• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.128-134
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• 2022

Nakdong river estuary is being operated with the goal of expanding the period of seawater inflow from this year to 2022 every month and creating a brackish water area within 15 km of the upstream of the river bank. In this study, the deep learning algorithm Long Short-Term Memory (LSTM) was applied to predict the salinity of the Nakdong Bridge (about 5 km upstream of the river bank) for the purpose of rapid decision making for the target brackish water zone and prevention of salt water damage. Input data were constructed to reflect the temporal and spatial characteristics of the Nakdong River estuary, such as the amount of discharge from Changnyeong and Hamanbo, and an optimal model was constructed in consideration of the hydraulic characteristics of the Nakdong River Estuary by changing the degree according to the sequence length. For prediction accuracy, statistical analysis was performed using the coefficient of determination (R-squred) and RMSE (root mean square error). When the sequence length was 12, the R-squred 0.997 and RMSE 0.122 were the highest, and the prior prediction time showed a high degree of R-squred 0.93 or more until the 12-hour interval.

• Journal of Korean Society on Water Environment
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• v.38 no.5
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• pp.220-230
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• 2022

Seasonal variability of water quality in the upland headwater streams in ten forested catchments (37.0~209.0 ha) was examined from April to November 2021. Here, seven physicochemical parameters were analyzed including pH, electrical conductivity (EC), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (T-N), total phosphorous (T-P), and BOD/TOC. The parameters were compared with those of lowerland rivers as middle and lower reaches within a watershed. The pH showed was low (6.4~6.9) during all the seasons, however, BOD and BOD/TOC in the fall season were 2-fold higher than in the spring and summer seasons. Based on environmental standards, the water quality level revealed that the upland headwater streams maintained the purity and cleanliness of water except for pH in the summer season. BOD/TOC of all the seasons and BOD of the fall season in the upland headwater streams were higher than that in the lowerland rivers, whereas the rest of the physicochemical parameters in the upland headwater streams were lower than that in the lowerland rivers. Additionally, the water quality level maintained the purity and cleanliness of water as "Good" in two reaches. The unique aspects of our study design enabled us to draw inferences about water quality characteristics with temporal and spatial analysis in upland headwater streams. This design will be useful for the long-term strategy of effective water quality management for integrated upland headwater streams and lowerland rivers within a watershed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.12
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• pp.899-907
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• 2022

According to the Unmanned aircraft system Traffic Management (UTM), drones are permitted to fly up to 150m above ground, which is located in the atmospheric boundary layer where there is considerable wind fluctuation due to turbulence. Although it is difficult to predict when turbulence will occur drone aviation safety could be enhanced by having a better understanding of the characteristics of vertical profile of wind in the flight area. We used wind lidar (WIndMast 350M) to observe vertical profiles of wind at the test site for aviation meteorological observation equipment located near Incheon International Airport in July and September, 2022. In this study, we utilized the observed wind profile data to propose a technique for obtaining information that could help improve the drone aviation safety. The Fourier transform analysis is used to evaluate the temporal characteristics of the horizontal wind speed at various vertical levels up to 350m. We also examined the relative contribution of the variance of wind having scales of less than an hour, a crucial scale for drone flight, to the variance of wind having all scales at each vertical altitude for days with and without precipitation.

• Journal of The Geomorphological Association of Korea
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• v.17 no.1
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• pp.15-27
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• 2010

This study investigated the sedimentary environment changes in the Baramarae beach of Anmyeondo through spatio-temporal surface sediment analysis. In the winter season 2009, surface sediments were classified into 7 sedimentary facies such as gravel, sand, gravelly sand, gravelly muddy sand, muddy sand, silty sand, and sandy silt. Time-series analysis of average grain size from 2002 to 2009 revealed that the average grain size of sediments became finer and sorting was much worse. On the contrary, during the same period, the grain size became coarsening-trend and sorting was much better in beach area. These different grain size patterns resulted from the different change characteristics of beach and tidal flats. The southwestern beach area was connected to the open sea and thus fine sediments were removed by the environments with relatively high-energy. The sedimentation of fine sediments in the bay resulted from the tidal current action and the reduction of energy by the topographic effects. Fine sediments in the outer part of southwestern tidal flats could be explained such that the Seomot isle blocked ocean waves and as a result, low-energy environments accelerated sedimentations of fine sediments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.725-736
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• 2023

The compliance of deep geological disposal facilities for high-level radioactive waste with safety objectives requires consideration of uncertainties owing to temporal changes in the disposal system. A comprehensive review and analysis of the characteristics of this evolution should be undertaken to identify the effects on multiple barriers and the biosphere. We analyzed the evolution of the buffer, backfill, plug, and closure regions during the early phase of the post-closure period as part of a long-term performance assessment for an operating license application for a deep geological repository in Finland. Degradation mechanisms generally expected in engineered barriers were considered, and long-term evolution features were examined for use in performance assessments. The importance of evolution features was classified into six categories based on the design of the Finnish case. Results are expected to be useful as a technical basis for performance and safety assessment in developing the Korean deep geological disposal system for high-level radioactive waste. However, for a more detailed review and evaluation of each feature, it is necessary to obtain data for the final disposal site and facility-specific design, and to assess its impact in advance.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• Information Systems Review
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• v.21 no.3
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• pp.111-129
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• 2019

Recently, artificial intelligence (AI)-enabled products and services such as smartphones, smart speakers, chatbots are being released due to advances in AI technology. Thus researchers making effort to reveal that consumers' intention to adopt AI-enabled products. Yet, little is known about the intended adoption of AI-enabled products. Because most of studies has been not consideredthe perceived utility value of consumers for each attribute by classified based on the characteristics of AI-enabled products. Therefore, the purpose of this study is to investigate the difference in importance between attributes that affect the intention to adopt of AI-enabled products. For this, first, identified and classified the attributes of AI-enabled products based on IS Success Model of DeLone and McLean. Second, measured the utility value of each attribute on the adoption of AI-enabled products through conjoint analysis. And we employed construal level theory to see whether there are differences in the relative importance of AI-enabled products attributes depending on the temporal distance. Third, we segmented the market based on the utility value of each respondent through cluster analysis and tried to understand the characteristics and needs of consumers in each segment market. We expect to provide theoretical implications for conceptually structured attributes and factors of AI-enabled products and practical implications for how development efforts of AI-enabled products are needed to reach consumers need for each segment.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.413-420
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• 2024

After Friedrich Nietzsche's advocacy of nihilism, many literary works, dramas, and films have depicted aspects of human psychology associated with nihilism. Animation, too, has been used to convey nihilism, with narratives infused with nihilistic themes produced as both TV series and theatrical animations. Particularly, animation, as a visual medium capable of realizing any imaginative image unlike other media, possesses distinctive characteristics from live-action cinematography and differs from comics in its temporal properties. Hence, this study aims to analyze how Nietzsche's defined three stages of nihilism are represented within animation characters and how they construct various scenarios, using the anime "Attack on Titan" as a case study. The research unfolds by first examining Nietzsche's types of nihilism and the three stages through a review of literature, while also investigating the portrayal of nihilism in mass media and considering the unique attributes of animation. Secondly, building upon the literature review, the analysis interprets the narrative and constructed world of the chosen case study from a nihilistic perspective, examining four major characters through the stages of passive nihilism, active nihilism, and eternal recurrence. The findings demonstrate that the anime conveys two messages regarding negation and affirmation of one's life and existence, thereby offering viewers an opportunity to deeply contemplate human existence. This study is considered significant as it examines how Nietzschean nihilism is portrayed within the popular entertainment medium of animation.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.661-676
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• 2017

This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.