• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.229-238
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• 2012

Within last 10 years, internet has become a daily activity, and humankind had to face the Data Deluge, a dramatic increase of digital data (Economist 2012). Due to exponential increase in amount of digital data, large scale data has become a big issue and hence the term 'big data' appeared. There is no official agreement in quantitative and detailed definition of the 'big data', but the meaning is expanding to its value and efficacy. Big data not only has the standardized personal information (internal) like customer information, but also has complex data of external, atypical, social, and real time data. Big data's technology has the concept that covers wide range technology, including 'data achievement, save/manage, analysis, and application'. To define the connected technology of 'big data', there are Big Table, Cassandra, Hadoop, MapReduce, Hbase, and NoSQL, and for the sub-techniques, Text Mining, Opinion Mining, Social Network Analysis, Cluster Analysis are gaining attention. The three features that 'bid data' needs to have is about creating large amounts of individual elements (high-resolution) to variety of high-frequency data. Big data has three defining features of volume, variety, and velocity, which is called the '3V'. There is increase in complexity as the 4th feature, and as all 4features are satisfied, it becomes more suitable to a 'big data'. In this study, we have looked at various reasons why companies need to impose 'big data', ways of application, and advanced cases of domestic and foreign applications. To correspond effectively to 'big data' revolution, paradigm shift in areas of data production, distribution, and consumption is needed, and insight of unfolding and preparing future business by considering the unpredictable market of technology, industry environment, and flow of social demand is desperately needed.

• Journal of the Korean Geosynthetics Society
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• v.9 no.4
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• pp.1-7
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• 2010

The soil nailing method have been developed on the basis of experimental works as well as theoretical backgrounds. As for the experimental research works, most of the data have been measured during the application of load in service. However, not only the soil-nailed structure behavior in service but also the failure behavior of the structure are the major concerns to evaluate and even establish a design method of soil-nailed walls. In this paper for the apprehension of behavior in the soil-nailed structure which the front of nail is connected, a relatively large-scale experiment was carried out to figure out the failure behavior of soil-nailed wall. A number of data have been acquired and analysis.

• Geotechnical Engineering
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• v.13 no.2
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• pp.91-100
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• 1997

The soil nailing method has been developed on the basis of experimental works as well as theoretical backgrounds. As for the experimental research works, most of the data have been measured during the application of load in service. However, not only the soil-nailed structure behavior in service but also the failure behavior of the structure is major concern to evaluate and even establish a design method of soil-nailed walls. In this study, a relatively large-scale experiment was carried out to figure out the failure behavior of soil-nailed wall. A number of data such as displacement of soil-nailed walls, soil pressure in soil-nailed walls, atrial strain and axial force of nail etc.'have been acquired and analysis.

• Steel and Composite Structures
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• v.3 no.3
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• pp.153-168
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• 2003

This paper discusses the results and observations from a large-scale fire test conducted on a slim floor system, comprising asymmetric beams, rectangular hollow section beams and a composite floor slab. The structure was subjected to a fire where the fire load (combustible material) was higher that that found in typical office buildings and the ventilation area was artificially controlled during the test. Although the fire behaviour was not realistic it was designed to follow as closely as possible the time-temperature response used in standard fire tests, which are used to assess individual structural members and forms the bases of current fire design methods. The presented test results are limited, due to the malfunction of the instrumentation measuring the atmosphere and member temperatures. The lack of test data hinders the presentation of definitive conclusions. However, the available data, together with observations from the test, provides for the first time a useful insight into the behaviour of the slim floor system in its entirety. Analysis of the test results show that the behaviour of the beam-to-column connections had a significant impact on the overall structural response of the system, particularly when the end-plate of one of the connections fractured, during the fire.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.298-304
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• 2024

In Korea, fires are the second most common type of disaster, causing large-scale damages. The installation of fire detectors is legislated to prevent fires and minimize damage. Conventional fire detectors have limitations in initial suppression of failures because they detect fires when large amounts of smoke and heat are generated. Additionally, frequent malfunctions in fire detectors may cause users to turn them off. To address these issues, recent studies focus on accurately detecting even small-scale fires using multi-sensor and deep-learning technologies. They also aim at quick fire detection and thermal decomposition using gas. However, these studies are not practical because they overlook the heavy computations involved. Therefore, we propose a fast and accurate fire detection system based on multi-sensor and deep-learning technologies. In addition, we propose a computation-reduction method for selecting sensors suitable for detection using the Pearson correlation coefficient. Specifically, we use a moving average to handle outliers and two-stage labeling to reduce false detections during preprocessing. Subsequently, a deep-learning model is selected as LSTM for analyzing the temporal sequence. Then, we analyze the data using a correlation analysis. Consequently, the model using a small data group with low correlation achieves an accuracy of 99.88% and a false detection rate of 0.12%.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.481-490
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• 2018

Recently, the spatiotemporal patterns of flood disasters have become more complex and unpredictable due to climate change. Flood hazard map including information on flood risk level has been widely used as an unstructured measure against flooding damages. In order to product a high-precision flood hazard map by combination of hydrologic and hydraulic modeling, huge digital information such as topography, geology, climate, landuse and various database related to social economic are required. However, in some areas, especially in developing countries, flood hazard mapping is difficult or impossible and its accuracy is insufficient because such data is lacking or inaccessible. Therefore, this study suggests a method to delineate large scale flood-prone area based on topographic factors produced by linear binary classifier and ROC (Receiver Operation Characteristics) using globally-available geographic data such as ASTER or SRTM. We applied the proposed methodology to five different countries: North Korea Bangladesh, Indonesia, Thailand and Myanmar. The results show that model performances on flood area detection ranges from 38% (Bangladesh) to 78% (Thailand). The flood-prone area detection based on the topographical factors has a great advantage in order to easily distinguish the large-scale inundation-potent area using only digital elevation model (DEM) for ungauged watersheds.

• Journal of Korea Water Resources Association
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• v.50 no.2
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• pp.75-87
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• 2017

In recent decades, extreme events have been significantly increased over the Korean Peninsula due to climate variability and climate change. The potential changes in hydrologic cycle associated with the extreme events increase uncertainty in water resources planning and designing. For these reasons, a reliable changing point analysis is generally required to better understand regime changes in hydrologic time series at watershed scale. In this study, a hierarchical changing point analysis approach that can apply in a watershed scale is developed by combining the existing changing point analysis method and hierarchical Bayesian method. The proposed model was applied to the selected stations that have annual rainfall data longer than 40 years. The results showed that the proposed model can quantitatively detect the shift in precipitation in the middle of 1990s and identify the increase in annual precipitation compared to the several decades prior to the 1990s. Finally, we explored the changes in precipitation and sea level pressure in the context of large-scale climate anomalies using reanalysis data, for a given change point. It was concluded that the identified large-scale patterns were substantially different from each other.

• Wind and Structures
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• v.13 no.4
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• pp.327-346
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• 2010

During the past decade, many electrical transmission tower structures have failed during downburst events. This study is a part of a research program aimed to understand the behaviour of transmission lines under such localized wind events. The present study focuses on the assessment of the dynamic behaviour of the line conductors under downburst loading. A non-linear numerical model, accounting for large deformations and the effect of pretension loading, is developed and used to predict the natural frequencies and mode shapes of conductors at various loading stages. A turbulence signal is extracted from a set of full-scale data. It is added to the mean component of the downburst wind field previously evaluated from a CFD analysis. Dynamic analysis is performed using various downburst configurations. The study reveals that the response is affected by the background component, while the resonant component turns to be negligible due large aerodynamic damping of the conductors.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1297-1309
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• 2016

In this paper, an efficient connected component labeling (CCL) method was proposed. The proposed method is based on GPU parallelism. The CCL is very important in various applications where images are analysed. However, the label of each pixel is dependent on the connectivity of adjacent pixels so that it is not very easy to be parallelized. In this paper, a GPU-based parallel CCL techniques were proposed and applied to the analysis of radar signal. Since the radar signals contains complex and large data, the efficiency of the algorithm is crucial when realtime analysis is required. The experimental results show the proposed method is efficient enough to be successfully applied to this application.

• Korean Journal of Breeding Science
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• v.43 no.2
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• pp.103-114
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• 2011

In recent years, genomic resources and information have accumulated at an ever increasing pace, in many plant species, through whole genome sequencing, large scale analysis of transcriptomes, DNA markers and functional studies of individual genes. Well-characterized species within key plant taxa, co-called "model systems", have played a pivotal role in nucleating the accumulation of genomic information and databases, thereby providing the basis for comparative genomic studies. In addition, recent advances to "Next Generation" sequencing technologies have propelled a new wave of genomics, enabling rapid, low cost analysis of numerous genomes, and the accumulation of genetic diversity data for large numbers of accessions within individual species. The resulting wealth of genomic information provides an opportunity to discern evolutionary processes that have impacted genome structure and the function of genes, using the tools of comparative analysis. Comparative genomics provides a platform to translate information from model species to crops, and to relate knowledge of genome function among crop species. Ultimately, the resulting knowledge will accelerate the development of more efficient breeding strategies through the identification of trait-associated orthologous genes and next generation functional gene-based markers.