• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.517-524
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• 2017

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.

• The Journal of Fisheries Business Administration
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• v.49 no.2
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• pp.21-35
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• 2018

Squid is a popular seafood in Korea. However, since the 2000s, the squid production has been declining. The unstable supply of the squid products may cause price fluctuations of fresh and chilled squid. These price fluctuations may be relatively more severe than them of other commodities, because the fresh and chilled squid can not be stored for a long period of time. Thus, this study analyzes the structural characteristics of price volatility and price asymmetry of fresh squid based on off-diagonal GARCH model. Data used to analysis of this study are daily wholesale and retail prices of fresh squid from January 1, 2006 to December 31, 2016 provided in the KAMIS. As theoretical approaches of this study, first of all, the stability of the time series is confirmed by the unit root test. Secondly, the causality between distribution channels is checked by the Granger causality test. Thirdly, the VAR model and the off-diagonal GARCH model are adopted to estimate asymmetry effect and price volatility spillover between distribution channels. Finally, the stability of the model is confirmed by multivariate Q-statistic and ARCH-LM test. In conclusion, fresh squid is found to have shock and volatility spillover between wholesale and retail prices as well as its own price. Also, volatility asymmetry effect is shown in own wholesale or retail price of fresh squid. Finally, this study shows that the decrease in the fresh squid retail price of t-1 period than the increase in the t-1 period has a greater impact on the volatility of the fresh squid wholesale price in t period.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Journal of Korea Port Economic Association
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• v.27 no.1
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• pp.263-280
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• 2011

The global financial crisis, triggered by the subprime mortgage crisis in 2007, has put the world economy into the recession with financial market turmoil. I tested whether variables were cointegrated or whether there was an equilibrium relationship. Also, Generalized impulse-response function (GIRF) and accumulation impulse-response function (AIRF) may be used to understand and characterize the time series dynamics inherent in economical systems comprised of variables that may be highly interdependent. Moreover, the IRFs enables us to simulate the response in freight to a shock in the USD/JPY exchange rate, Dow Jones industrial average index, Dow Jones volatility, Chinese Import volatility. The result on the cointegration test show that the hypothesis of no cointergrating vector could be rejected at the 5 percent level. Also, the empirical analysis of cointegrating vector reveals that the increases of USD/JPY exchange rate have negative relations with freight. The result on the impulse-response analysis indicate that freight respond negatively to volatility, and then decay very quickly. Consequently, the results highlight the potential usefulness of the multivariate time series techniques accounting to behavior of Freight.

• Environmental and Resource Economics Review
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• v.20 no.3
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• pp.531-565
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• 2011

All previous studies analyzing multivariate time series data of EUA (European Union Allowance) price commonly used endogenous variables within the four variables and included the period from April to June of 2006 in the analysis, when the price distortion occurred. This study uses graph theory and structural vector error correction model (SVECM) to analyze the daily time series data of the EUA (European Union Allowance) price. As endogenous variables, five variables are considered for the analysis, including prices of crude oil, natural gas, electricity and coal in addition to carbon price. Data period is Phase 2 period (April 21, 2008 to March 31, 2010) to avoid the EUA price distortion of Phase 1 period (2005~2007). Further, the monthly data including the economic variables as endogenous variables are analyzed.

• Journal of Navigation and Port Research
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• v.44 no.1
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• pp.44-52
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• 2020

More accurate forecasting of port cargo in the global long-term recession is critical for the implementation of port policy. In this study, the Busan Port container volume (export cargo and transshipment cargo) was estimated using the Vector Autoregressive (VAR) model and the vector error correction (VEC) model considering the causal relationship between the economic scale (GDP) of Korea, China, and the U.S. as well as ARIMA, a single volume model. The measurement data was the monthly volume of container shipments at the Busan port J anuary 2014-August 2019. According to the analysis, the time series of import and export volume was estimated by VAR because it was relatively stable, and transshipment cargo was non-stationary, but it has cointegration relationship (long-term equilibrium) with economic scale, interest rate, and economic fluctuation, so estimated by the VEC model. The estimation results show that ARIMA is superior in the stationary time-series data (local cargo) and transshipment cargo with a trend are more predictable in estimating by the multivariate model, the VEC model. Import-export cargo, in particular, is closely related to the size of our country's economy, and transshipment cargo is closely related to the size of the Chinese and American economies. It also suggests a strategy to increase transshipment cargo as the size of China's economy appears to be closer than that of the U.S.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• KDI Journal of Economic Policy
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• v.26 no.2
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• pp.3-62
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• 2004

This paper estimates Korea's natural rate of unemployment using various estimation methods such as pure time-series methods, reduced-form methods, and structural form methods, with discussion about relative advantages and disadvantages of each estimation method. This paper also provides the confidence interval of the estimated natural rate of unemployment by the Monte Carlo integration method. Though multivariate unobserved component model exhibits better performance in many aspects than other estimation methods, awareness should be raised for a potential misspecification problem of a multivariate unobserved component model. Considering that each method has its own advantages and disadvantages, it is recommended to make an inference on the natural rate of unemployment based on common results among various methods. Korea's natural rate of unemployment was estimated to be around 3.8~4.0% on average in the period of 1979:I~1987:IV, and to decline to 2.5~2.9% in the period of 1988:I~1997:IV. During the Asian crisis, it is estimated to peak at near 4.8% and to have been on a downward trend since then.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1245-1245
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145-154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.