• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.111-116
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• 2021

The hyper-spectral image is data obtained by dividing the electromagnetic wave band in the infrared region into hundreds of wavelengths. It is used to find or classify objects in various fields. Recently, deep learning classification method has been attracting attention. In order to use hyper-spectral image data as deep learning training data, a processing technique is required compared to conventional visible light image data. To solve this problem, we developed a software that selects specific wavelength images from the hyper-spectral data cube and performs the ground truth task. We also developed software to manage data including environmental information. This paper describes the configuration and function of the software.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.191-196
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• 2012

The tactical training system that was applied to wire at open terrain and field is no training accomplishment in conformity with battlefield situation portrayal. Therefore, this paper developed the tactical training and evaluation system for growing fruitfully that is installed as a tactical training monitoring system by USN WMN through controlled sensing interlock for collecting sensing data and guaranteed training equipment status and report training result for serviceability of manager. additionally we implemented controller for managing server of sending and manipulating of being gathered training data by sensor using USN and system as GUI methods. This system is easy for adapting new and improved protocol in order to processing many function of H/W and algorithms which is generated by controlled system is applicable easily.

• Research in Community and Public Health Nursing
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• v.8 no.2
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• pp.197-210
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• 1997

This study was conducted to test the effect of cognitive stimulation training on elderly persons with dementia. The design of the research was one group in pre-test-post-test design. The subjects were nine demented persons over sixty years, with mild to moderate cognitive impairment. Training was administered by research assistants on a one to one basis for thirty to forty minutes, three times a week for eight weeks. In order to evaluate the effect of cognitive stimulation training, we measured cognitive function before and after three training sessions each. Data were analyzed using descriptive statistics and a paired t-test analysis using a spss pc package, The results are as follows: 1) The recipients of the training program showed improvement in overall cognitive functioning. The MMSK - K score, recall levels of concepts, daily tasks, personal past history and performance of word fluency were significantly increased after training. 2) There was a significant improvement in cognitive functioning over the training period: recall levels of concepts, daily tasks, past personal history and performance of word fluency significantly increased over the training period progessively, The results suggest that cognitive stimulation training is effective in improving and maintaining overall cognitive function of elderly persons with dementia.

• Korean Business Review
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• v.19 no.2
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• pp.95-116
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• 2006

The purposes of this study are to understand the relationship between training program components and training performance in public sector. Training program components(training planning, trainer's teaching ability, trainee's motivation to learn, training contents) and the social support(superior's support, peer's support) that are supposed to affect the training performance were chosen by the literature review. There are 20 hypotheses based on literature reviews and tested by the data from questionnaires. It is turned out to be partly significant positive relationship between the training program components and training performance. The contribution and limitations of this research were discussed, and the future researches were mentioned.

• Journal of Sport and Applied Science
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• v.7 no.4
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• pp.13-18
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• 2023

Purpose: This study aims to determine what changes occur in body composition and performance by subjecting youth soccer players to weight training and combined training, which are known to be effective in improving anaerobic capacity, for 6 weeks. Research design, data, and methodology: This study was conducted on 30 high school soccer players from S City who had no current injuries or medical problems and had been registered as players with the Korea Football Association for more than 3 years. Subjects were divided into a weight training group (WTG, n=15) and a combined training group (CTG, n=15). Training lasted 6 weeks, and measurements were taken before and after training. Mean (M) and standard deviation (SD) were calculated to present the descriptive statistics of all dependent variables. Paired t-tests were used to test for within-group differences. Further, Independent t-tests were employed to test for between-group differences. Results: In terms of body composition, height significantly increased in WTG and CTG, and body fat percentage significantly decreased in CTG. As for performance, WTG's 20m sprint record decreased significantly. Conclusion: As a result, this study confirmed that weight training improved the 20m sprint ability of youth soccer players. Future research could provide more useful information by extending the study period and incorporating the physical characteristics of adolescents.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.111-117
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• 2013

In this paper, we have proposed novel technique to improve maintainability for training vehicle of underwater weapon system. In case of under water weapon, the fire procedure is related with operation of expulsion system in submarines. So the submarine crews should practice the complex fire procedure of weapon system by using training vehicle, which is safer and cheaper than operational weapon. After emitted from submarine, the training vehicle rise to the surface and should be withdrawn from the sea. The recovered training vehicle is transported to maintenance depot and pass through the recycling procedure including disassembling the vehicle, data acquisition & analysis, battery charge, replacing expandable components, testing the captive equipment, and assembling the vehicle. The disassembling & assembling of training vehicle which is composed of watertight section or airframe, is time-consuming work. So in this paper, we have studied the elements of recycling procedure and propose the method to exclude the assembling & disassembling work for maintainability improvement.

• Journal of IKEEE
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• v.22 no.1
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• pp.14-20
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• 2018

The purpose of training a convolutional neural network (CNN) is to obtain weight factors that give high classification accuracies. The initial values of hyper-parameters affect the training results, and it is important to train a CNN with a suitable hyper-parameter set of a learning rate, a batch size, the initialization of weight factors, and an optimizer. We investigate the effects of a single hyper-parameter while others are fixed in order to obtain a hyper-parameter set that gives higher classification accuracies and requires shorter training time using a proposed VGG-like CNN for training since the VGG is widely used. The CNN is trained for four datasets of CIFAR10, CIFAR100, GTSRB and DSDL-DB. The effects of the normalization and the data transformation for datasets are also investigated, and a training scheme using merged datasets is proposed.

• Korean Journal of Child Studies
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• v.12 no.1
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• pp.21-37
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• 1991

The present research studied the effectiveness of specific strategy training in memory awareness on children's memory performance. The subjects were 60 children, 30 six-year-olds and 30 eight-year-olds. Free recall scores and use of a rehearsal strategy (exposure durations) based on Belmont & Butterfield (1971) were used to measure children's performance in three memory tasks. All subjects were randomly assigned to one of three experimental groups: the control group with no training, the chunking and rehearsal strategy training group, and the chunking and rehearsal strategy training combined with memory awareness strategy. The data were analyzed with two-way ANOVA, three-way ANOVA with repeated measures, and Student-Newman-Keuls post hoc test. There were significant differences among the three groups both in the free recall score and in the use of the rehearsal strategy. The mnemonic strategic training with memory awareness strategy was the most effective on both free recall and use of rehearsal strategy. The effects of the mnemonic strategy training with memory awareness strategy were more effective for the 8-year-olds than the 6-year-olds.