• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.354-359
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• 2006

The analysis of large-scale water resources systems is often complicated by the presence of multiple reservoirs and diversions, the uncertainty of unregulated inflows and demands, and conflicting objectives. Reinforcement learning is presented herein as a new approach to solving the challenging problem of stochastic optimization of multi-reservoir systems. The Q-Learning method, one of the reinforcement learning algorithms, is used for generating integrated monthly operation rules for the Keum River basin in Korea. The Q-Learning model is evaluated by comparing with implicit stochastic dynamic programming and sampling stochastic dynamic programming approaches. Evaluation of the stochastic basin-wide operational models considered several options relating to the choice of hydrologic state and discount factors as well as various stochastic dynamic programming models. The performance of Q-Learning model outperforms the other models in handling of uncertainty of inflows.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.292-297
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• 1999

Evolving in artificial agent is an extremely difficult problem, but on the other hand, a challenging task. At present the studies mainly centered on single agent learning problem. In our case, we use simulated soccer to investigate multi-agent cooperative learning. Consider the fundamental differences in learning mechanism, existing reinforcement learning algorithms can be roughly classified into two types-that based on evaluation functions and that of searching policy space directly. Genetic Programming developed from Genetic Algorithms is one of the most well known approaches belonging to the latter. In this paper, we give detailed algorithm description as well as data construction that are necessary for learning single agent strategies at first. In following step moreover, we will extend developed methods into multiple robot domains. game. We investigate and contrast two different methods-simple team learning and sub-group loaming and conclude the paper with some experimental results.

• 융합보안논문지
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• 제5권2호
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• pp.37-42
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• 2005

정보통신기술의 발전과 지식정보 사회의 등장은 교육 및 훈련분야에도 거대한 변화를 가져왔다. 특히, 유비쿼터스 시대가 다가옴에 따라 e-Learning 또한 u-Learning으로 진화하려 하고 있다. 이는 지금까지와는 또 다른 형태로 교수-학습자 환경이 변화함을 말한다. 본 논문에서는 교육환경의 발전에 따른 다양한 학습 콘텐츠의 관리 방법을 제안, 구현하고 운영플랫폼 분석을 통하여 콘텐츠의 활용을 극대화 할 수 있는 LCMS를 제안하였다.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.170-182
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• 2019

In this paper, we use scratch to design and develop non-formal learning experiences that are linked with contents of secondary science textbook to educational programs. The goal of this paper is to develop a convenient and interesting program for non-formal learning in a learning environment using various smart device. Theoretical approaches to mobile education, such as smartphones, and smart education support policies continue to lead to various research efforts. Although most of the smart education systems developed for students who have difficulty in academic performance are utilized, they are limited to general students. To solve the problem, the learning environment was implanted by combining the scratch, which is an educational programming that can be easily written. The science education program proposed in this paper shows the result of process of programming using ICT device using scratch programming. In the evaluation stage, we were able to display the creations and evaluate each other, so that we could refine them more by sharing the completed ideas.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.248-256
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• 2021

The article discusses the evolution of the development of distance learning in world practice; investigated the essence and modern content of the concepts of "distance learning" and "distance education"; studied the principles of distance learning in the educational process; analyze the use of distance learning in higher educational institutions of Ukraine; substantiated the effectiveness of introducing distance learning into the higher education system; formed new management approaches in the distance learning system; proposals for the organization and improvement of distance learning at the university were developed on the basis of the analysis.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• 한국해양공학회지
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• 제36권3호
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.83-92
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• 2023

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

• 한국과학교육학회지
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• 제23권4호
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• pp.386-400
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• 2003

This paper presents an analysis of the Developmental Approaches in Science, Health and Technology (DASH) program, a K-6 curriculum developed by the Curriculum Research & Development Group (CRDG) at the University of Hawaii employing the curriculum analysis framework created by Posner. Using this framework the analyst found that the DASH design is based on the research on learning, teaching, and assessment now driving efforts to reform science education at the elementary level. DASH embraces the constructivist idea that learning is a personal and social process and the recapitulation model that new concepts are built out of theories previously learned. DASH provides an understandable, exciting, and memorable experience in the operations of science, health, and technology, and develops their capacity to use the skills and knowledge of science, health, and technology both in and outside school. A number of studies of DASH have examined its functionality, effectiveness of pedagogy and what students learn. The innovative nature of DASH necessitated a multidimensional assessment that included both quantitative and qualitative research techniques. Ongoing development of the DASH program in the research setting of a university laboratory school permits ever deeper connections with emerging curriculum theory and curriculum practice, and allows new linkages as ideas are tested in research classrooms.

• 한국멀티미디어학회논문지
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• 제22권1호
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• pp.1-9
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• 2019

Single image rain removal is a typical inverse problem which decomposes the image into a background scene and a rain streak. Recent works have witnessed a substantial progress on the task due to the development of convolutional neural network (CNN). However, existing CNN-based approaches train the network with synthetically generated training examples. These data tend to make the network bias to the synthetic scenes. In this paper, we present an unsupervised framework for removing rain streaks from real-world rainy images. We focus on the natural phenomena that static rainy scenes capture a common background but different rain streak. From this observation, we train siamese network with the real rain image pairs, which outputs identical backgrounds from the pairs. To train our network, a real rainy dataset is constructed via web-crawling. We show that our unsupervised framework outperforms the recent CNN-based approaches, which are trained by supervised manner. Experimental results demonstrate that the effectiveness of our framework on both synthetic and real-world datasets, showing improved performance over previous approaches.