• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.4
• /
• pp.575-594
• /
• 2010

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.1
• /
• pp.12-18
• /
• 2011

In this paper, we proposed Interval Type-2 polynomial Radial Basis Function Neural Networks. In the receptive filed of hidden layer, Interval Type-2 fuzzy set is used. The characteristic of Interval Type-2 fuzzy set has Footprint Of Uncertainly(FOU), which denotes a certain level of robustness in the presence of un-known information when compared with the type-1 fuzzy set. In order to improve the performance of proposed model, we used the linear polynomial function as connection weight of network. The parameters such as center values of receptive field, constant deviation, and connection weight between hidden layer and output layer are optimized by Conjugate Gradient Method(CGM) and Space Search Evolutionary Algorithm(SSEA). The proposed model is applied to gas furnace dataset and its result are compared with those reported in the previous studies.

• Journal of Communications and Networks
• /
• v.17 no.1
• /
• pp.1-11
• /
• 2015

In this paper, the transmission of an improper-complex second-order stationary data sequence is considered over a strictly band-limited frequency-selective channel. It is assumed that the transmitter employs linear modulation and that the channel output is corrupted by additive proper-complex cyclostationary noise. Under the average transmit power constraint, the problem of minimizing the mean-squared error at the output of a widely linear receiver is formulated in the time domain to find the optimal transmit and receive waveforms. The optimization problem is converted into a frequency-domain problem by using the vectorized Fourier transform technique and put into the form of a double minimization. First, the widely linear receiver is optimized that requires, unlike the linear receiver design with only one waveform, the design of two receive waveforms. Then, the optimal transmit waveform for the linear modulator is derived by introducing the notion of the impropriety frequency function of a discrete-time random process and by performing a line search combined with an iterative algorithm. The optimal solution shows that both the periodic spectral correlation due to the cyclostationarity and the symmetric spectral correlation about the origin due to the impropriety are well exploited.

• Structural Engineering and Mechanics
• /
• v.62 no.6
• /
• pp.749-758
• /
• 2017

In structural mechanics, traditional analyses methods usually employ matrix operations for obtaining displacement and internal forces of the structure under the external effects, such as distributed loads, earthquake or wind excitations, and temperature changing inter alia. These matrices are derived from the well-known principle of mechanics called minimum potential energy. According to this principle, a system can be in the equilibrium state only in case when the total potential energy of system is minimum. A close examination of the expression of the well-known equilibrium condition for linear problems, $P=K{\Delta}$, where P is the load vector, K is the stiffness matrix and ${\Delta}$ is the displacement vector, it is seen that, basically this principle searches the displacement set (or deformed shape) for a system that minimizes the total potential energy of it. Instead of using mathematical operations used in the conventional methods, with a different formulation, meta-heuristic algorithms can also be used for solving this minimization problem by defining total potential energy as objective function and displacements as design variables. Based on this idea the technique called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) is proposed. The method has been successfully applied for linear and non-linear analyses of trusses and truss-like structures, and the results have shown that the approach is much more successful than conventional methods, especially for analyses of non-linear systems. In this study, the application of TPO/MA, with Harmony Search as the selected meta-heuristic algorithm, to cables net system is presented. The results have shown that the method is robust, powerful and accurate.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.6
• /
• pp.647-666
• /
• 2009

Multiple-input multiple-output (MIMO) multiplexing is an attractive technology that increases the channel capacity without requiring additional spectral resources. The design of low complexity and high performance detection algorithms capable of accurately demultiplexing the transmitted signals is challenging. In this technical survey, we introduce the state-of-the-art MIMO detection techniques. These techniques are divided into three categories, viz. linear detection (LD), decision-feedback detection (DFD), and tree-search detection (TSD). Also, we introduce the lattice basis reduction techniques that obtain a more orthogonal channel matrix over which the detection is done. Detailed discussions on the advantages and drawbacks of each detection algorithm are also introduced. Furthermore, several recent author contributions related to MIMO detection are revisited throughout this survey.

• Communications for Statistical Applications and Methods
• /
• v.22 no.6
• /
• pp.543-556
• /
• 2015

We present a survey of contributions that defined the nature and extent of robust statistics for the last 50 years. From the pioneering work of Tukey, Huber, and Hampel that focused on robust location parameter estimation, we presented various generalizations of these estimation procedures that cover a wide variety of models and data analysis methods. Among these extensions, we present linear models, clustered and dependent observations, times series data, binary and discrete data, models for spatial data, nonparametric methods, and forward search methods for outliers. We also present the current interest in robust statistics and conclude with suggestions on the possible future direction of this area for statistical science.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.33A no.5
• /
• pp.172-179
• /
• 1996

In the paper, QCELP (qualcomm code excited linear predictive), CDMA (code division multiple access)'s vocoder algorithm, was analyzed. And then, a ptich parameter seaarch architecture for 16-bit programmable DSP(digital signal processor) for QCELP was designed. Because we speed up the parameter search through high speed DSP using two MACs, we can satisfy speech codec specifiction for the digital celluar. Also, we implemented in FIFO(first-in first-out) memory using register file to increase the access time of data. This DSP was designed using COMPASS, ASIC design tool, by top-down design methodology. Therefore, it is possible to cope with rapid change at mobile communication market.

• Korean Journal of Remote Sensing
• /
• v.8 no.1
• /
• pp.15-26
• /
• 1992

This paper describes an implementation of adaptive stereo matching for DBM generation. The matching method of two stereo satellite images to find corresponding points used in this paper is area-based matching, which is usually used in the field of making DBM. Same window size and search area used as in the conventional matching methods and we propose adaptive stereo matching algorithm in this paper. We cluster three areas which are consist of mountainous areas, cultivated areas and cities, and rivers and lakes by using proposed linear feature extracting method. These classified areas are matched by adaptive window size and search area, but rivers and lakes is excluded in this experiment. The matching time is three times faster than conventional methods.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.12
• /
• pp.204-213
• /
• 1999

This paper suggests a numerical method of finding optimal geometric path and minimum-time motion for a manipulator arm. To find the minimum-time motion, the optimal geometric path is searched first, and the minimum-time motion is searched on this optimal path. In the algorithm finding optimal geometric path, the objective function is minimizing the combination of joint velocities, joint-jerks, and actuator forces as well as avoiding several static obstacles, where global search is performed by adjusting the seed points of the obstacle models. In the minimum-time algorithm, the traveling time is expressed by the linear combinations of finite-term quintic B-splines and the coefficients of the splines are obtained by nonlinear programming to minimize the total traveling time subject to the constraints of the velocity-dependent actuator forces. These two search algorithms are basically similar and their convergences are quite stable.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.40-48
• /
• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.