• Hwankyungkyoyuk
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• v.21 no.2
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• pp.89-102
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• 2008

Because of the rapid industrialization and urbanization, urban dwellers are lack of opportunity to contact with nature, and hence alienated from it. In particular, primary school children who are very sensible to nature need more opportunities to learn nature by direct interactions with it. For this purpose, a movement for making and using ecological learning-place in play-ground within primary school. It has been found as a result of research on ecological learning-places in 7 primary schools in Daegu that such places, equipped with several ecological facilities, provide both pupils and local dwellers around schools with a place for ecological learning and for rest. But some of them have been left without care and hence can not be properly used, because of inappropriate site, insufficient facilities, and deficient programme for practical use. In conclusion, the paper reconfirms importance of ecological learning-place within grounds of primary school in terms of its educational, social and ecological effects, and suggests briefly some measures to encourage its construction and practical use.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.614-621
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• 2006

We have implemented a multiclassifier learning approach in a GA-based inductive learning environment that learns classification rules that are similar to rules used in PROSPECTOR. In the multiclassifier learning approach, a classification system is constructed with several classifiers that are obtained by running a GA-based learning system several times to improve the overall performance of a classification system. To implement the multiclassifier learning approach, we need a decision-making scheme that can draw a decision using multiple classifiers. In this paper, we introduce two decision-making schemes: one is based on combining posterior odds given by classifiers to each class and the other one is a voting scheme based on ranking assigned to each class by classifiers. We also present empirical results that evaluate the effect of the multiclassifier learning approach on the GA-based inductive teaming environment.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1044-1047
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• 2005

In this paper, we present the strategy of object search for distributed autonomous robotic systems (DARS). The DARS are the systems that consist of multiple autonomous robotic agents to whom required functions are distributed. For instance, the agents should recognize their surrounding at where they are located and generate some rules to act upon by themselves. In this paper, we introduce the strategy for multiple DARS robots to search a hidden object at the unknown area. First, we present an area-based action making process to determine the direction change of the robots during their maneuvers. Second, we also present Q learning adaptation to enhance the area-based action making process. Third, we introduce the coordinate system to represent a robot's current location. In the end of this paper, we show experimental results using hexagon-based Q learning to find the hidden object.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.429-431
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• 2003

The induced learning retardation method involves the temporary inhibition of the artificial neural network’s active units from participating in the error reduction process during training. This stimulates the less active units to contribute significantly to reduce the network error. However, some less active units are not sensitive to stimulation making them almost useless. The network can then be pruned by removing the less active units to make it smaller and more efficient. This study focuses on making the network more efficient and accurate by developing the induced learning retardation and pruning sequence training method. The developed procedure results to faster learning and more accurate artificial neural network for satellite image classification.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.309-316
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• 2021

The purpose of this study is to verify the effectiveness of community-based learning(CBL) on career decision-making self-efficiency of junior college students and explore the meaning. This study was conducted on 68 students and 10 departments participating in the CBL, which was supported by the D University Faculty Learning Development Center in Busan. First of all, does CBL affect the career decision-making self-efficiency for junior college students? Second, what is the meaning of CBL for career decisions for junior college students? The effectiveness of the CBL's before and after application surveys has shown statistically significant changes in the career decision-making self-efficiency. The meaning of CBL for learners' career decisions was derived from "improving understanding through on-site application of theory and creating confidence and commitment in their career paths by providing an opportunity to study." Through this, it can be seen that CBL is worth applying as a teaching method suitable for career guidance of junior college students.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.645-646
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• 2018

Recently, the concept of machine learning has been introduced as a decision making method through data processing. Machine learning uses the results of running based on existing data as a means of decision making. The data generated by the development of technology is vast. This data is called big data. It is important to extract the necessary data from these data. In this paper, we propose a method for extracting related data for constructing an ontology through machine learning. The results of machine learning can be given a relationship from a semantic perspective. it can be added to the ontology to support relationships depending on the needs of the application.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.145-156
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• 2015

Incorporation of individual learning and forgetting behaviors within worker-task assignment models produces a mixed integer nonlinear program (MINLP) problem, which is difficult to solve as a NP hard due to its nonlinearity in the objective function. Previous studies commonly assume homogeneity among workers in workforce scheduling that takes account of learning and forgetting characteristics. This paper expands previous researches by considering heterogeneous individual learning/forgetting, and investigates the impact of worker heterogeneity in initial expertise, steady-state productivity, learning and forgetting on system performance to assist manager's decision-making in worker-task assignments without tackling complex MINLP models. In order to understand the performance implications of workforce heterogeneity, this paper examines analytically how heterogeneity in each of the four parameters of the exponential learning and forgetting (L/F) model affects system performance in three cases : consecutive assignments with no break, n breaks of s-length each, and total b break-periods occurred over T periods. The study presents the direction of change in worker performance under different assignment schedules as the variance in initial expertise, steady-state productivity, learning or forgetting increases. Thus, it implies whether having more heterogenous workforce in terms of each of four parameters in the L/F model is desired or not in different schedules from the perspective of system productivity measurement.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.127-135
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• 2013

For the successful implementation of IT projects, individual consultant's competency in the project is very important. Especially, 3 key factors which are 1) Learning-by-Doing, 2) Learning-from-Others, and 3) Learning-by-Investment with individual consultant's competency, are required for solving various critical issues which can be occurred during implementing IT project. The objective of this research is to examine the effects of these learning processes on decision performance of consultants. Prior to setup the research model, we conducted 3 times in-depth interviews with IT consultants who have over 20 years IT project experiences. Through interviews with IT project expert, we tried to validate our research model and develop survey questionnaires. Over 100 consultants, who are working at SI companies those of Samsung SDS, LG CNS, SK C&C and other small SI companies, were participated to survey. In the contrary of our thoughts before conducted experiment, we got the interesting result from pilot experiment. Most influenced learning process was Learning-by-Doing and less influenced learning process was Learning-from-Others.

• Journal of Digital Convergence
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• v.7 no.3
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• pp.49-60
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• 2009

The aim of this study was to develop a model for teaching-teaming by applying Ubiquitous at a learning experience field, in which connect characteristics of both ubiquitous application learning and experience teaming, making use of them. A literature survey of concepts was conducted, with the main areas to find out relationships between ubiquitous application learning and experience learning. Experience learning by applying ubiquitous learning methods maximizes its efficiency of experience learning in considering ubiquitous learning methods's characteristics of dynamic, interaction, sharing. Also it makes communications through positive participation and active interaction, and leads to a process of internal examination. The research data suggests that critical factors of experiencing learning applying ubiquitous are acquiring information and memory, information integration and exquisiteness, emotional and social activity, producing activity, help activity.