• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.603-608
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• 2008

A simple equation to find a optimum speed of desiccant rotor is presented in this theoretical study. Usually the determination of optimum speed of desiccant rotor requires tedious and lengthy procedures by solving governing differential equations with many complicated parameters. The determining equation of optimal rotating speed is derivated from governing differential equations with three linearization assumptions, which simplify temperature profile linear along the desiccant rotor depth, psychrometric chart within a proper range, and relative humidity-sorption capacity relation. This study shows that the dominant parameters of optimal rotating speed of desiccant rotor are NTU, flow velocity, desiccant rotor depth, and temperature different between dehumidification and regeneration. The comparison shows the good agreement between complicated calculation results and simple theoretical equation prediction.

• Journal of Navigation and Port Research
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• v.36 no.7
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• pp.591-597
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• 2012

Generally an urban freeway means a primary arterial which provides road users with a free-flow speed, except for ramp junctions during rush hours. However, most road users suffer from traffic congestion in the basic segments as well as in the ramp junctions of urban freeway during rush hours, because most road users prefer urban freeways to local roads in the urban areas. This study then intends to analyze lane traffic characteristics of urban freeway basic segments having more freight vehicles during rush hours, find the lane showing a high correlation with the segment speed between lane speeds, and finally suggest a segment-speed predictive model by the lane speed of urban freeway basic segments during rush hours.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.6
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• pp.73-79
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• 2020

Although engineering plastics that are light-weight and have excellent mechanical performance have been widely applied in various industries in place of steel structures to reduce the burden of cost and time, there have been few studies related to their surface roughness. This study aims to evaluate the optimal effects of feed rate, cutting speed, and depth of cut as cutting parameters as well as nose angle on the surface characteristics of MC nylon in CNC lathe machining. To determine the best conditions under different nose radii, the experiments were performed based on the Taguchi L9(34) orthogonal array method, in which the resulting data was analyzed using the S/N ratio and ANOVA. Results indicate that the most significant contribution was feed rate followed by nose angle and cutting speed, whereas the depth of cut did not influence the performance. This study demonstrates that the suggested method for improving the surface finishing of MC nylon is efficient compared with results obtained from experimentation and prediction.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1829-1836
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• 2016

Today, the application area and scope of FANET(Flying Ad Hoc Network) has been extended. As a result, FANET related research are actively conducted, but there is no decision yet as the routing protocol for FANET. In this paper, we propose the OLSR-Pds (Prediction with direction and speed) which is added a method to predict status of link for OLSR protocol. The mobility of nodes are modeled using Gauss-Markov algorithm, and relative speed between nodes were calculated by derive equation of movement, and thereby we can predict link status. An experiment for comparing AODV, OLSR and, OLSR-Pds was conducted by three factors such as packet delivery ratio, end to end delay, and routing overhead. In experiment result, we were confirm that OLSR-Pds performance are superior in these three factors. OLSR-Pds has the disadvantage that requires time-consuming calculations for link state and required for computing resources, but we were confirm that OLSR-Pds is suitable for routing to the FANET environment because it has all the characteristics of proactive protocol and reactive protocol.

• 유체기계공업학회:학술대회논문집
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• 2001.11a
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• pp.250-257
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• 2001

Low NPSH and high pressure pumps are widely used for turbopump systems, which have an inducer and operate at high rotating speeds In this paper, a meanline method has been established for the preliminary design and performance prediction of pumps having an inducer for cavitating or non-cavitating conditions and at design or off-design points. The method was applied for the performance prediction of a fuel pump, which had been developed by Hyundai Mobis in collaboration with KeRC for a liquid rocket engine. The engine uses liquid methane and liquid oxygen as working fluids and rotates at 50,000 rpm KeRC carried out a model testing of the fuel pump with water as a working fluid at the reduced speed (10,000 ${\~}$ 15,000 rpm). Predicted performances by the method are shown to be in good agreement with experimental results for cavitating and non-cavitating conditions. The established meanline method can be used for the performance prediction and preliminary design of high speed pumps which have a inducer, impeller and volute.

• Journal of Korea Multimedia Society
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• v.20 no.4
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• pp.593-605
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• 2017

In applications which are covered wide range, it is possible that one or more number of Unmanned Aerial Vehicle(UAV) squadrons are used to perform a mission. In this case, it is most important to communicate seamlessly between the UAV squadrons. In this paper, we applied the modified OLSR(OSLR-Pds) which can prediction for state of the link for the communication in UAV squadron, and applied the modified AOMDV which can build multi-path for the communication between UAV Squadrons. The mobility of nodes are modeled using Gauss-Markov algorithm, and relative speed between nodes were calculated by derive equation of movement, and thereby we can predict link state for in a squadron and between squadrons. An experiment for comparing AODV, AOMDV and the proposed routing protocol was conducted by three factors such as packet delivery ratio, end to end delay, and routing overhead. In experiment result, we make sure that the proposed protocol performance are superior in these three factors. However, if the density of the nodes constituting FANET are too low, and if the moving speed of node is very slow, there is no difference to others protocols.

• Transactions of Materials Processing
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• v.29 no.4
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Journal of Ocean Engineering and Technology
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• v.33 no.2
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• pp.161-167
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• 2019

This paper focuses on a newly designed ice load formula based on the ARAON's 2016 Arctic field data in order to improve a structural design against ice loads. The strain gage signals from ARAON's hull plating were converted to the local ice pressure upon the hull plating using the influence coefficient matrix and finite element analysis. First, a traditional pressure-area relationship is derived by applying probabilistic approaches to handle the strains measured onboard the ARAON. Then, the local ice load prediction formula is re-analyzed after reviewing the ARAON's additional field data to consider information about the ship speed and thickness of the sea ice. It is shown that the newly developed pressure-area relationship well reflects the influence of other design parameters such as the ship speed and ice thickness in the prediction of local ice loads on Arctic vessels.

• Atmosphere
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• v.25 no.1
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• pp.1-18
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• 2015

The most objective way to overcome the limitation of numerical weather prediction model is to represent the uncertainty of prediction by introducing probabilistic forecast. The uncertainty of the numerical weather prediction system developed due to the parameterization of unresolved scale motions and the energy losses from the sub-scale physical processes. In this study, we focused on the growth of model errors. We performed ensemble forecast to represent model uncertainty. By employing the multi-physics scheme (PHYS) and the stochastic kinetic energy backscatter scheme (SKEBS) in simulating typhoon Rusa (2002), we assessed the performance level of the two schemes. The both schemes produced better results than the control run did in the ensemble mean forecast of the track. The results using PHYS improved by 28% and those based on SKEBS did by 7%. Both of the ensemble mean errors of the both schemes increased rapidly at the forecast time 84 hrs. The both ensemble spreads increased gradually during integration. The results based on SKEBS represented model errors very well during the forecast time of 96 hrs. After the period, it produced an under-dispersive pattern. The simulation based on PHYS overestimated the ensemble mean error during integration and represented the real situation well at the forecast time of 120 hrs. The displacement speed of the typhoon based on PHYS was closest to the best track, especially after landfall. In the sensitivity tests of the model uncertainty of SKEBS, ensemble mean forecast was sensitive to the physics parameterization. By adjusting the forcing parameter of SKEBS, the default experiment improved in the ensemble spread, ensemble mean errors, and moving speed.