• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.11-25
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• 2012

We present the method to infer Context-Free Grammars by applying genetic algorithm to the Binary Third-order Recurrent Neural Networks(BTRNN). BTRNN is a multiple-layered architecture of recurrent neural networks, each of which is corresponding to an input symbol, and is combined with external stack. All parameters of BTRNN are represented as binary numbers and each state transition is performed with any stack operation simultaneously. We apply Genetic Algorithm to BTRNN chromosomes and obtain the optimal BTRNN inferring context-free grammar of positive and negative input patterns. This proposed method infers BTRNN, which includes the number of its states equal to or less than those of existing methods of Discrete Recurrent Neural Networks, with less examples and less learning trials. Also BTRNN is superior to the recent method of chromosomes representing grammars at recognition time complexity because of performing deterministic state transitions and stack operations at parsing process. If the number of non-terminals is p, the number of terminals q, the length of an input string k, and the max number of BTRNN states m, the parallel processing time is O(k) and the sequential processing time is O(km).

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.217-221
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• 2013

Recently, researches for methods of generating binary feature descriptors have been actively done. Since matching of binary feature descriptors uses Hamming distance which is based on bit operations, it is much more efficient than that of previous general feature descriptors which uses Euclidean distance based on real number operations. However, since increase in the number of features linearly drops matching speed, in applications such as object tracking where real-time applicability is a must, there has been an increasing demand for methods of further improving the matching speed of binary feature descriptors. In this regard, this paper proposes a method that improves the matching speed greatly while maintaining the matching accuracy by splitting high dimensional binary feature descriptors to several low dimensional ones and sequentially analyzing their partial Hamming distances. To evaluate the efficiency of the proposed method, experiments of comparison with previous matching methods are conducted. In addition, this paper discusses schemes of generating binary feature descriptors for maximizing the performance of the proposed method. Based on the analysis on the performance of several generation schemes, we try to find out the most effective scheme.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1571-1574
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• 2017

In the present work, the gamma ray backscattering technique was used to determine the effective atomic numbers for certain binary alloys. With the help of a muffle furnace, the binary alloys were synthesized using the melt quenching technique with different compositions of $_{82}Pb$, $_{50}Sn$, and $_{30}Zn$. The intensity distribution of backscattered photons from radioactive isotope $^{22}Na$ (511 keV) was recorded with the help of GAMMARAD5 [$76mm{\times}76mm$ NaI(Tl) scintillator detector] and analyzed as a function of both atomic number and thickness of the target material. The effective atomic numbers for the same binary alloys were also computed theoretically using the atomic to electronic cross-section method with the help of the mass attenuation coefficient database of WinXCom (2001). Good agreement was observed between theoretical and experimental results for the effective atomic numbers of all the selected alloys.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.229-236
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• 2017

This paper presents an efficient object-based color image-retrieval algorithm that is suitable for the classification and retrieval of images from small to mid-scale datasets, such as images in PCs, tablets, phones, and cameras. The proposed method first finds salient regions by using regional feature vectors, and also finds several dominant colors in each region. Then, each salient region is partitioned into small sub-blocks, which are assigned 1 or 0 with respect to the number of pixels corresponding to a dominant color in the sub-block. This gives a binary map for the dominant color, and this process is repeated for the predefined number of dominant colors. Finally, we have several binary maps, each of which corresponds to a dominant color in a salient region. Hence, the binary maps represent the spatial distribution of the dominant colors in the salient region, and the union (OR operation) of the maps can describe the approximate shapes of salient objects. Also proposed in this paper is a matching method that uses these binary maps and which needs very few computations, because most operations are binary. Experiments on widely used color image databases show that the proposed method performs better than state-of-the-art and previous color-based methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4007-4024
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• 2020

It is tremendously important to construct decision trees to use as a tool for knowledge representation from a given decision table. However, the usual algorithms may split the decision table based on each value, which is not efficient for numerical attributes. The methodology of this paper is to split the given decision table into binary groups as like the CART algorithm, that uses binary split to work for both categorical and numerical attributes. The difference is that it uses split for each attribute established by the directed acyclic graph in a dynamic programming fashion whereas, the CART uses binary split among all considered attributes in a greedy fashion. The aim of this paper is to study the effect of binary splits in comparison with each value splits when building the decision trees. Such effect can be studied by comparing the number of nodes, local and global misclassification rate among the constructed decision trees based on three proposed algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.9-17
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• 2004

We present a performance improvement of Binary CDMA by flattening the correlation values. A Binary CDMA system, in which multi-leveled transmission signal of multi-code CDMA is clipped into a binary value, is cost-efficient since the strict linearity of the power amplifier is relieved. However, a loss of orthogonality among user channels due to the clipping causes the correlation values at the receiver to have a random distribution. If the correlation value for even a single channel goes too low, the average BER drops considerably. We developed a method of correlation flattening, where the binary chip pattern at the transmitter is adjusted so that the correlation values have averaged magnitude. Experimental results on several spreading codes show that the correlation flattening method increases the number of available channels at reduced BER.

• Journal of Broadcast Engineering
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• v.20 no.2
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• pp.257-264
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• 2015

In this paper, Enhanced binary block matching method for Constrained one-bit transform (C1BT) based motion estimation is proposed. Binary motion estimation exploits the Number of non-matched points (NNMP) as a block matching criterion instead of the Sum of Absolute Differences (SAD) for low complex motion estimation. The motion estimation using SAD could use the smaller block for more accurate motion estimation. In this paper the enhanced binary block matching method using smaller motion estimation block for C1BT is proposed to the more accurate binary matching. Experimental results shows that the proposed algorithm has better Peak Signal to Noise Ration (PSNR) results compared with conventional binary transform algorithms.

• Journal of Pharmaceutical Investigation
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• v.38 no.1
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• pp.1-8
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• 2008

Planetary ball mill was used to decrease and control the particle size of excipients. The effects of the weight of sample and the revolution number of mill, and grinding time on the particle size of the ground sample were analyzed by response surface methodology. The optimum conditions for the milling of microcrystalline cellulose were 38.82 g of the weight of sample and 259 rpm of the revolution number of mill, and 45 minutes of grinding time. The predicted value of the particle size at the these conditions was $19.02{\mu}m$, of which the experimental value at the similar conditions was $18.68{\mu}m$. The tensile strength of tablets of single-component powders, such as microcrystalline cellulose, hydroxypropylmethyl cellulose and starch, binary mixtures and ground binary mixtures of these powder were measured at various relative densities. It was found that the logarithm of the tensile strength of the tablets was proportional to the relative density. A simple model, based upon Ryshkewitch-Duckworth equation that was originally proposed for porous materials, has been developed in order to predict the relationship between the tensile strength and relative density of ground binary tablets based on the properties of the constituent single-component powders. The validity of the model has been verified with experimental results for ground binary mixtures. It has demonstrated that this model can well predict the tensile strength of ground binary mixtures based upon the properties of single-component powders, such as true density, and the compositions. When the tensile strength of the mixture of microcrystalline cellulose hydroxypropylmethyl cellulose (90:10) and the ground mixture of them were compared, the tensile strength of the ground mixture decreased widely from 45.3 to 5.6% compared to the mixture in case the relative density of tablets was in the range of $0.7{\sim}0.9$. When the tensile strength of the mixture of microcrystalline cellulose starch (80:20) and the ground mixture of them were compared, the tensile strength of the ground mixture decreased widely from 31.0 to 11.6% compared to the mixture in case the relative density of tablets was in the range of $0.7{\sim}0.9$.

• Journal of Creative Information Culture
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• v.5 no.3
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• pp.345-353
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• 2019

Several studies have been proposed to improve storage space efficiency by expressing binary trie data structure as a linear binary bit string. Compact binary tree approach generated using one binary trie increases the key search time significantly as the binary bit string becomes very long as the size of the input key set increases. In order to reduce the key search range, a hierarchical compact binary tree technique that hierarchically expresses several small binary compact trees has been proposed. The search time increases proportionally with the number and length of binary bit streams. In this paper, we generate several binary compact trees represented by full binary tries hierarchically. The search performance is improved by allowing a path for the binary bit string corresponding to the search range to be determined through simple numeric conversion. Through the performance evaluation using the worst time and space complexity calculation, the proposed method showed the highest performance for retrieval and key insertion or deletion. In terms of space usage, the proposed method requires about 67% ~ 68% of space compared to the existing methods, showing the best space efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.896-909
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• 2010

IEEE 802.11 medium access control (MAC) employs the distributed coordination function (DCF) as the fundamental medium access function. DCF operates with binary exponential backoff (BEB) in order to avoid frame collisions. However it may waste wireless resources because collisions occur when multiple stations are contending for frame transmissions. In order to solve this problem, a binary negative-exponential backoff (BNEB) algorithm has been proposed that uses the maximum contention window size whenever a collision occurs. However, when the number of contending stations is small, the performance of BNEB is degraded due to the unnecessarily long backoff time. In this paper, we propose the adaptive BNEB (A-BNEB) algorithm to maximize the throughput regardless of the number of contending stations. A-BNEB estimates the number of contending stations and uses this value to adjust the maximum contention window size. Simulation results show that A-BNEB significantly improves the performance of IEEE 802.11 DCF and can maintain a high throughput irrespective of the number of contending stations.