• Smart Structures and Systems
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• 제24권3호
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• pp.319-332
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• 2019

Structural health monitoring (SHM) is of great importance to super high-rise buildings. The Shanghai Tower is currently the tallest building in China, and a complete SHM system was simultaneously constructed at the beginning of the construction of the tower. Due to the variety of sensor types and the large number of measurement points in the SHM system, an online automatic structural health assessment method with few computations and no manual intervention is needed. This paper introduces a structural health assessment method for the Shanghai Tower that uses the coefficients of an autoregressive (AR) time series model as structural state indicators. An analysis of collected data indicates that the coefficients of the AR model are affected by environmental factors, and the principal component analysis method is used to remove the influence of environmental factors. Finally, the control chart method is used to track the changes in structural state indicators, and a plan for online automatic structure health state evaluation is proposed. This method is applied to long-term acceleration and inclination data from the Shanghai Tower and successfully identifies the changes in the structural state. Overall, the structural state indicators of the Shanghai Tower are stable, and the structure is in a healthy state.

• 천문학회보
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• 제43권2호
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• pp.47.1-47.1
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• 2018

Solar cycles are inherent to the Sun, which experiences temporal changes in its magnetic activity via the surface distribution of the solar magnetic field. This raises a fundamental question of how to derive the distribution characteristics of a solar-surface magnetic field that are responsible for individual solar cycles. We present a new approach to deriving as long-term and large-scale distribution characteristics of this quantity as was ever obtained; that is, we conducted a population ecological analysis of Wilcox Solar Observatory (WSO) Synoptic Charts which provide a more than 40-year time series of latitude-longitude maps of solar-surface magnetic fields. In this approach, solar-surface magnetic fields are assumed as hypothetical trees with magnetic polarities (magnetic trees) distributed on the Sun. Accordingly, we identified a peculiarity of cycle 23 with a longer period than an average period of 11 years; specifically we found that the negative surface magnetic field had much more clumped distributions than the positive surface magnetic field during the first one-third of this cycle, while the latter was dominant over the former. The Sun eventually spent more than one-third of cycle 23 recovering from these imbalances.

• 한국농공학회논문집
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• 제61권1호
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• pp.21-29
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• 2019

This study analyzed the variability of drought risk based on trend analysis of dry-seasonal dam inflow located in upper river basins. To this, we used areal averaged precipitation and dam inflow of three upper river dams such as Soyang dam, Chungju dam, and Andong dam. We employed Mann-Kendall trend analysis and change point detection method to identify the significant trends and changing point in time series. Our results showed that significant decreasing trends (95% confidence interval) in dry-seasonal runoff rates (= dam inflow/precipitation) in three-dam basins. We investigated potential causes of decreasing runoff rates trends using changes in potential evapotranspiration (PET) and precipitation indices. However, there were no clear relation among changes in runoff rates, PET, and precipitation indices. Runoff rate reduction in the three dams may increase the risk of dam operational management and long-term water resource planning. Therefore, it will be necessary to perform a multilateral analysis to better understand decreasing runoff rates.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.93-104
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• 2021

In recent years, there has been a substantial theoretical and empirical study on the role that financial market development has significantly played in promoting economic growth and development in the world. The development of an economy requires the financial industry to be developed. In the context of rapid economic development, global warming has become a serious problem with issues such as rising average temperatures, climate change, rising sea level, and increasing carbon dioxide emissions. This study aims to examine the influence of economic growth, financial development, transportation capacity, and environmental degradation. Using time-series data from 1986 to 2019 and environmental degradation being measured by CO2 emissions, the study employs a quantity of ample unit root tests, the structural break unit root tests, Autoregressive Distributed Lag (ARDL), and cointegration bounds test. The results show that there is a significant long-term cointegration among study variables. Empirical findings also indicate that an increase in per capita GDP and financial development worsens environmental quality whereas transportation capacity and foreign investment can improve environmental quality.

• 무역학회지
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• 제44권2호
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• pp.127-141
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• 2019

In this study, the relationship between Baltic Dry Index(BDI) and maritime trade volume in the dry cargo market was verified using the vector autoregressive (VAR) model. Data was analyzed from 1992 to 2018 for iron ore, steam coal, coking coal, grain, and minor bulks of maritime trade volume and BDI. Granger causality analysis showed that the BDI affects the trade volume of coking coal and minor bulks but the trade volume of iron ore, steam coal and grain do not correlate with the BDI freight index. Impulse response analysis showed that the shock of BDI had the greatest impact on coking coal at the two years lag and the impact was negligible at the ten years lag. In addition, the shock of BDI on minor cargoes was strongest at the three years lag, and were negligible at the ten years lag. This study examined the relationship between maritime trade volume and BDI in the dry bulk shipping market in which uncertainty is high. As a result of this study, there is an economic aspect of sustainability that has helped the risk management of shipping companies. In addition, it is significant from an academic point of view that the long-term relationship between the two time series was analyzed through the causality test between variables. However, it is necessary to develop a forecasting model that will help decision makers in maritime markets using more sophisticated methods such as the Bayesian VAR model.

• 무역학회지
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• 제44권6호
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• pp.187-199
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• 2019

Vietnam's economic growth has progressed rapidly since Doi Moi. Vietnam is a key driving force for global economic growth on behalf of China. Therefore, this study analyzed the factors of Vietnam's economic growth by using time series variables after Doi Moi. Study results show that educational expenditures affect ODA in the short term. In the long run, GDP and FDI are causally related to ODA. Based on the above findings, it can be seen that FDI and ODA played a significant role in Vietnam's economic growth. This finding suggests that in order for Vietnam's economy to grow further, the capital market should be more open to foreigners so that FDI and ODA can flow more smoothly. Since the inflow of FDI is also linked to educational expenditure, it is important to understand that the workforce is being upgraded in the Vietnamese labor market.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• 한국멀티미디어학회논문지
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• 제25권10호
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• pp.1483-1498
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• 2022

While public interests in the metaverse are growing recently in the Korean media and research, its understanding has not been fully established yet. In this study, we aimed to probe whether the rapid growth in media attention about the metaverse has increased its usage as a buzzword accompanied by an absence of scientific context. We analyzed publications and online news containing "metaverse" from 2020 to 2022. The data analysis methods are 1) time series frequency, 2) keyword network, 3) natural language model. The findings indicate the perception gap about metaverse between research and news articles broadened as its popularity has grown. Research about metaverse gradually expanded its connections with related topics-virtual and augmented realities-focusing on social changes in a remote environment. However, media reporting frequently used "metaverse" as a buzzword rather than explaining its scientific background, stimulating the proliferation of related topics and the dispersion of news content. This study further discusses the need for a media strategy to improve public conception of the long-term development of the metaverse.

• 한국지하수토양환경학회지:지하수토양환경
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• 제27권5호
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• pp.18-31
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• 2022

To evaluate the impact of drought on agriculture in N. Korea, SPI (standardized precipitation index) analysis was carried out by utilizing time-series precipitation data during 1996 - 2003 when severe drought occurred throughout the country. The SPI value was estimated to reach 12 in approximately 60% of the total period, indicating that agricultural productivity deteriorated rapidly due to the long-term drought. The national average drought cycle, based on SPI 12, was estimated as 32.5 months for the last 40 years. However, when examined on 20-year basis, the drought cycle was shortened by 10.6 months in last 20 years (30.3 months) as compared to previous 20 years (40.9 months). Annual crop production continued to increase mainly in rice and maize until the mid-1990s, but declined sharply thereafter due to the drought. After the drought period, the production of potatoes of which growth is more resistant to drought started to increase to the production level comparable to those of rice and soybean. It is expected that changes in the agricultural production environment in N. Korea will be inevitable due to the climate change. To this end, using the results of the drought cycle analysis, it is possible to analyze the changes in the agricultural production environment in N. Korea in the future.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.366-366
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• 2022

유역의 하천유량과 같은 수문 시계열을 모의 또는 예측하기 위한 수문 모델링에서 최근 기계 학습 방법을 활용한 연구가 활발하게 적용되고 있는 추세이다. 이러한 데이터 기반 모델링 접근법은 입출력 자료에서 관찰된 패턴을 학습하며, 특히, 장단기기억(Long Short-Term Memory, LSTM) 네트워크는 많은 연구에서 수문 시계열 예측에 대한 적용성이 검증되었으나, 장기간의 고품질 관측자료를 활용할 때 더 나은 예측성능을 보인다. 그러나 우리나라의 경우 장기간 관측된 고품질의 하천유량 자료를 확보하기 어려운 실정이다. 따라서 본 연구에서는 LSTM 네트워크의 학습 시 가용한 모든 유역의 자료를 통합하여 학습시켰을 때 하천유량 예측성능을 개선할 수 있는지 판단해보고자 하였다. 이를 위해, 우리나라 13개 댐 유역을 대상으로 대상 유역의 자료만을 학습한 모델의 예측성능과 모든 유역의 자료를 학습한 모델의 예측성능을 비교해 보았다. 학습은 2001년부터 2010년까지 기상자료(강우, 최저·최고·평균기온, 상대습도, 이슬점, 풍속, 잠재증발산)를 이용하였으며, 2011년부터 2020년에 대해 테스트 되었다. 다지점 통합학습을 통해 테스트 기간에 대해 예측된 각 유역의 일 하천유량의 KGE 중앙값이 0.74로 단일지점 학습을 통해 예측된 KGE(0.72)보다 다소 개선된 결과를 보여주었다. 다지점 통합학습이 하천유량 예측에 큰 개선을 달성하지는 못하였으며, 추가적인 가용 자료 확보와 LSTM 구성의 개선을 통해 추가적인 연구가 필요할 것으로 판단된다.