• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.5 s.98
• /
• pp.48-57
• /
• 1999

Applications of thresholding technique are based on the assumption that object and background pixels in a digital image can be distinguished by their gray level values. For the segmentation of more complex images, it is necessary to resort to multiple threshold selection techniques. This paper describes a new method for multiple threshold selection of gray level images which are not clearly distinguishable from the background. The proposed method consists of three main stages. In the first stage, a probability distribution function for a gray level histogram of an image is derived. Cluster points are defined according to the probability distribution function. In the second stage, fuzzy partition matrix of the probability distribution function is generated through the fuzzy clustering process. Finally, elements of the fuzzy partition matrix are classified as clusters according to gray level values by using max-membership method. Boundary values of classified clusters are selected as multiple threshold. In order to verify the performance of the developed algorithm, automatic inspection process of ball grid array is presented.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.41 no.10
• /
• pp.9-16
• /
• 2004

The state assignment for a finite state machine greatly affects the delay, area, power dissipation, and testabilities of the sequential circuits. In order to improve the testabilities and power consumption, a new state assignment technique based on m-block partition is introduced in this paper. The algorithm minimizes the dependencies between groups of state variables are minimized and reduces switching activity by grouping the states depending on the state transition probability. In the sequel the length and number of feedback cycles are reduced with minimal switching activity on the state variables. Experiment shows significant improvement in testabilities and Power dissipation for benchmark circuits.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.1
• /
• pp.37-43
• /
• 2023

Compressed still image or video bitstreams require protection from channel errors in a wireless channel. Embedded Zerotree Coding(EZW), SPIHT could have provided unprecedented high performance in image compression with low complexity. If bit error is generated by dint of wireless channel transmission problem, the loss of synchronization on between encoder and decoder causes serious performance degradation. But wavelet zerotree coding algorithms are producing variable-length codewords, extremely sensitive to bit errors. The idea is to partition the lifting coefficients. A many partition of lifting transform coefficients distributes channel error from wireless channel to each partition. Therefore synchronization problem that caused quality deterioration in still image and video stream was improved.

• Journal of the Korean Institute of Navigation
• /
• v.24 no.5
• /
• pp.361-371
• /
• 2000

This paper treats a genetic algorithm for ship scheduling problem in set packing formulation. We newly devised a partition based representation of solution and compose initial population using a domain knowledge of problem which results in saving calculation cost. We established replacement strategy which makes each individual not to degenerate during evolutionary process and applied adaptive mutate operator to improve feasibility of individual. If offspring is feasible then an improve operator is applied to increase objective value without loss of feasibility. A computational experiment was carried out with real data and showed a useful result for a large size real world problem.

• Korean Management Science Review
• /
• v.17 no.2
• /
• pp.1-12
• /
• 2000

In this study, we deals with the implementation of an optimal basis identification procedure for interior point methods. Our implementation is based on Megiddo’s strongly polynomial algorithm applied to Andersen and Ye’s approximate LP construction. Several techniques are explained such as the use of effective indicator for obtaining optimal partition when constructing the approximate LP, the efficient implementation of the problem reduction technique proposed by Andersen, the crashing procedure needed for fast dual phase of Megiddo’s algorithm and the construction of the stable initial basis. By experimental comparison, we show that our implementation is superior to the crossover scheme implementation.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.2
• /
• pp.41-47
• /
• 2016

The times of multiplication for encryption and decryption of cryptosystem is primarily determined by implementation efficiency of the modular exponentiation of $a^b$(mod m). The most frequently used among standard modular exponentiation methods is a standard binary method, of which n-ary($2{\leq}n{\leq}6$) is most popular. The n-ary($1{\leq}n{\leq}6$) is a square-and-multiply method which partitions $b=b_kb_{k-1}{\cdots}b_1b_{0(2)}$ into n fixed bits from right to left and squares n times and multiplies bit values. This paper proposes a variable-length partition algorithm that partitions $b_{k-1}{\cdots}b_1b_{0(2)}$ from left to right. The proposed algorithm has proved to reduce the multiplication frequency of the fixed-length partition n-ary method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.188-191
• /
• 2011

Path finding and path planning is crucial in today's world where time is an extremely valuable element. It is easy to plan the optimum path to a destination if provided a map but the same cannot be said for an unknown and unexplored environment. It will surely be exhaustive to search and explore for paths to reach the destination, not to mention planning for the optimum path. This is very much similar to finding for an exit of a maze. A very popular competition designed to tackle the maze solving ability of autonomous called Micromouse will be used as a guideline for us to design our maze. There are numerous ways one can think of to solve a maze such as Dijkstra's algorithm, flood fill algorithm, modified flood fill algorithm, partition-central algorithm [1], and potential maze solving algorithm [2]. We will analyze these algorithms from various aspects such as maze solving ability, computational complexity, and also feasibility to be implemented.

• Korean Journal of Remote Sensing
• /
• v.16 no.2
• /
• pp.145-156
• /
• 2000

Clustering is the process of partitioning a data set into meaningful clusters. As the data to process increase, a laster algorithm is required than ever. In this paper, we propose a clustering algorithm to partition a multispectral remotely sensed image data set into several clusters using a hash search algorithm. The processing time of our algorithm is compared with that of clusters algorithm using other speed-up concepts. The experiment results are compared with respect to the number of bands, the number of clusters and the size of data. It is also showed that the processing time of our algorithm is shorter than that of cluster algorithms using other speed-up concepts when the size of data is relatively large.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.6
• /
• pp.99-105
• /
• 1998

This paper is to propose the fuzzy regression model using genetic algorithm which is fuzzy nonlinear regression model. Genetic algorithm is used to classify the input data for better fuzzy regression analysis. From this partition. each data can be have the grade of membership function which is belonged to a divided data group. The data group, from optimal partition of the region of each variable, have different fuzzy parameters of fuzzy linear regression model one another. We compound the fuzzy output of each data group so as to obtain the final fuzzy number for a data. We show the efficiency of this method by means of demonstration of a case study.

• Journal of Korea Multimedia Society
• /
• v.10 no.11
• /
• pp.1516-1522
• /
• 2007

We had proposed the FPA(Fixed Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. The algorithm worked not bad in many area, but it lead to some overhead for memory usage and lengthy computation in the multi classes area. We propose an Repetitive FPA algorithm which repetitively partitioning pattern space in the multi classes area. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.