• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1162-1171
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• 1998

This paper presents a data structure that implements a mergable double-ended priority queue : namely an improved relaxed min-max-pair heap. By means of this new data structure, we suggest a parallel algorithm to merge priority queues organized in two relaxed heaps of different sizes, n and k, respectively. This new data-structure eliminates the blossomed tree and the lazying method used to merge the relaxed min-max heaps in [9]. As a result, employing max($2^{i-1}$,[(m+1/4)]) processors, this algorithm requires O(log(log(n/k))${\times}$log(n)) time. Also, on the MarPar machine, this method achieves a 35.205-fold speedup with 64 processors to merge 8 million data items which consist of two relaxed min-max heaps of different sizes.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.73-82
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• 1995

This paper presents a data structure that implements a mergeable double-ended priority queue ; namely, an improved relaxed min-max-pair heap. It suggests a sequential algorithm to merge priority queues organized in two relaxed min-max heaps : kheap and nheap of sizes k and n, respecrively. This new data sturuture eliminates the blossomed tree and the lazying method used to merge the relaxed min-max heaps in [8]. As a result, the suggested method in this paper requires the time complexity of O(log(log(n/k))*log(k)) and the space complexity of O(n+), assuming that $k{\leq}{\lfloor}log(size(nheap)){\rfloor}$ are in two heaps of different sizes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1630-1636
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• 2022

The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1195-1200
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• 2013

In this paper, we proposed an enhanced Max-Min neural network by auto-tuning of learning rate using fuzzy control method. For the reduction of training time required in the competition stage, the method was proposed that arbitrates dynamically the learning rate by applying the numbers of the accuracy and the inaccuracy to the input of the fuzzy control system. The experiments using real concrete crack images showed that the enhanced Max-Min neural network was effective in the recognition of direction of the extracted cracks.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.367-367
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian membership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.118-126
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• 1996

In object analysis by image processing, finding lines plays a universal role. And these lines can be easily found by detecting ridges and valleys in digital gray scale images. In this paper, a new method of detecting ridges and valleys by using local min/max operations was presented. This method detects ridges and valleys of desired width by using erosion and dilation properties of local min/max operations, and requires no information of ridge or valley direction. Therefore the method is efficient and computationally simple in comparision with the conventional analytical method.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.776-782
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• 2004

Generally, Max-Min CRI (Compositional Rule of Inference ) method by Zadeh and Mamdani is used in the conventional fuzzy inference. However, owing to the problems of Max-Min CRI method, the inference often results in significant error regions specifying the difference between the desired outputs and the inferred outputs. In this paper, I propose a New Max-Min CRI method which can solve some problems of the conventional Max-Min CRI method. And then this method is simulated in a D.C.series motor, which is a bench marking system in control systems, and showed that the new method performs better than the other fuzzy inference methods.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.96-104
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• 1998

A new gray-scale thinning algorithm using local min/max operations is proposed. In this method, erosion and dilation properties of local min/max operations are using for generating new rides and detecting ridges in gray scale image, and gray-scale skeletons are gradually obtained by accumulating the detected ridges. This method can be applicable to the unsegmented image in which object are not specified, and the obtained skeletons correspond to the ridges (high gray values) of an input image.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.371-380
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• 2013

The purpose of this study was to develop the equation of $\dot{V}O_{2max}$ by $sub_{max}imal$ PACER method for obese middle school boys. For this, $_{max}$imal test using Bruce protocol in lab was performed and then PACER $_{max}imal$ test with portable $\dot{V}O_{2max}$ equipment. To decide the level of submaximal test, during PACER with portable equipment, we found the section in which target hreat rate(over 75%$HR_{max}$) and then per section(75%,80%,85%,90%,95%) metabolic responses were recorded, with which we analyzed multiple regression by stepwise method. Model 1(at 90%$HR_{max}$): $\dot{V}O_{2max}$(ml/kg/min) = 142.721-0.275(repetition)-0.48(HR)+0.177(weight)-1.536(age)[%error 3.90ml/kg/min; performance until 2 stage(13 repetition)]. Model 2(at 95%$HR_{max}$): $\dot{V}O_{2max}$(ml/kg/min) = 182.851-0.103(repetition)-0.744(HR)+0.186(weight)-0.324(age)[%error 4.51ml/kg/min; performance until 3 stage(25 repetitions)]. estimated $\dot{V}O_{2max}$ from Model 1 was different about $3.25{\pm}6.32ml/kg/min$(%error=6.84%), otherwise model 2 was $3.16{\pm}4.54ml/kg/min$(%error=5.75%). considering %HRmax, as the submaximal test model 1 might be fit more than model 2 for obese middle school boys.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.149-161
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• 1994

The Max-Min CRI method , a traditional inference method , has three problems: subjective formulation of membership functions, error-prone weighting strategy, and inefficient compositional rule of inference. Because of these problems, there is an insurmountable error region between desired output and inferred output. To overcome these problems, we propose an Intelligent Neuro-Fuzzy System (INFS) based on fuzzy thoery and self-organizing functions of neural networks. INFS makes use of neural networks(Error Back Propagation) to solve the first problem, and NCRI(New Max-Min CRI) method for the second. With a proposed similarity measure, NCRI method is an improved method compared to the traditional Max-Min CRI method. For the last problem, we propose a new defuzzification method which combines only the appropriate rules produced by the rule selection level. Applying INFS to a D.C. series motor, we can conclude that the error region is reduced and NCRI method performs better than Max-Min CRI method.