• 한국보육지원학회지
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• 제9권2호
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• pp.173-189
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• 2013

본 연구는 유아의 정보처리능력인 동시처리 및 순차처리능력과 기초학습능력이 어떠한 관계가 있는지, 그리고 읽기, 쓰기, 기초수학을 포함하는 기초학습능력에 대한 정보처리능력의 상대적 설명력이 어떠한지를 알아보고자 하였다. 취학직전 만 5~6세 유아 99명을 대상으로 유아그림기초학습능력검사와 K-ABC 검사를 실시하여 기초학습능력과 정보처리능력을 측정하였다. 본 연구결과로써 첫째, 유아의 정보처리능력과 기초학습능력 간 상관을 살펴본 결과, 유아의 순차처리능력과 기초학습능력 간, 동시처리능력과 기초학습능력 간 유의미한 정적 상관이 있는 것으로 나타났다. 각 하위영역인 읽기, 쓰기, 기초수학 능력과 동시처리 및 순차처리능력 간에도 유의미한 정적 상관이 있는 것으로 나타났다. 둘째, 유아의 기초학습능력에 대한 정보처리능력의 상대적 설명력을 알아본 결과, 유아의 정보처리능력 중 동시처리능력이 기초학습능력에 22%의 설명력을 가지며, 여기에 순차처리능력이 첨가됨으로써 설명력이 3% 증가하여 25%의 설명력을 가지는 것으로 나타났다.

• Journal of Information Processing Systems
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• 제16권5호
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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.

• 대한감각통합치료학회지
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• 제16권3호
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• pp.11-21
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• 2018

목적 : 본 연구는 대학생의 감각처리 유형과 학습유형, 학습전략 간에 상관관계를 조사하고자 실시되었다. 연구방법 : 부산소재 K대학교의 학생 115명을 대상으로 실시하였다. 측정도구는 청소년/성인 감각프로파일(Adolescent/Adult Sensory Profile; AASP)과 학습과정 설문지(Study Process Questionnaire; SPQ)와 학습동기전략 설문지(Motivated Strategies for Learning Questionnarie; MSLQ)를 사용하여 감각처리 유형, 학습유형, 학습전략에 대한 설문을 실시하였다. 수집된 자료는 SPSS/WIN 20.0을 이용하여 카이 제곱 검정(chi square test), 피어슨 상관계수(Pearson correlation coefficient)로 분석하였다. 결과 : 감각처리 유형과 학습유형의 상관관계에서 감각등록저하 유형은 표층형 학습유형(p=0.03), 감각추구 유형은 심층형 학습유형(p=0.02)과 상관관계가 있었다. 감각처리 유형과 학습전략의 상관관계에서 감각추구 유형은 조직화 학습전략(p=0.00), 감각민감 유형은 조직화 학습전략(p=0.03)및 초인지 학습전략(p=0.00)과 상관관계가 있었다. 감각추구 성향의 차이에 따라 group A와 B로 나누었을 때 학습유형(p=0.00) 및 학습전략(p=0.03)에서 유의한 차이가 나타났다. 결론 : 감각처리 유형과 학습유형, 학습전략 간에 상관관계가 나타났으며 감각처리 유형에 따라 학습유형, 학습전략이 달라짐으로 개인에게 맞는 학습 유형과 학습전략을 선택함에 있어 기초자료로써 활용되어지는데 의의가 있다.

• 수산해양교육연구
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• 제29권3호
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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.

• 전자통신동향분석
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• 제33권4호
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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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• 제45권2호
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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.

• 수산해양교육연구
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• 제21권2호
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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.

• 한국표면공학회지
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• 제56권6호
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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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• 제23권2호
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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.

• 한국정보처리학회논문지
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• 제6권5호
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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.