• ETRI Journal
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• v.39 no.3
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• pp.364-372
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• 2017

For the reliability of controllers in a software defined network (SDN), a dynamic and self-learning schedule method (DSL) is proposed. This method is original and easy to deploy, and optimizes the combination of multiple controllers. First, we summarize multiple controllers' combinations and schedule problems in an SDN and analyze its reliability. Then, we introduce the architecture of the schedule method and evaluate multi-controller reliability, the DSL method, and its optimized solution. By continually and statistically learning the information about controller reliability, this method treats it as a metric to schedule controllers. Finally, we compare and test the method using a given testing scenario based on an SDN network simulator. The experiment results show that the DSL method can significantly improve the total reliability of an SDN compared with a random schedule, and the proposed optimization algorithm has higher efficiency than an exhaustive search.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.1-7
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• 2021

In this paper, We focused the issue of creating a socially problematic nurse schedule. The nurse schedule should be prepared in consideration of three shifts, appropriate placement of experienced workers, the fairness of work assignment, and legal work standards. Because of the complex structure of the nurse schedule, which must reflect various requirements, in most hospitals, the nurse in charge writes it by hand with a lot of time and effort. This study attempted to automatically create an optimized nurse schedule based on legal labor standards and fairness. We developed an I/O Q-Learning algorithm-based model based on Python and Web Application for automatic nurse schedule. The model was trained to converge to 100 by creating an Fairness Indicator Score(FIS) that considers Labor Standards Act, Work equity, Work preference. Manual nurse schedules and this model are compared with FIS. This model showed a higher work equity index of 13.31 points, work preference index of 1.52 points, and FIS of 16.38 points. This study was able to automatically generate nurse schedule based on reinforcement Learning. In addition, as a result of creating the nurse schedule of E hospital using this model, it was possible to reduce the time required from 88 hours to 3 hours. If additional supplementation of FIS and reinforcement Learning techniques such as DQN, CNN, Monte Carlo Simulation and AlphaZero additionally utilize a more an optimized model can be developed.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.2
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• pp.84-93
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• 2021

Airline schedule is the most basic data for flight operations and has significant importance to an airline's management. It is crucial to know the airline's current schedule status in order to effectively manage the company and to be prepared for abnormal situations. In this study, machine learning techniques were applied to actual schedule data to examine the possibility of whether the airline's fleet state could be artificially learned without prior information. Given that the schedule is in categorical form, One Hot Encoding was applied and t-SNE was used to reduce the dimension of the data and visualize them to gain insights into the airline's overall fleet status. Interesting results were discovered from the experiments where the initial findings are expected to contribute to the fields of airline schedule health monitoring, anomaly detection, and disruption management.

• Science of Emotion and Sensibility
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• v.17 no.1
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• pp.3-12
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• 2014

Quick and accurate learning of diverse electronic products has become an important daily task. In particular, software occupies core status in the control and operation of the products. This research tested the effects of learning method, schedule, and task difficulty in the learning of software. Using 2 (learning method: experiential vs. verbal) ${\times}$ 2 (learning schedule: spaced vs. massed) ${\times}$ 2 (difficulty: easy vs. difficult) between-subjects design, Experiment 1 tested participants' learning of file control using Windows Movie Maker. There was no effect of learning schedule on task completion time, but participants in experiential learning were faster in the completion of evaluation task compared with those in verbal learning condition. Importantly, as task difficulty increases participants in verbal condition showed markedly lower performance than those in experiential condition, which suggests that experiential learning is more effective with more difficult learning task. That is, in case of learning simple operation of software verbal learning using linguistic manual or instruction could be sufficient; on the other hand in case of learning complex operation learning from experience or tutorial mode would be more effective. Additional studies which manipulated task difficulty (Expt. 2) and inter-trial learning interval (Expt. 3) did not produce meaningful results.

• ETRI Journal
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• v.46 no.3
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• pp.473-484
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• 2024

We introduce a machine learning-based web application to help travel agents plan a package tour schedule. K-nearest neighbor (KNN) classification predicts the optimal tourists' dwelling time based on a variety of information to automatically generate a convenient tour schedule. A database collected in collaboration with an established travel agency is fed into the KNN algorithm implemented in the Python language, and the predicted dwelling times are sent to the web application via a RESTful application programming interface provided by the Flask framework. The web application displays a page in which the agents can configure the initial data and predict the optimal dwelling time and automatically update the tour schedule. After conducting a performance evaluation by simulating a scenario on a computer running the Windows operating system, the average response time was 1.762 s, and the prediction consistency was 100% over 100 iterations.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.176-184
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• 2023

Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.87-88
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• 2012

• The Journal of the Korea Contents Association
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• v.2 no.1
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• pp.7-15
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• 2002

In this paper, 1 design a mode of educational contents for Problem Based Learning(PBL) fitted education using ICT. I design a teaching and loaming schedule for PBL using ICT. I search pertinent items of educational contents to provide to student in PBL and design a mood fitted them. ,also, 1 design a pertinent mode of system to carry out a teaching and teaming schedule. Therefor, a teaching and learning schedule designed in this paper will apply easily. Because PBL manages ill-structured problem reflected the actuality and is high ratio which student participate in instruction, 1 expect that we take the effect of instruction using ICT in PBL.

• Physical Therapy Korea
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• v.12 no.1
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• pp.36-44
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• 2005

The purpose of this study was to examine the effects of self-controlled learning using the (KR) feedback schedule versus the yoked KR on the acquisition and retention of balance training for individuals with hemiplegics. Sixteen hemiplegics were randomly assigned to either a self-controlled or yoked KR group. All subjects were ambulatory with or without an assistive device. The self-controlled group was provided with feedback whenever they requested it, whereas the yoked group had no influence on the feedback schedule. All subjects performed 10 acquisition trials and 10 retention trials the day after acquisition. The data were analyzed using an independent t-test and a Mann-Whitney U test. Participants in the self-controlled group achieved significantly more effective learning than the yoked group during the acquisition and retention test except anterior/posterior (AP) body sway. These results suggest that a feedback schedule which is controlled by the individuals with hemiplegics may be more effective in balancing training than a yoked KR which is not controlled by the subject.

• Journal of Korean Academy of Nursing Administration
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• v.7 no.2
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• pp.361-375
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• 2001

This research aims to produce and implement web-based continuing nurse education programs in response to nurses' educational needs, and to verify them, thus preparing for the times that the program will be offered via web. This research designed, produced and implemented 'emergency nursing' and 'medical fee schedule management' subjects based on Jung, In-seong's(1997) web-based instructional system design, and then compared the learning achievements of web-based learning group of 38 people with those of face-to-face learning group of 39 people. The questionnaire have been developed by these researchers to measure pre-learning knowledge on 'emergency nursing' and 'medical fee schedule management.' Data collected for this research have been given statistical analysis, using SPSS 10.0 for Windows Program. As a result of giving Mann-Whitney test, with respect to pre-learning prior knowledge level, there was no significant difference between the web-based learning group and the face-to-face learning group(Z=-.092, p=.926), while after completing learning, there was a significant difference in the learning achievements between the web-based learning group and the face-to-face learning group(Z=-2.406, p=.008). That is, this research revealed this: the web-based learning group and the face-to face learning group with both having no significant difference in the pre-learning level, after receiving the continuing education each with different methods(face-to-face education and web-based education), showed that the web-based learning groups attained higher learning achievements than the face-to-face learning groups. This result proves the effect of the web-based education to be no worse or even better than that of the face-to-face education, provided that choices of appropriate themes and quality courses composition, as well as systematic design development effective implementation are guaranteed.