• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1197-1210
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• 2022

Recently, users' complaints on drinking water quality are increasing according to emerging interest in the drinking water service issues such as pipe aging and various water quality accidents. In the case of drinking water quality complaints, not only the water pollution but also the inconvenience on the chlorine residual for disinfection are included, thus various efforts, such as rechlorination treatment, are being attempted in order to keep the chlorine concentration supplied evenly. In this research, for a more accurate water quality simulation of water distribution network, the water quality reaction coefficients were estimated, and an optimization method of chlorination/ rechlorination scheduling was proposed consideirng satisfaction of water quality standards and chlorine residual equalization. The proposed method was applied to a large-scale real water network, and various chlorination schemes were comparatively analyzed through the grid search algorithm and optimized based on the suitability and uniformity of supplied chlorine residual concentration.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.737-742
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• 2017

Recently, unconventional high-rise building shapes have attracted attention as a landmark of metropolitan cities and the search for innovative building forms in architecture is ongoing. In this study, $Isotruss^{(R)}$ grid(ITG) used in smaller scale structures was applied to building structural systems and its structural performance was examined. The structural behavior of an ITG was compared with that of a diagrid structure as a reference structure. The stiffness-based design method of the diagrid system was used for the preliminary design stage of member sizing in an ITG. The structural design of 16, 32, and 48-story buildings was carried out for the two systems with the same size. The angle of the inclined columns for ITG and diagrid was $59^{\circ}$ and $68.2^{\circ}$, respectively. The lateral stiffness, steel tonnage of the exterior frame, axial strength ratio, story drift ratio, and natural frequency of the two systems were compared. Based on the analysis result of 6 buildings, the two systems had similar structural capacity; 93.3% and 88.7% of the lateral load was carried by the perimeter frame in the ITG system and diagrid system, respectively. This suggests that the ITG system is better in arranging core columns. Therefore, the proposed ITG system has not only a unique façade, but also substantial structural capacity equivalent to the existing system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.321-335
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• 2023

Monitoring sediment loads in natural rivers is the key process in river engineering, but it is costly and dangerous. In practice, suspended loads are directly measured, and total loads, which is a summation of suspended loads and bed loads, are estimated. This study proposes a real-time sediment discharge monitoring system using the horizontal acoustic Doppler current profiler (H-ADCP) and support vector regression (SVR). The proposed system is comprised of the SVR model for suspended sediment concentration (SVR-SSC) and for total loads (SVR-Q_TL), respectively. SVR-SSC estimates SSC and SVR-Q_TL mimics the modified Einstein procedure. The grid search with K-fold cross validation (Grid-CV) and the recursive feature elimination (RFE) were employed to determine SVR's hyperparameters and input variables. The two SVR models showed reasonable cross-validation scores (R²) with 0.885 (SVR-SSC) and 0.860 (SVR-Q_TL). During the time-series sediment load monitoring period, we successfully detected various sediment transport phenomena in natural streams, such as hysteresis loops and sensitive sediment fluctuations. The newly proposed sediment monitoring system depends only on the gauged features by H-ADCP without additional assumptions in hydraulic variables (e.g., friction slope and suspended sediment size distribution). This method can be applied to any ADCP-installed discharge monitoring station economically and is expected to enhance temporal resolution in sediment monitoring.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.359-365
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• 2011

This study aims at filtering ALS points into ground and non-ground effectively through labeling and window based algorithm by utilizing 2D adjacency based on scan line. Firstly, points adjacency is constructed through minimal search based on scan line. Connected component labeling algorithm is applied to classify raw ALS points into ground and non-ground by utilizing the adjacency structure. Then, some small objects are removed by morphology filtering, and isolated ground points are restored by IDW estimation. The experimental results shows that the method provides good filtering performance( about 97% accuracy) for diverse sites, and the overall processing takes less time than converting raw data into TIN or raster grid.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.268-276
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• 2015

A ship's sailing route or plan is determined by the master as the decision maker of the vessel, and depends on the characteristics of the navigational environment and the conditions of the ship. The trajectory, which appears as a result of the ship's navigation, is monitored and stored by a Vessel Traffic Service center, and is used for an analysis of the ship's navigational pattern and risk assessment within a particular area. However, such an analysis is performed in the same manner, despite the different navigational environments between coastal areas and the harbor limits. The navigational environment within the harbor limits changes rapidly owing to construction of the port facilities, dredging operations, and so on. In this study, a support vector machine was used for processing and modeling the trajectory data. A K-fold cross-validation and a grid search were used for selecting the optimal parameters. A complicated traffic route similar to the circumstances of the harbor limits was constructed for a validation of the model. A group of vessels was composed, each vessel of which was given various speed and course changes along a specified route. As a result of the machine learning, the optimal route and voyage data model were obtained. Finally, the model was presented to Vessel Traffic Service operators to detect any anomalous vessel behaviors. Using the proposed data modeling method, we intend to support the decision-making of Vessel Traffic Service operators in terms of navigational patterns and their characteristics.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.151-161
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• 2009

In Location-Based Services (LBSs), users requesting a location-based query send their exact location to a database server and thus the location information of the users can be misused by adversaries. Therefore, a privacy protection method is required for using LBS in a safe way. In this paper, we propose a new cloaking region generation algorithm using grids for privacy protection in LBSs. The proposed algorithm creates a m inimum cloaking region by finding L buildings and then performs K-anonymity to search K users. For this, we make use of not only a grid-based index structure, but also an efficient pruning techniques. Finally, we show from a performance analysis that our cloaking region generation algorithm outperforms the existing algorithm in term of the size of cloaking region.

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.87-103
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• 2006

Advances in computer and network technology provide new computing environment that were only possible with supercomputers before. In order to provide the environment, a distributed runtime system has to be provided, but most of the conventional distributed runtime systems lack in providing dynamic and flexible system reconfiguration depending on workload variance, due to a static architecture of fixed master node and slave working nodes. This paper proposes and implements a new model for distributed job allocation and management which is a distributed runtime system is P2P environment for flexible and dynamic system reconfiguration. The implemented systems enables job program transfer and management, remote compile and execution among cooperative developers based on P2P standard protocol Jxta platform. Since it makes dynamic and flexible system reconfiguration possible, the proposed method has some advantages in that it can collect and utilize idle computing resources immediately at a needed time for distributed job processing. Moreover, the implemented system's effectiveness and performance increase are shown by applying and processing the crawler jobs, in a distributed way, for collecting a large amount of data needed for internet search.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.246-247
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• 2018

Since the routes within VTS areas include harbour limit of major ports, there are sections where the traffic volume increases and the routes are normally narrow according to the geographical conditions. In the case of ports and VTS areas located on the west coast of Korea, it is affected by strong current due to large tidal differences. In this paper, we propose a method to produce useful information according to the change of navigation environment by analyzing the characteristics of ship's movement according to tidal stream or current. The SVR seaway model, support vector regression, and grid search were conducted in order to extract models.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.