• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.121-128
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• 2018

A Korean morphological analyzer adopts sequence-to-sequence (seq2seq) model, which can generate an output sequence of different length from an input. In general, a seq2seq based Korean morphological analyzer takes a syllable-unit based sequence as an input, and output a syllable-unit based sequence. Syllable-based morphological analysis has the advantage that unknown words can be easily handled, but has the disadvantages that morpheme-based information is ignored. In this paper, we propose a reranking model as a post-processor of seq2seq model that can improve the accuracy of morphological analysis. The seq2seq based morphological analyzer can generate K results by using a beam-search method. The reranking model exploits morpheme-unit embedding information as well as n-gram of morphemes in order to reorder K results. The experimental results show that the reranking model can improve 1.17% F1 score comparing with the original seq2seq model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.5
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• pp.29-36
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• 2019

In modern society in which the fourth industrial revolution has come to the fore and rapid technology innovations are taking place, a phenomenon of making and selling small quantities of various products that consumers want instead of mass producing one item has emerged. As the market is moving toward the multi-item small-sized production system, there is a need for a system in which a machine independently judges and carries out machining and post-processing. In order for a machine to judge processing on its own, it is necessary to measure the force applied to a product. This study aimed to develop a large-scale dynamometer that enables three-axis measurement using octagonal ring load cells. As for the device's configuration, four octagonal ring load cells, which were previously researched, were used to enable three-axis measurement. It was reconfigured by modifying the attachment position of the octagonal ring load cells' strain gauge and the Wheatstone bridge of each axis, and a system was set up to allow the monitoring of data measured through the monitor. The configured device calculated a strain rate by an experiment, and this rate was compared with the theoretical strain rate to find a correction value. The correction value was entered into a formula, deriving a modified formula. The modified formula was entered into the device, which completed the large-scale dynamometer.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.61-67
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• 2021

In this paper, we propose a method of extracting and recognizing related information for research from images of the unstructured pulmonary function test papers using character detection and recognition techniques. Also, we develop a post-processing method to reduce the character recognition error rate. The proposed structuring method uses a character detection model for the pulmonary function test paper images to detect all characters in the test paper and passes the detected character image through the character recognition model to obtain a string. The obtained string is reviewed for validity using string matching and structuring is completed. We confirm that our proposed structuring system is a more efficient and stable method than the structuring method through manual work of professionals because our system's error rate is within about 1% and the processing speed per pulmonary function test paper is within 2 seconds.

• Journal of Life Science
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• v.31 no.4
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• pp.410-417
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• 2021

Next-generation sequencing (NGS) is a high-throughput technique for sequencing large numbers of DNA fragments that are prepared from a genome. This sequencing technique has been used to elucidate whole genome sequences of living organisms and to analyze complementary DNA (cDNA) or chromatin immunoprecipitated DNA (ChIPed DNA) at the genome level. After NGS, the use of proper tools is important for processing and analyzing data with reasonable parameters. However, handling large-scale sequencing data and programing for data analysis can be difficult. The Galaxy platform, a public web service system, provides many different tools for NGS data analysis, and it allows researchers to analyze their data on a web browser with no deep knowledge about bioinformatics and/or programing. In this study, we explain the procedure for preparing chromatin immunoprecipitation-sequencing (ChIP-seq) libraries and steps for analyzing ChIP-seq data using the Galaxy platform. The data analysis steps include the NGS data upload to Galaxy, quality check of the NGS data, premapping processes, read mapping, the post-mapping process, peak-calling and visualization by window view, heatmaps, average profile, and correlation analysis. Analysis of our histone H3K4me1 ChIP-seq data in K562 cells shows that it correlates with public data. Thus, NGS data analysis using the Galaxy platform can provide an easy approach to bioinformatics.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.393-402
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• 2009

The purpose of this study was to verify whether there are any differences in the level of physical self-efficacy, depression and stress of meddle-aged men according to the stage of exercise change. As for the study objects, men at the ages between 40 and 50, residing in Seoul and mostly having temporary jobs, office jobs and professional jobs, were surveyed with the questionnaire prepared, and 299 copies of the questionnaire in total were collected and used for the final analysis. SPSS 12.0 was used for the processing of the data, while the results were deduced by using exploratory analysis, reliability analysis, 1-way ANOVA and correlation analysis. The study results were as follows: Firstly, their Physical self-efficacy showed significant differences according to the stage of exercise change of the study objects, and as a result of the post-hoc analysis, it was found that as the stage became higher, their physical self-efficacy became higher as well. Secondly, the level of depression showed significant differences according to the stage of exercise change of the study objects, and as a result of the post-hoc analysis, it was found that as the stage generally became higher, their depression showed lower levels. Thirdly, their stress showed significant differences according to the stage of exercise change of the study objects, and as a result of the post-hoc analysis, it was found that the preparation stage had a lower level of stress than the maintenance stage while the before-plan stage had a lower level of stress than the before-plan stage and the maintenance stage. Fourthly, it was found that as the stage of exercise change became higher, their stress tended to decrease while their self-efficacy tended to increase.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.21-26
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• 2020

An autonomous driving system is based on the deep learning system built by big data which are obtained by various IoT sensors. The miniaturization and high performance of the IoT sensors are needed for diverse devices including the autonomous driving system. Specially, the miniaturization of the sensors leads to compel the miniaturization of the fixer structures. In the viewpoint of the miniaturization, metallic structure is a best solution to attach the small IoT sensors to the main body. However, it is hard to manufacture the small metallic structure with a conventional machining process or manufacturing cost greatly increases. As one of solutions for the problems, in this work, metallic FDM (Fused depositon modeling) based on metallic filament was proposed and the FDM process was investigated to fabricate the small metallic structure. Final part was obtained by the post-process that consists of debinding and sintering. In this work, the relationship between infill rate and the density of the part after the post-process was investigated. The investigation of the relationship is based on the fact that the infill rate and the density obtained from the post-processing is not same. It can be said that this work is a fundamental research to obtain the higher density of the printed part.

• Fisheries and Aquatic Sciences
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• v.26 no.2
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• pp.105-116
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• 2023

Improved fish post-harvest technologies (IFPT) have been promoted as more efficient methods of fish processing, preservation, and value addition than the traditional methods prevalent in developing countries. The adoption rates, however, do not appear to be convincing. The purpose of this study was to determine the socio-demographic and psychological factors that influence intention of Kenyan farmed fish traders to use IFPT. The technology acceptance model (TAM) was used to properly explain the impact of TAM constructs such as perceived usefulness (PU), perceived ease of use (PEOU), and attitude (ATT), as well as socio-demographic factors such as gender, age, education level and fish trading experience on traders' intention to use the technologies. A cross-sectional survey was conducted to collect data using a semi-structured questionnaire from 146 traders in Busia, Siaya and Kakamega counties. At a significance level of p = 0.05, a linear regression model was used to examine the socio-demographic and psychological determinants of the traders' behavioral intention to use the improved technologies. The regression analysis revealed that PU (β = 0.443; p = 0.000), PEOU (β = 0.364; p = 0.000) and ATT (β = 0.615; p = 0.000) influence traders' intention to use IFPT, with ATT having the highest influence on intention. However, the traders' socio-demographic characteristics have no effect on their intention to use the technologies, as the coefficients for gender (β = 0.148; p = 0.096), age (β = 0.016; p = 0.882), level of education (β = -0.135; p = 0.141) and fish trading experience (β = 0.017; p = 0.869) are all insignificant. These findings show that the traders intend to use IFPT and will use them when it is in their best economic interests.

• Therapeutic Science for Rehabilitation
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• v.13 no.1
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• pp.75-86
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• 2024

Objective : The purpose of this study was to examine the effects of an occupational therapy program on the school readiness, focusing on adaptation skills and daily life skills, in children with developmental delays. Methods : The study involved a boy with developmental delay, aged 5 years and 8 months. The program was conducted twice a week, with a total of 8 sessions spread over 4 weeks. The Canadian Occupational Performance Measure (COPM) was employed, targeting class preparation and use of the toilet. Pre-post tests and follow-up evaluations were carried out to compare changes. Data analysis involved video recordings of the subject's performance. Results : The COPM results indicated improvements in both the performance and satisfaction levels for class preparation and toilet use. Processing skills showed seven improvements in class preparation and eight improvements in toilet use during post-testing. Activity performance observations further confirmed improvements in both class preparation and toilet use during post-test and follow-up evaluations. Conclusion : Occupational therapy improves school readiness (adaptation skill, daily living activity skill) for children with developmental delays, and has a positive effect on overall school readiness.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.173-184
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• 2002

This paper discuss an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, Kim et al. suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multidimensional index using a feature vector as indexing attributes. For query processing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verify the superiority of our approach, we perform extensive experiments. The results reveal that our approach achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.