• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.2
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• pp.223-229
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• 2023

Seawater batteries are next-generation secondary batteries that use seawater as a cathode. They utilize marine resources to provide competitive prices, high eco-friendliness, and a structure suitable for marine applications. Based on these advantages, pouch types and prismatic types have been studied and developed assuming natural seawater exposure. However, because of the electrical characteristics of the secondary battery, its capacity and internal resistance vary depending on the use environment. These characteristics are not only utilized for predicting the life of a battery but also have a direct effect on the capacity and power suitable for a specific situation. Therefore, the internal resistance was analyzed in this study by measuring the capacity depending on the seawater battery use environment and the state-of-charge-open-circuit-voltage measurement method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.351-353
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• 2021

In recent years, while the demand for electricity is rapidly increasing, fire accidents due to negligence in management of switchboards. In particular, switchboards for industrial and electrical resource control can cause serious problems. Thus, for the safety management of power switchboard, a secondary response is conducted to control firing when a specific condition value is satisfied, but in this case, it is highly likely that a considerable amount of time has elapsed after firing. In this paper, we propose a method to acquire big data for the development of a switchboard temperature and power control system that can actively respond to the current situation by monitoring and learning the temperature of the switchboard's busbar connection in real time. Specifically, a method for periodically acquiring and managing data such as temperature and power from various scattered sensors is proposed.

• Korea Trade Review
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• v.48 no.2
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• pp.27-43
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• 2023

The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.43-48
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• 2023

Globally, there is a collaborative effort to achieve global carbon neutrality in response to climate change. In the case of South Korea, greenhouse gas emissions are rapidly increasing, presenting an urgent situation that requires resolution. In this context, this study developed a thermal energy collection device named a 'steam trap' and created an AI model capable of predicting future electricity usage by collecting energy usage data through steam traps. The average accuracy of electricity usage prediction with this AI model was 96.7%, demonstrating high precision. Consequently, the AI model enables the prediction and management of days with high electricity consumption and identifies which facilities contribute to elevated power usage. Future research aims to optimize energy consumption efficiency through efficient equipment operation using anomaly detection in steam traps and standardizing energy management systems, with the ultimate goal of reducing greenhouse gas emissions.

• Journal of Aerospace System Engineering
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• v.18 no.3
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• pp.48-55
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• 2024

In recent years, as the commercialization of unmanned aerial vehicles (UAVs) has been actively promoted, much attention has been focused on developing a technology to ensure the safety of UAVs. In general, the UAV has the potential to enter an uncontrollable state caused by sudden maneuvers, disturbances, and pilot error. To prevent entering an uncontrolled situation, it is essential to predict the flight state of the UAV. In this paper, we propose a flight state prediction technique based on an improved CNN-LSTM hybrid mode to enhance the flight state prediction performance. Simulation results show that the proposed prediction technique offers better state prediction performance than the existing prediction technique, and can be operated in real-time in an on-board environment.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.454-460
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• 2009

In this paper, we propose a residual echo and noise reduction scheme by using linear predictor for hands-free telephony applications. The proposed scheme whitens residual echo by the linear prediction during the non double-talk. But whitened residual echo signal still has speech characteristics. In this scheme, the whitened residual echo signal is more whitened by using the power of the linear prediction error signal and the linear predicted signal. After whitening process, near-end speech and ambient noise is present during double-talk but white noise will appear during non double-talk situation. By linearly predicting again the combined signal of the near-end speech and the whitened signal, the ambient noise is removed. Through computer simulation, it is shown that the proposed method performs well at the side of AIC (acoustic interference cancellation).

• Journal of Daesoon Thought and the Religions of East Asia
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• v.3 no.2
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• pp.71-92
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• 2024

The world we live in is becoming more convenient thanks to the inventions of science and technology. Still, the world is also becoming more and more unpredictable with the current situation of VUCA (Volatility, Uncertainty, Complexity, Ambiguity). The Covid-19 pandemic brought the biggest global disaster ever with 774,631,444 infected people and 7,031,216 deaths (WHO on February 11, 2024) but it seems that humanity is gradually forgetting this disaster. Meanwhile the economic stimulus packages worth trillions of dollars from governments after the pandemic have further caused the world debt bubble to swell. The bubble burst scenario is something that many economic experts fear. Apparently, in the transitional period of the early decades of the 21st century, the world's economic, cultural, political, social, natural, and environmental aspects have undergone profound transformations: from the real estate and finance crises in the United States since 2008; through the melting of the Arctic ice over the past several decades; to the double disaster of the earthquake and tsunami in Japan in 2011. Especially, in the context of the world economic crisis after the COVID-19 pandemic, the human achievements of the past thousands of years are in jeopardy of being wiped out in an instant. Many people are predicting a bad scenario for a chain collapse. Facing the signals of an imminent economic catastrophe based on the appearance of "the Gray Rhino, Black Swan and White Elephant," many drawn in by Eschatological thought declare that Doomsday will occur shortly. This is the time for many other people to hope for the incoming Messiah. The Messiah is said to appear when people feel despair or suffer a great disaster because faith in the Savior can help them overcome adversity mentally. This research will find out how adherents of Buddhism view and deal with civilizational crises by examining history via symbols associated with Maitreya as based upon the Buddhist Messiah, Maitreya.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.579-588
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• 2024

The present paper investigates the curvature ductility of confined reinforced concrete (RC) beams with normal (NSC) and high strength concrete (HSC). For the purpose of predicting the curvature ductility factor, an analytical model was developed based on the equilibrium of internal forces of confined concrete and reinforcement. In this context, the curvatures were calculated at first yielding of tension reinforcement and at ultimate when the confined concrete strain reaches the ultimate value. To best simulate the situation of confined RC beams in flexure, a modified version of an ancient confined concrete model was adopted for this study. In order to show the accuracy of the proposed model, an experimental database was collected from the literature. The statistical comparison between experimental and predicted results showed that the proposed model has a good performance. Then, the data generated from the validated theoretical model were used to train the artificial neural network (ANN) prediction model. The R² values for theoretical and experimental results are equal to 0.98 and 0.95, respectively which proves the high performance of the ANN model. Finally, a parametric study was implemented to analyze the effect of different parameters on the curvature ductility factor using theoretical and ANN models. The results are similar to those extracted from experiments, where the concrete strength, the compression reinforcement ratio, the yield strength, and the volumetric ratio of transverse reinforcement have a positive effect. In contrast, the ratio and the yield strength of tension reinforcement have a negative effect.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of the Korean Geotechnical Society
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• v.30 no.1
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• pp.65-74
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• 2014

Cemented soils have been widely used in road and dam construction, and recently ground improvement of soft soils. The strength of such cemented soils can be tested by using cored sample or laboratory-prepared specimen through unconfined compression or triaxial tests. It takes time to core a sample or prepare a testing specimen in the laboratory. In a certain situation, it is necessary to determine the in-situ strength of cemented soils very quickly and on time. In this study, the relation between unconfined compressive strength and shear wave velocity was investigated for predicting the in-situ strength of cemented soils. A small cemented specimen with 5 cm in diameter and 10 cm in height was prepared by Nakdong river sand and ordinary Portland cement. Its cement ratios were 4, 8, 12, and 16% and air cured for 7, 14, and 28 days. For recycling of resources, a blast furnace slag was also used with sodium hydroxide as an alkaline activator. The shear wave velocity for cemented soils was measured and then unconfined compressive strength test was carried out. As a cement ratio increased, the shear wave velocity and unconfined compressive strength increased due to increased density and denser structure. The relation between unconfined compressive strength and shear wave velocity increased nonlinearly for cemented soils with less than 16% of cement ratio.