• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.263-271
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• 2021

Optoelectronic shooting training systems are used in shooting training sites to improve the accuracy of shooting by tracking the trajectories of gun movements. However, optoelectronic-based systems have limitations in terms of cost, complexity of installation, and the risk that electronic targets may be broken. In this study, we developed and verified a shooting training system that measures postural tremors using a low-cost accelerometer. The acceleration sensor module was designed to be attached to the air cylinder of a gun. Postural tremors were evaluated based on amplitude, frequency, and spatial pattern index, which were computed using acceleration data. The postural tremor indices between the accelerometer and optoelectronic-based system were highly correlated (left-right and up-down directions: r = 0.76 and r = 0.70, respectively). We validated the developed shooting training system using an independent two-sample t-test, which identified a significant difference (p < 0.05) in the calculated postural tremor index according to the athlete's shooting score (i.e., best and worst shots).

• Journal of Educational Research in Mathematics
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• v.9 no.1
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• pp.263-278
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• 1999

Computer with various powerful functions has profound potential for mathematics instruction and learning. As computer technology progress, its applicability to mathematics education become more comprehensive. Not only its functional development but various psychological positions also changed the way computer technology utilized in mathematics education. In behaviorist's perspective, computer viewed as a teaching machine and constructivist viewed computer as microworld where students could explore various mathematical contents. Both theoretical positions emphasized individual aspect of learning because behaviorist tried to individualize learning using computer and constructivist focused on the process of individual construction. But learning is not only a individual event but also a social event. Therefore we must take social aspect into account. This is especially important when it comes to computer based learning. So far, mathematics loaming with computer weighed individual aspect of loaming. Even in microworld environment, learning should be mediated by teacher and collaborative learning activities. In this aspect, the roles of teacher and peers are very important and socio-cultural perspective sheds light on the computer based learning. In socio-cultural perspective, the idea of scaffold is very important in learning and students gradually internalize the social dimension and scaffolding is gradually faded. And in the zone of proximal development, teacher and more competent peers guide students to formulate their own understanding. In sum, we must take following points into account. First of all, computer should not be viewed as a medium for individualized teaming. That is, interaction with computer should be catalyst for collaborative activities with peers. So, exploration in computer environment has to be followed by small group activities including small group discussion. Secondly, regardless of the role that computer would play, teacher should play a crucial role in computer based learning. This does not mean teacher should direct every steps in learning process. Teacher's intervention should help student construct actively. Thirdly, it is needed to conceptualize computer in learning situation as medium. This would affect learning situation and result in the change of pre-service and in-service teacher training. Computer to be used effectively in mathematics classroom, researches on assessment of computer based learning are needed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.137-143
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• 2014

Smart education in the information window, type talent in modern society is changing as talented smart shoes, smart automation creative talent through education, training can be called education revolution. In other words, the advent of smart devices, such as knowledge and information and to actively growing individual customized training paradigm change in the way education and training to reflect this approach. Therefore, in this paper, a smart learning environment based technologies for implementing the system was designed to be the next generation of cloud computing and N screen-based smart education system was studied. From this, educational functions and features in a smart media environment, based on the analysis of the utilization of a smart education system, which maximizes the system design were studied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.368-391
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• 2018

Face recognition (FR) with a single sample per person (SSPP) is common in real-world face recognition applications. In this scenario, it is hard to predict intra-class variations of query samples by gallery samples due to the lack of sufficient training samples. Inspired by the fact that similar faces have similar intra-class variations, we propose a virtual sample generating algorithm called k nearest neighbors based virtual sample generating (kNNVSG) to enrich intra-class variation information for training samples. Furthermore, in order to use the intra-class variation information of the virtual samples generated by kNNVSG algorithm, we propose image set based multimanifold discriminant learning (ISMMDL) algorithm. For ISMMDL algorithm, it learns a projection matrix for each manifold modeled by the local patches of the images of each class, which aims to minimize the margins of intra-manifold and maximize the margins of inter-manifold simultaneously in low-dimensional feature space. Finally, by comprehensively using kNNVSG and ISMMDL algorithms, we propose k nearest neighbor virtual image set based multimanifold discriminant learning (kNNMMDL) approach for single sample face recognition (SSFR) tasks. Experimental results on AR, Multi-PIE and LFW face datasets demonstrate that our approach has promising abilities for SSFR with expression, illumination and disguise variations.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Journal of the Korean Society for Library and Information Science
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• v.11
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• pp.43-75
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• 1984

Information science is the study how in formation is transferred and all the intermediate steps of collecting, organizing, interpreting, storing, retrieving, disseminating and trans foming information. Professional education means the transfer of knowledge, the development of cognitive abilities and the infusion of professional attitudes. Training may be defined as practice-based instruction in the development and use of professional skills. Each is affected by the confluence of social, economic and technological realities of the environment where the learning takes place. We have witnessed controversy about methods of curriculum revision and change. Should information science courses be added to the traditional library science curriculum or should the new approaches be integrated within the subject matter of each individual course? The article is based upon the assumption that education for librarianship is at a turning point. To provide this information, 25 curricula of colleges and universities were analysed to assist in the study. Also 32 information professionals were asked to assist in the study. In the experimental part of this study, curricula based on the education and training of library and information profession als were examined. The most frequently offered compulsory course 'Introduction to Information Science' exposes students to a new way of looking at library and information problems. Information retrieval, library automation, computer programming, data processing, indexing and abstraction, communication, system analysis has offered. These indicate a curriculum slowly shift from traditional librarianship to an emphasis on computerization and automation. Also from a questionnaire listing 58 events might influence library and information science education.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1111-1123
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• 2023

In recent times, semantic image segmentation methods using deep learning models have been widely used for monitoring changes in surface attributes using remote sensing imagery. To enhance the performance of various UNet-based deep learning models, including the prominent UNet model, it is imperative to have a sufficiently large training dataset. However, enlarging the training dataset not only escalates the hardware requirements for processing but also significantly increases the time required for training. To address these issues, transfer learning is used as an effective approach, enabling performance improvement of models even in the absence of massive training datasets. In this paper we present three transfer learning models, UNet-ResNet50, UNet-VGG19, and CBAM-DRUNet-VGG19, which are combined with the representative pretrained models of VGG19 model and ResNet50 model. We applied these models to building extraction tasks and analyzed the accuracy improvements resulting from the application of transfer learning. Considering the substantial impact of learning rate on the performance of deep learning models, we also analyzed performance variations of each model based on different learning rate settings. We employed three datasets, namely Kompsat-3A dataset, WHU dataset, and INRIA dataset for evaluating the performance of building extraction results. The average accuracy improvements for the three dataset types, in comparison to the UNet model, were 5.1% for the UNet-ResNet50 model, while both UNet-VGG19 and CBAM-DRUNet-VGG19 models achieved a 7.2% improvement.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.107-116
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• 2004

The purpose of the present study was to identify how and how much teachers utilize ICT in their instruction and to analyze teachers' perception about the environmental factors to promote ICT implementation after their participation in a teacher training for Project-Based Learning (PBL) with ICT. The results showed that there was a significant change in the application of PBL into classroom and the class preparation before and after the training. Lack of time to prepare the instruction using ICT was identified as the most important reason not to utilize ICT in their classroom. While teachers perceived time and rewards among the environmental factors were lack, dissatisfaction with status quo, incentives, knowledge and skill, participation and leadership were perceived as being present in their school.

• Journal of Biomedical Engineering Research
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• v.31 no.5
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• pp.359-364
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• 2010

The purpose of the brain-computer (machine) interface (BCI or BMI) is to provide a method for people with damaged sensory and motor functions to use their brain to control artificial devices and restore lost ability via the devices. Functional electrical stimulation (FES) is a method of applying low level electrical currents to the body to restore or to improve motor function. The purpose of this study was to develop a SSVEP-based BCI rehabilitation training system with FES for spinal cord injured individuals. Six electrodes were attached on the subjects' scalp ($PO_Z$, $PO_3$, $PO_4$, $O_z$, $O_1$ and $O_2$) according to the extended international 10-20 system, and reference electrodes placed at A1 and A2. EEG signals were recorded at the sampling rate of 256Hz with 10-bit resolution using a BIOPAC system. Fast Fourier transform(FFT) based spectrum estimation method was applied to control the rehabilitation system. FES control signals were digitized and transferred from PC to the microcontroller using Bluetooth communication. This study showed that a rehabilitation training system based on BCI technique could make successfully muscle movements, inducing electrical stimulation of forearm muscles in healthy volunteers.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.57-64
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• 2021

In this paper, we propose a device that can control the primary control surface of an aircraft by recognizing speech commands. The speech command consists of 19 commands, and a learning model is constructed based on a total of 2,500 datasets. The training model is composed of a CNN model using the Sequential library of the TensorFlow-based Keras model, and the speech file used for training uses the MFCC algorithm to extract features. The learning model consists of two convolution layers for feature recognition and Fully Connected Layer for classification consists of two dense layers. The accuracy of the validation dataset was 98.4%, and the performance evaluation of the test dataset showed an accuracy of 97.6%. In addition, it was confirmed that the operation was performed normally by designing and implementing a Raspberry Pi-based control device. In the future, it can be used as a virtual training environment in the field of voice recognition automatic flight and aviation maintenance.