• Proceedings of the KSME Conference
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• 2000.04b
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• pp.520-524
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• 2000

A ejector system is one of the fluid machinery, which has been mainly used as an exhaust pump or a vacuum pump. The ejector system has often been pointed out to have only a limited efficiency because it is driven by pure shear action and the mixing action between primary and secondary streams. In the present work, numerical simulations were conducted to investigate the effects of the geometry and the mass flow ratio of supersonic ejector-diffuser systems on their mixing performance. A fully implicit finite volume scheme was applied to solve the axisymmetric Navier-Stokes equations, and the standard ${\kappa}-{\varepsilon}$ turbulence model was used to close the governing equations. The flow fields of the supersonic ejector-diffuser systems were investigated by changing the ejector throat area ratio and the mass flow ratio. The existence of the second throat strongly affected the shock wave structure inside the mixing tube as well as the spreading of the under-expanded jet discharging from the primary nozzle, and served to enhance the mixing performance.

• Journal of Korea Water Resources Association
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• v.41 no.6
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• pp.559-564
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• 2008

A practical method is derived for determining the unit hydrograph and S-curve from complex storm events by using a smoothed unit kernel approach. The using a unit kernel yields more convenient way of constructing a unit hydrograph and its S-curve than a conventional method. However, with use of real data, the unit kernel oscillates and is unstable so that a unit hydrograph and S-curve cannot easily obtained. The use of non-parametric ridge regression with a Laplacian matrix is suggested for deriving an event averaged unit kernel which reduces the computational efforts when dealing with the Nash instantaneous unit hydrograph as a basis of the kernel. A method changing the unit hydrograph duration is also presented. The procedure shown in this work will play an efficient role when any unit hydrograph works is involved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.291-294
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• 2001

This Current research for software architecture views and models a software system as a set of components and connectors. Components are ions of system level computational entities, connectors are ions of component interrelationships. In his paper, we focus attention on connectors for the Java Beans-based systems that are built using object integration technologies like CORBA. We present connector model in lava Beans-based system for object-oriented component integration. We start with a discussion of related work of software architecture research and of Object-Oriented modeling that focuses on the description of component collaborations. We propose connectors as transferable ions of system level component interconnection and inter-operation. Connectors are architectural ions of component coordination in the architecture of a system only. Connectors describe a collaboration rationale for component adaptations, which are then modeled in the concrete architecture of a system.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.50-53
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• 2016

Image processing techniques play an increasingly important role in many aspects of our daily life. For example, it has been shown to improve agricultural productivity in a number of ways such as plant pest detecting or fruit grading. However, massive quantities of images generated in real-time through multi-devices such as remote sensors during monitoring plant growth lead to the challenges of big data. Meanwhile, most current image processing systems are designed for small-scale and local computation, and they do not scale well to handle big data problems with their large requirements for computational resources and storage. In this paper, we have proposed an IPABigData (Image Processing Algorithm BigData) platform which provides algorithms to support large-scale image processing in agriculture based on Hadoop framework. Hadoop provides a parallel computation model MapReduce and Hadoop distributed file system (HDFS) module. It can also handle parallel pipelines, which are frequently used in image processing. In our experiment, we show that our platform outperforms traditional system in a scenario of image segmentation.

• 유체기계공업학회:학술대회논문집
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• 2005.12a
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• pp.405-412
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• 2005

Numerical and experimental investigations were conducted to assess the aerodynamic performance of several centrifugal compressors. In order to impose an appropriate physics at the interface between impeller and vaned diffuser numerically, two different techniques, frozen rotor and stage models, were applied and the simulation results were compared with the corresponding prototype test data. An equivalent sand-grain roughness height was utilized in the present computational study to consider a relative surface roughness effect on the stage performance simulated. From a series of investigations, it was found that the stage model is more suitable than the frozen rotor scheme for the steady interactions between impeller and diffuser in turbocompressor applications. It is supposed that the solution by frozen rotor scheme is inclined to overrate the non-uniformity of the flow fields. The predicted aerodynamic performance accounting for surface roughness effect shows favorable agreement with experimental data. Simulations based on the aerodynamically smooth surface assumption tend to overestimate the stage performance.

• Speech Sciences
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• v.11 no.2
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• pp.193-210
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• 2004

In this paper, we develop a real time lip-synch system that activates 2-D avatar's lip motion in synch with an incoming speech utterance. To realize the 'real time' operation of the system, we contain the processing time by invoking merge and split procedures performing coarse-to-fine phoneme classification. At each stage of phoneme classification, we apply the support vector machine (SVM) to reduce the computational load while retraining the desired accuracy. The coarse-to-fine phoneme classification is accomplished via two stages of feature extraction: first, each speech frame is acoustically analyzed for 3 classes of lip opening using Mel Frequency Cepstral Coefficients (MFCC) as a feature; secondly, each frame is further refined in classification for detailed lip shape using formant information. We implemented the system with 2-D lip animation that shows the effectiveness of the proposed two-stage procedure in accomplishing a real-time lip-synch task. It was observed that the method of using phoneme merging and SVM achieved about twice faster speed in recognition than the method employing the Hidden Markov Model (HMM). A typical latency time per a single frame observed for our method was in the order of 18.22 milliseconds while an HMM method applied under identical conditions resulted about 30.67 milliseconds.

• Environmental Engineering Research
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• v.25 no.4
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• pp.545-553
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• 2020

Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.4
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• pp.63-82
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• 2008

The explosively growing attractiveness of the Web is commencing significant demands for a structuring analysis on various web objects. The larger the substantial number of web objects are available, the more difficult for the clients(i.e. common web users and web robots) and the servers(i.e. Web search engine) to retrieve what they really want. We have in mind focusing on the structure of web objects by introducing optimization models for more convenient and effective information retrieval. For this purpose, we represent web objects and hyperlinks as a directed graph from which the optimal structures are derived in terms of rooted directed spanning trees and Top-k trees. Computational experiments are executed for synthetic data as well as for real web sites' domains so that the Lagrangian Relaxation approaches have exploited the Top-k trees and Hop constraint resolutions. In the experiments, our methods outperformed the conventional approaches so that the complex web graph can successfully be converted into optimal-structured ones within a reasonable amount of computation time.

• Management Science and Financial Engineering
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• v.19 no.2
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• pp.19-29
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• 2013

Refuse collection network design, one of major decision problems in reverse logistics, is the problem of locating collection points and allocating refuses at demand points to the opened collection points. As an extension of the previous models, we consider capacity and maximum allowable distance constraints at each collection point. In particular, the maximum allowable distance constraint is additionally considered to avoid the impractical solutions in which collection points are located too closely. Also, the additional distance constraint represents the physical distance limit between collection and demand points. The objective is to minimize the sum of fixed costs to open collection points and variable costs to transport refuses from demand to collection points. After formulating the problem as an integer programming model, we suggest an optimal branch and bound algorithm that generates all feasible solutions by a simultaneous location and allocation method and curtails the dominated ones using the lower bounds developed using the relaxation technique. Also, due to the limited applications of the optimal algorithm, we suggest two heuristics. To test the performances of the algorithms, computational experiments were done on a number of test instances, and the results are reported.