• 한국수학교육학회지시리즈A:수학교육
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• 제42권2호
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• pp.121-135
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• 2003

This article retrospects the developmental process of the psychology of learning and its' influence on mathematics education. At the end of the article, brain-based learning science is introduced to examine its possibility to improve the psychology of learning mathematics. Behaviorists points of views such as Skinner, Guthrie, and Gagne were summarized to discuss the influences on the learning and teaching of mathematics. Gestalt' theories and Constructivism are also included in the discussion of developmental process of learning psychology. In elaboration of the brain-based learning science, recent research findings and the possibility of it's impact on mathematics education were discussed. Since mathematics itself is the most abstract subject it could be more challenging to identify the teaming process of mathematics compared with other areas. The possibilities of identifying the teaming process of mathematics are cautiously anticipated with a help of new paradigm.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.258-266
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• 2024

E-learning systems have proliferated in recent years, particularly in the wake of the global COVID-19 pandemic. For kids, there isn't a specific online learning platform available, though. To do this, new conceptual models of training and learning software that are adapted to the abilities and preferences of end users must be created. Young pupils: those in kindergarten, preschool, and elementary school are unique subjects with little research history. From the standpoint of software technology, young students who have never had access to a computer system are regarded as specific users with high expectations for the functionality and interface of the software, social network connectivity, and instantaneous Internet communication. In this study, we suggested creating an electronic learning management system that is web-based and appropriate for primary school pupils. User-centered design is the fundamental technique that was applied in the development of the system that we are proposing. Test findings have demonstrated that students who are using the digital environment for the first time are studying more effectively thanks to the online learning management system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1106-1108
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• 1993

This paper deals with an industrial application of a fuzzy feedback combined learning control to an industrial batch free radical polymerization reactor. As a result, the plant has reduced the batch reaction time by 50 minute and stabilized both by 40 percent reduction of the standard deviations of product qualities, such as the total solid content and the graft gum, and by 45 percent reduction of the standard deviation of the batch reaction end time.

• 한국항해학회지
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• 제21권3호
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• pp.55-66
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• 1997

In this paper, applications of neural networks to vibration control of flexible single link robot manipulator are ocnsidered. The architecture of neural networks is a hidden layer, which is comprised of self-recurrent one. Tow neural networks are utilized in a control system ; one as an identifier is called neuro identifier and the othe ra s a controller is called neuro controller. The neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by dynamic error-backpropagation algorithm(DEA). To guarantee concegence and to get faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. When a flexible manipulator is ratated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlle dinsuch as way, that the motor is rotated by a specified angle. while simulataneously stabilizing vibration of the flexible manipulators so that it is arrested as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large body motions, as well as the flexural vibrations. Therefore, dynamic models for a flexible single link manipulator is derived, and LQR controller and nerual networks controller are composed. The effectiveness of the proposed nerual networks control system is confirmed by experiments.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.110-117
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• 2021

The purpose of the study is to develop effective teaching methods to strengthen the major learning capabilities of electronic engineering learners through inquiry learning using computing thinking ability. To this end, first, in the electronic engineering curriculum, we performed teaching-learning through an inquiry and learning model related to mathematics, probability, and statistics under the theme of various majors in electronic engineering, focusing on understanding computing thinking skills. Second, an efficient electronic engineering subject inquiry class operation using computing thinking ability was conducted, and electronic engineering-linked education contents based on the components of computer thinking were presented. Third, by conducting a case study on inquiry-style teaching using computing thinking skills in the electronic engineering curriculum, we identified the validity of the teaching method to strengthen major competency. In order to prepare for the 4th Industrial Revolution, by implementing mathematics, probability, statistics-related linkage, and convergence education to foster convergent talent, we tried to present effective electronic engineering major competency enhancement measures and cope with innovative technological changes.

• 방송공학회논문지
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• 제26권5호
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• pp.566-574
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• 2021

전기화재의 원인중의 하나는 직렬 아크이다. 최근까지 아크 신호를 검출하기 위해 다양한 기법들이 진행되고 있다. 시간 신호에 푸리에 변환, 웨이블릿 변환, 또는 통계적 특징 등을 활용하여 아크 검출을 하는 방법들이 소개되었지만, 변환 및 특징 추출은 부가적인 처리 시간이 요구되는 단점이 있다. 반면에 최근의 딥러닝 모델은 종단간 학습으로 특징 추출 과정없이 직접 원시 데이터를 활용한다. 그러나, 딥러닝의 문제는 연산 복잡도가 높다는 것이다. 이 문제는 단말기에 딥러닝 연산 모듈을 넣기가 어렵게 한다. 따라서 본 논문에서는 복잡도가 상대적으로 낮은 기계학습 기법중에 로지스틱회기 (logistic regression)를 이용하여 아크 검출을 하는 기법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권2호
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• pp.729-748
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• 2021

Vocal detection is one of the fundamental steps in musical information retrieval. Typically, the detection process consists of feature extraction and classification steps. Recently, neural networks are shown to outperform traditional classifiers. In this paper, we report our study on how to improve detection accuracy further by carefully choosing the parameters of the deep network model. Through experiments, we conclude that a feature-classifier model is still better than an end-to-end model. The recommended model uses a spectrogram as the input plane and the classifier is an 18-layer convolutional neural network (CNN). With this arrangement, when compared with existing literature, the proposed model improves the accuracy from 91.8% to 94.1% in Jamendo dataset. As the dataset has an accuracy of more than 90%, the improvement of 2.3% is difficult and valuable. If even higher accuracy is required, the ensemble learning may be used. The recommend setting is a majority vote with seven proposed models. Doing so, the accuracy increases by about 1.1% in Jamendo dataset.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.25-28
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• 2020

Automatic segmentation of brain tissues such as WM, GM, and CSF from brain MRI scans is helpful for the diagnosis of many neurological disorders. Accurate segmentation of these brain structures is a very challenging task due to low tissue contrast, bias filed, and partial volume effects. With the aim to improve brain MRI segmentation accuracy, we propose an end-to-end convolutional based U-SegNet architecture designed with multi-scale kernels, which includes cascaded dilated convolutions for the task of brain MRI segmentation. The multi-scale convolution kernels are designed to extract abundant semantic features and capture context information at different scales. Further, the cascaded dilated convolution scheme helps to alleviate the vanishing gradient problem in the proposed model. Experimental outcomes indicate that the proposed architecture is superior to the traditional deep-learning methods such as Segnet, U-net, and U-Segnet and achieves high performance with an average DSC of 93% and 86% of JI value for brain MRI segmentation.