• 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 Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• The KSFM Journal of Fluid Machinery
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• v.15 no.4
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• pp.61-66
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• 2012

Nowadays, precise prediction of the pump performance becomes more important than ever before in high-value industries such as power plants and large ships. The power consumed in such pumps of large head and capacity definitely affects the efficiency of the entire system. In this study, we report the theoretical and CFD results used in prediction of the performance change caused by the reduction of impeller diameter. We have found that the theoretical calculation is somehow useful at least in estimating the very beginning condition for the CFD main calculation.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.181-190
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• 1996

Pipelined architecture improves processor performance by overlapping the execution of several different instructions. The effect of control hazard stalls the pipeline and reduces processor performance. In order to reduce the effect of control hazard caused by branch, we proposes a new approach combining both branch prediction and two paths strategy. In addition, we verify the performance improvement in a proposed approach by utilizing system performance metric CPI rather than BEP.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.7 s.94
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• pp.1851-1863
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• 1993

A procedure for the prediction of the off-design performance of a gas turbine engine is proposed. The system performance at off-design speed is predicted by coupling the thermodynamic models of a compressor and a turbine. The off-design performance of a compressor is obtained using the stage-stackimg method, while the Ainlay-Mathieson method is used for a turbine. The procedure is applied to a single-shaft gas turbine and its predictability is found satisfactory. The results also show that the net work output increases with the increase of the turbine inlet temperature, while the thermal efficiency is marginal. The maximum thermal efficiency at design point is obtained between the highest pressure ratio and design pressure ratio.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.2
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• pp.27-31
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• 2023

Purpose: Establishment of a real-time monitoring system for odor control in traditional markets in Gangwon-do and a system for linking prevention facilities. Research design, data and methodology: Build server and system logic based on data through real-time monitoring device (sensor-based). A temporary data generation program for deep learning is developed to develop a model for odor data. Results: A REST API was developed for using the model prediction service, and a test was performed to find an algorithm with high prediction probability and parameter values optimized for learning. In the deep learning algorithm for AI modeling development, Pandas was used for data analysis and processing, and TensorFlow V2 (keras) was used as the deep learning library. The activation function was swish, the performance of the model was optimized for Adam, the performance was measured with MSE, the model method was Functional API, and the model storage format was Sequential API (LSTM)/HDF5. Conclusions: The developed system has the potential to effectively monitor and manage odors in traditional markets. By utilizing real-time data, the system can provide timely alerts and facilitate preventive measures to control and mitigate odors. The AI modeling component enhances the system's predictive capabilities, allowing for proactive odor management.

• The KIPS Transactions:PartA
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• v.14A no.6
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• pp.357-362
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• 2007

In distributed systems like cluster systems, in order to get more performance improvement, the initial task placement system precisely estimates and correctly assigns the resource requirement by the process. The resource-based initial job placement scheme needs the prediction of resource usage of a task in order to fit it to the most suitable hosts. However, the wrong prediction of resource usage causes serious performance degradation in dynamic load balancing systems. Therefore, in this paper, to resolve the problem due to the wrong prediction, we propose a new load metric. By the new load metric, the resource-based initial job placement scheme can work without priori knowledge about the type of process. Simulation results show that the dynamic load balancing system using the proposed approach achieves shorter execution times than the conventional approaches.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.31-43
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• 2008

Travel time prediction is an indispensable to many advanced traveler information systems(ATIS) and intelligent transportation systems(ITS). In this paper we propose a method to predict travel time using $Na{\ddot{i}}ve$ Bayesian classification method which has exhibited high accuracy and processing speed when applied to classily large amounts of data. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. For a given route, we consider time-varying average segment velocity to perform more accuracy of travel time prediction. We compare the proposed method with the existing prediction algorithms like link-based prediction algorithm [1] and Micro T* algorithm [2]. It is shown from the performance comparison that the proposed predictor can reduce MARE (mean absolute relative error) significantly, compared with the existing predictors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.184-191
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• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• Atmosphere
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• v.25 no.2
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• pp.367-374
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• 2015

Korean Peninsula has high potential for occurrence of aviation turbulence. A Korean aviation Turbulence Guidance (KTG) system focused on the Korean Peninsula, named Korean-Peninsula KTG (KP-KTG) system, is developed using the high resolution (horizontal grid spacing of 1.5 km) Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration (KMA). The KP-KTG system is constructed first by selection of 15 best diagnostics of aviation turbulence using the method of probability of detection (POD) with pilot reports (PIREPs) and the LDAPS analysis data. The 15 best diagnostics are combined into an ensemble KTG predictor, named KP-KTG, with their weighting scores computed by the values of area under curve (AUC) of each diagnostics. The performance of the KP-KTG, represented by AUC, is larger than 0.84 in the recent two years (June 2012~May 2014), which is very good considering relatively small number of PIREPs. The KP-KTG can provide localized turbulence forecasting in Korean Peninsula, and its skill score is as good as that of the operational-KTG conducting in East Asia.