• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.09a
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• pp.27-30
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• 2001

Flow and transport at fracture intersections, and their effects on network scale transport, are investigated in three-dimensional random fracture networks. Fracture intersection mixing rules complete mixing and streamline routing are defined in terms of fluxes normal to the intersection line between two fractures. By analyzing flow statistics and particle transfer probabilities distributed along fracture intersections, it is shown that for various network structures with power law size distributions of fractures, the choice of intersection mixing rule makes comparatively little difference in the overall simulated solute migration patterns. The occurrence and effects of local flows around an intersection (local flow cells) are emphasized. Transport simulations at fracture intersections indicate that local flow circulations can arise from variability within the hydraulic head distribution along intersections, and from the internal no flow condition along fracture boundaries. These local flow cells act as an effective mechanism to enhance the nondiffusive breakthrough tailing often observed in discrete fracture networks. It is shown that such non-Fickian (anomalous) solute transport can be accounted for by considering only advective transport, in the framework of a continuous time random walk model. To clarify the effect of forest environmental changes (forest type difference and clearcut) on water storage capacity in soil and stream flow, watershed had been investigated.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.459-468
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• 2008

A data stream which is a massive unbounded sequence of data elements continuously generated at a rapid rate. Many recent research activities for emerging applications often need to deal with the data stream. Such applications can be web click monitoring, sensor data processing, network traffic analysis. telephone records and multi-media data. For this. data processing over a data stream are not performed on the stored data but performed the newly updated data with pre-registered queries, and then return a result immediately or periodically. Recently, many studies are focused on dealing with a data stream more than a stored data set. Especially. there are many researches to optimize continuous queries in order to perform them efficiently. This paper proposes a query optimization algorithm to manage continuous query which has multiple join operators(Multi-way join) over data streams. It is called by an Extended Greedy query optimization based on a greedy algorithm. It defines a join cost by a required operation to compute a join and an operation to process a result and then stores all information for computing join cost and join cost in the statistics catalog. To overcome a weak point of greedy algorithm which has poor performance, the algorithm selects the set of operators with a small lay, instead of operator with the smallest cost. The set is influenced the accuracy and execution time of the algorithm and can be controlled adaptively by two user-defined values. Experiment results illustrate the performance of the EGA algorithm in various stream environments.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.4
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• pp.430-436
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• 2014

This paper relates to the study about the street security light management system. The purpose of the wireless remote management system is to manage street security lights efficiently. The system is composed of three components like light controller, CDMA gateway and web based remote management server. The zigbee solution is adopted to make local wireless network between street security lights. The CDMA network is used for the wireless communication between street security light controller and the remote control center. The gateway to interconnect zigbee network and CDMA was designed with low power 32 bits Cortex M3 micro-controller. For the data communication between the management server and the gateway, SMS and socket based TCP streaming is used. The management server sends SMS to the gateway to deliver light control and management requests, and the gateway replies with the light controllers report via TCP streaming. By using both SMS and TCP streaming communication, it was verified that simple cost effective management is possible for street security lights. We tried real test for 95 street security lights in real environment during two months and analyzed the practical possibility for mass supply.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.181-187
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• 2018

With a view to deepening the research stream on the factors positively influencing the export growth and the employment increase of South Korean small and medium companies, this research has developed the research model about the impact of small and medium companies' external technology R&D collaboration network heterogeneity on their export growth and employment increase, and has empirically tested the research model with the 2,200 data collected from the small and medium companies in the South Korea. This study illuminates the two points from the empirical testing results. One point is that small and medium companies' external technology R&D collaboration network heterogeneity significantly and positively influences their employment increase from technology development. The other point is that small and medium companies' export growth from technology R&D partially mediates the impact of the external technology R&D collaboration network heterogeneity on their employment increase from technology R&D.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.79-95
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• 2016

In order to improve the Quality-of-Service (QoS) of latency sensitive applications in next-generation cellular networks, multi-path is adopted to transmit packet stream in real-time to achieve high-quality video transmission in heterogeneous wireless networks. However, multi-path also introduces two important challenges: out-of-order issue and reordering delay. In this paper, we propose a new architecture based on Software Defined Network (SDN) for flow aggregation and flow splitting, and then design a Multiple Access Radio Scheduling (MARS) scheme based on relative Round-Trip Time (RTT) measurement. The QoS metrics including end-to-end delay, throughput and the packet out-of-order problem at the receiver have been investigated using the extensive simulation experiments. The performance results show that this SDN architecture coupled with the proposed MARS scheme can reduce the end-to-end delay and the reordering delay time caused by packet out-of-order as well as achieve a better throughput than the existing SMOS and Round-Robin algorithms.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.4
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• pp.293-298
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• 2004

To satisfy the requirements for QoS and acceptance ratio of the users using multimedia content service, it is required to control mechanism for QoS assurance and allocation of the stream server' resources based on CoS(Class of Service). To compare performance of the algorithm, we have classified the user by two classes (super class, base class) and control the acceptance ratio of user's requests by user's class information. We have experimented the test of network resources and test of processing time under server/client environment and agent environment. MA-URFA based on agent increases the acceptance ratio of super class and utilization ratio of network resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.500-521
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• 2021

Echocardiography, an ultrasound scan of the heart, is regarded as the primary physiological test for heart disease diagnoses. How an echocardiogram is interpreted also relies intensively on the determination of the view. Some of such views are identified as standard views because of the presentation and ease of the evaluations of the major cardiac structures of them. However, finding valid cardiac views has traditionally been time-consuming, and a laborious process because medical imaging is interpreted manually by the specialist. Therefore, this study aims to speed up the diagnosis process and reduce diagnostic error by providing an automated identification of standard cardiac views based on deep learning technology. More importantly, based on a brand-new echocardiogram dataset of the Asian race, our research considers and assesses some new neural network architectures driven by action recognition in video. Finally, the research concludes and verifies that these methods aggregating dynamic information will receive a stronger classification effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3599-3619
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• 2019

In human activity recognition system both static and motion information play crucial role for efficient and competitive results. Most of the existing methods are insufficient to extract video features and unable to investigate the level of contribution of both (Static and Motion) components. Our work highlights this problem and proposes Static-Motion fused features descriptor (SMFD), which intelligently leverages both static and motion features in the form of descriptor. First, static features are learned by two-stream 3D convolutional neural network. Second, trajectories are extracted by tracking key points and only those trajectories have been selected which are located in central region of the original video frame in order to to reduce irrelevant background trajectories as well computational complexity. Then, shape and motion descriptors are obtained along with key points by using SIFT flow. Next, cholesky transformation is introduced to fuse static and motion feature vectors to guarantee the equal contribution of all descriptors. Finally, Long Short-Term Memory (LSTM) network is utilized to discover long-term temporal dependencies and final prediction. To confirm the effectiveness of the proposed approach, extensive experiments have been conducted on three well-known datasets i.e. UCF101, HMDB51 and YouTube. Findings shows that the resulting recognition system is on par with state-of-the-art methods.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.44.1-44.1
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• 2015

We set up a project to make spectrometers for single dish observations of the Korean VLBI Network (KVN), a new future multi-beam receiver of the ASTE (Atacama Submillimeter Telescope Experiment), and the total power (TP) antennas of the Atacama Large Millimeter/submillimeter Array (ALMA). Traditionally, spectrometers based on ASIC (Application-Specific Integrated circuit) and FPGA (Field-Programmable Gate Array) have been used in radio astronomy. It is, however, that a Graphics Processing Unit (GPU) technology is now viable for spectrometers due to the rapid improvement of its performance. A high-resolution spectrometer should have the following functions: poly-phase filter, data-bit conversion, fast Fourier transform, and complex multiplication. We wrote a program based on CUDA (Compute Unified Device Architecture) for a GPU spectrometer. We measured its performance using two GPU cards, Titan X and K40m, from NVIDIA. A non-optimized GPU code can process a data stream of around 2 GHz bandwidth, which is enough for the KVN spectrometer and promising for the ASTE and ALMA TP spectrometers.