• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.114-131
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• 2023

Personal Mobility (PM)-related accidents have increased rapidly since PM use was activated. In response to the increase in these accidents, the government strengthened regulations for PM users on May 13, 2021. The number of the accidents in which the PM user was a victim decreased significantly. In contrast, the increasing number of accidents in which PM user was the offender did not decrease significantly. In most of these accidents, the PM user was the offender who crashed into pedestrians. Hence, the safety of pedestrians is threatened. Therefore, this study analyzed the factors, such as the regulations, urban built environment, and personal characteristics, affecting the severity of PM-pedestrian accidents by focusing on PM-pedestrian crashes. This study analyzed the PM-pedestrian accidents in Seoul from 2020 to 2021 using binary logistic regression model. Through these results, this study proposed the policy implications.

• Human Ecology Research
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• v.53 no.2
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• pp.131-141
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• 2015

The purpose of this study is to examine a potential association between community factors and the establishment of Local Healthy Family Support Centers (LHFSCs). Community factors were population size, community size, local finance independency, number of workplaces per 1,000 people, number of colleges, political party affiliation of mayor, and political party affiliation of congressman. Data of this study were collected from the census indicators of 222 communities from 2004 to 2014 and analyzed by frequency, mean, geographical information system mapping, and the binary logit analysis. The results of this study are as follows. First, LHFSCs are less likely to be established in communities in the provinces of Gangwon, Chungbuk, and Gyeongbuk. Second, the population size was positively related to the establishment of LHFSCs. Third, finance independency was positively associated with the establishment of LHFSCs. Forth, a mayor was more likely to establish LHFSCs if they were affiliated with the ruling conservative political party. However, the establishment of LHFSCs was not affected by other factors such as community scale, number of workplaces per 1,000 people, the number of colleges, and party affiliation of congressman. Thus, the conclusion suggests family policy implications to improve the geographical imbalance of LHFSCs based on the analysis results.

• Journal of Satellite, Information and Communications
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• v.11 no.2
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• pp.14-17
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• 2016

This paper describes the implementation of automatic number-braille converter based on image processing for the blind people. The algorithm is consists of four main steps. First step is binary image conversion of the input image obtained by the camera. the second step is segmentation operation by means of dilation and labelling of the character. Next step is calculation of cross-correlation between segmented text image and pre-defined text-pattern image. The final step is generation of brail output which is relevant to input image. The computer simulation result was showing 91.8% correct conversion rate for arabian numbers which is printed in A4-sheet and practical possibility was also confirmed by using implemented automatic number-braille converter based on DSP image processing board.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.285-290
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• 2009

An association rule mining has been studied to find hidden data pattern in data mining. A realization of fast processing method have became a big issue because it treated a great number of transaction data. The time which is derived by association rule finding method geometrically increase according to a number of item included data. Accordingly, the process to reduce the number of rules is necessarily needed. We propose the T-algorithm that is efficient rule reduction algorithm. The T-algorithm can reduce effectively the number of association rules. Because that the T-algorithm compares transaction data item with binary format. And improves a support and a confidence between items. The performance of the proposed T-algorithm is evaluated from a simulation.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1011-1020
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• 2004

A lot of work has been done to improve the I/O performance of such a system that store and manage a massive amount of data by distributing them across multiple disks and access them in parallel. Most of the previous work has focused on an efficient mapping from a grid ceil, which is determined bY the interval number of each dimension, to a disk number on the assumption that each dimension is split into disjoint intervals such that entire data space is GRID-like partitioned. However, they have ignored the effects of a GRID partitioning scheme on declustering performance. In this paper, we enhance the performance of mapping function based declustering algorithms by applying a good GRID par-titioning method. For this, we propose an estimation model to count the number of grid cells intersected by a range query and apply a GRID partitioning scheme which minimizes query result size among the possible schemes. While it is common to do binary partition for high-dimensional data, we choose less number of dimensions than needed for binary partition and split several times along that dimensions so that we can reduce the number of grid cells touched by a query. Several experimental results show that the proposed estimation model gives accuracy within 0.5% error ratio regardless of query size and dimension. We can also improve the performance of declustering algorithm based on mapping function, called Kronecker Sequence, which has been known to be the best among the mapping functions for high-dimensional data, up to 23 times by applying an efficient GRID partitioning scheme.

• Bulletin of the Korean Chemical Society
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• v.25 no.4
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• pp.460-465
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• 2004

Hydrofluoric acid has been used as a novel stripping agent for molybdenum(VI) after its extraction with Cyanex 301. In the extraction step, the effects of parameters such as type and initial concentration of acid, type of diluent, extractant concentration, metal concentration and temperature have been studied. In the stripping step, the effects of various stripping agents on stripping efficiency have been investigated. Hydrofluoric acid has been chosen as an effective stripping agent, and the effects of concentration of hydrofluoric acid, stripping time, volume of hydrofluoric acid and the number of stages of stripping have been studied. Molybdenum(VI) has been effectively separated from a large number of elements in binary mixtures, with a very high tolerance limit. Finally, the optimized method has been extended for the analysis of Mo(VI) in spent molybdenum catalysts.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.83-88
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• 2004

The Classification of defected oil-seals using a vision system with the artificial neural network is presented. The artificial neural network fur classification consists of 27 input nodes, 10 hidden nodes, and one output node. The selection of the number of the input nodes is based on an observation that the difference among the defected, non-defected, and smeared oil-seals is greatly pronounced in the 26 step gray-scale level thresholding. The number of the hidden nodes is chosen as a result of a trade-off between accuracy and computing time. The back-propagation algorithm is used for teaching the network. The proposed network is capable of successfully classifying the defected from the smeared oil-seals which tend to be classified as the defected ones using the binary thresholding. It is envisaged that the proposed method improves the reliability and productivity of the automotive vision inspection system.

• Journal of Communications and Networks
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• v.12 no.5
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• pp.475-479
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• 2010

The clipping value, defined as the log-likelihood ratio (LLR) in the case wherein all the list of candidates have the same binary value, is investigated, and an effective method to estimate it is presented for iterative tree search detection. The basic principle behind the method is that the clipping value of a channel bit is equal to the LLR of the maximum probability of correct decision of the bit to the corresponding probability of erroneous decision. In conjunction with multilevel bit mappings, the clipping value can be calculated with the parameters of the number of transmit antennas, $N_t$; number of bits per constellation point, $M_c$; and variance of the channel noise, $\sigma^2$, per real dimension in the Rayleigh fading channel. Analyses and simulations show that the bit error performance of the proposed method is better than that of the conventional fixed-value method.

• The Journal of Information Technology
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• v.12 no.2
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• pp.39-47
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• 2009

Residue Number System is used for the purpose of increasing the speed of processing in the many application parts of Image Processing, Computer Graphic, Neural Computing, Digital Signal Processing etc, since it has the characteristic of parallelism and no carry propagation at each moduli. DRNS has the twice RNS Conversion, it is used to decreases the size of the operator in RNS. But it has a week point on the Second Residue to First Residue Conversion time. So, in this paper SRTFR(Second Residue to First Residue) Converter using MRC(Mixed Radix Conversion) is designed to decrease the size of RTB(Residue to Binary) Converter. Since the proposed SRTFR Converter using MRC(Mixed Rdix Convertion) has a pipeline processing. Also, modular operation is applied to at each partitioned SAM(Subtraction and Addition) and MA(Multiplication and addition). In the following study, the more effective design on MA is needed.