• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.34-50
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• 2023

Quantum error correction is a key technology for achieving fault-tolerant quantum computation. Finding the best decoding solution to a single error syndrome pattern counteracting multiple errors is an NP-hard problem. Consequently, error decoding is one of the most expensive processes to protect the information in a logical qubit. Recent research on quantum error decoding has been focused on developing conventional and neural-network-based decoding algorithms to satisfy accuracy, speed, and scalability requirements. Although conventional decoding methods have notably improved accuracy in short codes, they face many challenges regarding speed and scalability in long codes. To overcome such problems, machine learning has been extensively applied to neural-network-based error decoding with meaningful results. Nevertheless, when using neural-network-based decoders alone, the learning cost grows exponentially with the code size. To prevent this problem, hierarchical error decoding has been devised by combining conventional and neural-network-based decoders. In addition, research on quantum error decoding is aimed at reducing the spacetime decoding cost and solving the backlog problem caused by decoding delays when using hardware-implemented decoders in cryogenic environments. We review the latest research trends in decoders for quantum error correction with high accuracy, neural-network-based quantum error decoders with high speed and scalability, and hardware-based quantum error decoders implemented in real qubit operating environments.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.39-46
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• 2010

Sounding data is the essential source for the safety of ships navigation system, and fundamental to the reasonable usage and maintenance of the ocean as well. As IT tech, positioning equipment such as GPS and INS, echo sounder are developed, recently, the precise submarine topography database bas been built by Multi-Beam Echo Sounder. However, MBES data includes some inevitable error caused by several factor, and some data have errors where the terrain is wobble. The error, which causes the $moir\acute{e}$ pattern error is the main factor hindering the accuracy of MBES data results, and therefore it is necessary to figure out the main cause of the error for the improvement of the accuracy by removing error data. On this research, the main cause of the error data is studied by analyzing motion sensor value of data including the $moir\acute{e}$ pattern error. Thus, as the result of examination, it turns out that the $moir\acute{e}$ pattern error is related to the standard deviation of Roll, and error data values are results of the non-correspondence between Swath data and Roll values caused by the drastic change of Roll values. Accordingly, the error data is removed by comparing between the gradient of Swath data and Roll values. Finally, as the result of removing error data, it is expected to be able to estimate the quality of MBES using the standard deviation of Motion sensor's Roll value, and calculate the additive error factor, which minimize non-corresponding data, and also this research must be contributed to improve the accuracy of sounding for small vessels with lots of motion in the bad circumstance for navigation.

• The korean journal of orthodontics
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• v.52 no.4
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• pp.287-297
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• 2022

Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

• Journal of Environmental Science International
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• v.15 no.8
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• pp.775-784
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• 2006

In this study, CN value was estimated by using detailed soil map and land cover characteristic against upper basin of Kumho watermark located on the upper basin of Kumho river and the hydrologic morphological characteristic factors were extracted from the basin by using the DEM document. Also the runoff analysis was conducted by the WMS model in order to study how the assumed CN value affects the runoff characteristic. First of all, as a result of studying the soil type in this study area, mostly D type soil was Identified by the application of the 1987 classification criteria. However, by that in 1995, B type soil and C type soil were distributed more widely in that area. When CN value was classified by the 1995 classification criteria, it was estimated lower than in 1987, as a result of comparing the estimated CNs by those standars. Also it was assumed that CN value was underestimated when the plan for Geum-ho river maintenance was drawn up. As a result of the analysis of runoff characteristic, the pattern of generation of the classification criteria of soil groups appeared to be similar, but in the case of the application of the classification criteria in 1995, the peak rate of runoff was found to be smaller on the whole than in the case of the application of the classification criteria in 1987. Also when the statistical data such as the prediction errors, the mean squared errors, the coefficient of determination and other data emerging from the analysis, was looked over in total, it seemed appropriate to apply the 1995 classification criteria when hydrological soil classification group was applied. As the result of this study, however, the difference of the result of the statistical dat was somewhat small. In future study, it is necessary to follow up evidence about soil application On many more watersheds and in heavy rain.

• 한국해양학회지
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• v.29 no.3
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• pp.228-238
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• 1994

The change of sea-level is a good indicator of the change of climate during the Quaternary period. The sea-levels in the world have been changing very irregularly during that time. The pattern of the Quaternary sea-level change was assumed to be a stochastic fractal in this study. We measured fractal dimensions of the Holocene sea-levels of the Hudson river estuary and the Delaware coast. A box counting method gave almost the same values. i.e., D=1.358 for the Hudson sea-level changes and D+1.346 for the Delaware sea-level changes. the ability of the inverse method of fractal interposea-levels. IFIF reproduction the realistic sea-levels for the both of them. The delaware sea-level data made less statistical errors for the interpolation of IFIF than the Hudson and the Delaware sea-levels. IFIF reproduction the realistic sea-levels for the both of them. The Delaware sea-level data made less statistical errors for the interpolation of IFIF than the Hudson sea-level data. This suggests that the Delaware sea-level data are more reliable than the Hudson sea-level data was calculated from the fractal dimension of the Delaware sea-level data. Fractal interpolation functions (FIF) was used to reconstruct the peleosea-levels of the Korean coasts and the Atlantic Ocean coasts of the United States. The Korean Peleosea-level change generacted by FIF is different from the peleosea-level change of the eastern U.S.. The Korean peleosea-levels are much higher than the eastern U.S. Paleosea-levels, comparing to each other from the present to 8,000 BP.

• Spatial Information Research
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• v.16 no.3
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• pp.279-290
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• 2008

Landslide, one of the serious natural disasters, has Incurred a large loss of human and material resources. Recently, many forecasting or alarm systems based on various kinds of measuring equipment have been developed to reduce the damage of landslide. However, only a few of these equipments are guaranteed to evaluate the safety of whole side of land slope with their accessibility to the slope. In this study, we performed some experiments to evaluate the applicability of a terrestrial LiDAR as a surveying tool to measure the displacement of a land slope surface far a slope collapsing protection system. In the experiments, we had applied a slope stability method to a land slope and then forced to this slope with a load increasing step by step. In each step, we measured the slope surface with both a total station and a terrestrial LiDAR simultaneously. As the result of Slope Fracturing analysis using all targets, the LiDAR system showed that three was 1cm RMSE on X-axis, irregularity errors on Y-axis and few errors on Z-axis compare with Total Station. As the result of Slope Fracturing analysis using continuous targets, the pattern of Slope Fracturing was different according to the location of continuous targets and we could detect a continuous change which couldn't be found using Total station. The accuracy of the LiDAR data was evaluated to be comparable to that of the total station data. We found that a LiDAR system was appropriate to measuring the behaviour of land slope. The LiDAR data can cover the whole surface of the land slope, whereas the total station data are available on a small number of targets. Moreover, we extracted more detail information about the behavior of land slope such as the volume and profile changes using the LiDAR data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.94-105
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• 2020

Recently, most studies of 3D printing in construction have focused on the development of 3D printers and materials suitable for construction 3D printers. In comparison, there has been little research on design support tools that enable representative BIM data of building modeling tools to be applied to 3D printing. In addition, existing 3D printing slicing programs are commercialized around manufacturing, showing that they are unsuitable for construction 3D printing. Therefore, this research aims to develop a design support tool for 3D printing for buildings. The developed design support tool was validated based on arbitrary BIM data. Verification showed that wall pattern generation was modeled accurately without errors, and a calculation of the construction period showed that the formula presented in this study was valid. Furthermore, the maximum length of the mesh split was set to 100mm to minimize errors when converting to STL files.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.129-139
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• 2019

Mapping urban areas using the earth observation satellites is useful for monitoring urban expansions and measuring urban developments. In this research, the different thresholds for detecting the urban areas separately from the remote-sensing index images (normalized-difference built-up index(NDBI) and urban index(UI) images) generated from the Landsat-8 image acquired in Daegu, South Korea were evaluated through the following steps: (1) the NDBI and UI images were separately generated from the given Landsat-8 image; (2) the different thresholds (-0.4, -0.2, and 0) for detecting the urban areas separately from the NDBI and UI images were evaluated; and (3) the accuracy of each detected urban area was assessed. The experiment results showed that the threshold -0.2 had the best performance for detecting the urban areas from the NDBI image, while the threshold -0.4 had the best performance for detecting the urban areas from the UI image. Some misclassification errors, however, occurred in the areas where the bare soil areas were classified into urban areas or where the high-rise apartments were classified into other areas. In the future research, a robust methodology for detecting urban areas, including the various types of urban features, with less misclassification errors will be proposed using the satellite images. In addition, research on analyzing the pattern of urban expansion will be carried out using the urban areas detected from the multi-temporal satellite images.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.405-414
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• 2011

Korean grammar checkers typically detect context-dependent errors by employing heuristic rules that are manually formulated by a language expert. These rules are appended each time a new error pattern is detected. However, such grammar checkers are not consistent. In order to resolve this shortcoming, we propose new method for generalizing error decision rules to detect the above errors. For this purpose, we use an existing thesaurus KorLex, which is the Korean version of Princeton WordNet. KorLex has hierarchical word senses for nouns, but does not contain any information about the relationships between cases in a sentence. Through the Tree Cut Model and the MDL(minimum description length) model based on information theory, we extract noun classes from KorLex and generalize error decision rules from these noun classes. In order to verify the accuracy of the new method in an experiment, we extracted nouns used as an object of the four predicates usually confused from a large corpus, and subsequently extracted noun classes from these nouns. We found that the number of error decision rules generalized from these noun classes has decreased to about 64.8%. In conclusion, the precision of our grammar checker exceeds that of conventional ones by 6.2%.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.6
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• pp.1234-1240
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• 2009

This study was to compare the effectiveness and validity of various data-mining algorithm for Sasang type diagnostic test. We compared the sensitivity and specificity index of nine attribute selection and eleven class classification algorithms with 31 data-set characterizing Sasang typology and 10-fold validation methods installed in Waikato Environment Knowledge Analysis (WEKA). The highest classification validity score can be acquired as follows; 69.9 as Percentage Correctly Predicted index with Naive Bayes Classifier, 80 as sensitivity index with LWL/Tae-Eum type, 93.5 as specificity index with Naive Bayes Classifier/So-Eum type. The classification algorithm with highest PCP index of 69.62 after attribute selection was Naive Bayes Classifier. In this study we can find that the best-fit algorithm for traditional medicine is case sensitive and that characteristics of clinical circumstances, and data-mining algorithms and study purpose should be considered to get the highest validity even with the well defined data sets. It is also confirmed that we can't find one-fits-all algorithm and there should be many studies with trials and errors. This study will serve as a pivotal foundation for the development of medical instruments for Pattern Identification and Sasang type diagnosis on the basis of traditional Korean Medicine.