• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.169.3-169
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• 2001

This paper introduces a new approach to analysis of error convergence for a class of iterative learning control systems. First, a nonlinear plant is represented using a Takagi-Sugeno(T-S) fuzzy model. Then each iterative learning controller is designed for each linear plant in the T-S fuzzy model. From the view point of two-dimensional(2-D) system theory, we transform the proposed learning systems to a 2-D error equation, which is also established in the form of T-S fuzzy model. We analysis the error convergence in the sense of induced 2 L -norm, where the effects of disturbances and initial conditions on 2-D error are considered. The iterative learning controller design problem to guarantee the error convergence can be reduced to linear matrix inequality problems. In comparison with others, our learning algorithm ...

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.86-93
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• 2022

To combine the science and technology creativity necessary in the era of the 4th Industrial Revolution, it is necessary to cultivate talents who can discover new knowledge and create new values by combining various knowledge with self-directed mathematical competencies. This research attempted to lay the foundation for the curriculum for fostering future creative convergence talent by preparing, executing, and reflecting on the learning plan after learners themselves understand their level and status through self-directed learning. Firstly, We would like to present a teaching-learning plan based on the essential capabilities of the future society, where the development of a curriculum based on mathematics curriculum and intelligent informatization are accelerated. Secondly, an educational design model system diagram was presented to strengthen the self-directed learning ability of mathematics subjects in the electronic engineering curriculum. Consequently, through a survey, we would like to propose the establishment of an educational system necessary for the 4th industry by analyzing learning ability through self-directed learning teaching methods of subjects related to mathematics, probability, and statistics.

• Journal of Korean Neurosurgical Society
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• v.64 no.3
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• pp.447-459
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• 2021

Objective : Oblique lateral interbody fusion (OLIF) is becoming the preferred treatment for degenerative lumbar diseases. As beginners, we performed 143 surgeries over 19 months. In these consecutive cases, we analyzed the learning curve and reviewed the complications in our experience. Methods : This was a retrospective study; however, complications that were well known in the previous literature were strictly recorded prospectively. We followed up the changes in estimated blood loss (EBL), operation time, and transient psoas paresis according to case accumulation to analyze the learning curve. Results : Complication-free patients accounted for 43.6% (12.9%, early stage 70 patients and 74.3%, late stage 70 patients). The most common complication was transient psoas paresis (n=52). Most of these complications occurred in the early stages of learning. C-reactive protein normalization was delayed in seven patients (4.89%). The operation time showed a decreasing trend with the cases; however, EBL did not show any significant change. Notable operation-induced complications were cage malposition, vertebral body fracture, injury to the ureter, and injury to the lumbar vein. Conclusion : According to the learning curve, the operation time and psoas paresis decreased. It is important to select an appropriately sized cage along with clear dissection of the anterior border of the psoas muscle to prevent OLIF-specific complications.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.509-514
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• 2015

In this paper, we introduce a new pattern classifier which is based on the learning algorithm of Extreme Learning Machine the sort of artificial neural networks and fuzzy set theory which is well known as being robust to noise. The learning algorithm used in Extreme Learning Machine is faster than the conventional artificial neural networks. The key advantage of Extreme Learning Machine is the generalization ability for regression problem and classification problem. In order to evaluate the classification ability of the proposed pattern classifier, we make experiments with several machine learning data sets.

• Journal of the Korea Convergence Society
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• v.7 no.2
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• pp.33-41
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• 2016

This study was convergence study conducted to examine the effects of problem-based learning(PBL) on self-directed learning ability, problem solving skills, academic self-efficacy, motivation toward learning in adult nursing care. This study was one-group pretest-posttest design. 51 students in the second year were recruited. After the PBL education, The participants showed much improvement in two areas of problem solving skills(t=3.30, p=.002) and Motivation toward Learning(t=3.004, p= .004). In the case of self-directed learning ability(t=1.451, p= .153) and academic self-efficacy(t=-1.04, p= .304), these have not showed a statistically significant change. The results indicate that the PBL education is effective in improving some areas of students' learning competency. Further study is needed to develop PBL programs for various clinical topics and evaluate the effectiveness on the learning outcomes.

• Journal of Animal Science and Technology
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• v.64 no.5
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• pp.830-841
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• 2022

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

• Annual Conference of KIPS
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• 2021.11a
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.608-615
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• 1998

In this paper a second-order iterative learning control algorithm with feedback is proposed for the trajectory-tracking control of nonlinear dynamic systems with unidentified parameters. In contrast to other known methods, the proposed teaming control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is given in detail, and the sufficient condition for the convergence of the algorithm is provided. We also discuss the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial condition. The effectiveness of the proposed algorithm is shown by computer simulation result. It is shown that, by adding a feedback term in teaming control algorithm, convergence speed, robustness to disturbances and robustness to unmatched initial conditions can be improved.

• Journal of Engineering Education Research
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• v.24 no.6
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• pp.79-85
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• 2021

In a future society where cutting-edge science technology such as artificial intelligence becomes commonplace, the demand for talented people with basic knowledge of mathematics and science is expected to increase continuously, and the educational infrastructure suitable for the characteristics of future generations is still insufficient. In particular, in the case of students taking convergence courses including practical training, there was a problem in communication with the instructor. In this study, we looked at the current status of distance learning at domestic universities that came suddenly due to the global pandemic of COVID-19. In addition, a case study of the use of technology was conducted to facilitate the interaction between instructors and learners through case analysis of distance classes in convergence subjects. Therefore, this study aims to introduce the case of developing lecture contents for smooth convergence education in a non-face-to-face educational environment targeting the developed AI convergence courses and applying them to the education of enrolled students.