• Journal of IKEEE
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• v.26 no.1
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• pp.19-26
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• 2022

In this paper, we propose an algorithm that applies a different variable lambda to each loss value to solve the performance degradation caused by domain differences in LwF, and show that the retention of past knowledge is improved. The lambda value could be variably adjusted so that the current task to be learned could be well learned, by the variable lambda method of this paper. As a result of learning by this paper, the data accuracy improved by an average of 5% regardless of the scenario. And in particular, the performance of maintaining past knowledge, the goal of this paper, was improved by up to 70%, and the accuracy of past learning data increased by an average of 22% compared to the existing LwF.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.89-106
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• 2022

Recently, in Korea, the number of unemployed people who have lost their jobs involuntarily due to closure of workplaces, layoffs, and poor management stood at 788,000 in October 21, an increase of 44,000 from last August (Statistics Office, 2021). The average retiring age of workers is 49.7, so regardless of their intention, they often end up retiring early unavoidably. Meanwhile, it has been found that eight out of ten workers have startup intention; therefore, now their startup is regarded to be essential, not selective. The purpose of this study is to investigate if workers' entrepreneurial orientation, continuous learning, satisfaction with remuneration, and employment prospect are correlated with entrepreneurial intention and examine if innovative behavior mediates the relations. To sum up the results, first, innovativeness and risk sensitivity in entrepreneurship are found to have positive effects on workers' entrepreneurial intention. Restless challenges and innovative thinking at work are crucial variables to enhance entrepreneurial intention. Second, satisfaction with remuneration influences entrepreneurial intention positive effects, and continuous learning and employment prospect, too, have positive effects on entrepreneurial intention. As employment instability is increasing at work due to the rapidly changing corporate environment, Considering whether the current organization will strive for survival or prepare to start a business for sustainable economic activity, it is judged that there is a willingness to start a business for better compensation even if the satisfaction of compensation is high. In addition, it was confirmed that the possibility of employability with the career desired by the organization as well as the securing of practical competency and expertise through continuous learning are important variables in increasing the will to start a business. Third, relations between entrepreneurial orientation, satisfaction with remuneration, continuous learning, employment prospect, and entrepreneurial intention are found to be mediated by innovative behavior; however, its mediative effect in relations between innovativeness, risk sensitivity, and entrepreneurial intention in entrepreneurship is dismissed. Innovative behavior at work are found to be major variables to elevate entrepreneurial intention in relations between continuous learning, employment prospect, and satisfaction with remuneration.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.6-10
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• 1996

This paper presents an overview of research results obtained by the authors in a series of publications. Methods are developed both for time-varying and time-invariant for linear and nonlinear. for time domain and frequency domain . and for discrete-time and continuous-time systems. Among the topics presented are: 1. Learning control based on integral control concepts applied in the repetition domain. 2. New algorithms that give improved transient response of the indirect adaptive control ideas. 4. Direct model reference learning control. 5 . Learning control based frequency domain. 6. Use of neural networks in learning control. 7. Decentralized learning controllers. These learning algorithms apply to robot control. The decentralized learning control laws are important in such applications becaused of the usual robot decentralized controller structured.

• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.59-82
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• 2023

The present research aims to examine whether learning from the supervisor influences readiness for change with the mediating impact of planfulness. Drawing upon the theory of planned behavior, it is hypothesized that learning from the supervisor positively impacts planfulness ability in individuals, which in turn enhances the readiness for change. Through using convenience sampling, the sample of 451 was collected from employees working full-time in the manufacturing and I.T. service organizations in India. Structural equation modeling and regression analysis indicate that learning from the supervisor is positively associated with readiness for change and planfulness. Additionally, planfulness fully mediated the relationship between learning from the supervisor and readiness to change. The findings of the present research highlight that continuous support and learning from the supervisor enhances the planfulness ability of the individual and consequently enhances individual readiness for change. The current research is pioneering in testing the hypothetical model associating learning from the supervisor, planfulness, and readiness for change.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.11-20
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• 2012

Stream data shows a sequence of values changing continuously over time. Due to the nature of stream data, its trend is continuously changing according to various time intervals. Therefore the prediction of stream data must be carried out simultaneously with respect to multiple intervals, i.e. Continuous Multiple Prediction(CMP). In this paper, we propose a Continuous Integrated Hierarchical Temporal Memory (CIHTM) network for CMP based on the Hierarchical Temporal Memory (HTM) model which is a neocortex leraning algorithm. To develop the CIHTM network, we created three kinds of new modules: Shift Vector Senor, Spatio-Temporal Classifier and Multiple Integrator. And also we developed learning and inferencing algorithm of CIHTM network.

• Structural Engineering and Mechanics
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• v.47 no.4
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• pp.495-511
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• 2013

In this paper, a new efficient optimization algorithm called Teaching-Learning-Based Optimization (TLBO) is used for the least weight design of trusses with continuous design variables. The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several truss structures are analyzed to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.977.1-1000
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• 1993

This paper presents a fuzzy neural network, called the fuzzy counterpropagation network, that structures its inputs and generates its outputs in a manner based on counterpropagation networks. The fuzzy counterpropagation network is developed by incorporating the concept of fuzzy clustering into the hidden layer responses. Three learning algorithms are introduced for use with the proposed network. Simulations demonstrate that fuzzy counterpropagation networks with the proposed learning algorithms work well on approximating bipolar and continuous functions.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.519-524
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• 2009

Recently, reinforcement learning methods have drawn much interests in the area of machine learning. Dominant approaches in researches for the reinforcement learning include the value-function approach, the policy search approach, and the actor-critic approach, among which pertinent to this paper are algorithms studied for problems with continuous states and continuous actions along the line of the actor-critic strategy. In particular, this paper focuses on presenting a method combining the so-called ACFRL(actor-critic fuzzy reinforcement learning), which is an actor-critic type reinforcement learning based on fuzzy theory, together with the RLS-NAC which is based on the RLS filters and natural actor-critic methods. The presented method is applied to a control problem for crawling robots, and some results are reported from comparison of learning performance.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1054-1061
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• 2003

Q-Learning, a representative algorithm of reinforcement learning, experiences repeatedly until estimation values about all state-action pairs of state space converge and achieve optimal policies. When the state space is high dimensional or continuous, complex reinforcement learning tasks involve very large state space and suffer from storing all individual state values in a single table. We introduce Q-Map that is new function approximation method to get classified policies. As an agent learns on-line, Q-Map groups states of similar situations and adapts to new experiences repeatedly. State-action pairs necessary for fine control are treated in the form of rule. As a result of experiment in maze environment and mountain car problem, we can achieve classified knowledge and extract easily rules from Q-Map

• Educational Technology International
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• v.19 no.2
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• pp.229-254
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• 2018

Universities in Korea have driven universities' new attempts to adopt more learner-centered and active learning in English. Problem-based Learning (PBL) is one of the well-known constructive teaching and learning methodologies in higher education. Our research goal was to design and develop the optimal PBL practices for a college physics course taught in English to promote learning and course satisfaction. For four semesters, we have tried and adjusted PBL components, and looked at the trend of the exam scores and group work achievement in each semester. We found that the number of problems and the duration of problem solving are the critical factors that influence the effect of PBL in a college physics course taught in English by going through iterative implementation. The iterative process of applying, designing, and constructing PBL to physics classes was meaningful not only in that we have found the optimal PBL model for learning a college physics course, but also in that we have been reflecting on the continuous interaction with learners during the course.