• Ecology and Resilient Infrastructure
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• v.3 no.4
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• pp.247-255
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• 2016

Non-point pollutants from surface runoff during rainfall exert adverse effects on urban river water quality management. In particular, the first flush effect during the initial phase of rainfall can deliver significant amounts of pollutant loads to surface waters with extremely high concentrations. In this study, a sustainable first flush effect management system was developed by using settling and filtration that require no additional power or chemicals. A pilot scale experiment has shown that the removal of total suspended solid (TSS), total nitrogen (TN) and total phosphorus (TP) are in ranges of 84 - 95%, 31 - 46%, and 42 - 86%, respectively. An Integrated Stormwater Runoff Management System (ISTORMS) was also developed to efficiently manage the developed system by linking weather forecast, flow rate and water quality modeling of surface runoff and automatic monitoring systems in fields and in the system. This study can provide effective solutions for the management of urban river in terms of both quantity and quality.

• Korean Journal of Construction Engineering and Management
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• v.18 no.3
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• pp.63-73
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• 2017

In contemporary society, it is increasingly common to spend more time indoors. As such, there is a continually growing desire to build comfortable and safe indoor environments. Along with this trend, however, there are some serious indoor-environment challenges, such as the quality of indoor air and Sick House Syndrome. To address these concerns the government implements various systems to supervise and manage indoor environments. For example, green building certification is now compulsory for public buildings. There are three categories of green building certification related to indoor air in Korea: Health-Friendly Housing Construction Standards, Green Standard for Energy & Environmental Design(G-SEED), and Indoor Air Certification. The first two types of certification, Health-Friendly Housing Construction Standards and G-SEED, evaluate data in a drawing plan. In comparison, the Indoor Air Certification evaluates measured data. The certification using data from a drawing requires a considerable amount of time compared to other work. A 2D tool needs to be employed to measure the area manually. Thus, this study proposes an automatic assessment process using a Building Information Modeling(BIM) model based on 3D data. This process, using open source Industry Foundation Classes(IFC), exports data for the certification system, and extracts the data to create an Excel sheet for the certification. This is expected to improve the work process and reduce the workload associated with evaluating indoor air conditions.

• Phonetics and Speech Sciences
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• v.12 no.2
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• pp.29-37
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• 2020

Recently, the neural network-based deep learning algorithm has dramatically improved performance compared to the classical Gaussian mixture model based hidden Markov model (GMM-HMM) automatic speech recognition (ASR) system. In addition, researches on end-to-end (E2E) speech recognition systems integrating language modeling and decoding processes have been actively conducted to better utilize the advantages of deep learning techniques. In general, E2E ASR systems consist of multiple layers of encoder-decoder structure with attention. Therefore, E2E ASR systems require data with a large amount of speech-text paired data in order to achieve good performance. Obtaining speech-text paired data requires a lot of human labor and time, and is a high barrier to building E2E ASR system. Therefore, there are previous studies that improve the performance of E2E ASR system using relatively small amount of speech-text paired data, but most studies have been conducted by using only speech-only data or text-only data. In this study, we proposed a semi-supervised training method that enables E2E ASR system to perform well in corpus in different domains by using both speech or text only data. The proposed method works effectively by adapting to different domains, showing good performance in the target domain and not degrading much in the source domain.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.431-447
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• 2021

Recently, the use of high-resolution satellites is increasing in many areas. In order to supply useful satellite images stably, it is necessary to establish automatic precision geometric correction technic. Geometric correction is the process that corrected geometric errors of satellite imagery based on the GCP (Ground Control Point), which is correspondence point between accurate ground coordinates and image coordinates. Therefore, in the automatic geometric correction process, it is the key to acquire high-quality GCPs automatically. In this paper, we proposed iterative precision geometry correction method. we constructed an image pyramid and repeatedly performed GCP chip matching, outlier detection, and precision sensor modeling in each layer of the image pyramid. Through this method, we were able to acquire high-quality GCPs automatically. we then improved the performance of geometric correction of high-resolution satellite images. To analyze the performance of the proposed method, we used KOMPSAT-3 and 3A Level 1R 8 scenes. As a result of the experiment, the proposed method showed the geometric correction accuracy of 1.5 pixels on average and a maximum of 2 pixels.

• Tunnel and Underground Space
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• v.34 no.5
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• pp.461-476
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• 2024

As the paradigm of urban development has recently changed to development of underground space, the road tunnels and railway tunnels are increasing to relieve traffic congestion. This technical notes is related to the development of underground spaces using NATM (New Austrian Tunneling Method). Limitations of conventional 2D blasting pattern design method were analyzed, and BIM-based automatic design method was developed to overcome them. Since it was developed to facilitate modeling of all safety facilities along a alignment using coordinates and GIS data, it can overcome the limitations of the number of safety facilities that can be considered and time required for conventional design. In the conventional design, the results of borehole test blasting were used to predict the blasting impact. However, the developed technology is possible to recalculate by applying the measurement results obtained from actual tunnel blasting, enabling rapid re-evaluation of the blasting impact on all safety facilities during construction, leading economical design. As a result of applying it to GTX-A5 and 6 sites, it took about 5 minutes, which is 1/480 compared to the conventional design method. In addition, the construction cost was reduced by about 8 billion won/km and the period was reduced by about 41 days/km. It is expected to be used as technical basis for calculating the optimal blasting pattern in the BIM-based design and construction management process.

• Journal of KIISE
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• v.43 no.3
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• pp.370-379
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• 2016

Recently personal media were produced in a variety of ways as a lot of smart devices have been spread and services using these data have been desired. Therefore, research has been actively conducted for the media analysis and recognition technology and we can recognize the meaningful object from the media. The system using the media ontology has the disadvantage that can't classify the media appearing in the video because of the use of a video title, tags, and script information. In this paper, we propose a system to automatically classify video using the objects shown in the media data. To do this, we use a description logic-based reasoning and a rule-based inference for event processing which may vary in order. Description logic-based reasoning system proposed in this paper represents the relation of the objects in the media as activity ontology. We describe how to another rule-based reasoning system defines an event according to the order of the inference activity and order based reasoning system automatically classify the appropriate event to the category. To evaluate the efficiency of the proposed approach, we conducted an experiment using the media data classified as a valid category by the analysis of the Youtube video.

• Journal of Korea Spatial Information System Society
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• v.1 no.1 s.1
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• pp.5-18
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• 1999

Recently, the necessity of automatic knowledge extraction from spatial data stored in spatial databases has been increased. Spatial data mining can be defined as the extraction of implicit knowledge, spatial relationships, or other knowledge not explicitly stored in spatial databases. In order to extract useful knowledge from spatial data, an object management system that can store spatial data efficiently, provide very fast indexing & searching mechanisms, and support a distributed computing environment is needed. In this paper, we designed and implemented an open object management system for spatial data mining, that supports efficient management of spatial, aspatial, and knowledge data. In order to develop this system, we used Open OODB that is a widely used object management system. However, the lark of facilities for spatial data mining in Open OODB, we extended it to support spatial data type, dynamic class generation, object-oriented inheritance, spatial index, spatial operations, etc. In addition, for further increasement of interoperability with other spatial database management systems or data mining systems, we adopted international standards such as ODMG 2.0 for data modeling, SDTS(Spatial Data Transfer Standard) for modeling and exchanging spatial data, and OpenGIS Simple Features Specification for CORBA for connecting clients and servers efficiently.

• Journal of the Korean association of regional geographers
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• v.17 no.4
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• pp.432-442
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• 2011

Climatic variables such as temperature and precipitation tend to vary both in space and in time simultaneously. Thus, it is necessary to include space-time autocorrelation into conventional spatial interpolation methods for reliable time-series mapping. This paper introduces and applies space-time variogram modeling and space-time kriging to generate time-series temperature maps using hourly Automatic Weather System(AWS) temperature observation data for a one-month period. First, temperature observation data are decomposed into deterministic trend and stochastic residual components. For trend component modeling, elevation data which have reasonable correlation with temperature are used as secondary information to generate trend component with topographic effects. Then, space-time variograms of residual components are estimated and modelled by using a product-sum space-time variogram model to account for not only autocorrelation both in space and in time, but also their interactions. From a case study, space-time kriging outperforms both conventional space only ordinary kriging and regression-kriging, which indicates the importance of using space-time autocorrelation information as well as elevation data. It is expected that space-time kriging would be a useful tool when a space-poor but time-rich dataset is analyzed.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.10
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• pp.7-16
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• 2007

In this paper, we introduce a new design environment for efficient multiprocessor system-on-chip design space exploration. The design environment takes a process network model as input system specification. The process network model has been widely used for modeling signal processing applications because of its excellent modeling power. However, it has limitation in predictability, which could cause severe problem for real time systems. This paper proposes a new approach that enables static analysis of a process network model by converting it to a hierarchical synchronous dataflow model. For efficient design space exploration in the early design step, mapping application to target architectures has been a crucial part for finding better solution. In this paper, we propose an efficient mapping algorithm. Our mapping algorithm supports both single bus architecture and multiple bus architecture. In the experiments, we show that the automatic conversion approach of the process network model for static analysis is performed successfully for several signal processing applications, and show the effectiveness of our mapping algorithm by comparing it with previous approaches.

• Journal of radiological science and technology
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• v.35 no.2
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• pp.133-139
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• 2012

For the examination of nuclear medicine, myocardial scan is a good method to evaluate a hemodynamic importance of coronary heart disease. but, the automatized qualitative measurement is additionally necessary to improve the decoding efficiency. we suggests the creation of cardiac three-dimensional model and model of three-dimensional cardiac thickness as a new measurement. For the experiment, cardiac reduced cross section was obtained from SPECT. Next, the pre-process was performed and image segmentation was fulfilled by level set. for the modeling of left cardiac thickness, it was realized by applying difference equation of two-dimensional laplace equation. As the result of experiment, it was successful to measure internal wall and external wall and three-dimensional modeling was realized by coordinate. and, with laplace formula, it was successful to develop the thickness of cardiac wall. through the three-dimensional model, defects were observed easily and position of lesion was grasped rapidly by the revolution of model. The model which was developed as the support index of decoding will provide decoding information to doctor additionally and reduce the rate of false diagnosis as well as play a great role for diagnosing IHD early.