• Journal of The Korean Association of Information Education
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• v.15 no.3
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• pp.375-385
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• 2011

Computer education is moving its focus from skill oriented education to improving students' creativity and problem solving ability. Thus, the importance of programming education is being strengthened. However, programming education was biased to grammar oriented language that has been limits of students' interest. Robot programming is problem solving itself, and by allowing students to directly see the robot which is the output of programming, can help interest and motivate to the students. In fact, it is still observed that the students are facing difficulties due to various kinds of errors during the programming education. Therefore, this study categorizes and analyzes the errors students are facing during robot programming, and based on that, a support tool to help treat errors developed. The developed supporting system for error solving reduces the frequency of errors and provides the set of coding instruction, NXC language and error message in Korean, examples and detailed information for each stage of education, function removing major coding errors, and code sorting and indication of row number. This study also confirmed that the supporting tool is helpful in reducing and solving errors after input.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.287-289
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• 2021

Smart farming education service based on network are important factors for farming sector. The lack of time and space has to lead to their limited appliance to farmers. Limited information support and low background knowledge in farming production is a lot of trial and error in farming production. Smart farming education as a service based on cloud provide the farming information that is the farming knowledge, farming skill, and farmer's experiences and knowhow, etc. And real-time information such as climate change, soil environment and market trends is very important. This paper proposes a framework for applying farming education service based on cloud. It consists of smart farming function and smart farming education function

• The Journal of the Korea Contents Association
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• v.7 no.5
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• pp.36-46
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• 2007

This paper presents an automated English scoring system designed to help non-native speakers of English, Korean-speaking learners in particular. The system is developed to help the 3rd grade students in junior high school improve their English grammar skills. Without human's efforts, the system identifies grammar errors in English sentences, provides feedback on the detected errors, and scores the sentences. Detecting grammar errors in the system requires implementing a special type of rules in addition to the rules to parse grammatical sentences. Error production rules are implemented to analyze ungrammatical sentences and recognize syntactic errors. The rules are collected from the junior high school textbooks and real student test data. By firing those rules, the errors are detected followed by setting corresponding error flags, and the system continues the parsing process without a failure. As the final step of the process, the system scores the student sentences based on the errors detected. The system is evaluated with real English test data produced by the students and the answers provided by human teachers.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2788-2790
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• 2002

In this paper, we designed neural network predictive two degree of freedom PID controller to control sway of crane Crane's trolley arrive minimum oscillation of transfer body and establishment position in minimum time. When various establishment location and surrounding disturbance were approved based on mathematical modeling of crane, controller designed to become effective control location error and oscillation angle of two control variables that simultaneously can predictive control. We wish to develop automatic travel control system through anti-sway skill of crane.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.1-8
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• 2019

Identifying behavioral errors of seafarers that have led to marine accidents is a basis for research into prevention or mitigation of marine accidents. The purpose of this study is to estimate the optimal probability distribution function needed to model behavioral errors of crew members into three behaviors (i.e., Skill-, Rule-, Knowledge-based). Through use of behavioral data obtained from previous accidents, we estimated the optimal probability distribution function for the three behavioral errors and verified the significance between the probability values derived from the probability distribution function. Maximum Likelihood Estimation (MLE) was applied to the probability distribution function estimation and variance analysis (ANOVA) used for the significance test. The obtained experimental results show that the probability distribution function with the smallest error can be estimated for each of the three behavioral errors for eight types of marine accidents. The statistical significance of the three behavioral errors for eight types of marine accidents calculated using the probability distribution function was observed. In addition, behavioral errors were also found to significantly affect marine accidents. The results of this study can be applied to predicting marine accidents caused by behavioral errors.

• Atmosphere
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• v.30 no.2
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• pp.103-113
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• 2020

In this study, we analyzed and developed the monitoring system in order to confirm the effect of observations on forecast sensitivity on ensemble-based data assimilation. For this purpose, we developed the Ensemble Forecast Sensitivity to observation (EFSO) monitoring system based on Local Ensemble Transform Kalman Filter (LETKF) system coupled with Korean Integrated Model (KIM). We calculated 24 h error variance of each of observations and then classified as beneficial or detrimental effects. In details, the relative rankings were according to their magnitude and analyzed the forecast sensitivity by region for north, south hemisphere and tropics. We performed cycle experiment in order to confirm the EFSO result whether reliable or not. According to the evaluation of the EFSO monitoring, GPSRO was classified as detrimental observation during the specified period and reanalyzed by data-denial experiment. Data-denial experiment means that we detect detrimental observation using the EFSO and then repeat the analysis and forecast without using the detrimental observations. The accuracy of forecast in the denial of detrimental GPSRO observation is better than that in the default experiment using all of the GPSRO observation. It means that forecast skill score can be improved by not assimilating observation classified as detrimental one by the EFSO monitoring system.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.8
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• pp.647-654
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• 2016

Controlling a mobile robot using conventional control devices requires skill and experience, and is not intuitive, especially in complex environments. For human-mobile robot cooperation, the direct-teaching method with impedance control has been used most frequently in complex environments. This thesis proposes a new direct-teaching method for a mobile robot utilizing variable impedance control. This includes analysis of user intention, which is changed by force and moment. A fuzzy inference technique is proposed in this thesis for identification of user intension. The direct teaching of a mobile robot based on variable impedance control through fuzzy inference is experimentally verified by comparing its efficiency to that of the conventional impedance control-based direct teaching of a mobile robot. Experimental data, such as the total time consumed, path error time, and the total energy used by the user, were recorded. The results showed that the efficiency of variable impedance control was increased.

• Journal of Korean Academy of Nursing Administration
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• v.6 no.1
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• pp.109-122
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• 2000

The present study examined the causal relationships among nurses' perceived autonomy, job satisfaction, work environment (work overload, role conflict, situational support, head nurses' leadership), personal aspects(experiences, need for achievement, professional knowledge and skill) by constructing and testing a theoretical framework. Based on literature review nurses' perceived autonomy and job satisfaction were conceived of as outcomes of the interplay among work environment and personal characteristics. Work environment factors involved work overload, role conflict, situational support, and head nurses' leadership (task oriented leadership, relation oriented leadership). Personal charateristics included experiences, need for achievement, and professional knowledge and skill. Three large general hospital in Chonbuk were selected to participate. The total sample of 516 registered nurses represents a response rate of 92 percent. Data for this study was collected from July to September in 1998 by Questionnaire. Path analyses with LISREL 7.16 program were used to test the fit of the proposed conceptual model to the data and to examine the causal relationship among variables. The result showed that both the proposed model and the modified model fit the data excellently. It needs to be notified, however, that path analisis can not count measurement errors; measurement error can attenuate estimates of coefficient and explanatory power. Nevertheless the model revealed relatively high explanatory power. 42 percent of nurses' perceived autonomy was explained by predicted variables; 32 percent of nurses' job satisfaction was explained by by predicted variables. Tn predicting nurses' perceived autonomy the findings of this study clearly demonstrated the work overload might be the most important variable of all the antecedent variables. Head nurses' relation oriented leadership, situational supports, need for achievement, and role conflict were also found to be important determinants for nurses' perceived autonomy. As for the job satisfaction, role conflict, situational supports, need for the achievement, and head nurses' relation oriented leadership were in turn important predictors. Unexpectedly the result showed perceived autonomy have few influence on job satisfaction. The results were discussed, including directions for the future research and practical implication drawn from the research were suggested.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.259-261
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• 2017

SRK-BB(Skill-, Rule-, Knowledge-Based Behavior)는 주어진 사건을 처리할 때 인간이 행하는 행동을 체계적으로 식별하기 위한 하나의 이론이다. 이러한 SRK-BB에 대한 결과는 주어진 임무에 대한 '성공'과 '실패'로 나타낼 수 있다. 만약, 어느 사건에 대한 SRK-BB를 식별할 수 있고, 이에 대한 '성공/실패'의 결과를 알 수 있다면, SRK-BB를 이용하여 이들 사이에 연계된 확률적인 관계를 정립할 수 있다. 한편, 해양사고의 결과를 분석한 해양안전심판원의 재결서 또는 재결요약서에는 다양한 사고(즉, 실패한 사건)에 대해서 해기사가 어떠한 행동을 취했는지 상세하게 기록되어 있다. 이러한 해양안전심판원의 자료를 분석하면 실패한 해양사고에 대한 방대한 해기사의 SRK 분포를 확보할 수 있다. 본 연구의 목적은 다양한 해양사고에 나타난 해기사들의 행동을 SRK-BB로 식별한 후 해기사들이 추후 야기할 수 있는 인적오류를 예측하기 위한 모델 구축에 있다. 인적오류 모델을 구축하기 위해서는 우선 해양사고에 포함된 SRK 분포 분석이 필요하고, 시스템적인 입출력 관계를 통해서 SRK에 의한 인적오류의 결과를 예측하기 위한 예측 모델이 필요하다. 본 연구에서는 해기사의 인적오류에 의한 사고를 어떻게 SRK 분포를 이용하여 예측할 수 있는지에 대한 개념을 설명하고, 해양사고 데이터에서 획득한 SRK 분포의 의미와, SRK 분포를 이용하여 어떻게 해기사가 야기할 사고를 예측할 수 있는지에 대한 연구접근 방법을 소개하고자 한다.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.769-779
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• 2017

As the occurrence of drought is recently on the rise, the reliable drought forecasting is required for developing the drought mitigation and proactive management of water resources. This study developed a probabilistic hydrological drought forecasting method using the Bayesian Networks and drought propagation relationship to estimate future drought with the forecast uncertainty, named as the Propagated Bayesian Networks Drought Forecasting (PBNDF) model. The proposed PBNDF model was composed with 4 nodes of past, current, multi-model ensemble (MME) forecasted information and the drought propagation relationship. Using Palmer Hydrological Drought Index (PHDI), the PBNDF model was applied to forecast the hydrological drought condition at 10 gauging stations in Nakdong River basin. The receiver operating characteristics (ROC) curve analysis was applied to measure the forecast skill of the forecast mean values. The root mean squared error (RMSE) and skill score (SS) were employed to compare the forecast performance with previously developed forecast models (persistence forecast, Bayesian network drought forecast). We found that the forecast skill of PBNDF model showed better performance with low RMSE and high SS of 0.1~0.15. The overall results mean the PBNDF model had good potential in probabilistic drought forecasting.