• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.28-36
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• 2018

There was a difference in recognition of respirators according to the educational performance environment. they were showed higher recognition of respirators of group by internal and external mix trainer, less than 6 months, over 1hour, more than 5 times, variety of education. To identify the relationship between types of job classification(typical and atypical)and the levels of recognition of respirators, a total of 153 workers in a business workplace. mainly, typical workers showed higher recognition of respirators than atypical workers. Training of correct wearing showed high demands both typical and atypical workers. Descriptive statistics(SAS ver 9.2)was performed. the results of recognition of respirators were analyzed the mean and standard deviation by t-test, and anova, fit factor is used geometric means(geometric standard deviation), paired t-test, Wilcoxon analysis(P=0.05). Particulate filtering facepiece respirators (PFFR) is one of the most widely used items of personal protective equipments, and a tight fit of the respirators on the wearers is critical for the protection effectiveness. In order to effectively protect the workers through the respirators, it is important to find and evaluate the ways that can be readily applicable at the workplace to improve the fit of the respirators. This study was designed to evaluate effects of mask style (cup or foldable type) and donning training on fit factors (FF) of the respirators, since these are available at various workplace, especially at small business workplace. A total of 40 study subjects, comprised of employment type workers in metalworking industries, were enrolled in this study. The FF were quantitatively measured before and after training related to the proper donning and use of cup or foldable-type respirators. The pass/fail criterion of FF was set at 100. After the donning training for the cup-type mask, fit test were increased by 769%. but foldable-type mask was also increased after the donning training, the GM of FF for the foldable-type mask and it's increase rate were smaller as compared to the cup-type mask. Furthermore, the differences of the increase rates of the GM of FF in employment type of the subjects were not significantly for the foldable-type mask. These results imply that the raining on the donning and use of PFFR can enhance the protection effectiveness of cup or foldable-type mask, and that the training effects for the foldable-type mask is less significant than that for the cup-type mask. Therefore, it is recommended that the donning training and fit tests should be conducted before the use of the PFFR, and listening to workers opinion regularly.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.21-29
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• 2018

In the era of the 4-th industrial revolution, the concept of smart factory is emerging. There are efforts to predict the occurrences of facility errors which have negative effects on the utilization and productivity by using data analysis. Data composed of the situation of a facility error and the type of the error, called the facility error log, is required for the prediction. However, in many manufacturing companies, the types of facility error are not precisely defined and categorized. The worker who operates the facilities writes the type of facility error in the form with unstructured text based on his or her empirical judgement. That makes it impossible to analyze data. Therefore, this paper proposes a framework for constructing a phrase network to support the identification and classification of facility error types by using facility error logs written by operators. Specifically, phrase indicating the types are extracted from text data by using dictionary which classifies terms by their usage. Then, a phrase network is constructed by calculating the similarity between the extracted phrase. The performance of the proposed method was evaluated by using real-world facility error logs. It is expected that the proposed method will contribute to the accurate identification of error types and to the prediction of facility errors.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.155-164
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• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.249-260
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• 2021

This study is a meta-analysis study conducted to integrate and analyze the results of flip-learning studies for Korean nursing students. We searched PubMed, EMBASE, Cochrane, CINAHL, and Korean databases. Randomized controlled trials (RCTs) and Non-Randomized controlled trials (Non-RCTs) evaluating the effects of flipped learning for Korean nursing students were included. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using a random-effects meta-analysis. The entire effect size in flipped learning was big in effect size (SMD = 1.21; 95% CI = 0.84 to 1.63; I2 = 93.9; n = 23) compared to the control groups. The analysis results of subgroups according to the classification of Bloom showed that flipped learning was found to have a significant effect on psychomotor domain, cognitive domain, and affective domain. A total of 10 literature analyses, this meta-analysis showed that flipped learning on Korean nursing students is effective in psychomotor, cognitive, and affective domain than the traditional teaching method. The flip learning can be integrated into theoretical and practical nursing education to improve the academic performance of nursing students.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.93-106
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• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.

• Applied Chemistry for Engineering
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• v.32 no.4
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• pp.361-370
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• 2021

Carbon fiber reinforced polymer (CFRP) is one of the composite materials, which has a unique property that is lightweight but strong. The CFRPs are widely used in various industries where their unique characteristics are required. In particular, electric and unmanned aerial vehicles critically need lightweight parts and bodies with sufficient mechanical strengths. Vehicles using the battery as a power source should simultaneously meet two requirements that the battery has to be safely protected. The vehicle should be light of increasing the mileage. The CFRP has considered as the one that satisfies the requirements and is widely used as battery housing and other vehicle parts. On the other hand, in the battery area, carbon fibers are intensively tested as battery components such as electrodes and/or current collectors. Furthermore, using carbon fibers as both structure reinforcements and battery components to build a structural battery is intensively investigated in Sweden and the USA. This mini-review encompasses recent research trends that cover the classification of structural batteries in terms of functionality of carbon fibers and issues and efforts in the battery and discusses the prospect of structural batteries.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.425-434
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• 2021

This study is subjected to two linked research. First is the creation process of dance work applying noise and non-musical sounds in our daily life based on understanding the classification, perception, and factor of the sound, through Murray Schafer's concept of 'soundscape'. Second is to find the value of new type of choreography and musical effect of creation process of the dance work . According to methodological research of practice-based research, three stages which is practice, theory and evaluation were accumulated as somatic data, And the analysis was provided a basis by presenting in a figuration, form of the movement and method of specialization with reference to the paper 『space design and forming practice』(2003). As a result, the creation process was able to discover the musical effect of the sound in daily life and new method of choreography, and also find the possibilities that sound could convey the theme of the dance work, the meaning of the movement and the overall atmosphere of the work to audience. In addition, It is expected that will have been made another new creation environment by potential that music has based on concept 'soundscape'.

• Korean Journal of Plant Resources
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• v.34 no.1
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• pp.79-88
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• 2021

This research was conducted to investigate the genetic diversity and select useful accession with agronomic characteristics of garlic (Allium sativum L.). germplasms at Garlic Research Institute in Chungbuk Agricultural Research and Extension Service. Morphological diversity and relationships among 160 germplasms collected from 26 countries were assessed by methods of clustering and principal component analysis. Among 11 types of leaves and bulbs characteristics, emergence days of leaf showed the highest variation with coefficient of variation of 84.8%, and the bulb weight and the number of scales showed higher variability with 24.3%. Correlation analysis based on 11 quantitative traits showed that bulb weight and bulb length have very high positive correlation with bulb quantity. Plant height, leaf length, and number of leaves showed positive correlation with bulb weight as collections with better performance in growth produced large bulb with higher quality. The cluster analysis based on 5 principal components generated 6 clusters with an average distance of 1.6 among clusters. Domestic genetic resources were the largest with 36 species (22.5%) in group II.