• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.89-93
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• 2014

Reviews are very important for potential consumer' making choices. They are also used by manufacturers to find problems of their products and to collect competitors' business information. But someone write fake reviews to mislead readers to make wrong choices. Therefore detecting fake reviews is an important problem for the E-commerce sites. Support Vector Machines (SVMs) are very important text classification algorithms with excellent performance. In this paper, we propose a new incremental algorithm based on weight and the extension of Karush-Kuhn-Tucker(KKT) conditions and Convex Hull for online Review Spam Detection. Finally, we analyze its performance in theory.

• International Journal of Contents
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• v.15 no.3
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.1-8
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• 2004

For the linearized differential algebraic equation of the nonlinear constrained system, exact initial values of the acceleration are needed to solve itself. It may be very troublesome to perform the inverse operation for obtaining the incremental quantities since the mass matrix contains the zero element in the diagonal. This fact makes the mass matrix impossible to be positive definite. To overcome this singularity phenomenon the mass matrix needs to be modified to allow the feasible application of predictor and corrector in the iterative computation. In this paper the proposed numerical algorithm based on the modified mass matrix combines the conventional implicit algorithm, Newton-Raphson method and Newmark method. The numerical example presents reliabilities for the proposed algorithm via comparisons of the 4th order Runge-kutta method. The proposed algorithm seems to be satisfactory even though the acceleration, Lagrange multiplier, and energy show unstable behaviour. Correspondingly, it provides one important clue to another algorithm for the enhancement of the numerical results.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.474-478
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• 1999

Genetic algorithm is an efficient search method using principles of natural selection and population genetics. In conventional genetic algorithms, however, the size of gene pool should be increased to insure a convergency. Therefore, many memory spaces and much computation time were needed. Also, since child chromosomes were generated by chromosome crossover and gene mutation, the algorithms have a complex structure. Thus, in this paper, a compact stereo matching algorithm using a population-based incremental teaming based on probability vector is proposed to reduce these problems. The PBIL method is modified for matching environment. Since the Proposed algorithm uses a probability vector and eliminates gene pool, chromosome crossover, and gene mutation, the matching algorithm is simple and the computation load is considerably reduced. Even if the characteristics of images are changed, stable outputs are obtained without the modification of the matching algorithm.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.19-27
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• 2009

This paper presents a newly developed optimization algorithm, the Bee Optimization Algorithm (BeeOA), to solve the economic power dispatch (EPD) problem. The authors have developed a derivative free and global optimization technique based on the working of the honey bee. The economic power dispatch problem is a nonlinear constrained optimization problem. Classical optimization techniques fail to provide a global solution and evolutionary algorithms provide only a good enough solution. The proposed approach has been examined and tested on two test systems with different objectives. A simple power dispatch problem is tested first on 6 generators and then the algorithm is demonstrated on 13 thermal unit systems whose incremental fuel cost function takes into account the value point loading effect. The results are promising and show the effectiveness and robustness of the proposed approach over recently reported methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.10
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• pp.480-488
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• 2001

This paper presents a technique that can obtain an optimal solution for the Security-Constrained Economic Dispatch (SCED) problems using the Interior Point Method (IPM) while taking into account of the power flow constraints. The SCED equations are formulated by using only the real power flow equations from the optimal power flow. Then an algorithm is presented that can linearize the SCED equations based on the relationships among generation real power outputs, loads, and transmission losses to obtain the optimal solutions by applying the linear programming (LP) technique. The objective function of the proposed linearization algorithm are formulated based on the fuel cost functions of the power plants. The power balance equations utilize the Incremental Transmission Loss Factor (ITLF) corresponding to the incremental generation outputs and the line constraints equations are linearized based on the Generalized Generation Distribution Factor (GGDF). Finally, the application of the Primal Interior Point Method (PIPM) for solving the optimization problem based on the proposed linearized objective function is presented. The results are compared with the Simplex Method and the promising results ard obtained.

• Transactions of Materials Processing
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• v.16 no.8
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• pp.590-595
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• 2007

This paper introduces a new approach to take account of bending history in finite element inverse analysis during sheet metal forming process. A modified membrane element was adopted for finite element inverse analysis so that bending-unbending energy was additionally imposed in the total plastic energy, predicting bending-unbending regions using the geometry of the final shape and tools. An algorithm was applied to a cylindrical cup deep drawing process. The blank shape and the distribution of the thickness strain were compared with those obtained from the incremental finite element analysis in order to evaluate the effect of the bending history. The algorithm reduced the difference between the results of the inverse analysis from those of the incremental analysis due to bending history. The analysis was also carried out with the variation of the thickness of the initial blank to investigate the effect of bending deformation. The results showed that the difference was remarkably reduced as the thickness of the initial blank increased. This indicates that the finite element inverse analysis cooperated with the suggested scheme is useful to obtain more accurate results, especially when bending effects are significant.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.4
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• pp.617-624
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• 2014

Smart phone usage has increased exponentially and open source based Android OS occupy significant market share. However, various malicious applications that use the characteristic of Android threaten users. In this paper, we construct an efficient malicious application detector by using the principle component analysis and the incremental k nearest neighbor algorithm, which consider an required permission, of Android applications. The cross validation is exploited in order to find a critical parameter of the algorithm. For the performance evaluation of our approach, we simulate a real data set of Contagio Mobile.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.9
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• pp.74-82
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• 2013

This paper presents a novel maximum power point tracking for a photovoltaic power (PV) system with a direct control plan. Maximum power point tracking (MPPT) must usually be integrated with photovoltaic (PV) power systems so that the photovoltaic arrays are able to deliver maximum available power. The maximum available power is tracked using specialized algorithms such as Perturb and Observe (P&O) and incremental Conductance (indCond) methods. The proposed method has the direct control of the MPPT algorithm to change the duty cycle of a dc-dc converter. The main difference of the proposed system to existing MPPT systems includes elimination of the proportional-integral control loop and investigation of the effect of simplifying the control circuit. The proposed method thus has not only faster dynamic performance but also high tracking accuracy. Without a conventional controller, this method can control the dc-dc converter. A simulation model and the direct control of MPPT algorithm for the PV power system are developed by Matlab/Simulink, SimPowerSystems and Matlab/Stateflow.

• Journal of KIISE:Software and Applications
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• v.29 no.12
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• pp.957-965
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• 2002

Garbage collection in the CLDC typically employs a stop-the-world GC algorithm which is performing a complete garbage collection when needed. This technique is unsuitable for the interactive Java embedded system because this can lead to long and unpredictable delays. In this paper, We present a garbage collection algorithm which reduces the average delay time and supports the interactive environment. Our garbage collector is composed of the allocator and the collector. The allocator determines the allocation position of free-list according to object size, and the collector uses an incremental mark-sweep algorithm. The garbage collector is called periodically by the thread scheduling policy and the allocator allocates the objects of marked state during collection cycle. Also, we introduce a color toggle mechanism that changes the meaning of the bit patterns at the end of the collection cycle. We compared the performance of our implementation with stop-the-world mark-sweep GC. The experimental results show that our algorithm reduces the average delay time and that it provides uniformly low response times.