• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1007-1021
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• 2022

Autonomous driving of unmanned aerial vehicles may have to pay expensive cost to create and switch new routes if unexpected obstacles exist or local map updates occured by the control system due to incorrect route information. Integrating digital twins into the path-following process requires more computing resources to quickly switch the wrong path to an alternative path, but it can quickly update the path during flight. In this study, we design a DEVS-based simulation environment which can modify optimized paths through short-term simulation of multi-virtual UAVs for applying digital twin concepts to path follow. Through simulation, we confirmed the possibility of increasing the mission stability of UAV.

• Kyungpook Mathematical Journal
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• v.63 no.1
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• pp.45-60
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• 2023

In this paper we suggest an enhanced Geraghty-type contractive mapping for examining the existence properties of classical nonlinear operators with or without prior degenerates. The nonlinear operators are proved to exist with the imposition of the Geraghty-type condition in a non-empty closed subset of complete metric spaces. To showcase some efficacies of the Geraghty-type condition, convergent rate and stability are deduced. The results are used to study some asymptotic properties of perturbed integral and hypergeometric operators. The results also extend and generalize some existing Geraghty-type conditions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.8
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• pp.632-638
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• 2013

This paper describes on the flame stability and combustion instability of coal derived synthetic gas especially for gases of Buggenum IGCC in Netherlands and Taean IGCC in Korea. These combustion characteristics were observed by conducting ambient-pressure elevated-temperature combustion tests in GE7EA model combustor when varying heat input and nitrogen dilution ratio. Flame stability map is plotted according to the flame structure by dividing all regimes into six, and only regime I and II are identified to be stable. Both syngases of Taean and Buggenum with nitrogen integration corresponds to regime II in which syngas burnt stably and flame coupled with outer recirculation flow. Stable regime of Buggenum is larger than that of Taean when considering only $H_2$/CO ratio due to higher content of hydrogen. However, when considering nitrogen dilution, syngas of Taean is burnt more stably than that of Buggenum since more nitrogen in Buggenum has negative effect on the stability of flame.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of the Korean Geosynthetics Society
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• v.18 no.4
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• pp.225-242
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• 2019

Rockfall failure at the access road to Seokguram were occurred due to the earthquake on September 12, 2016. A detailed investigation was carried out in order to find out the cause of the rockfall, to identify the risk of the entire sites, and to prepare proper countermeasure methods and mitigation. We checked for geological and topographical characteristics of overall slopes alongside the access road to Seokguram and made a face map. In addition, we analyzed topographical factors caused by the earthquake through calculating a degree of slope, degree of bearing, upslope contributing area, and wetness index with the use of shading relief map. As a result, we confirmed that the large rockfall occurred with a weak section. In this study, we also evaluated the overall slope stability of the entire access road to Seokguram in order to classify it into danger and caution zones depending on the risk of collapse.

• Journal of the Korean Society of Combustion
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• v.17 no.1
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• pp.48-57
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• 2012

Laminar lifted propane coflow-jet flames diluted with nitrogen were experimentally investigated to determine heat-loss-related self-excitation regimes in the flame stability map and elucidate the individual flame characteristics. There exists a critical lift-off height over which flame-stabilizing effect becomes minor, thereby causing a normal heat-loss-induced self-excitation with O(0.01 Hz). Air-coflowing can suppress the normal heat-loss-induced self-excitation through increase of a Peclet number; meanwhile it can enhance the normal heat-lossinduced self-excitation through reducing fuel concentration gradient and thereby decreasing the reaction rate of trailing diffusion flame. Below the critical lift-off height. the effect of flame stabilization is superior, leading to a coflow-modulated heat-loss-induced self-excitation with O(0.001 Hz). Over the critical lift-off height, the effect of reducing fuel concentration gradient is pronounced, so that the normal heat-loss-induced self-excitation is restored. A newly found prompt self-excitation, observed prior to a heat-loss-induced flame blowout, is discussed. Heat-loss-related self-excitations, obtained laminar lifted propane coflow-jet flames diluted with nitrogen, were characterized by the functional dependency of Strouhal number on related parameters. The critical lift-off height was also reasonably characterized by Peclet number and fuel mole fraction.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.946-951
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• 2004

ITIS is applied to the several tunnel construction sites in Korea. Tunnel construction properties which are acquired from these sites are transferred to information management server(SQL 2000 server)by client application program in real time. Access permission to DB server depends on the user's roles. Some functions which cannot be embodied in SQL Server are serviced through XML and GMS server is used for spatial data based on GIS part. This system is supposed to give engineers the advantages which are not only easy handling of the program and computerized documentation on every information during construction but also analyzing the acquired data in order to predict the structure of ground and rock mass to be excavated later and show the guideline of construction. Neung-Dong tunnel and Mu-Gua express way tunnel are now under construction and with this system they have 3D visualized map of the geology and tunnel geometry and accumulate database of construction information such as tunnel face mapping results, special notes and pictures of construction and 3D monitoring data, all matters on the stability of rock bolts and shotcrete, and so on. Ground settlement prediction program included in ITIS, based on the artificial neural network(ANN) and supported by GIS technology is applying to the subway tunnel. This prediction tool can make it possible to visualize the ground settlement according to the excavation procedures by contouring the calculated result on 3D GIS map and to assess the damage of buildings in the vicinity of construction site caused by ground settlement.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.33-40
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• 2010

The hand over between different radio access networks, especially where their coverage overlaps, suffers various complications since the different access networks provide different service characteristics. In this paper, it is about the system which can control the wireless LAN network management system and the wireless LAN access point of variety vendor which is in different areas. Successful development of mobile sink wireless LAN network management system which supports multi-vendor devices and have center-local interoperability structure. In proposed system, speed of the mobile station give about 10% improvement when the map domain size is 5 than the existing system. Through a data plane and management system of message way, it give a stability system and consistency network.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.12
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• pp.1483-1487
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• 2013

Water injection pump outputs oil with high pressure during this process, seawater is injected into the well to recover the well pressure and maintain high productivity. A water injection pump has high productivity, and therefore, it serves as a key piece of equipment in marine plants. In this light, water injection pumps are being studied widely in industry. In this study, the rotor dynamics is analyzed to determine the natural frequency according to the bearing stiffness and operation speed change. This study aims to establish the pump reliability through critical speed, stability, and unbalance response analysis.