• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.10 no.4
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• pp.483-494
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• 1998

This study describes the characteristics on impeller discharge flow of a centrifugal compressor with vaneless diffuser. Distorted flow at impeller exit was investigated by measuring of unsteady velocity fluctuation using hot-wire anemometer. As a result, a wake region appears near shroud side and moves to suction side and also to hub side as flow rate decreases. Jet, wake, and their boundary region which can be defined in jet-wake flow model are clearly observed at a high flow rate for the flow coefficient of 0.64, however, as flow rate decreases to the flow coefficient of 0.19, the classification of their regions disappears. Turbulence intensity also increases as flow rate decreases. Measurement error from uncertainty analysis is estimated about 4% at the flow coefficient of 0.19

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2191-2201
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• 2012

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.852-855
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• 2003

This paper reports the RFC(Recursive Flow Classification) algorithm that is available on multiple fields. It is easy to be implemented by both software and hardware. For high speed classification of packets, the implementation of RFC is essential by hardware. Hence, in this paper, RFC algorithm is simulated by Verilog-HDL, and it verify the efficiency of the algorithm. The result shows that the algorithm can perform a packet classification within several cycles. It is not only much faster than software implementation but also enough to support OC192c.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2261-2280
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• 2014

Recently, network traffic has become more complex and diverse due to the emergence of new applications and services. Therefore, the importance of application-level traffic classification is increasing rapidly, and it has become a very popular research area. Although a lot of methods for traffic classification have been introduced in literature, they have some limitations to achieve an acceptable level of performance in real-time application-level traffic classification. In this paper, we propose a novel application-level traffic classification method using payload size sequence (PSS) signature. The proposed method generates unique PSS signatures for each application using packet order, direction and payload size of the first N packets in a flow, and uses them to classify application traffic. The evaluation shows that this method can classify application traffic easily and quickly with high accuracy rates, over 99.97%. Furthermore, the method can also classify application traffic that uses the same application protocol or is encrypted.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.3
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• pp.287-294
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• 2007

When we use the circular pipes for wastewater and storm water, we should be known the characteristics of the flow for accurate design. To elevate the design accuracy, we want to know the profile of flow. The roughness coefficient in the Manning equation is constant, but in actuality changed with the relative depth in circular pipe. This study was conducted to calculate the relative normal depth in changing the roughness coefficient (named relative roughness coefficient) with the relative depth in the analysis of gradually varied flow in the circular pipe by Newton-Raphson method. We performed the analysis of gradually varied flow using the relative normal depth and the relative roughness coefficient. We presented the 12 flow profiles with the relative depth and the relative roughness coefficient in circular pipe. The flow classification considering relative depth in circular pipe is available to analyse gradually varied flow profiles.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.106-106
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• 2015

The information of flow regimes continues to be norm in water resource and watershed management, in that stream flow regime is a crucial factor influencing water quality, geomorphology, and the community structure of stream biota. The objectives of this study were to estimate Korean stream flows from landscape variables, classify stream flow gages using hydraulic characteristics, and then apply these methods to ungaged biological monitoring sites for effective ecological assessment. Here I used a linear modeling approach (MLR, PCA, and PCR) to describe and predict seasonal flow statistics from landscape variables. MLR models were successfully built for a range of exceedance discharges and time frames (annual, January, May, July, and October), and these models explained a high degree of the observed variation with r squares ranging from 0.555 (Q95 in January) to 0.899 (Q05 in July). In validation testing, predicted and observed exceedance discharges were all significantly correlated (p<0.01) and for most models no significant difference was found between predicted and observed values (Paired samples T-test; p>0.05). I classified Korean stream flow regimes with respect to hydraulic and hydrologic regime into four categories: flashier and higher-powered (F-HP), flashier and lower-powered (F-LP), more stable and higher-powered (S-HP), and more stable and lower-powered (S-LP). These four categories of Korean streams were related to with the characteristics of environmental variables, such as catchment size, site slope, stream order, and land use patterns. I then applied the models at 684 ungaged biological sampling sites used in the National Aquatic Ecological Monitoring Program in order to classify them with respect to basic hydrologic characteristics and similarity to the government's array of hydrologic gauging stations. Flashier-lower powered sites appeared to be relatively over-represented and more stable-higher powered sites under-represented in the bioassessment data sets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.3
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• pp.612-622
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• 2017

Long-lived flowed termed as elephant flows in data center networks have a tendency to consume a lot of bandwidth, leaving delay-sensitive short-lived flows referred to as mice flows choked behind them. This results in non-trivial delays for mice flows, eventually degrading application performance running on the network. Therefore, a datacenter network should be able to classify, detect, and visualize elephant flows as well as provide QoS guarantees in real-time. In this paper we aim to focus on: 1) a proposed framework for real-time detection and visualization of elephant flows in SDN using sFlow. This allows to examine elephant flows traversing a switch by double-clicking the switch node in the topology visualization UI; 2) an approach to guarantee QoS that is defined and administered by a SDN controller and specifications offered by OpenFlow. In the scope of this paper, we will focus on the use of rate-limiting (traffic-shaping) classification technique within an SDN network.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.519-519
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• 2023

Estuarine dams are a recent and global phenomenon. While estuarine dams can provide the benefit of improved freshwater resources, they can also alter estuarine processes. Due to the wide range of estuarine types and estuarine dam configurations, the effect of estuarine dams on estuaries is not well understood in general. To develop a systematic understanding of the effect of estuarine dam location and freshwater discharge interval on a range of estuarine types (strongly stratified, partially mixed, periodically stratified, and well-mixed), this study used a coupled hydrodynamic-sediment dynamic numerical model (COAWST) and compared flow, sediment transport, and morphological conditions in the pre- and post-dam estuaries. For each estuarine type, scenarios with dam locations at 20, 55 and 90 km from the mouth and discharge intervals of a discharge every 0.5, 3, and 7 days were investigated. The results were analyzed in terms of change in tide, river discharge, estuarine classification, and sediment flux mechanism. The estuarine dam location primarily affected the tide-dominated estuaries, and the resonance length was an important length scale affecting the tidal currents and Stokes return flow. When the location was less than the resonance length, the tidal currents and Stokes return flow were most reduced due to the loss of tidal prism, the dead-end channel, and the shift from mixed to standing tides. The discharge interval primarily affected the river-dominated estuaries, and the tidal cycle period was an important time scale. When the interval was greater than the tidal cycle period, notable seaward discharge pulses and freshwater fronts occurred. Dams located near the mouth with large discharge interval differed the most from their pre-dam condition based on the estuarine classification. Greater discharge intervals, associated with large discharge magnitudes, resulted in scour and seaward sediment flux in the river-dominated estuaries, and the dam located near the resonance length resulted in the greatest landward tidal pumping sediment flux and deposition in the tide-dominated estuaries.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.6
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• pp.814-822
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• 1986

Flow patterns and its transitions in vertical two phase flow of air-water isothermal flow are identified objectively by void output signals and moments computed from the Probability Density Function which is associated with the statistical measurement for time average local void fractions using conductance probe. It has been shown that the probe output signals, PDF distributions and its moments are deterministic criteria of flow pattern and transition classification.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.