• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2022년도 추계학술대회
• /
• pp.188-190
• /
• 2022

본 논문에서는 하이브리드 러닝의 목적 및 교수-학습 원리를 기반으로 AI 교육 시스템의 구성안에 대해 제안하였다. 이를 위해 하이브리드 러닝의 4가지 구성요소를 바탕으로 AI 교육을 효과적으로 운영하기 위한 온·오프라인 학습환경(메타버스 기반, 앱 기반, 면대면 기반) 등의 시스템 개념 구성도와 시스템에 필요한 DB 구성도를 설계하였다. 본 연구에서 제안한 AI 교육 시스템 모형은 학습자의 수준 및 요구에 따라 AI 교육의 효과성을 극대화하고 AI 교육을 통한 컴퓨팅 사고력 함양에 있어 더 효과적인 학습자 중심의 학습 환경을 구축하는 데 도움이 될 것으로 기대한다.

• International Journal of Precision Engineering and Manufacturing
• /
• 제1권1호
• /
• pp.79-83
• /
• 2000

Although it is well known that fuzzy learning controller is powerful for nonlinear systems, it is very difficult to apply a learning method if they are unstable. An unstable system diverges for impulse input. This divergence makes it difficult to learn the rules unless we can find the initial rules to make the system table prior to learning. Therefore, we introduced LQR(Linear Quadratic Regulator) technique to stabilize the system. It is a state feedback control to move unstable poles of a linear system to stable ones. But, if the system is nonlinear or complicated to get a liner model, we cannot expect good results with only LQR. In this paper, we propose that the LQR law is derived from a roughly approximated linear model, and next the fuzzy controller is tuned by the adaptive on-line learning with the real nonlinear plant. This hybrid controller of LQR and fuzzy learning was superior to the LQR of a linearized model in unstable nonlinear systems.

• 전자공학회논문지B
• /
• 제31B권12호
• /
• pp.116-123
• /
• 1994

A modified Hybrid learning rule(MHLR) is proposed, which is derived from combining the Back Propagation algorithm that is known as an excellent classifier with modified Hebbian by changing the orginal Hebbian which is a good feature extractor. The network architecture of MHLR is multi-layered neural network. The weights of MHLR are calculated from sum of the weight of BP and the weight of modified Hebbian between input layer and higgen layer and from the weight of BP between gidden layer and output layer. To evaluate the performance, BP, MHLR and the proposed Hybrid learning rule (HLR) are simulated by Monte Carlo method. As the result, MHLR is the best in recognition rate and HLR is the second. In learning speed, HLR and MHLR are much the same, while BP is relatively slow.

• Computers and Concrete
• /
• 제33권6호
• /
• pp.739-754
• /
• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2011년도 춘계학술대회 논문집
• /
• pp.224-229
• /
• 2011

In this paper, in order to improve safety of hybrid electric vehicle a fault detection algorithm is introduced. The proposed algorithm uses SVDD techniques. Two methods for learning a lot of data are used in this technique. One method is to learn the data incrementally. Another method is to remove the data that does not affect the next learning. Using lines connecting support vectors selection of removing data is made. Using this method, lot of computation time and storage can be saved while learning many data. A battery data of commercial hybrid electrical vehicle is used in this study. In the study fault boundary via SVDD is described and relevant algorithm for virtual fault data is verified. It takes some time to generate fault boundary, nevertheless once the boundary is given, fault diagnosis can be conducted in real time basis.

• 대한의용생체공학회:의공학회지
• /
• 제42권5호
• /
• pp.232-240
• /
• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• 한국인터넷방송통신학회논문지
• /
• 제13권6호
• /
• pp.187-193
• /
• 2013

다중 에이전트 시스템에서 학습을 통해 여러 에이전트들의 행동을 어떻게 조절할 것인가는 매우 중요한 문제이다. 가장 중요한 문제는 여러 에이전트가 서로 효율적인 협동을 통해 목표를 성취하는 것과 다른 에이전트들과 충돌을 방지하는 것이다. 본 논문에서는 혼성 학습 전략을 제안하였다. 제안된 방법은 다중에이전트를 효율적으로 제어하기 위해 에이전트들 사이의 공간적 관계를 이용하였다. 실험을 통해 제안된 방법은 에이전트들과 충돌을 피하면서 에이전트들의 목표에 빠르게 수렴함을 알 수 있었다.

• 한국언어정보학회지:언어와정보
• /
• 제14권2호
• /
• pp.159-181
• /
• 2010

This paper presents a hybrid approach to the sentiment analysis of online texts. The sentiment of a text refers to the feelings that the author of a text has towards a certain topic. Many existing approaches employ either a pattern-based approach or a machine learning based approach. The former shows relatively high precision in classifying the sentiments, but suffers from the data sparseness problem, i.e. the lack of patterns. The latter approach shows relatively lower precision, but 100% recall. The approach presented in the current work adopts the merits of both approaches. It combines the pattern-based approach with the machine learning based approach, so that the relatively high precision and high recall can be maintained. Our experiment shows that the hybrid approach improves the F-measure score for more than 50% in comparison with the pattern-based approach and for around 1% comparing with the machine learning based approach. The numerical improvement from the machine learning based approach might not seem to be quite encouraging, but the fact that in the current approach not only the sentiment or the polarity information of sentences but also the additional information such as target of sentiments can be classified makes the current approach promising.

• International Journal of Computer Science & Network Security
• /
• 제21권8호
• /
• pp.238-246
• /
• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• 한국산업융합학회 논문집
• /
• 제25권4_2호
• /
• pp.663-671
• /
• 2022

Practice through virtual reality can increase the educational effect regardless of time and place, and it is an educational method that is being pursued even in the situation of COVID-19. On the other hand, for VR-based education, related technology development and content development must be made, and experiential methods (flipped learning, blended learning, hybrid learning) must be provided in the educational process. The development scenario was developed with the contents of drone qualification test (ultra-light unmanned multicopter) and drone practice and the possibility of non-face-to-face self-directed learning (flipped learning, blended learning, hybrid learning). It is expected that the quality of vocational education related to drones and the effect of high education will be improved through the contents, and it is thought that it will be possible to suggest a direction for the development of various vocational education contents in non-face-to-face education.