• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.7
• /
• pp.1411-1417
• /
• 2009

In this paper, we consider the design of $H_{\infty}$ control of linear discrete-time systems having no mathematical model. The basic approach is to use Q-learning which is a reinforcement learning method based on actor-critic structure. The model-free control design is to use not the mathematical model of the system but the informations on states and inputs. As a result, the derived iterative algorithm is expressed as linear matrix inequalities(LMI) of measured data from system states and inputs. It is shown that, for a sufficiently rich enough disturbance, this algorithm converges to the standard $H_{\infty}$ control solution obtained using the exact system model. A simple numerical example is given to show the usefulness of our result on practical application.

• International journal of advanced smart convergence
• /
• v.7 no.4
• /
• pp.27-39
• /
• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• Science of Emotion and Sensibility
• /
• v.13 no.1
• /
• pp.225-234
• /
• 2010

To investigate the role of positive emotion in education, we have reviewed the previous studies on positive emotion, learning and motivation. In the present study, we examined the definition of positive emotion, and influences of positive emotion on cognition, creativity, social relationship, psychological resource such as life satisfaction, and interactive relationship among positive emotion, motivation and learning. To investigate the role of positive emotion on motivation and learning more scientifically, we examined the recent results of neuroscience. In other words, we have reviewed diverse research on positive emotion, learning and motivation based on brain-based learning. We also examined the research of autonomy-supportive environment as the specific example of improving positive emotion. As one of the most effective methods for emotional education, we discussed brain-based learning, the new research field. As the future prospects, we discussed the implications, possibilities and limitations of brain-based learning.

• Journal of Engineering Education Research
• /
• v.23 no.4
• /
• pp.76-83
• /
• 2020

The purpose of this study is to conduct Project Based Learning (PBL) in collaboration with industry experts to operate practical subjects in an industry-university-linked teaching method. PBL is a teaching method in which students can learn through actively engaging in real-world and personally meaningful projects. For a long period of time, PBL methodologies have been found to be especially effective in engineering education. This case study deals with the operational results of a practice subject which has been conducted over three years from 2017 to 2019 in Korea University of Technology and Education. The course is for the 4^th grade students in the school of mechatronics engineering. The results of the surveyed learning outcomes (for example, Program Outcomes and Course Learning Outcomes) have been analyzed and reflected in the next years for the Continuous Quality Improvement. By working on practical projects linked to industry, students have been able to develop so-called 4C's capabilities which are Critical Thinking, Creativity, Communication and Collaboration.

• Journal of Korean Association for Spatial Structures
• /
• v.24 no.2
• /
• pp.83-90
• /
• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.2012-2030
• /
• 2021

Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

• Journal of the Korea Convergence Society
• /
• v.11 no.3
• /
• pp.19-26
• /
• 2020

In this paper, we propose a method to analyze the loss region of the dictionary-based super resolution result learned for image quality improvement and to map the learning data according to the analyzed loss region. In the conventional learned dictionary-based method, a result different from the feature configuration of the input image may be generated according to the learning image, and an unintended artifact may occur. The proposed method estimate loss information of low resolution images by analyzing the reconstructed contents to reduce inconsistent feature composition and unintended artifacts in the example-based super resolution process. By mapping the training data according to the final interpolation feature map, which improves the noise and pixel imbalance of the estimated loss information using a Gaussian-based kernel, it generates super resolution with improved noise, artifacts, and staircase compared to the existing super resolution. For the evaluation, the results of the existing super resolution generation algorithms and the proposed method are compared with the high-definition image, which is 4% better in the PSNR (Peak Signal to Noise Ratio) and 3% in the SSIM (Structural SIMilarity Index).

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.331-337
• /
• 1997

In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general descriptions of concepts from specific instances of these concepts. In many real life situations however new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overall set of instances. The method of learning presented here is based on a rough set concept proposed by Pawlak[2]. It is shown an algorithm to fund minimal set of rules using reduct change theorems giving criteria for minimum recalculation and an illustrative example.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.3
• /
• pp.363-368
• /
• 2004

In this paper, the robustness property of 2nd-order iterative learning control(ILC) method for a class of linear and nonlinear discrete-time dynamic systems is studied. 2nd-order ILC method has the PD-type learning algorithm based on both time-domain performance and iteration-domain performance. It is proved that the 2nd-order ILC method has robustness in the presence of state disturbances, measurement noise and initial state error. In the absence of state disturbances, measurement noise and initialization error, the convergence of the 2nd-order ILC algorithm is guaranteed. A numerical example is given to show the robustness and convergence property according to the learning parameters.

• Journal of Educational Research in Mathematics
• /
• v.12 no.1
• /
• pp.1-16
• /
• 2002

The 7th Curriculum emphasizes that in mathematics classes, mathematical concepts be understood and mathematical problems be solved through student's own exploratory activities including the use of data, manipulatives, andtechnological devices. Following the main idea of the Seventh Mathematics Curriculum, this paper dealt with instructional methods applied suitably and effectively in mathematics classes, and focused on the 'exploratory learning in groups' method in mathematics education. For this purpose, this paper reviewed and summarized theories related to general pedagogy and of mathematics education. Based on the results, it investigated appropriate instructional methods in mathematics education. In particular, this paper focused on studying the exploratory learning method while investigating its properties and understand- ing the relationship between the 'exploratory learning in groups' method and the discussion-centered method. Finally, in order to show the usefulness of the exploratory learning method, this paper developed an example of a teaching module using the exploratory learning method in addition to discussion and lecture-centered methods by the use of manipulatives. The main goal of the module was to make students understand the principle of multiplication of integers.