• Journal of the Korea Society of Computer and Information
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• 제19권10호
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• pp.1-12
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• 2014

This paper implements and improves the performance of high computational subtractive clustering algorithm using a single instruction, multiple data (SIMD) based many-core processor. In addition, this paper implements five different processing element (PE) architectures (PEs=16, 64, 256, 1,024, 4,096) to select an optimal PE architecture for the subtractive clustering algorithm by estimating execution time and energy efficiency. Experimental results using two different medical images and three different resolutions ($128{\times}128$, $256{\times}256$, $512{\times}512$) show that PEs=4,096 achieves the highest performance and energy efficiency for all the cases.

• Computers and Concrete
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• 제21권6호
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Transactions of the Society of Information Storage Systems
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• 제6권1호
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• pp.31-36
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• 2010

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanical actuating part so fast data transfer rate and high storage capacity about 1Tb/cm3 can be realized. In this research, to correct errors of binary data stored in holographic data storage system, a new method for reduction errors is suggested. First, find cluster centers using subtractive clustering algorithm then reduce intensities of pixels around cluster centers. By using this error reduction method following results are obtained ; the effect of Inter Pixel Interference noise in the holographic data storage system is decreased and the intensity profile of data page becomes uniform therefore the better data storage system can be constructed.

• Nuclear Engineering and Technology
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• 제42권4호
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• pp.414-425
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• 2010

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into "families". On the basis of the decision maker's preferences, each family is then synthetically represented by a "head of the family" solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions.

• Journal of the Institute of Electronics Engineers of Korea TE
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• 제39권3호
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• pp.51-58
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• 2002

The conventional code division multiple access(CDMA) detector shows severe degradation in communication quality as the number of users increases due to multiple access interferences(MAI). This problem thus restricts the user capacity. Various multiuser detection algorithms have been proposed to overcome the MAI problem. The existing detectors can be generally classified into one of the two categories : linear multiuser detection and subtractive interference cancellation detectors. In the linear multiuser detection, a linear transform is applied to the soft outputs of the conventional detector. In the subtractive interference cancellation detection, estimates of the interference are generated and subtracted out from the received signal. There has been great interest in the family of the subtractive interference cancellation detection because the linear multiuser detection exhibits the disadvantage of taking matrix inversion operations. The successive interference cancellation (SIC) and the parallel interference cancellation (PIC) are the two most popular structures in the subtractive interference cancellation detector. The SIC structure is very simple in hardware complexity, but has the disadvantage of increased processing delay time, while the PIC structure is good in performance, but shows the disadvantage of increased hardware complexity. In this paper we propose a mixed structure form of SIC and PIC in order to achieve good performance as well as simple hardware complexity. A performance analysis of the proposed scheme has been made, and the superior characteristics of the mixed structure are demonstrated by extensive computer simulations. 

• Journal of the Institute of Electronics and Information Engineers
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• 제53권2호
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• Journal of Korea Water Resources Association
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• 제37권6호
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• pp.449-460
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• 2004

The purpose of this study is to test the applicability of neuro-fuzzy system for monthly dam inflow forecasts by using weather forecasting information. The neuro-fuzzy algorithm adopted in this study is the ANFIS(Adaptive neuro-fuzzy Inference System) in which neural network theory is combined with fuzzy theory. The ANFIS model can experience the difficulties in selection of a control rule by a space partition because the number of control value increases rapidly as the number of fuzzy variable increases. In an effort to overcome this drawback, this study used the subtractive clustering which is one of fuzzy clustering methods. Also, this study proposed a method for converting qualitative weather forecasting information to quantitative one. ANFIS for monthly dam inflow forecasts was tested in cases of with or without weather forecasting information. It can be seen that the model performances obtained from the use of past observed data and future weather forecasting information are much better than those from past observed data only.

• Smart Structures and Systems
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• 제16권6호
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• 정보저장시스템학회:학술대회논문집
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• 정보저장시스템학회 2005년도 추계학술대회 논문집
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• pp.129-133
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• 2005

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanical actuating part therefore fast data transfer rate and high storage capacity about $1Tb/cm^3$ can be realized. In this paper, to reduce errors of binary data stored in holographic data storage system, a new method for bit error reduction is suggested. First, find cluster centers using subtractive clustering algorithm then reduce intensities of pixels around cluster centers and fuzzy rules. Therefore, By using this error reduction method following results are obtained ; the effect of Inter Pixel Interference noise is decreased and the intensity profile of data page becomes uniform therefore the better data storage system can be constructed.

• Nuclear Engineering and Technology
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• 제40권1호
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• pp.69-76
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• 2008

Most pressurized water reactors use Venturi flow meters to measure the feedwater flow rate. However, fouling phenomena, which allow corrosion products to accumulate and increase the differential pressure across the Venturi flow meter, can result in an overestimation of the flow rate. In this study, a soft-sensing model based on fuzzy support vector regression was developed to enable accurate on-line prediction of the feedwater flow rate. The available data was divided into two groups by fuzzy c means clustering in order to reduce the training time. The data for training the soft-sensing model was selected from each data group with the aid of a subtractive clustering scheme because informative data increases the learning effect. The proposed soft-sensing model was confirmed with the real plant data of Yonggwang Nuclear Power Plant Unit 3. The root mean square error and relative maximum error of the model were quite small. Hence, this model can be used to validate and monitor existing hardware feedwater flow meters.