• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.4
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• pp.381-392
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• 2022

Objectives: Objective of this study is to review briefly exposure characteristics, monitoring instruments and threshold limit values for extremely low frequency-magnetic field (ELF-MF) methods. This study was undertaken through brief literature review. We performed a literature search in PubMed to identify ELF-MF studies conducted in workplaces. Initial search keywords such as 'extremely low frequency-magnetic field (ELF-MF)' and 'electromagnetic fields (EMF)' combined or singly. We limited our review to occupational rather than general nonworkplace environmental exposures. Methods: The contents we reviewed: key industry and occupations generating ELF-MF, several direct-reading instruments monitoring ELF-MF and threshold limit values (TLV) preventing health effects may be caused by the exposure to ELF-MF. Results: The industries related to the generation and supply of electricity, electrolytic installations, welding, and induction heating and more were regarded as high ELF-MF exposure industries. All jobs handling or employed performed in power cable lines, electrical wiring, and electrical equipment are found to be exposed to ELF-MF. Threshold or ceiling limit, 1,000 µT, is established to prevent acute effects of exposure to low-frequency EMFs on the nervous system: the direct stimulation of nerve and muscle tissues and the induction of retinal phosphenes. The International Agency for Research on Cancer (IARC) has classified ELF-MF as possibly carcinogenic to humans chiefly based on epidemiological studies on childhood leukemia. However, a causal relationship between magnetic fields and several types of cancer including childhood leukemia has not been established nor has any other long-term effects. Risk management using precautionary measures, has been initiated by the US and EU to prevent chronic health effects related to ELF-MF exposure in workplaces. Conclusion: This study recommends the implementation of various measures such as theestablishment of occupational exposure limit values for ELF-MF and precautionary principle to prevent potential chronic occupational health effects may be caused by ELF-MF in Korea.

• Composites Research
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• v.35 no.6
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• pp.378-393
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• 2022

As an emerging power generation technology, triboelectric nanogenerators (TENGs) have received increasing attention due to their boundless promise in energy harvesting and self-powered sensing applications. The recent rise of soft robotics has sparked widespread enthusiasm for developing flexible and soft sensors and actuators. TENGs have been regarded as promising power sources for driving actuators and self-powered sensors, providing a unique approach for the development of soft robots with soft sensors and actuators. In this review, TENG-based soft robots with different morphologies and different functions are introduced. Among them, the design of biomimetic soft robots that imitate the structure, surface morphology, material properties, and sensing/generating mechanisms of nature has greatly benefited in improving the performance of TENGs. In addition, various bionic soft robots have been well improved compared to previous driving methods due to the simple structure, self-powering characteristics, and tunable output of TENGs. Furthermore, we provide a comprehensive review of various studies within specific areas of TENG-enabled soft robotics applications. We first explore various recently developed TENG-based soft robots and a comparative analysis of various device structures, surface morphologies, and nature-inspired materials, and the resulting improvements in TENG performance. Various ubiquitous sensing principles and generation mechanisms used in nature and their analogous artificial TENG designs are demonstrated. Finally, biomimetic applications of TENG enabled in tactile displays as well as in wearable devices, artificial electronic skin and other devices are discussed. System designs, challenges and prospects of TENGs-based sensing and actuation devices in the practical application of soft robotics are analyzed.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1031-1039
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• 2022

As one of the hydrological factors closely related to landslides, precipitation indirectly affects slope stability by generating external forces. Groundwater level fluctuations have attracted more attention lately as factors that directly affect slope stability have become more prominent. Therefore, this study attempted to analyze the relationship between variables through changes in precipitation, groundwater levels, and land displacement. A time series-based analysis was conducted using satellite-based precipitation and point-based groundwater levels in conjunction with the PSInSAR technique to simulate land displacement in urban and mountainous areas. There was a sharp rise in groundwater levels in both urban and mountain areas during heavy rainfall, and a continuous decrease in urban areas when rainfall was low. 6 mm of displacements was observed in the mountainous area as a results of soil outflow from the topsoil layer, which was accompanied by an increased groundwater level. Meanwhile, different results were found in urban area. In response to the rise in groundwater level, the land displacement increases due to the expansion of soil skeletons, while the decrease seems to be attributed to anthropogenic influences. Overall, there was no consistent relationship between groundwater levels and land displacement, which appears to be caused by factors other than hydrological factors. Additional consideration of environmental factors could contribute to a deeper understanding of the relationship between the two factors.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.19-31
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• 2022

In Machine learning, we usually divide the entire data into training data and test data, train the model using training data, and use test data to determine the accuracy and generalization performance of the model. In the case of models with low generalization performance, the prediction accuracy of newly data is significantly reduced, and the model is said to be overfit. This study is about a method of generating training data based on central limit theorem and combining it with existed training data to increase normality and using this data to train models and increase generalization performance. To this, data were generated using sample mean and standard deviation for each feature of the data by utilizing the characteristic of central limit theorem, and new training data was constructed by combining them with existed training data. To determine the degree of increase in normality, the Kolmogorov-Smirnov normality test was conducted, and it was confirmed that the new training data showed increased normality compared to the existed data. Generalization performance was measured through differences in prediction accuracy for training data and test data. As a result of measuring the degree of increase in generalization performance by applying this to K-Nearest Neighbors (KNN), Logistic Regression, and Linear Discriminant Analysis (LDA), it was confirmed that generalization performance was improved for KNN, a non-parametric technique, and LDA, which assumes normality between model building.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.

• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.1-13
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• 2022

This study aimed to explore 'Becoming Science Teachers' seen in pre-service elementary teachers' inquiry into the features and roles of tree stems, based on Deleuze's Theory of Becoming. As research subjects, this study selected 30 pre-service elementary teachers and made them carry out textbook activities and advanced activities for about a month. Based on the results of these activities, they kept reflective inquiry diaries, and this study analyzed them and found out how far they attained according to Deleuze's mechanism of generating differences. Interestingly, it was found out that most of them attained the phase of escape and even the phase of deterritorialization and showed the highest frequency of establishing various know-hows about inquiries needed to teach students in the future. However, it was also analyzed that over 60% of the diaries still remained in phases prior to the phase of deterritorialization through escape. When analyzing rhizomatic thought that helped attain deterritorialization through escape, this study found out that all the 6 types were seen and functioned to attain deterritorialization. Therefore, it is necessary to provide challenging inquiry environments that pre-service elementary teachers have never experienced before so that they can go through a phased process for overcoming striated space in inquiry and attain reterritorialization.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.359-366
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• 2022

In this paper, we present the Korean menu-ordering Sentence Text-to-Speech (TTS) system using conformer-based FastSpeech2. Conformer is the convolution-augmented transformer, which was originally proposed in Speech Recognition. Combining two different structures, the Conformer extracts better local and global features. It comprises two half Feed Forward module at the front and the end, sandwiching the Multi-Head Self-Attention module and Convolution module. We introduce the Conformer in Korean TTS, as we know it works well in Korean Speech Recognition. For comparison between transformer-based TTS model and Conformer-based one, we train FastSpeech2 and Conformer-based FastSpeech2. We collected a phoneme-balanced data set and used this for training our models. This corpus comprises not only general conversation, but also menu-ordering conversation consisting mainly of loanwords. This data set is the solution to the current Korean TTS model's degradation in loanwords. As a result of generating a synthesized sound using ParallelWave Gan, the Conformer-based FastSpeech2 achieved superior performance of MOS 4.04. We confirm that the model performance improved when the same structure was changed from transformer to Conformer in the Korean TTS.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.1-9
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• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• Journal of The Korean Association For Science Education
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• v.42 no.1
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• pp.33-49
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• 2022

This study aims to explore, using both quantitative and qualitative data analyzing the structural relationship between creative process and product, the types of elementary students' scientific creativity. For this, 105 fifth-graders responded to a scientific creativity test that assesses creative process and product, and four students who scored the highest were interviewed. In the interview, they were asked about the cognitive process they used in generating the creative product. Then, correlation analysis and structural equation modeling were used, along with the interview data, to type the students. The main findings of the study are as follows. First, the structural equation modeling of creative process and product gave satisfactory results in absolute and incremental fit indexes. Second, among the three components of creative process - knowledge, inquiry skill-observation, and creative thinking skills -, only creative thinking skills had significant effects on creative product. Third, divergent thinking skills had the strongest correlation with the creative product, followed by convergent thinking skills. Associational thinking skills did not have significant correlation. Fourth, elementary students' scientific creativity could be categorized into Creative Type, Useful Type, Original Type, and Non-creative Type, based on their creative product. The Non-creative Type could be further classified into Common Type, Repetitive Type, Non-response Type, Irrelevant Type, and Abstract Type. Fifth, most students used either knowledge or observation in their creative process, making them either Knowledge-oriented Type or Observation-oriented Type. In addition, there were DT Type, DT-CT Type, and DT-CT-AT Type among the students, based on the kinds of creative thinking skills they mainly used in the process. This study provides implications for educators and researchers in scientific creativity education.

• Journal of Soil and Groundwater Environment
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• v.27 no.4
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• pp.10-21
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• 2022

In the South Korea, 47% of abandoned mines are suffering from the mining hazards such as the mine drainage (MD), the mine tailings and the waste rocks. Among them the mine drainage which has a low pH and the high concentration of heavy metals can directly contaminate rivers or soil and cause serious damages to human health. The natural/artificial treatment facilities by using neutralizers and coagulants for the mine drainage have been operated in domestic and most of heavy metals in mind drainage are precipitated and removed in the form of metal hydroxide, alumino-silicate or carbonate, generating a large amount of mine drainage treated sludge ('MDS' hereafter) by-product. The MDS has a large surface area and many functional groups, showing high efficiency on the fixation of heavy metals. The purpose of this study is to develop a ingenious heavy metal stabilizer that can effectively stabilize arsenic (As) and heavy metals in soil by recycling the MDS (two types of MDS: the acid mine drainage treated sludge (MMDS) and the coal mine drainage treated sludge (CMDS)). Various analyses, toxicity evaluations, and leaching reduction batch experiments were performed to identify the characteristics of MDS as the stabilizer for soils contaminated with As and heavy metals. As a result of batch experiments, the Pb stabilization efficiency of both of MDSs for soil A was higher than 90% and their Zn stabilization efficiencies were higher than 70%. In the case of soil B and C, which were contaminated with As, their As stabilization efficiencies were higher than 80%. Experimental results suggested that both of MDSs could be successfully applied for the As and heavy metal contaminated soil as the soil stabilizer, because of their low unit price and high stabilization efficiency for As and hevry metals.