• The Journal of Information Technology
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• v.6 no.1
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• pp.71-84
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• 2003

In this paper, it is presented the efficient system for data recognition using the proposed binary code images. The proposed algorithm finds the position of binary image. Through the process of the block region classification, it is classified each block with the edge region using the value of gray level only. Each block region is divided horizontal and vertical edge region. If horizontal edge region blocks are classified over six blocks in any region, the proposed algorithm should search the vertical edge region in the start point of the horizontal edge region. If vertical edge region blocks were found over ten blocks in vertical region, the code image would found. Practical code region is acquired from the rate of the total edge region that is computed from the binary image that is processed with the average value. In case of the wrong rate, it is restarted the code search in the point after start point and the total process is followed. It has a short time than the before process time because it had classified block information. The block processing is faster thant the total process. The proposed system acquires the image from the digital camera and makes binary image from the acquired image. Finally, the proposed system extracts various characters from the binary image.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.209-214
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• 2020

The equitable partitioning problem(EPP) is classified as [0/1] binary skill existence or nonexistence and integer skill levels such as [1,2,3,4,5]. There is well-known a polynomial-time optimal solution finding algorithm for binary skill EPP. On the other hand, tabu search a kind of metaheuristic has apply to integer skill level EPP is due to unknown polynomial-time algorithm for it and this problem is NP-hard. This paper suggests heuristic greedy algorithm with polynomial-time to find the optimal solution for integer skill level EPP. This algorithm descending sorts of skill level frequency for each field and decides the lower bound(LB) that more than the number of group, packing for each group bins first, than the students with less than LB allocates to each bin additionally. As a result of experimental data, this algorithm shows performance improvement than the result of tabu search.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.175-178
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• 2004

This paper describes a new IP address lookup algorithm using a binary search on multiple balanced trees stored in one memory. The proposed scheme has 3 different tables; a range table, a main table, and multiple sub-tables. The range table includes $2^8$ entries of 22 bits wide. Each of the main table and sub-table entries is composed of fields for a prefix, a prefix length, the number of sub-table entries, a sub-table pointer, and a forwarding RAM pointer. Binary searches are performed in the main table and the multiple sub-tables in sequence. Address lookups in our proposed scheme are achieved by memory access times of 11 in average, 1 in minimum, and 24 in maximum using 267 Kbytes of memory for 38.000 prefixes. Hence the forwarding table of the proposed scheme is stored into L2 cache, and the address lookup algorithm is implemented in software running on general purpose processor. Since the proposed scheme only depends on the number of prefixes not the length of prefixes, it is easily scaled to IPv6.

• International Journal of Contents
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• v.2 no.1
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• pp.34-38
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• 2006

In this paper, an overview of anti-collision algorithm for RFID system of a standard EPC Class1 protocol is presented, and the binslotted dynamic search algorithm (BDS) based upon the slotted ALOHA and binary tree procedure is proposed and analyzed. Also, the performance is evaluated as comparing the BDS algorithm with the standard bin-slotted algorithm (BSA) through the simulation program. The performance of the proposed BDS algorithm is improved by dynamically identifying the collided-bit position and the collided bins stored in the stack of the reader. As the results, the number of request command that a reader send to tags in the reader s interrogation zone and the total recognition time are decreased to 59% as compared with BSA algorithm. Therefore, the tag identification performance is fairly improved by resolving a collision problem using the proposed BDS algorithm.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.11
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• pp.863-869
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• 2008

This paper investigated the new calibration algorithm of a straight-type five-hole pressure probe for measuring three-dimensional flow velocity components. This new calibration algorithm was used for velocity data reduction from the calibration map and based on the combination of a look-up, a binary search algorithm and a geometry transformation including the translation and reflection of nodes in a calibration map. The calibration map was expanded up to the application angle, ${\pm}55^{\circ}$ of a probe. This velocity data reduction method showed a perfect performance without any kind of interpolating errors in calculating yaw and pitch angles from the calibration map. Moreover, when it was applied to an actual flow field including a swirling flow, a good result came out on the whole.

• Journal of Sensor Science and Technology
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• v.25 no.2
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• pp.91-102
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• 2016

The objective of this paper is to propose a heuristic algorithm to optimize the railway track maintenance scheduling, a NP-hard problem, by reflecting conditions of the actual field more quickly and easily. We develop the mechanism based on Binary Artificial Bee Colony (BABC) and Binary Harmony Search (BHS), and verify their performance through simulation experiments. Our proposed BABC and BHS mechanisms were applied to problems composed of 30, 60, 100, and 200 operations for railway track maintenance scheduling to carry out experiments and analysis. On comparing it with the results solved by CPLEX, it is found that the mechanism could present an optimal solution within limited time by user.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.402-410
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• 2013

In this paper, we propose a new fast block matching algorithm for block matching using the temporal and spatially correlation of the video sequence and local statistics of neighboring motion vectors. Since the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vectors of neighboring blocks around the same block of the previous frame and the current frame and the predictor candidate point on each division region by binary-tree structure. Experimental results show that the proposed algorithm has the capability to dramatically reduce the search points and computing cost for motion estimation, comparing to fast FS(full search) motion estimation and other fast motion estimation.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.119-122
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• 2003

This paper presents an efficient huffman decoding method based on the multiple branch technique. In the proposed search method, the internal node which does not contain a leaf node are removed for decrease the searching time and the memory consumption. The proposed search method gives 44％ of improved in searching time and 34％ of decreased in memory requirement compared to the binary search method.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.23-34
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• 2023

The purpose of this paper is to review global stochastic optimization algorithms(GSOA) in case binary response experimentation is used and to compare the performances of them. GSOAs utilise estimator of probability of success $\^p$ instead of population probability of success p, since p is unknown and only known by its estimator which has stochastic characteristics. Hill climbing algorithm algorithm, simple random search, random search with random restart, random optimization, simulated annealing and particle swarm algorithm as a population based algorithm are considered as global stochastic optimization algorithms. For the purpose of comparing the algorithms, two types of test functions(one is simple uni-modal the other is complex multi-modal) are proposed and Monte Carlo simulation study is done to measure the performances of the algorithms. All algorithms show similar performances for simple test function. Less greedy algorithms such as Random optimization with Random Restart and Simulated Annealing, Particle Swarm Optimization(PSO) based on population show much better performances for complex multi-modal function.