• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.695-704
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• 2021

This study aims to determine the effect of changes in learning systems and its effects on students' learning outcomes amid the Covid-19 pandemic. The sample of this study are the students who are in Jakarta, Indonesia. "Non-probability random sampling" technique has been used to select the samples while the sampling method used is "purposive sampling", where criteria are used to select samples. The samples in this study are 200 people taken randomly using Google Form. Concentration ability and learning interest can affect learning outcomes with the mediation of learning comfort and a good learning environment. As well as physical distancing can moderate the effect of concentration ability and learning interest on learning outcomes. The ability to concentrate on improving learning outcomes requires psychomotor improvement. Whereas interest in learning with indicators of learning awareness can improve learning outcomes. A clean environment is a strength in the learning comfort and the community environment can be recommended in the learning environment. The implementation of the restriction of gathering becomes an important point of physical distancing. The other novelties are the learning comfort and the learning environment as mediating variables and physical distancing as moderating variables in one study at a time.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.155-165
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• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.45-46
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• 2012

• International conference on construction engineering and project management
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• 2022.06a
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• pp.119-127
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• 2022

Most of the construction works are conducted outdoors, so the construction workers are affected by weather conditions such as temperature, humidity, and wind velocity which can be evaluated the thermal comfort as environmental factors. In our previous researches, it was found that construction accidents are usually occurred in the discomfort ranges. The safety management, therefore, should be planned in consideration of the thermal comfort and measured by a specialized simulation tool. However, it is very complex, time-consuming, and difficult to model. To address this issue, this study is aimed to develop a framework of a prediction model for improving the prediction accuracy about outdoor thermal comfort considering environmental factors using machine learning algorithms with hyperparameter tuning. This study is done in four steps: i) Establishment of database, ii) Selection of variables to develop prediction model, iii) Development of prediction model; iv) Conducting of hyperparameter tuning. The tree type algorithm is used to develop the prediction model. The results of this study are as follows. First, considering three variables related to environmental factor, the prediction accuracy was 85.74%. Second, the prediction accuracy was 86.55% when considering four environmental factors. Third, after conducting hyperparameter tuning, the prediction accuracy was increased up to 87.28%. This study has several contributions. First, using this prediction model, the thermal comfort can be calculated easily and quickly. Second, using this prediction model, the safety management can be utilized to manage the construction accident considering weather conditions.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.375-380
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• 2022

High-speed railway bridges carry a risk of dynamic response amplification due to resonance caused by train loads, and running safety and riding comfort must therefore be reviewed through dynamic analysis in accordance with design codes. The running safety and ride comfort calculation procedure, however, is time consuming and expensive because dynamic analyses must be performed for every 10 km/h interval up to 110% of the design speed, including the critical speed for each train type. In this paper, a deep-learning-based prediction system that can predict the running safety and ride comfort in advance is proposed. The system does not use dynamic analysis but employs a deep learning algorithm. The proposed system is based on a neural network trained on the dynamic analysis results of each train and speed of the railway bridge and can predict the running safety and ride comfort according to input parameters such as train speed and bridge characteristics. To confirm the performance of the proposed system, running safety and riding comfort are predicted for a single span, straight simple beam bridge. Our results confirm that the deck vertical displacement and deck vertical acceleration for calculating running safety and riding comfort can be predicted with high accuracy.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.426-432
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• 2022

HVAC systems play a critical role in reducing energy consumption in buildings. Integrating occupants' thermal comfort evaluation into HVAC control strategies is believed to reduce building energy consumption while minimizing their thermal discomfort. Advanced technologies, such as visual sensors and deep learning, enable the recognition of occupants' discomfort-related actions, thus making it possible to estimate their thermal discomfort. Unfortunately, it remains unclear how accurate a deep learning-based classifier is to recognize occupants' discomfort-related actions in a working environment. Therefore, this research evaluates the classification performance of occupants' discomfort-related actions while sitting at a computer desk. To achieve this objective, this study collected RGB video data on nine college students' cold discomfort-related actions and then trained a deep learning-based classifier using the collected data. The classification results are threefold. First, the trained classifier has an average accuracy of 93.9% for classifying six cold discomfort-related actions. Second, each discomfort-related action is recognized with more than 85% accuracy. Third, classification errors are mostly observed among similar discomfort-related actions. These results indicate that using human action data will enable facility managers to estimate occupants' thermal discomfort and, in turn, adjust the operational settings of HVAC systems to improve the energy efficiency of buildings in conjunction with their thermal comfort levels.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.61-86
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• 2022

The purpose of this study is to investigate the effect of learning space Servicescape on the user satisfaction level and continuance intention and to identify moderating effect of the learning activity. The six Servicescape factors are selected after literature review and in-depth interviews; cleanliness, comfort, convenience, aesthetics, accessibility, and flexibility. The online survey is given to the university students at four-year private universities in Seoul metropolitan area. The result shows that among the learning space Servicescape factors, cleanliness, comfort, convenience, and accessibility have a significant impact on the user's satisfaction and the user's satisfaction response determines the continuance intention to the learning space. It is also found that the factors of cleanliness and comfort have a negative moderating effect on user satisfaction. This study implies that the result provides methods to develop the space arrangement for university libraries that provide the better-support to students' learning experience.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.1
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• pp.50-55
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• 2013

Subjects with 0.7[clo]'s amount of clothing were estimated on their thermal comfort, concentrativeness, heart rate variability, stress and fatigue degree when given variation in relative humidity to 30, 40, 50, 60, 70, and 80[RH%], in an environmental test room of temperature 25[$^{\circ}C$], illumination 1000[lux] and air velocity 0.02[m/sec], by using EEG, learning ability and HRV. At the result, it was at 50~60[RH%] of relative humidity that subject's thermal comfort and concentrativeness were at the highest while stress were at the lowest, and it was at 60[RH%] of relative humidity that heart rate variability was most stabilized. It was found that when temperature and humidity of the environmental test room are at 25[$^{\circ}C$] and 50~60[RH%], subject's productivity and psychological state are least affected.

• Journal of The Korean Association For Science Education
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• v.22 no.5
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• pp.991-1009
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• 2002

This qualitative study investigated two preservice teachers' conceptual ecologies in professional development during the science teacher preparation program. The notion of a conceptual ecology contains nature of knowledge, science and science teaching, learning, and content knowledge and comfort level. The data were collected during the participants' preservice year and their practicum experience. Both data collections and analyzing were from the various sources of interviews, teaching observations, journals, and information and profiles by the participants' supervisor. Two preservice teachers serve as cases representative of this study. Results show that problems preventing the preservice teachers from moving closer to conceptual change teaching were their understandings of the nature of science and the nature of knowledge. The preservice teachers' views about knowledge come from, and what knowledge is, are largely shaped by the nature of science and learning drive pedagogy and classroom practice. Knowledge of and comfort with the subject matter are also important.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.907-913
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• 1999

For an automated transportation system like PRT(Personal Rapid Transit) system or IVHS, an efficient vehicle-merging algorithm is required for smooth operation of the network. For management of merging, collision avoidance between vehicles, ride comfort, and the effect on traffic should be considered. This paper proposes an unmanned vehicle-merging algorithm that consists of two procedures. First, a longitudinal control algorithm is designed to keep a safe headway between vehicles in a single lane. Secondly, 'vacant slot and ghost vehicle' concept is introduced and a decision algorithm is designed to determine the sequence of vehicles entering a converging section considering energy consumption, ride comfort, and total traffic flow. The sequencing algorithm is based on fuzzy rules and the membership functions are determined first by an intuitive method and then trained by a learning method using a neural network. The vehicle-merging algorithm is shown to be effective through simulations based on a PRT model.