• Advanced Industrial SCIence
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• v.3 no.1
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• pp.17-22
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• 2024

History, which began with the emergence of mankind, has a means of recording. Today, we can check the past through data. Generated data may only be generated and stored at a certain moment, but it is not only continuously generated over a certain time interval from the past to the present, but also occurs in the future, so making predictions using it is an important task. In order to find out trends in the use of time series data among numerous data, this paper analyzes the concept of time series data, analyzes Recurrent Neural Network and Long-Short Term Memory, which are mainly used for time series data analysis in the machine learning field, and analyzes the use of these models. Through case studies, it was confirmed that it is being used in various fields such as medical diagnosis, stock price analysis, and climate prediction, and is showing high predictive results. Based on this, we will explore ways to utilize it in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Safety and Health at Work
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• v.15 no.2
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• pp.220-227
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• 2024

Background: Though the artificial neural network (ANN) technique has been used to predict noise-induced hearing loss (NIHL), the established prediction models have primarily relied on cross-sectional datasets, and hence, they may not comprehensively capture the chronic nature of NIHL as a disease linked to long-term noise exposure among workers. Methods: A comprehensive dataset was utilized, encompassing eight-year longitudinal personal hearing threshold levels (HTLs) as well as information on seven personal variables and two environmental variables to establish NIHL predicting models through the ANN technique. Three subdatasets were extracted from the afirementioned comprehensive dataset to assess the advantages of the present study in NIHL predictions. Results: The dataset was gathered from 170 workers employed in a steel-making industry, with a median cumulative noise exposure and HTL of 88.40 dBA-year and 19.58 dB, respectively. Utilizing the longitudinal dataset demonstrated superior prediction capabilities compared to cross-sectional datasets. Incorporating the more comprehensive dataset led to improved NIHL predictions, particularly when considering variables such as noise pattern and use of personal protective equipment. Despite fluctuations observed in the measured HTLs, the ANN predicting models consistently revealed a discernible trend. Conclusions: A consistent correlation was observed between the measured HTLs and the results obtained from the predicting models. However, it is essential to exercise caution when utilizing the model-predicted NIHLs for individual workers due to inherent personal fluctuations in HTLs. Nonetheless, these ANN models can serve as a valuable reference for the industry in effectively managing its hearing conservation program.

• Journal of Astronomy and Space Sciences
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• v.32 no.4
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• pp.367-377
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• 2015

Atmospheric drag force is an important source of perturbation of Low Earth Orbit (LEO) orbit satellites, and solar activity is a major factor for changes in atmospheric density. In particular, the orbital lifetime of a satellite varies with changes in solar activity, so care must be taken in predicting the remaining orbital lifetime during preparation for post-mission disposal. In this paper, the System Tool Kit (STK$^{(R)}$) Long-term Orbit Propagator is used to analyze the changes in orbital lifetime predictions with respect to solar activity. In addition, the STK$^{(R)}$ Lifetime tool is used to analyze the change in orbital lifetime with respect to solar flux data generation, which is needed for the orbital lifetime calculation, and its control on the drag coefficient control. Analysis showed that the application of the most recent solar flux file within the Lifetime tool gives a predicted trend that is closest to the actual orbit. We also examine the effect of the drag coefficient, by performing a comparative analysis between varying and constant coefficients in terms of solar activity intensities.

• Computers and Concrete
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• v.8 no.5
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• pp.511-524
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• 2011

Although concrete is believed to be a durable material, concrete structures have been degraded by severe environmental conditions such as the effects of chloride and chemical, abrasion, and other deterioration processes. Therefore, durability evaluation has been required to ensure the long term serviceability of structures located in chloride exposed environments. Recently, probability-based durability analysis and design have proven to be reliable for the service-life predictions of concrete structures. This approach has been successfully applied to durability estimation and design of concrete structures. However, currently it is difficult to find an appropriate method engineers can use to solve these probability-based diffusion problems. In this paper, computer software has been developed to facilitate probability-based durability analysis and design. This software predict the chloride diffusion using the Monte Carlo simulation method based on Fick's second law, and provides durability analysis and design solutions. A graphic user interface (GUI) is adapted for intuitive and easy use. The developed software is very useful not only for prediction of the service life but for the durability design of the concrete structures exposed to chloride environments.

• Journal of Astronomy and Space Sciences
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• v.11 no.1
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• pp.131-145
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• 1994

The Cosmic Ray Experiment (CRE) is one of the modules flown on board the KITSAT-1 satellite and consistes of two sub-systems: the Total Dose Experiment (TDE) and the Cosmic Particl Experiment(CPE). The purpose of CRE is to characterize the space radiation environment as encountered by an Earth-orbiting spacecraft. KITSAT-1 orbit is dominated by the inner Van Allen radiation belt. This region has a large population of high energy protons which contributes significantly to both long-term and transient radiation effects. The data shows that the inner Van Allen radiation belt is very stable and the solar activity influences the CPE, TDE data and SEU(Single Event Upset) rates. The result also shows that much larger high energy particle flux is recorded than the predictions of the CREME code.

• Journal of Environmental Health Sciences
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• v.44 no.1
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• pp.31-43
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• 2018

Objectives: Effective genetic toxicology and molecular biology research techniques and strategies that are highly correlated with the carcinogenic inhalation toxicity test and related research are required. The aim of this study was to maximize the utilization of chemical substances to prevent workers' occupational diseases. Methods: We surveyed the literature, domestic and international references, and the status of relevant domestic and foreign professional organizations. Expert advisory opinions were reflected, and experts were consulted by participating in domestic and overseas academic conferences. Results: The current status of domestic and international genotoxic toxicity evaluation was examined through various documents from related organizations. Cell models for in vitro lung toxicology were investigated and summarized, and the human resources and performance results of genetic toxicity studies and pilot projects were compared and analyzed by holding an advisory meeting. We examined domestic and international genotoxicity guidelines and investigated new test methods for the development of genotoxicity and carcinogenicity. Ultimately, we described long-term future predictions, including the implementation of our researchers' recommendations and occupational genetic toxicology forecasts for future worker health protection. Conclusions: This research project aims to establish current genetic toxicology and molecular biology research techniques and strategies that can maximize the linkage with the carcinogenic inhalation toxicity test and research in the future. We expanded the study of genetic toxicity and establish a foundation forgenetic toxicity in accordance with research trends in Korea and abroad.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1023-1025
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• 2019

Coal and Natural gas are two biggest contributors to a generation of energy throughout the world. Most of these resources create environmental pollution while making energy affecting the natural habitat. Many approaches have been proposed as alternatives to these sources. One of the leading alternatives is Solar Energy which is usually harnessed using solar farms. In artificial intelligence, the most researched area in recent times is machine learning. With machine learning, many tasks which were previously thought to be only humanly doable are done by machine. Neural networks have two major subtypes i.e. Convolutional neural networks (CNN) which are used primarily for classification and Recurrent neural networks which are utilized for time-series predictions. In this paper, we predict energy generated by solar fields and optimal angles for solar panels in these farms for the upcoming seven days using environmental and historical data. We experiment with multiple configurations of RNN using Vanilla and LSTM (Long Short-Term Memory) RNN. We are able to achieve RSME of 0.20739 using LSTMs.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• Geomechanics and Engineering
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• v.35 no.1
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• pp.55-65
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• 2023

Soft clay is widely spread in nature and encountered in geotechnical engineering applications. The creep property of soft clay greatly affects the long-term performance of its upper structures. Therefore, it is vital to establish a reasonable and practical creep constitutive model. In the study, two updated hyperbolic equations based on the volumetric creep and deviatoric creep are respectively proposed. Subsequently, three creep constitutive models based on different creep behavior, i.e., V-model (use volumetric creep equation), D-model (use deviatoric creep equation) and VD-model (use both volumetric and deviatoric creep equations) are developed and compared. From the aspect of prediction accuracy, both V-model and D-model show good agreements with experimental results, while the predictions of the VD-model are smaller than the experimental results. In terms of the parametric sensitivity, D-model and VD-model are lower sensitive to parameter M (the slope of the critical state line) than V-model. Therefore, the D-model which is developed by incorporating the updated deviatoric creep equation is suggested in engineering applications.