• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1486-1494
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• 2021

Character recognition is a technology required in various platforms, such as smart parking and text to speech, and many studies are being conducted to improve its performance through new attempts. However, with low-quality image used for character recognition, a difference in resolution of the training image and test image for character recognition occurs, resulting in poor accuracy. To solve this problem, this paper designed an end-to-end learning neural network that combines image super-resolution and character recognition so that the character recognition model performance is robust against various quality data, and implemented an alternative whole learning algorithm to learn the whole neural network. An alternative end-to-end learning and recognition performance test was conducted using the license plate image among various text images, and the effectiveness of the proposed algorithm was verified with the performance test.

• Journal of IKEEE
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• v.28 no.1
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• pp.6-13
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• 2024

This paper proposes an approach to detect abnormal data in automotive controller area network (CAN) using an unsupervised learning model, i.e. autoencoder and Gaussian kernel density estimation function. The proposed autoencoder model is trained with only message ID of CAN data frames. Afterwards, by employing the Gaussian kernel density estimation function, it effectively detects abnormal data based on the trained model characterized by the optimally determined number of frames and a loss threshold. It was verified and evaluated using four types of attack data, i.e. DoS attacks, gear spoofing attacks, RPM spoofing attacks, and fuzzy attacks. Compared with conventional unsupervised learning-based models, it has achieved over 99% detection performance across all evaluation metrics.

• The Journal of Korean Association of Computer Education
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• v.15 no.6
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• pp.21-31
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• 2012

The purpose of this study was to examine acceptance factors that affect the intention to use digital textbook. The results are as follow. First, the quality characteristic factor has significant effects on the ease of use, the usefulness of use, and the intention of use. Second, the ease of use appeared to be a significant impact on the usefulness of the use. The usefulness of digital textbook appeared to be a significant impact on the intention of use. The results show that the quality characteristics consisted of six components: personalization, mobility, stability, accuracy, responsiveness, and accessibility. The results can suggest important implications for the development of digital textbook contents and system.

• Journal of Korean Library and Information Science Society
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• v.40 no.2
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• pp.223-242
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• 2009

This is an exploratory study targeting initial users to evaluate the usability of an aggregator-supplied full-text database and to analyze the differences in task performance and perceptions based on participant characteristics. The differences in completion time and perceptions on ease of use, terminology, subject categories and satisfaction were analyzed based on the participant characteristics such as Kolb learning style, gender and participation in user instruction on full-text databases. The results indicated that there was a statistically significant difference in completion time based on the Kolb learning style. Also a statistically significant difference was found in perceptions on ease of use and terminology based on gender. However no statistically significant difference was found in perceptions on the usability of the database based on the participation in user instruction.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.285-286
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• 2016

We aim to propose the prediction modeling method of ship's position with extracting ship's trajectory model through pattern recognition based on the data that are being collected in VTS centers at real time. Support Vector Machine algorithm was used for data modeling. The optimal parameters are calculated with k-fold cross validation and grid search. We expect that the proposed modeling method could support VTS operators' decision making in case of complex encountering traffic situations.

• 한국정보교육학회:학술대회논문집
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• 2010.01a
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• pp.37-44
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• 2010

현대 사회는 미래의 지식과 정보의 시대에 대비한 인재를 양성하기 위해서 컴퓨터교육에 대한 필요성이 날로 커져가고 있다. 이에 제7차 교육과정에서도 컴퓨터 교육의 활성화를 위하여 연간 34시간 이상 컴퓨터를 교육하도록 명시하고 있지만 가장 기초적이라 할 수 있는 초등학교에서의 컴퓨터 교육이 교사들에 의해 제대로 시행되고 있지 않고 있으며, 그에 대한 교사들의 인식과 실태를 파악하여 개선방안을 제시하는데 목적이 있다. 본 연구에서 얻은 결과를 바탕으로 초등학교 컴퓨터과학 교육 활성화를 위한 개선방안을 다음과 같이 제안하고자 한다. 첫째, 정보통신윤리 개정 지침안에 대한 교사 교육이 의무화되어야 하며, 컴퓨터과학 교수학습 방법 및 교육과정 운영에 필요한 프로그램의 기능 연수가 개설되어야 할 것이다. 둘째, 정보통신기술 교재 교사용 지도서가 전문화, 체계화 되어야 한다. 셋째, 컴퓨터과학 교육을 수업 현장에서 쉽게 실현할 수 있는 다양한 자료를 개발하여 보급해야 한다. 넷째, 초등학교 컴퓨터 교육 과정의 재정비가 필요하다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.275-275
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• 2015

최근 기후변화에 따른 극한 강우사상의 증가로 인하여 농업용 저수지의 재해 위험도가 증가하고 있는 추세이며, 사고가 발생할 때 마다 파손/붕괴된 시설물을 보수하는 대응형 유지관리체계에서 벗어나 기반시설의 성능과 생애주기 등을 고려하여 재해 발생을 사전에 예보 및 경보를 알릴 수 있는 예방적 관리체계로의 전환이 필요하다. 한국농어촌공사는 전국 1,500개 저수지에서 10분 단위 수위자료를 측정하고 있으며, 이를 분석하여 재해예방에 활용할 수 있는 기반이 조성되어 있으나 이에 대한 관리가 이루어지지 않고 있고 수집된 자료를 활용하여 재해 징후를 분석할 수 있는 재해 예방적 분석기술이 마련되어 있지 않은 실정이다. 본 연구에서는 농업용 저수지 수위자료를 이용한 저수지 이상거동을 판별하기 위하여 전국 34개 한국농어촌공사 관할 저수의 시계열 수위자료의 특성(Feature)을 분석하고자 한다. 시계열 자료의 시계열 특성을 분석하기 위하여 한국농어촌공사 관할의 전국 34개 저수지를 선정하여 분석을 실시하였다. 대상저수지는 지역별, 저수용량, 안정등급, 붕괴발생, 1개 지사관할 저수지로 각각 구분하여 선정하였으며, 각 저수지의 수위 측정기간(최소 5개년)에 대한 자료를 수집하였다. 농업용 저수지의 시계열 수위 자료의 특성을 분석하기 위하여 자료의 전처리를 수행하였다. 자료의 전처리는 시계열 수위자료의 잡음 특성, 기상자료 관련 변동특성 등 분류(Classification)에 영향을 미치는 노이즈 요소를 제거하는 과정이다. 전처리과정을 거친 자료는 특징(Feature) 추출 과정을 거치게 되고, 추출된 특징의 적합성에 따라 분류 알고리듬 성능에 많은 영향을 미친다. 따라서 시계열 자료의 특성을 파악하고 특징을 추출하는 것은 이상치 탐지에 있어 매우 중요한 과정이다. 본 연구에서는 시계열 자료 특징 추출 방법으로 물리적인 한계치, 확률적인 문턱값(Threshold), 시계열 패턴, 주변 저수지와의 시계열 상관분석 등을 적용하였으며, 이를 데이터베이스로 구축하여 이후 분류알고리듬 학습에 적용하여 정상치와 이상치를 판별하는데 이용될 수 있도록 하였다. 따라서 본 연구에서 제시되는 농업용 저수지의 시계열 특성은 다양한 분류알고리듬에 적용할 수 있으며, 이를 통하여 저수지 이상거동 판별을 위한 최적을 분류알고리듬의 선택에 도움이 될 것이다.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.331-358
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• 2024

In the rapid digital transformation era, kiosks have become a common element in daily life. However, their widespread deployment has introduced new challenges for socially marginalized groups, including individuals with disabilities and the elderly. This study aims to evaluate the usability of kiosks for individuals with low vision and propose improvement strategies. The study was conducted with eight low-vision university students from A University in Gyeongsangbuk-do and four non-disabled university students from Daegu. Usability was assessed through experiments involving a self-service certificate issuance kiosk and a fast-food restaurant kiosk, using Jakob Nielsen's five usability evaluation criteria: learnability, efficiency, memorability, error prevention, and satisfaction. The results revealed that individuals with low vision faced significant difficulties with small text size, low contrast, no physical buttons, and lack of screen zoom functionality. To address these issues, the study recommends enhancements such as increasing text size and contrast, incorporating physical buttons, adding zoom functionality, ensuring consistent UI design, and providing auditory feedback. This study provides foundational data for enhancing information accessibility for individuals with low vision. It offers critical insights into kiosk design and policy recommendations, thereby contributing to the mitigation of the digital divide.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.21-28
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• 2006

Most of the commercialized wearable text input devices are wrist-worn keyboards that have adopted the minimization method of reducing keys. Generally, a drastic key reduction in order to achieve sufficient wearability increases KSPC(Keystrokes per Character), decreases text entry performance, and requires additional effort to learn a new typing method. We are faced with wearability-usability tradeoff problems in designing a good wearable keyboard. To address this problem, we introduced a new keyboard minimization method of reducing key pitch. From a series of empirical studies, we found the potential of a new method which has a keyboard with a 7mm key pitch, good wearability and social acceptance in terms of physical form factors, and allows users to type 15.0WPM in 3 session trials. However, participants point out that a lack of passive haptic feedback in keying action and visual feedback on users' input deteriorate the text entry performance. We have developed the One-key Keyboard that addresses this problem. The traditional desktop keyboard has one key per character, but the One-key Keyboard has only one key ($70mm{\times}35mm$) on which a 10*5 QWERTY key array is printed. The One-key Keyboard detects the position of the fingertip at the time of the keying event and figures out the character entered. We conducted a text entry performance test comprised of 5 sessions. The participants typed 18.9WPM with a 6.7% error rate over all sessions and achieved up to 24.5WPM. From the experiment's results, the One-key Keyboard was evaluated as a potential text input device for wearable computing, balancing wearability, social acceptance, input speed, and learnability.