• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 제11회 음성통신 및 신호처리 워크샵 논문집 (SCAS 11권 1호)
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• pp.407-410
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• 1994

In this paper, we describe the new unsuperivised learning algorithm, SASOM. It can solve the defects of the conventional SOM that the state of network can't converge to the minimum point. The proposed algorithm uses the object function which can evaluate the state of network in learning and adjusts the learning rate flexibly according to the evaluation of the object function. We implement the simulated annealing which is applied to the conventional network using the object function and the learning rate. Finally, the proposed algorithm can make the state of network converged to the global minimum. Using the two-dimensional input vectors with uniform distribution, we graphically compared the ordering ability of SOM with that of SASOM. We carried out the recognitioin on the new algorithm for all Korean phonemes and some continuous speech.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.239-246
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• 2003

Statisticians, or data miners, are often requested to assess the importances of input variables in the given supervised learning model. For the purpose, one may rely on separate ad hoc measures depending on modeling types, such as linear regressions, the neural networks or trees. Consequently, the conceptual consistency in input variable importance measures is lacking, so that the measures cannot be directly used in comparing different types of models, which is often done in data mining processes, In this short communication, we propose a unified approach to the importance measurement of input variables. Our method uses sensitivity analysis which begins by perturbing the values of input variables and monitors the output change. Research scope is limited to the models for continuous output, although it is not difficult to extend the method to supervised learning models for categorical outcomes.

• 간호행정학회지
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• 제12권2호
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• pp.287-294
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• 2006

Purpose: The purpose of this study was to identify the change of critical thinking disposition and problem solving process in students who experienced problem-based learning. Method: This research design was one group pre-post test design. Twenty-five nursing students who participated in ‘'Nursing Process' course with two PBL packages for a semester in 2004 were the subjects of this study. The data were analyzed by repeated measures of ANOVA, and content analysis. Result: The problem defining in problem solving process was improved significantly, but there was no significant difference in the critical thinking disposition. Conclusion: The results of this study suggest that PBL has a positive effect on nursing students' problem solving process, But for a more significant effect on a continuous base for critical thinking of nursing students, faculties should use web based and simulation-based education for self directed learning along with clinical situation-based scenarios.

• 음성과학
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• 제10권1호
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• pp.71-84
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• 2003

In the acoustic modeling for large vocabulary speech recognition, a sparse data problem caused by a huge number of context-dependent (CD) models usually leads the estimated models to being unreliable. In this paper, we develop a new clustering method based on the C45 decision-tree learning algorithm that effectively encapsulates the CD modeling. The proposed scheme essentially constructs a supervised decision rule and applies over the pre-clustered triphones using the C45 algorithm, which is known to effectively search through the attributes of the training instances and extract the attribute that best separates the given examples. In particular, the data driven method is used as a clustering algorithm while its result is used as the learning target of the C45 algorithm. This scheme has been shown to be effective particularly over the database of low unknown-context ratio in terms of recognition performance. For speaker-independent, task-independent continuous speech recognition task, the proposed method reduced the percent accuracy WER by 3.93% compared to the existing rule-based methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.71-77
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• 1995

In this paper are proposed robust iterative learning control(ILC) algorithms for both linear continuous time-invariant system and linear discrete-time system. In contrast to conventional methods, the proposed learning algorithms are constructed based on both time domain performance and iteration-domain performance. The convergence of the proposed learning algorithms is proved. Also, it is shown that the proposed method has robustness in the presence of external disturbances and the convergence accuracy can be improved. A numerical external disturbances and the convergence accuracy can be improved. A numerical example is provided to show the effectiveness of the proposed algorithm.

• Structural Engineering and Mechanics
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• 제81권5호
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• 한국콘텐츠학회논문지
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• 제5권5호
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• pp.325-333
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• 2005

웹 기반 교수-학습체계를 설계 시에 학습자의 효율적인 상호작용을 통해서 학습효과를 높이기 위해서는 많은 것들이 고려되어야 한다. 무엇보다도 웹 환경에서 학습자에게 효율성과 관심을 유도하기 위해서 웹 기반 시뮬레이션이 권장된다. 이러닝에서 잘 설계된 시뮬레이션 기반 콘텐츠는 학습자에게 지속적인 관심을 제공하기 때문에 효율적인 학습 설계가 필요하다. 현재까지 많은 이러닝 시스템에서는 각기 다른 기법으로 웹 기반 시뮬레이션을 제공하고 있으나 학습자의 학습 스타일과 성취 수준을 고려한 콘텐츠의 재구성이 요구되고 있다. 본 논문에서는 XML 웹 서비스를 통해서 이러닝 시스템간에 시뮬레이션 객체를 공유하는 시스템을 제안하였다. 실험결과 제안 시스템이 기존 시스템보다 자기주도적 학습 활동과 상호 작용에서 효율적임을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

• International Journal of Contents
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• 제18권2호
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• pp.58-67
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• 2022

As the Internet rapidly evolves, online learning has emerged as the third largest scenario in the field of education. Online education, different from the two traditional learning scenarios of the school and society, is characterized with broader learning types and higher freedom. In today's post-pandemic era, art education, which relies on face-to-face teaching, is of particular significance to expand online education methods. Based on the UTAUT model, this paper posits seven hypotheses about the willingness to continue learning in online art education. After collecting valid data through a questionnaire, a detailed empirical analysis was conducted via SPSS and AMOS. The results of empirical analysis show that less than half of the respondents had experienced the online art education, mirroring that this is a market worth developing. Based on the findings, learning habit does not significantly impact art learners' willingness to continue learning online. This result and other verified hypotheses are detailed in the discussion part of this paper. This study proves that UTAUT can better explain user behavior than the traditional information system model prior to the improvement, and also has strong explanatory power in the field of art education. The conclusion also posits some operational suggestions from the perspective of practitioners in this field, thereby providing a theoretical basis for art education practitioners.