• Journal of Trauma and Injury
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• v.35 no.4
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• pp.240-247
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• 2022

Purpose: Studies on the relationship between obesity and injuries, especially those sustained in pedestrian traffic accidents, are lacking. We aimed to assess the effects of obesity on the severity of injury at the time of admission to the emergency room in patients who experienced pedestrian traffic accidents. Methods: This study included trauma patients registered in the Korean Trauma Database from July 1, 2018 to December 31, 2020, whose mechanism of injury was pedestrian traffic accidents and who were treated at a single institution. Those aged below 15 years were excluded. Patients were assigned to nonobese and obese groups based on a body mass index of 25 kg/m². An Injury Severity Score of 25 or greater was considered to indicate a critical injury. Results: In total, 679 cases of pedestrian traffic accidents were registered during the study period, and 543 patients were included in the final analysis. Of them, 360 patients (66.3%) and 183 patients (33.7%) were categorized as nonobese and obese, respectively. The median age was significantly higher in the nonobese group than in the obese group (60 vs. 58 years). Multivariate analysis demonstrated that the odds ratio for critical injury in obese patients was 1.59 (95% confidence interval, 1.01-2.48) compared with nonobese patients. Conclusions: Obesity affected the likelihood of sustaining severe injuries in pedestrian traffic accidents. Future studies should analyze the effects of body mass index on the pattern and severity of injuries in patients with more diverse injury mechanisms using large-scale data.

• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.5-17
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• 2023

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.453-458
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• 2024

Due to global issues such as climate crisis and rising energy costs, there is an increasing focus on energy conservation and management. In the case of South Korea, approximately 53.5% of the total energy consumption comes from industrial complexes. In order to address this, we aimed to improve issues through the 'Shared Network Utility Plant' among companies using similar energy utilities to find energy-saving points. For effective energy conservation, various techniques are utilized, and stable data supply is crucial for the reliable operation of factories. Many anomaly detection and alert systems for checking the stability of data supply were dependent on Energy Management Systems (EMS), which had limitations. The construction of an EMS involves large-scale systems, making it difficult to implement in small factories with spatial and energy constraints. In this paper, we aim to overcome these challenges by constructing a data collection system and anomaly detection alert system on embedded devices that consume minimal space and power. We explore the possibilities of utilizing anomaly detection alert systems in typical institutions for data collection and study the construction process.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.828-837
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• 2008

We propose a method to select representative views of single objects and classes of objects for 3D object retrieval and classification. Our method is based on projected 2D shapes, or views, of the 3D objects, where the representative views are selected by applying affinity propagation to cluster uniformly sampled views. Affinity propagation assigns prototypes to each cluster during the clustering process, thereby providing a natural criterion to select views. We recursively apply affinity propagation to the selected views of objects classified as single classes to obtain representative views of classes of objects. By enabling classification as well as retrieval, effective management of large scale databases for retrieval can be enhanced, since we can avoid exhaustive search over all objects by first classifying the object. We demonstrate the effectiveness of the proposed method for both retrieval and classification by experimental results based on the Princeton benchmark database [16].

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.7
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• pp.4243-4247
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• 2013

The relationship between the X-ray repair cross-complementing group 3 (XRCC3) Thr241Met polymorphism and gliomas remains inclusive or controversial. For better understanding of the effect of XRCC3 Thr241Met polymorphism on glioma risk, a meta-analysis was performed. All eligible studies were identified through a search of PubMed, Elsevier Science Direct, Excerpta Medica Database (Embase) and Chinese Biomedical Literature Database (CBM) before May 2013. The association between the XRCC3 Thr241Met polymorphism and gliomas risk was conducted by odds ratios (ORs) and 95% confidence intervals (95% CIs). A total of nine case-control studies including 3,533 cases and 4,696 controls were eventually collected. Overall, we found that XRCC3 Thr241Met polymorphism was significantly associated with the risk of gliomas (T vs. C: OR=1.10, 95%CI=1.01-1.20, P=0.034; TT vs. CC: OR=1.30, 95%CI=1.03-1.65, P=0.027; TT vs. TC/CC: OR=1.29, 95%CI=1.01-1.64, P=0.039). In the subgroup analysis based on ethnicity, the significant association was found in Asian under four models (T vs. C: OR=1.17, 95%CI=1.07-1.28, P=0.00; TT vs. CC: OR=1.79, 95%CI=1.36-2.36, P=0.00; TT vs. TC/CC: OR=1.75, 95%CI=1.32-2.32, P=0.00; TT/TC vs. CC: OR=1.11,95% CI=1.02-1.20). This meta-analysis suggested that the XRCC3 Thr241Met polymorphism is a risk factor for gliomas, especially for Asians. Considering the limited sample size and ethnicities included in the meta-analysis, further large scale and well-designed studies are needed to confirm our results.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.187-195
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• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.501-508
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• 2011

A knowledge map, which has been recently applied in various fields, is discovering characteristics hidden in a large amount of information and showing a tangible output to understand the meaning of the discovery. In this paper, we suggested a knowledge map for research trend analysis based on keyword-relation networks which are constructed by using a database of the domestic journal articles in the computer engineering field from 2000 through 2010. From that knowledge map, we could infer influential changes of a research topic related a specific keyword through examining the change of sizes of the connected components to which the keyword belongs in the keyword-relation networks. In addition, we observed that the size of the largest connected component in the keyword-relation networks is relatively small and groups of high-similarity keyword pairs are clustered in them by comparison with the random networks. This implies that the research field corresponding to the largest connected component is not so huge and many small-scale topics included in it are highly clustered and loosely-connected to each other. our proposed knowledge map can be considered as a approach for the research trend analysis while it is impossible to obtain those results by conventional approaches such as analyzing the frequency of an individual keyword.

• Journal of Korean Society for Geospatial Information Science
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• v.1 no.1 s.1
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• pp.141-152
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• 1993

This study aims to generate digital elevation model from digital satellite imagery. Digital elevation model is being increasingly used for geo-spatial information system database development and for digital map production. Image matching technique was applied to acquire conjugate image coordinates and the algorithm for digital elevation model generation is presented in this study The exterior orientation parameters of the satellite imagery is determined by bundle adjustment and standard correlation was applied for image matching conjugate of image points. The window as well as the searching area have to be defined in image matching. Different sizes of searching area were tested to study the appropriate size of the searching area. Various coordinate transformation methods were applied to improve the computation speed as well as the geometric accuracy. The results were then statistically analysed after which the searching area is determined with the safety factor. To evaluate the accuracy of digital elevation model, 3-D coordinates were extracted from 1/5000 scale topographic map and this was compared to the digital elevation model generated from satellite imagery. The algorithm for generation of digital elevation model generated from satellite imagery is presented in this study which will prove effective in the database development of geo-spatial information system and in digital elevation modelling of large areas.

• Journal of the Korean Geotechnical Society
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• v.26 no.3
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• pp.59-68
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• 2010

Systematic risk management is necessary in grand scaled urban construction because of the existence of complicated and various risk factors. Problems of obstructions, adjacent structures, safety, environment, traffic and geotechnical properties need to be solved because urban construction is progressed in limited space not as general earthwork. Therefore the establishment of special risk management system is necessary to manage not only geotechnical properties but also social and cultural uncertainties. This research presents the technique analysis by the current state of risk management technique. Risk factors were noticed and the importance of each factor was estimated through survey. The systemically categorized database was established. Risk extraction module, matrix and score module were developed based on the database. Expected construction budget and time distribution can be computed by Monte Carlo analysis of probabilities and influences. Construction budgets and time distributions of before and after response can be compared and analyzed 80 the risks are manageable for entire whole construction time. This system will be the foundation of standardization and integration. Procurement, efficiency improvement, effective time and resource management are available through integrated management technique development and application. Conclusively decrease in cost and time is expected by systemization of project management.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.