• Journal of Environmental Science International
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• v.22 no.5
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• pp.543-554
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• 2013

Acoustic Doppler Current Profilers (ADCPs) are increasingly popular in the river research and management communities being primarily used for estimation of stream flows. ADCPs capabilities, however, entail additional features that are not fully explored, such as morphological representation of river or reservoir bed based upon multi-beam depth measurements. In addition to flow velocity, ADCP measurements include river bathymetry information through the depth measurements acquired in individual 4 or 5 beams with a given oblique angle. Such sounding capability indicates that multi-beam ADCPs can be utilized as an efficient depth-sounder to be more capable than the conventional single-beam eco-sounders. The paper introduces the post-processing algorithms required to deal with raw ADCP bathymetry measurements including the following aspects: a) correcting the individual beam depths for tilt (pitch and roll); b) filtering outliers using SMART filters; d) transforming the corrected depths into geographical coordinates by UTM conversion; and, e) tag the beam detecting locations with the concurrent GPS information; f) spatial representation in a GIS package. The developed algorithms are applied for the ADCP bathymetric dataset acquired from Han-Cheon in Jeju Island to validate themselves applicability.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1166-1169
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• 2011

In this paper, we design a Circle dot Code, In our scheme, we design a dot patterns for increasing maximum capacity and also for increasing robustness to Affine Transformation. Our code Can be extended according number of data circle. We use three data circle vision code. In this type code, after acquiring camera images for the Circle dot Codes, and perform error correction decoding using four position symbols and six CRC symbols. We perform graph based dot code analysis which determines the topological distance between dot pixels. Our code can be bridged the real world and ubiquitous computing environment.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.2
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• pp.97-104
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• 2018

In this study, a structure satisfying the fatigue constraint is designed by applying the topology optimization based on the phase field design method. In order to predict life based on the stress value, high cycle fatigue failure theory in which stress acts within the range of elastic limit is discussed and three fatigue theories of modified-Goodman, Smith-Watson-Topper and Gerber theory are applied. To calculate the global maximum stress, a modified P-norm stress correction method is used. As a result, it is possible to obtain topology optimization results that minimize the volume while satisfying the fatigue constraints. By applying the phase field design method, a simple shape with a minimized gray scale was obtained, and the maximum stress value acting on the optimization result became very close to the allowable stress value due to the modified P-norm stress method. While previous studies does not consider the stress correction factor, this study proposes the determination method regarding the stress correction factor considering loading effects related to axial stress components.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.275-279
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• 2004

This study was done to detect the topographic and terrain change of the vicinity of the west coast. To make the basic map of the change in topology and terrain, the mosaic images were made using the images from the satellite, which were given the geometric correction based on the GCP (Ground Control Point) and DEM (Digital Elenation Model) data. The accuracy of the images was examined by .empaling them with CCP through 1:25,000's digital map. After that, among the resultant images of the 1970s and 2000s, those of Sihwa, Hwaong and Ansan, the lands reclaimed by drainage were compared to observe the change in the area. From this study, the accuracy of the images of the west coast from satellite could be acquired and the change of the topology and terrain was detected effectively. From the results, it was known that, in case of the land the topological change was not so big due to the development in the reclaimed land or the bare land. In Sihwa, the size of the land was increased 180 $\textrm{km}^2$ and that of the seashore was decreased 110 km. in Hwaong the size was increased 50 $\textrm{km}^2$ and in Ansan the city space was increased 71 $\textrm{km}^2$ due to the formation of the industrial complex.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1388-1391
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• 2010

In this paper, we present a design and implementation of U-learning system using pen based augmented reality approach. Student has been given a smart pen and a smart study book, which is similar to the printed material already serviced. However, we print the study book using CMY inks, and embed perceptually invisible dot patterns using K ink. Smart pen includes (1) IR LED for illumination, IR pass filter for extracting the dot patterns, and (3) camera for image captures. From the image sequences, we perform topology analysis which determines the topological distance between dot pixels, and perform error correction decoding using four position symbols and five CRC symbols. When a student touches a smart study books with our smart pen, we show him/her multimedia (visual/audio) information which is exactly related with the selected region. Our scheme can embed 16 bit information, which is more than 200% larger than previous scheme, which supports 7 bits or 8 bits information.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.