• ETRI Journal
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• 제39권5호
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• pp.621-631
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• 2017

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

• 한국컴퓨터정보학회논문지
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• 제24권11호
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• pp.119-126
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• 2019

부분역최적화는 역최적화의 흥미로운 변형으로, 주어진 최적화문제와 그 문제의 부분해가 주어지면 이 부분해가 최적해에 포함되도록 문제를 최소한으로 수정하는 문제이다. 이 논문은 라인위에서 정의되는 순환외판원문제(TSP)를 다루는데, 이는 배달시스템, 창고 선반에서 물건을 수집하는 것, 등의 많은 응용을 가진다. 라인 위에서 위치하는 n개의 일이 주어지고 이 중 연속적으로 처리해야하는 일 k개가 부분적으로 주어진다. 각각의 일은 라인 위의 특정 장소에 위치하고 라인을 움직이는 서버에 의해 처리되어야 한다. 우리의 임무는 k개의 일이 최적해에서 연속적으로 처리되도록 n개의 일의 위치를 라인 위에서 최소한으로 조정하는 것이다. 이 논문에서 이 문제와 이 문제의 다양한 변종을 다항시간 내에 푸는 알고리즘을 개발한다. 구체적으로, 서버가 특정한 Forward Trip이라는 특정한 내부 알고리즘을 사용하는 경우와 일반적인 최적 알고리즘을 사용하는 경우에 대한 부분역최적화를 다룬다.

• 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.

• 대한기계학회논문집A
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• 제36권7호
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• pp.805-811
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• 2012

신뢰성 기반 최적설계의 효과적인 수행을 위하여 개발된 단일루프 단일벡터 방법은 신뢰성 해석의 계산과정을 제거함으로써 최적설계 시 발생하는 과도한 계산비용을 줄일 수 있다. 하지만 성능함수의 오목한 정도가 심할 경우, 수렴을 하지 못하고 발산하는 경향을 보인다. 때문에 일반적인 단일루프 단일벡터 방법은 낮은 수렴성과 부정확성 문제를 내포하고 있다. 본 연구에서는 공액경사도법을 이용한 단일루프 단일벡터 방법을 제안한다. 공액경사도법은 이전 반복과정의 최대가능손상점에서 계산된 방향 벡터들을 이용하여 현재 설계점에서의 최대가능손상점을 산출하기 위한 새로운 방향벡터를 구하고 이 방향벡터를 이용하여 현재점에서의 최적화를 수행한다. 이를 다양한 수학예제에 적용하고 다른 방법들과 수치적 성능 비교를 통해 제안한 방법의 유용성을 검증한다. 공액경사도법을 이용한 단일루프 단일벡터 방법은 성능함수 특성에 크게 영향을 받지 않으며 수렴성을 크게 향상시킬 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2422-2443
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• 2020

This paper studies a single-user Mobile Edge Computing (MEC) system where mobile device (MD) includes an application consisting of multiple computation components or tasks with dependencies. MD can offload part of each computation-intensive latency-sensitive task to the AP integrated with MEC server. In order to accomplish the application faultlessly, we calculate out the optimal task offloading strategy in a time-division manner for a predetermined execution order under the constraints of limited computation and communication resources. The problem is formulated as an optimization problem that can minimize the energy consumption of mobile device while satisfying the constraints of computation tasks and mobile device resources. The optimization problem is equivalently transformed into solving a nonlinear equation with a linear inequality constraint by leveraging the Lagrange Multiplier method. And the proposed dual Bi-Section Search algorithm Bi-JOTD can efficiently solve the nonlinear equation. In the outer Bi-Section Search, the proposed algorithm searches for the optimal Lagrangian multiplier variable between the lower and upper boundaries. The inner Bi-Section Search achieves the Lagrangian multiplier vector corresponding to a given variable receiving from the outer layer. Numerical results demonstrate that the proposed algorithm has significant performance improvement than other baselines. The novel scheme not only reduces the difficulty of problem solving, but also obtains less energy consumption and better performance.

• Journal of the Korean Data and Information Science Society
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• 제28권6호
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• pp.1271-1278
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• 2017

Although the $L_0$-penalty is the most natural choice to identify the sparsity structure of the model, it has not been widely used due to the computational bottleneck. Recently, the adaptive ridge procedure is developed to efficiently approximate a $L_q$-penalized problem to an iterative $L_2$-penalized one. In this article, we proposed to apply the adaptive ridge procedure to solve the $L_0$-penalized weighted support vector machine (WSVM) to facilitate the corresponding optimization. Our numerical investigation shows the advantageous performance of the $L_0$-penalized WSVM compared to the conventional WSVM with $L_2$ penalty for both simulated and real data sets.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2004년도 추계학술대회
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• Journal of Electrical Engineering and Technology
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• 제4권3호
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• pp.365-369
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• 2009

Identification is considered to be among the main applications of inverse theory and its objective for a given physical system is to use data which is easily observable, to infer some of the geometric parameters which are not directly observable. In this paper, a parameter identification method using inverse problem methodology is proposed. The minimisation of the objective function with respect to the desired vector of design parameters is the most important procedure in solving the inverse problem. The conjugate gradient method is used to determine the unknown parameters, and Tikhonov's regularization method is then used to replace the original ill-posed problem with a well-posed problem. The simulation and experimental results are presented and compared.

• 한국컴퓨터정보학회논문지
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• 제15권7호
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• pp.11-17
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• 2010

본 논문에서는 클래스 불균형 학습을 위한 이차 최적화 문제의 해를 구하는 개선된 SMO 학습 알고리즘을 제안한다. 클래스에 서로 다른 정규화 값이 부여되는 지지벡터기계의 최적화 문제의 구현에 SMO 알고리즘이 적합하며, 제안된 알고리즘은 서로 다른 클래스에서 선택된 두 라그랑지 변수의 현재 해를 구하는 학습 단계를 반복한다. 제안된 학습 알고리즘은 UCI 벤치마킹 문제에서 테스트되어 클래스 불균형 분포를 반영하는 g-mean 평가를 이용한 일반화 성능이 SMO 알고리즘과 비교되었다. 실험 결과에서 제안된 알고리즘은 SMO에 비해 적은 클래스 데이터의 예측율을 높이고 학습시간을 단축시킬 수 있다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.1145-1148
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• 2002

This paper presents a new method which is calculated to use only three vanishing points in order to compute the dimensions of object and its pose from a single image of perspective projection taken by a camera and the problem of recovering 3D models from three vanishing points of box scene. Our approach is to compute only three vanishing points without this information such as the focal length, rotation matrix, and translation from images in the case of perspective projection. We assume that the object can be modeled as a linear function of a dimension vector ν. The input of reconstruction is a set of correspondences between features in the model and features in the image. To minimize each the dimensions of the parameterized models, this reconstruction of optimization can be solved by the standard nonlinear optimization techniques with a multi-start method which generates multiple starting points for the optimizer by sampling the parameter space uniformly.