• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Composites Research
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• v.34 no.6
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• pp.394-399
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• 2021

Piecewise Integrated Composite (PIC) is a new concept to design composite structures of multiple stacking angles both for in-plane direction and through the thickness direction in order to improve stiffness and strength. In the present study, PIC beam was suggested based on 3D training data instead of 2D data, which did offer a limited behavior of beam characteristics, with enhancing the stiffness accompanied by reduced tip deformation. Generally training data were observed from the designated reference finite elements, and preliminary FE analysis was conducted with respect to regularly distributed reference elements. Also triaxiality values for each element were obtained in order to categorize the loading state, i.e. tensile, compressive or shear. The main FE analysis was conducted to predict the mechanical characteristics of the PIC beam.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.235-241
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• 2022

Several studies using Convolutional Neural Network (CNN) have been conducted to detect and classify the sounds of marine mammals in underwater acoustic data collected through passive acoustic monitoring. In this study, the possibility of automatic classification of bearded seal sounds was confirmed using a CNN model based on the underwater acoustic spectrogram images collected from August 2017 to August 2018 in East Siberian Sea. When only the clear seal sound was used as training dataset, overfitting due to memorization was occurred. By evaluating the entire training data by replacing some training data with data containing noise, it was confirmed that overfitting was prevented as the model was generalized more than before with accuracy (0.9743), precision (0.9783), recall (0.9520). As a result, the performance of the classification model for bearded seals signal has improved when the noise was included in the training data.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.291-296
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• 2024

This study analyzed various machine learning models that predict employment outcomes after vocational training using pre-assessment data of disabled vocational trainees. The study selected and utilized the most appropriate machine learning models based on a data set containing various personal characteristics, including trainees' gender, age, and type of disability. Through this analysis, the goal is to improve the employment rate and job satisfaction of disabled trainees using only pre-assessment data. As a result, it presents a universal approach that can be applied not only to people with disabilities, but also to vocational trainees from a variety of backgrounds. This is expected to make an important contribution to the development and implementation of tailored vocational training programs, ultimately helping to achieve better employment outcomes and job satisfaction.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.83-91
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• 2018

Purpose - The purpose of this study is to explore the introduction and activation of smart training for the effective training of vocational ability development of companies in the 4th industrial revolution era, we analyze the present status of smart training introduction and related difficulties and propose concrete activation plan. Research design, data, and methodology - Through the online survey, we tried to confirm the recognition of corporate about smart training. Questionnaires include what are the benefits, expectations, and difficulties of smart training, etc. The survey was conducted from August 21, 2017 to September 4, 2017. A total of 69 companies participated in the questionnaire. The questionnaire results were analyzed through frequency analysis and contents analysis. Based on the results of the questionnaire, we found out the cause of inhibition of smart training activation and suggested activation strategies. Results - The main reason for the provision of smart training is the expectation of the training performance and the recognition that it is possible to provide training in a flexible manner. The effectiveness of smart training operation was evaluated as a high level of contribution to the development of creative training course and the capacity of training institute. As a result of checking factors that hinders the activation of smart training, the most important reason is that the time and cost burden of the training institutes is excessive. The lack of expertise in the design of smart training courses and the burden of employers and trainees. Conclusions - In order to activate smart training, it is necessary to find solutions to the obstacles at the internal or external level of training institutions. The internal barriers to the training organization are lack of internal competence for preparation and course management. In this regard, we need to consider providing consulting, best practices or guidance in the process of designing and operating smart training. On the other hand, as an external obstacle factor, it is necessary to provide incentives to participate in smart training. In addition, further research is needed on strategies that can lead to participation in smart training from the viewpoint of employers and learners.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.14 no.2
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• pp.88-93
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• 2003

Whether respiratory muscle training is of benefit to the singing students is controversial. The purpose of the study is to investigate pulmonary function and the maximal inspiratory(MIP) and expiratory pressure(MET), and maximum phonation time in five female singing students before and after the specially programmed respiratory muscle training during 2 months. All singing students had average 4.8 years of formal classical voice training. Respiratory muscle training machine (Ultrabreath) was used to train respiratory muscle. Pulmonary function test data on simple pulmonary function, flow volume curve, static lung volumes are obtained from Vmax 6200. The MIP and MEP were measured using Spirovis, and the MPT were measured using hand-held stopwatch. Any pulmonary function test variables are not changed after respiratory muscle training. However, MIP and MEP were significantly increased between before and after respiratory muscle training. MPT increased significantly after training, compared to the pre-trained. MIP, MEP, and MPT after training in female singing students were 26%, 25% and 33% higher than those before training. The result indicated that the specially programmed respiratory muscle training is beneficial to improve respiratory muscle strength and vocal function without an increment in pulmonary function.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.6 no.2
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• pp.169-184
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• 1999

The purpose of this study was to identify the effects of breathing biofeedback training on the stress of nursing students in clinical practice. The research design was a nonequivalent control group pretest-posttest design. The subjects of this study were 39 nursing students from the College of Nursing of K University. The study was conducted from July 20 to September 3, 1998. The subjects were assigned to one of two groups : the experimental group (19 students), and the control group (20 students). The breathing biofeedback training was performed 12 times with the experimental group. The level of psychological stress was measured using the State Anxiety Inventory, Profile of Mood State, and Visual Analogue Stress Scale. The level of physiological stress was measured using pulse rate and blood pressure. The data were analyzed using descriptive statistics, $x^2-test$, t-test, and repeated measures of ANOVA. The results of study are as follows : 1) State anxiety scores were not significantly different between the experimental group and the control group after the biofeedback training. 2) Profile of mood state scores were not significantly different between the experimental group and the control group after the biofeedback training. 3) Visual Analogue Stress Scale scores were significantly different between the experimental group and the control group after the biofeedback training(F=11.68, p=0.002). 4) Pulse rates were not significantly different between the experimental group and the control group after the biofeedback training. 5) Systolic blood pressures were significantly different between the experimental group and the control group after the biofeedback training(F=5.44, p=0.025). 6) Diastolic blood pressures were not significantly different between the experimental group and the control group after the biofeedback training. On the basis of the above findings, the following recommendations for further study are made ; 1) Identification of the effects of breathing biofeedback training at times of high stress during clinical practice. 2) Identification fo the effects of stress reduction according to the frequency of the breathing biofeedback training.

• Journal of The Korean Society of Integrative Medicine
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• v.3 no.4
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• pp.37-42
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• 2015

Purpose: This study was conducted to test the impact of The Dynamic Visual-Motor integration training has effect on the visual perception reaction velocity. Dynavision were used to measure data from the participating 24 students(K college). Method : The participants were the 24 students of 'K' College in Busan in there twenties. They were divided into the The Dynamic Visual-Motor integration training group and the control group. To know if the Dynamic Visual-Motor integration training has effect on the visual perception reaction velocity, the Dynamic Visual-Motor integration training was implemented triweekly for 4 weeks. In Dynamic Visual-Motor integration training the ball should be grasped with one hand and threw by an arm. Only the balls threw beyond the objective point were counted. The visual perception reaction velocity and the number of response were measured before and after experiment by Dynavision. Result : Firstly, the visual perception reaction velocity was increased in Dynamic Visual-Motor integration training group compared with control group. Secondly, the number of response was also increased in Dynamic Visual-Motor integration training group compared with control group. Conclusion : As a result of The Dynamic Visual-Motor integration training has an effect on the visual perception reaction velocity and the number of response. The Dynamic Visual-Motor integration training seems to be effective for cerebral apoplexy patient who has visual perceptional disability or cerebral palsy child in training for visual perceptional development or daily living activities development. Study participated by more detailed and practical patients in hospital is needed.

• Industry Promotion Research
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• v.1 no.1
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• pp.59-64
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• 2016

This study verified the effect of the experience of job-training on youth employees' wage by analyzing panal data. The study results showed that the objective of job-training(enhancing job performance, ${\beta}=.336$), satisfaction of job-training(${\beta}=.-.256$) and the type of job-training(cyber lecture, ${\beta}=.334$) significantly affected youth employees' wage. Also, age, education, marital status, hour of job-training, objective of job-training and satisfaction of job-training significantly affected the differences between groups. This study provided practical implication to prepare effective job-training policy by confirming the effect of the experience of job-training on youth employees' wage and verifying the effectiveness of job-training.