• The KSFM Journal of Fluid Machinery
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• v.9 no.6 s.39
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• pp.54-62
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• 2006

An experiment about performance characteristics is conducted on a double chamber vane-type rotor. Three different rotors, which have 6, 8 and 9 vanes, are applied to the driver and various lift holes at the rear plate are used to increase the effective vane height. The inner diameter of a double chamber cylinder is ${\phi}27mm$, and the length of the cylinder is 65 mm. The maximum offset length between the rotor outer surface and the cylinder inner surface is 4.5 mm. In this study, specific output torques and powers are measured, and also noise and vibration are measured at the real operating situation. The operating torque on the double chamber is increased to 17% compared to the operating torque obtained at the single chamber which has the same size. The experimental results of noise and vibration show that the operating sound and vibration are directly related to the operating power generated by the double chamber rotor.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.391-404
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• 2019

Customers' fixed characteristics have often been used to predict customer behavior. It has recently become possible to track customer web logs as customer activities move from offline to online. It has become possible to collect large amounts of web log data; however, the researchers only focused on organizing the log data or describing the technical characteristics. In this study, we predict the decision-making time until each customer makes the first reservation, using Airbnb customer data provided by the Kaggle website. This data set includes basic customer information such as gender, age, and web logs. We use various methodologies to find the optimal model and compare prediction errors for cases with web log data and without it. We consider six models such as Lasso, SVM, Random Forest, and XGBoost to explore the effectiveness of the web log data. As a result, we choose Random Forest as our optimal model with a misclassification rate of about 20%. In addition, we confirm that using web log data in our study doubles the prediction accuracy in predicting customer behavior compared to not using it.