• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.477-480
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• 2003

The development of distant education on the internet has undergone a great change. However, many of the educational ways force to students to acquire the technical skills by repeatedly practising the contents. These learning styles have certain advantages, for example, providing feedback without delay and constant learning, but they also have disadvantages. For instance, they reduce the motivation and interest level of the students. And if the students don't participate in class, they simply will not improve in their ability. This study provides a solution which will has an influence on the other classes. We can utilize self-learning, interactive and leveled studying with animation and teaching aids.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.113-117
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• 2018

Human emotions are revealed by various factors. Words, actions, facial expressions, attire and so on. But people know how to hide their feelings. So we can not easily guess its sensitivity using one factor. We decided to pay attention to behaviors and facial expressions in order to solve these problems. Behavior and facial expression can not be easily concealed without constant effort and training. In this paper, we propose an algorithm to estimate human emotion through combination of two results by gradually learning human behavior and facial expression with little data through the deep learning method. Through this algorithm, we can more comprehensively grasp human emotions.

• The Mathematical Education
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• v.51 no.1
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• pp.77-88
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• 2012

This qualitative research provides a situation model, which is designed for promoting learning of eigenvalue and eigenvector. This study also demonstrates the usefulness of the model through a small groups discussion. Particularly, participants of the discussion were asked to decide the numbers of milk cows in order to make constant amounts of cheese production. Through such discussions, subjects understood the notion of eigenvalue and eigenvector. This study has following implications. First of all, the present research finds significance of situation model. A situation model is useful to promote learning of mathematical notions. Subjects learn the notion of eigenvalue and eigenvector through the situation model without difficulty. In addition, this research demonstrates potentials of small groups discussion. Learners participate in discussion more actively under small group debates. Such active interaction is necessary for situation model. Moreover, this study emphasizes the role of teachers by showing that patience and encouragement of teachers promote students' feeling of achievement. The role of teachers are also important in conveying a meaning of eigenvalue and eigenvector. Therefore, this study concludes that experience of learning the notion of eigenvalue and eigenvector thorough situation model is important for teachers in future.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.295-300
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• 1998

In this paper, we point out the possibility of the divergence of control input caused by the estimation error of delay-time when general iterative learning algorithms are applied to a class of linear dynamic systems with time-delay in which delay-time is not exactly measurable, and then propose a new type of iterative learning algorithm in order to solve this problem. To resolve the uncertainty of delay-time, we propose an algorithm using holding mechanism which has been used in digital control system and/or discrete-time control system. The control input is held as constant value during the time interval of which size is that of the delay-time uncertainty. The output of the system tracks a given desired trajectory at discrete points which are spaced auording to the size of uncertainty of delay-time with the robust property for estimation error of delay-time. Several numerical examples are given to illustrate the effeciency of the proposed algorithm.

• Educational Technology International
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• v.11 no.2
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• pp.43-75
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• 2010

The purpose of this study is to identify patterns of learner behaviors, conflicting and facilitating factors during collaborative work in an online learning community(OLC). This study further seeks to investigate the difference of learner behaviors between high- and low-performing groups, and conflicting and facilitating factors. The online postings from four groups(19 students) in the spring semester(study 1) and six groups(24 students) in the fall semester(study 2) were analyzed. A coding scheme was generated based on constant comparison using the qualitative data analysis tool, NVivo. The analysis identified 7 categories of learner behaviors in both studies. Among the seven categories, information seeking and co-construction were most frequently observed in both studies. One evident difference between the high- and low-performing groups was that the high-performing groups revealed more incidents of learner behaviors in both studies. In addition, six categories of conflicting factors and five categories of facilitating factors were emerged in both studies. The inefficiency of work category was one of the most frequently observed categories in both studies. Interestingly, the high-performing groups showed more incidents of conflicting factors than the low-performing groups. This study revealed two different types of conflicting factors and there is a need for different moderating strategies depending on its type. Based on the results of the study, effective design strategies for an OLC to facilitate active learning were suggested.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.32-42
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• 2000

This paper presents an on-line learning algorithm for sequential construction of generalized radial basis function networks (GRBFNs) to model nonlinear systems from empirical data. The GRBFN, an extended from of standard radial basis function (RBF) networks with constant weights, is an architecture capable of representing nonlinear systems by smoothly integrating local linear models. The proposed learning algorithm has a two-stage learning scheme that performs both structure learning and parameter learning. The structure learning stage constructs the GRBFN model using two construction criteria, based on both training error criterion and Mahalanobis distance criterion, to assign new hidden units and the linear local models for given empirical training data. In the parameter learning stage the network parameters are updated using the gradient descent rule. To evaluate the modeling performance of the proposed algorithm, simulations and their results applied to two well-known benchmarks are discussed.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.261-268
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• 2020

Machine learning is achieved by making a cost function from a given digital signal data and optimizing the cost function. The cost function here has local minimums in the cost function depending on the amount of digital signal data and the structure of the neural network. These local minimums make a problem that prevents learning. Among the many ways of solving these methods, our proposed method is to change the learning step-size. Unlike existed methods using the learning rate (step-size) as a fixed constant, the use of multivariate function as the cost function prevent unnecessary machine learning and find the best way to the minimum value. Numerical experiments show that the results of the proposed method improve about 3%(88.8%→91.5%) performance using the proposed method rather than the existed methods.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.1-6
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• 2019

The proposed model is developed to minimize the loss of information in incomplete data including missing data. The first step is to transform the learning data to compensate for the loss information using the data extension technique. In this conversion process, the attribute values of the data are filled with binary or probability values in one-hot encoding. Next, this conversion data is input to the deep learning model, where the number of entries is not constant depending on the cardinality of each attribute. Then, the entry values of each attribute are assigned to the respective input nodes, and learning proceeds. This is different from existing learning models, and has an unusual structure in which arbitrary attribute values are distributedly input to multiple nodes in the input layer. In order to evaluate the learning performance of the proposed model, various experiments are performed on the missing data and it shows that it is superior in terms of performance. The proposed model will be useful as an algorithm to minimize the loss in the ubiquitous environment.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.116-124
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• 2022

Information and communication technologies used in the social sphere are born due to the development of computer technologies. The main task of the distance learning process in higher education institutions is not to provide information, but to teach how to obtain and use it. The purpose of the article: to identify innovative approaches in the training of specialists in higher education institutions in the context of distance learning. Various innovative approaches to organizing the work of students of higher educational institutions in the context of distance learning are considered. Based on the conducted research, it is concluded that each of the approaches described by us outlines the study of the phenomenon of professional training of a specialist in the condition of distance learning. All the described approaches significantly contribute to the improvement of professional training of specialists, encourage students to self-improvement, professional development and enrich their professional competence in modern conditions. The emergence and spread of innovative technologies means not only a change in the activity itself and its inherent means and mechanisms of its implementation, but also a significant restructuring of goals, value orientations, specific knowledge, skills and abilities. Therefore, the current stage of the development of civilization, scientific and technological progress requires the emergence of such specialists who would have broad humanitarian thinking, would have good psychological training, would be able to build professional activities according to laws that take into account the relationship between economic productivity and creativity, as well as the desire of the individual for constant renewal, self-realization. Only such qualities will help you master the specifics of innovative technologies well. We see the prospects in the study of innovative approaches to training specialists in higher education institutions in the condition of distance learning in foreign countries.