• Korean Journal of Childcare and Education
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• v.9 no.2
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• pp.173-189
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• 2013

The purpose of this study was to examine the relationship between children's information processing ability and basic learning abilities. To collect the data, two tests were given to 99 children. The Korean K-ABC(Moon & Byun, 1997) and Pictorial Basic Learning Abilities for Children(Kim, 2011) were used to examine the relationship between children's information processing and basic learning abilities. The collected data were analyzed by correlation analysis and multiple regression analysis. According to the results of this study, there was a significant positive correlation between information processing(sequential processing, simultaneous processing) and basic learning abilities including reading, writing, and basic mathematics. And information processing significantly affected basic learning abilities. Namely, simultaneous processing explained 22% of basic learning abilities and by adding sequential processing, the explanation was increased to 25%. In conclusion, the results of this study suggest various implications about children's basic learning abilities. These implications will help teachers and parents to understand their children's learning.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

• The Journal of Korean Academy of Sensory Integration
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• v.16 no.3
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• pp.11-21
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• 2018

Objective : The purpose of this study is to investigate correlation of sensory processing patterns, learning styles and learning strategies for university students. Methods : Participants of this study are 115 students from K university in Busan, South Korea. Measurements are Adolescent/Adult Sensory Profile (AASP) for sensory processing patterns, the Study Process Questionnaire (SPQ) for learning styles, and the Motivated Strategies for Learning Questionnaire (MSLQ) for learning strategies. The data collected was analyzed by SPSS/WIN 20.0 for chisuare test and Pearson corelation coefficient. Results : For sensory processing patterns and learning styles, there were correlation between low registration type and surface type of learning (p=0.03), and between sensory seeking type and deep type of learning (p=0.02). For sensory processing patterns and learning strategies, sensory seeking type was correlated with organized learning strategy (p=0.00), and sensory sensitivity type was correlated with organizational learning strategy (p=0.03) and meta-cognitive learning strategy (p=0.00). Conclusion : This study found that there is correlation between sensory processing patterns, learning styles and learning strategies with implying learning styles and learning strategies can be different depends on sensory procession pattern. The results of this study can be used as a basic data to select learning type and learning strategy appropriate for an individual based on his or her sensory processing patterns.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.909-919
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• 2017

The purpose of this study was to apply the learning clinic program to the maladjusted children to help the cognitive processing style, sense type and learning strategy. The results were as follows. First, the cognitive processing style of low-grade elementary school children is divided into the concept of sequential low-order style, which analyzes information sequentially and consecutively, concrete thinking style that processes real and direct information coming in from outside, and invisible principle or information. The abstract cognitive thinking style improved after the process before the program proceeded. However, There was no meaningful result in the simultaneous processing cognitive style which had excellent intuition and emotion and likes change. Second, the temporal lobe in which the linguistic activity is viewed, heard and spoken in the sensory type, the function of the occipital lobe in which the character or the language is processed is improved, but the function of the parietal lobe in moving and manipulating the body is not significant. Finally, factors that contribute to learning such as sincerity, learning initiative, study method, study habits, and concentration are helpful in learning and school life.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.54-60
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• 2018

In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.

• ETRI Journal
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• v.45 no.2
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• pp.318-328
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• 2023

Recently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep-learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep-learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep-learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator-scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type-based operator-scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling-based operator-scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively.

• Journal of Fisheries and Marine Sciences Education
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• v.21 no.2
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• pp.278-290
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• 2009

The purpose of this study is to investigate the relationship between learning skills and cognitive functions on elementary school students. In this study CAS and Learning Skills Test(LST) were administered with 3 to 6 grade, 60 students from 5 elementary schools. The data were analyzed according to Pearson's correlation and Stepwise Multiple Regression Analysis. The results are as follows. Firstly, girls and older students showed significantly higher ability than boys and younger students on the learning skills. And girls significantly outperformed boys on the planning function and attention function and on the simultaneous cognitive function was the other way round. Secondly, learning skills were explained 41% by two variables that the planning function and the successive function which are sub factors of the cognitive function. And then, planning and successive processing effected to self-management, attention and planning to test-taking skills, successive processing and attention to class-participation skills, and successive processing to information processing.

• Journal of Surface Science and Engineering
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• v.56 no.6
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• pp.353-370
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• 2023

Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1303-1311
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• 1999

In order to learn in dynamic environments, reinforcement learning algorithms like Q-learning, TD(0)-learning, TD(λ)-learning have been proposed. however, most of them have a drawback of very slow learning because the reinforcement value is given when they reach their goal state. In this thesis, we have proposed a reinforcement learning method that can approximate fast to the goal state in maze environments. The proposed reinforcement learning method is separated into global learning and local learning, and then it executes learning. Global learning is a learning that uses the replacing eligibility trace method to search the goal state. In local learning, it propagates the goal state value that has been searched through global learning to neighboring sates, and then searches goal state in neighboring states. we can show through experiments that the reinforcement learning method proposed in this thesis can find out an optimal solution faster than other reinforcement learning methods like Q-learning, TD(o)learning and TD(λ)-learning.