• The Journal of Korean Academic Society of Nursing Education
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• v.23 no.1
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• pp.118-130
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• 2017

Purpose: The purpose of this study was to identify the relationship between the satisfaction with clinical practice and clinical performance ability by types of self-directed learning ability of nursing students. Methods: This was a triangular study that was conducted to understand clinical performance ability. The subjects were 260 junior and senior students from a university in P city. The data were collected from April 22 to December 30, 2015. Data were collected by Q-card, Q-block an assessment tool, a structured self-reporting survey and a questionnaire. Results: We classified the self-directed learning abilities into four types: Type 1: a self-reflective person; Type 2: a person who prepares for the future; Type 3: a person with a sense of responsibility and obligation; and Type 4: an enthusiastic learner. We found that clinical performance ability was higher for Type 4 than Type 3. We found that clinical performance satisfaction with clinical practice was also higher for the Type 4 individual than a Type 3 person. Conclusion: To improve students' clinical performance ability, we need plans and support to lead students toward becoming an 'enthusiastic learner' type of person with self-directed learning ability. It is necessary to increase students' satisfaction with clinical practice.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.515-522
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• 2024

Energy consumption is steadily increasing, and buildings in particular account for more than 20% of the total energy consumption around the world. As an effort to cost-effectively manage the energy consumption of buildings, many research groups have recently focused on Smart Building Energy Management Systems (BEMS), which are deepening the research depth by applying artificial intelligence(AI). In this paper, we propose a reinforcement learning-based energy control method for smart energy buildings integrated with V2G station, which aims to reduce the total energy cost of the building. The results of performance evaluation based on the energy consumption data measured in the real-world building shows that the proposed method can gradually reduce the total energy costs of the building as the learning process progresses.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.166-169
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• 2003

Reinforcement learning if a kind of machine learning. It aims to adapt an agent to a given environment with a clue to a reward and a penalty. Q-learning [8] that is a representative reinforcement learning system treats a reward and a penalty at the same time. There is a problem how to decide an appropriate reward and penalty values. We know the Penalty Avoiding Rational Policy Making algorithm (PARP) [4] and the Penalty Avoiding Profit Sharing (PAPS) [2] as reinforcement learning systems to treat a reward and a penalty independently. though PAPS is a descendant algorithm of PARP, both PARP and PAPS tend to learn a local optimal policy. To overcome it, ion this paper, we propose the Multi Best method (MB) that is PAPS with the multi-start method[5]. MB selects the best policy in several policies that are learned by PAPS agents. By applying PS, PAPS and MB to a soccer game environment based on the SoccerBots[9], we show that MB is the best solution for the soccer game environment.

• The Journal of Korean Academic Society of Nursing Education
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• v.6 no.2
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• pp.359-375
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• 2000

PBL can be defined as an active, self-directed and student-centered learning, and an opposite way of classroom teacher-centered learning which has been traditional role learning. PBL enables students think more efficiently and effectively when puzzling through the patient problems. The purpose of this study is to find out the perception of student nurse about PBL, the characteristics and the structure of the type for PBL. The research process is as follow : First, the researcher selected 35 statements for PBL with the content analysis of in depth interview and the literature review. Second, the researcher asks 38 student nurse to classify the statement cards. The result of the research is that the type of student nurse's PBL perception is divided into 4 types(Affirmative type, Negative type, Suspicious type, and Preferable type), and the explanative total variance is 44 percent. In relation to this, if PBL well combined and adapted in our traditional curriculum will change our nursing education in better direction.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.22.2-22
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• 2001

Recently, Reinforcement Learning (RL) methods in MDP have been extended and applied to the POMDP problems. Currently, hierarchical RL methods are widely studied. However, they have the drawback that the learning time and memories are exhausted only for keeping the hierarchical structure, though they aren´t necessary. On the other hand, our "Labeling Q-learning (LQ-learning) proposed previously, has no hierarchical structure, but adopts a characteristic internal memory mechanism. Namely, LQ-1earning agent percepts the state by pair of observation and its label, and the agent can distinguish states, which look as same, but obviously different, more exactly. So to speak, at each step t, we define a new type of perception of its environment ～ot = (ot, $\theta$t), where of is conventional observation, and $\theta$t is the label attached to the observation. Then the conventional ...

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.88-92
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• 2018

Currently drone industry has become one of the fast growing markets and the technology for unmanned aerial vehicles are expected to continue to develop at a rapid rate. Especially small unmanned aerial vehicle systems have been designed and utilized for the various field with their own specific purposes. In these fields the path planning problem to find the shortest path between two oriented points is important. In this paper we introduce a path planning strategy for an autonomous flight of unmanned aerial vehicles through reinforcement learning with self-positioning technique. We perform Q-learning algorithm, a kind of reinforcement learning algorithm. At the same time, multi sensors of acceleraion sensor, gyro sensor, and magnetic are used to estimate the position. For the functional evaluation, the proposed method was simulated with virtual UAV environment and visualized the results. The flight history was based on a PX4 based drones system equipped with a smartphone.

• Journal of Platform Technology
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• v.11 no.6
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• pp.21-27
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• 2023

In this paper, we introduce a reinforcement learning-based algorithm for personalized tourist path recommendations. The algorithm employs a reinforcement learning agent to explore tourist regions and identify optimal paths that are expected to enhance tourism experiences. The concept of tourism experience is defined through points of interest (POI) located along tourist paths within the tourist area. These metrics are quantified through aggregated evaluation scores derived from reviews submitted by past visitors. In the experimental setup, the foundational learning model used to find tour paths is the Deep Q-Network (DQN). Despite the limited availability of historical tourist behavior data, the agent adeptly learns travel paths by incorporating preference scores of tourist POIs and spatial information of the travel area.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.73-78
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• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.648-660
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• 2018

The purpose of this study is to find out the perception of the satisfaction of the undergraduate curriculum in the college undergraduate degree. The purpose of this study is to classify the structure of satisfaction of major curriculum, and to describe the characteristics of types of curriculum satisfaction in the major curriculum. The results of the type analysis are as follows. The first type (N = 5): In-depth major curriculum teaching method satisfaction type, the second type (N = 4): Practical learning class satisfaction type, the third type (N = 3) 4 types (N = 3): Employment Establishment centered class satisfaction type, 5th type (N = 3): Theory centered class satisfaction type, 6th type (N = 2) It is analyzed that there are various features for each type. In the future, we will revise and refine it with detailed Q methodological questions and analytical techniques, and analyze various opinions of respondents more concrete and objectively.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.292-302
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• 2021

The purpose of this study was to use Google Classroom for Cooking Practice Subject. We would like to present a practice class operation method suitable for the current educational environment and conditions. If the Untact Class in the Practice Subject is conducted in the future, we want to cultivate the core competencies and practical skills of the major. In addition, Q Methodology, which can be extracted by type, was applied by analyzing subjective opinions or perception structures for the cooking major students who are currently experiencing the curriculum. The survey period was conducted from March 23 to April 30, 2020 for first-year Cooking Major students taking the Cooking Practice Subject. Type 1 (N=11): Development of cooking training kit, Type 2 (N=7): Special lectures from industry experts, Type 3 (N=7): Development of practice form self-directed learning, respectively. Based on this study, it is expected to contribute to the Q Methodology on the development of the curriculum for the operation of the cooking practice subject, the effectiveness of education, and the application of learning methods.