• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.100-104
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• 2003

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.288-289
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• 2019

This paper studies the secrecy capacity for a wireless cooperative network with perfect channel state information at the relays, and receiver. A similar assumption is also made for the instance where there exist a direct link between the transmitter and receiver. Physical Layer security techniques are employed in wireless networks to mitigate against the activity of eavesdroppers. It offers a viable alternative to computationally intensive encryption. In this paper the design of a protocol utilizing jamming (via jamming nodes) for better security and relaying (via relay nodes) for the amplify-and-forward (AF) operation, is investigated. A a signal-to-noise variant of secrecy known as secrecy gap is explored because of its use of lesser computational power - preferable for practical systems. Thus we maximize this signal-to-noise approach instead of the conventional secrecy capacity maximization method. With this, an iterative algorithm using geometric programming (GP) and semi-definite programming (SDP) is presented with appreciable benefits. The results show here highlight the benefits of using fractional components of the powers of the relays to offer better secrecy capacity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.5
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• pp.539-545
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• 2016

In this paper, the use of an amplify-and-forward (AF) helper station in a cellular network with device-to-device (D2D) communication links is considered to enhance D2D rates and control the interference caused by D2D users to the cellular network. Two design criteria for the AF helper station are considered to improve the overall system quality-of-service (QoS). One is maximization of the worst D2D user rate under a constraint on interference caused by D2D users to the cellular network and the other is its dual, i.e., minimization of interference caused by D2D users to the cellular network with minimum rate guarantee for each D2D user. It is shown that the considered problems reduce to semi-definite programming (SDP) problems. Numerical results show that the proposed AF helper station significantly improves the system performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2546-2566
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• 2016

This paper investigates an efficient transmission scheme for the remote wireless sensors to receive information which is rarely discussed in the integrated remote wireless sensor and multibeam satellite networks (IWSMSNs). The networks can be employed to exchange sensing information for emergency scenario, ocean scenario, and so on, which are isolated from available terrestrial networks. As the efficient transmission link is important to the IWSMSNs, we propose a hybrid full frequency (HFF) precoding by taking advantage of frequency reuse and multiple-input multiple-output (MIMO) precoding. Considering energy efficiency and sinks fairness are crucial to transmission link, thus the HFF precoding problems are formulated as transmit power minimization (TPM) and max-min fair (MMF) received signal to interference plus noise ratio (SINR) problems, which can be transformed to indefinite quadratic optimization programs. Then this paper presents a semi-definite programming (SDP) algorithm to solve the problems for the IWSMSNs. The promising potential of HFF for the real IWSMSNs is demonstrated through simulations.

• Communications of the Korean Mathematical Society
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• v.9 no.3
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• pp.753-765
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• 1994

The linearly constrained quadratic programming(QP) considered is : $$ min f(x) = c^T x + \frac{1}{2}x^T Hx $$ $$ (1) subject to A^T x \geq b,$$ where $c,x \in R^n, b \in R^m, H \in R^{n \times n)}$, symmetric, and $A \in R^{n \times n}$. If there are bounds on x, these are included in the matrix $A^T$. The Hessian matrix H may be positive definite or negative semi-difinite. For large problems H and the constraint matrix A are assumed to be sparse.

• Journal of applied mathematics & informatics
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• v.10 no.1_2
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• pp.83-99
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• 2002

Optimality conditions are derived for a nonlinear program in which a support function appears in the objective as well as in each constraint function. Wolfe and Mond-Weir type duals to this program are presented and various duality results are established under suitable convexity and generalized convexity assumptions. Special cases that often occur in the literature are those in which a support function is the square root of a positive semi- definite quadratic form or an Lp norm. It is pointed out that these special cases can easily be generated from our results.

• Journal of the Korean Mathematical Society
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• v.41 no.5
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• pp.821-864
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• 2004

Both parametric and parameter-free necessary and sufficient optimality conditions are established for a class of nondiffer-entiable nonconvex optimal control problems with generalized fractional objective functions, linear dynamics, and nonlinear inequality constraints on both the state and control variables. Based on these optimality results, ten Wolfe-type parametric and parameter-free duality models are formulated and weak, strong, and strict converse duality theorems are proved. These duality results contain, as special cases, similar results for minmax fractional optimal control problems involving square roots of positive semi definite quadratic forms, and for optimal control problems with fractional, discrete max, and conventional objective functions, which are particular cases of the main problem considered in this paper. The duality models presented here contain various extensions of a number of existing duality formulations for convex control problems, and subsume continuous-time generalizations of a great variety of similar dual problems investigated previously in the area of finite-dimensional nonlinear programming.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.1-6
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• 2007

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. Recently, with the objective of generalizing the SVDD which treats all training data with equal importance, the so-called D-SVDD (density-induced support vector data description) was proposed incorporating the idea that the data in a higher density region are more significant than those in a lower density region. In this paper, we consider the problem of further improving the D-SVDD toward the use of a partial reference set for testing, and propose an LMI (linear matrix inequality)-based optimization approach to solve the improved version of the D-SVDD problems. Our approach utilizes a new class of density-induced distance measures based on the RSDE (reduced set density estimator) along with the LMI-based mathematical formulation in the form of the SDP (semi-definite programming) problems, which can be efficiently solved by interior point methods. The validity of the proposed approach is illustrated via numerical experiments using real data sets.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.1
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• pp.35-41
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• 2010

In developing modern aircraft, the reconfiguration control that can improve the safety and the survivability against the unexpected failure by partitioning control surfaces into several parts has been actively studied. This paper deals with the reconfiguration control using model predictive control method considering the saturation of control surfaces under the control surface failure. Linearized aircraft model at trim condition is used as the internal model of model predictive control. We propose the controller that performs optimization using LMI (linear matrix inequalities) based semi-definite programming in case that control surface saturation occurs, otherwise, uses analytic solution of the model predictive control. The performance of the proposed control method is evaluated by nonlinear simulation under the flight scenario of control surface failure.