ratio (SINR), which is approximated using limited feedback data at the base stations (BSs) of multiple user multiple-input multiple-output (MU-MIMO) systems. In the proposed system, the codebook vector index, the quantization error obtained from the correlation between the measured channel and the codebook vector, and the measured value of the largest singular value are fed back from each user to the BS. The proposed method not only generates precoding vectors that are orthogonal to the precoding vectors of the previously selected users and are highly correlated with the codebook vector of each user but also adopts the quantization error in approximating the SINR, which eventually provides a significantly more accurate SINR than the conventional SINR-based user selection techniques. Computer simulations show that the proposed method enhances the sum rate of the conventional SINR-based methods by at least 2.4 (2.62) bps/Hz when the number of transmit antennas and number of receive antennas per user terminal is 4 and 1(2), respectively, with 100 candidate users and an SNR of 30 dB.

• 전력전자학회논문지
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• 제25권4호
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• pp.279-285
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• 2020

An open-end winding (OEW) permanent magnet synchronous motor with dual inverters can synthesize large voltages for a motor with the same DC link voltage. This ability has the advantage of reducing the use of DC/DC boost converters or high voltage batteries. However, zero-sequence voltage (ZSV), which is caused by the difference in the combined voltage between the primary and secondary inverters, can generate a zero-sequence current (ZSC) that increases system losses. Among the methods for eliminating this phenomenon, combining voltage vector eliminated ZSV cannot be accomplished by the conventional Pulse Width Modulation(PWM) method. In this study, a PWM carrier generation method using functionalization to generate a switching pattern to suppress ZSC is proposed and applied to analyze the control influence of the center-zero vector in the switching sequence about the current ripple.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5143-5158
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• 2018

Improving the intrusion detection system (IDS) is a pressing need for cyber security world. With the growth of computer networks, there are constantly daily new attacks. Machine Learning (ML) is one of the most important fields which have great contribution to address the intrusion detection issues. One of these issues relates to the imbalance of the diverse classes of network traffic. Accuracy paradox is a result of training ML algorithm with imbalanced classes. Most of the previous efforts concern improving the overall accuracy of these models which is truly important. However, even they improved the total accuracy of the system; it fell in the accuracy paradox. The seriousness of the threat caused by the minor classes and the pitfalls of the previous efforts to address this issue is the motive for this work. In this paper, we consolidated stratified sampling, cost function and weighted Support Vector Machine (WSVM) method to address the accuracy paradox of ID problem. This model achieved good results of total accuracy and superior results in the small classes like the User-To-Remote and Remote-To-Local attacks using the improved version of the benchmark dataset KDDCup99 which is called NSL-KDD.

• 한국컴퓨터정보학회논문지
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• 제16권4호
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• pp.1-13
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• 2011

본 논문에서는 MHI(Motion History Image)의 형태학적 정보를 이용하여 동작을 인식하는 제스처 인식(Gesture Recognition) 시스템을 제안한다. 입력되는 영상으로부터 동작에 관한 정보를 제공하는 MHI를 획득하고, 이 MHI로부터 x, y 각각의 좌표에 대한 기울기(gradient) 영상을 추출한다. 각각의 기울기 영상에 형태 문맥기법(shape context method)을 적용하여 형태 정보를 추출하고, 추출된 형태 정보 값들을 특징 값으로 사용한다. 이렇게 획득한 특징값들을 최종적으로 SVM(Support Vector Machine) 분류기로 학습 및 분류하여 동작을 인식한다. 제안하는 시스템은 MHI의 형태학적인 정보들을 사용함으로써 동작의 방향성을 인식할수 있고 다수 사람의 동작 인식이 가능하다. 뿐만 아니라 간단한 특징 추출 방법으로 높은 인식률의 시스템을 구현하였다.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.364-377
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• 2008

Due to the difficulty in solving the Hamilton-Jacobi-Isaacs equation, the nonlinear optimal control approach is not very practical in general. To overcome this problem, Ezal et al. (2000) first solved a linear optimal control problem for the linearized model of a nonlinear system given in the strict-feedback form. Then, using the backstepping procedure, a nonlinear feedback controller was designed where the linear part is same as the linear feedback obtained from the linear optimal control design. However, their construction is based on the cancellation of the high order nonlinearity, which limits the application to the smooth ($C^{\infty}$) vector fields. In this paper, we develop an alternative method for backstepping procedure, so that the vector field can be just $C^1$, which allows this approach to be applicable to much larger class of nonlinear systems.

• 제어로봇시스템학회논문지
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• 제21권7호
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• pp.662-668
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• 2015

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.