• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.450-453
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• 2001

Linear motor is characterized by its high velocity, high acceleration and good positioning accuracy. In recent years, linear motor is often used as a fast feed mechanism for high-speed machine tools. For the effective application of linear motors to machine tools, many demands on machine conceptions must be fulfilled. In this paper, some important construction concepts such as bending deformation of machine table, frictional force on the linear guidance and thermal behavior of linear motors are presented.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.1
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• pp.1-8
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• 2012

Recently, the needs of system performances such as working speed and processing accuracy in machine tools have been increased. Especially, the speed increment generates harmful heat at both moving part of the machine tools and handicrafts. The heat is a main drawback to progress accuracy of the processing. Hence, a cooler system to control temperature is inevitable for the machine tools. In general, two representative control schemes, hot-gas bypass and variable speed control of a compressor, have been adopted in the water cooler system. In this paper, comparisons of system performances according to the control schemes in a cooler for machine tools were conducted in detail. Each proportional-integral feedback controller for the two different control systems is designed. The system performances, especially the temperature control accuracy and coefficient of performance which is a criterion of energy saving, were mainly analyzed through various experiments using 1RT water cooler system with different two types of control scheme. These evaluations will provide useful information to choose suitable water cooler system for the engineers who design controllers of the cooler system for machine tools.

• Journal of ILASS-Korea
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• v.28 no.1
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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• pp.311-317
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• 2005

NC controller parameters are fixed when the controller is combined with a machine. However, the characteristics of controller could be changed as it has being used by the machine or other environmental conditions. Ultimately, it results in tool positioning accuracy changing. The loading torque in servo motor also influences on the positioning accuracy. This study focus on a measuring and analysing method for verifying the angular positioning accuracy of NC servo motor. We used a high resolution A/D converter for acquiring analogue signal of rotary encoder in servo motor. Generating tool path by the combination of axial movements (X,Y,Z) is compared with the encoder signals with the servo motor torque. The current variation signal is also read from the servo motor power using a hall sensor and converted to the motor torque. The method of analysing proposed in this study will be used for determining the gains (tuning) of parameter in NC controller, when the controller is set up at a machine initially or the controller condition is changed during the work.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.106-118
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• 2019

In order to develop pedestrian navigation service that provides optimal pedestrian routes based on pedestrian satisfaction levels, it is required to develop a prediction model that can estimate a pedestrian's satisfaction level given a certain condition. Thus, the aim of the present study is to develop a pedestrian satisfaction prediction model based on three machine learning algorithms: Logistic Regression, Random Forest, and Artificial Neural Network models. The 2009, 2012, 2013, 2014, and 2015 Pedestrian Satisfaction Survey Data in Seoul, Korea are used to train and test the machine learning models. As a result, the Random Forest model shows the best prediction performance among the three (Accuracy: 0.798, Recall: 0.906, Precision: 0.842, F1 Score: 0.873, AUC: 0.795). The performance of Artificial Neural Network is the second (Accuracy: 0.773, Recall: 0.917, Precision: 0.811, F1 Score: 0.868, AUC: 0.738) and Logistic Regression model's performance follows the second (Accuracy: 0.764, Recall: 1.000, Precision: 0.764, F1 Score: 0.868, AUC: 0.575). The precision score of the Random Forest model implies that approximately 84.2% of pedestrians may be satisfied if they walk the areas, suggested by the Random Forest model.

• Computers and Concrete
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• v.30 no.3
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• pp.225-236
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• 2022

In this article, Multi-Gene Genetic Programming (MGGP) is proposed for the estimation of the compressive strength of concrete. MGGP is known to be a powerful algorithm able to find a relationship between certain input space features and a desired output vector. With respect to most conventional machine learning algorithms, which are often used as "black boxes" that do not provide a mathematical formulation of the output-input relationship, MGGP is able to identify a closed-form formula for the input-output relationship. In the study presented in this article, MGPP was used to predict the compressive strength of plain concrete, concrete with fly ash, and concrete with furnace slag. A formula was extracted for each mixture and the performance and the accuracy of the predictions were compared to the results of Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) algorithms, which are conventional and well-established machine learning techniques. The results of the study showed that MGGP can achieve a desirable performance, as the coefficients of determination for plain concrete, concrete with ash, and concrete with slag from the testing phase were equal to 0.928, 0.906, 0.890, respectively. In addition, it was found that MGGP outperforms ELM in all cases and its' accuracy is slightly less than ANN's accuracy. However, MGGP models are practical and easy-to-use since they extract closed-form formulas that may be implemented and used for the prediction of compressive strength.

• Smart Structures and Systems
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• v.30 no.3
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• pp.287-301
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• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4102-4111
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• 2023

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.7
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• pp.276-281
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• 2007

In this paper, we use Support Vector Machine to predict the susceptibility of chronic hepatitis from single nucleotide polymorphism data. Our data set consists of SNP data for 328 patients based on 28 SNPs and patients classes(chronic hepatitis, healthy). We use leave-one-out cross validation method for estimation of the accuracy. The experimental results show that SVM with SNP is capable of classifying the SNP data successfully for chronic hepatitis susceptibility with accuracy value of 67.1%. The accuracy of all SNPs with health related feature(sex, age) is improved more than 7%(accuracy 74.9%). This result shows that the accuracy of predicting susceptibility can be improved with health related features. With more SNPs and other health related features, SVM prediction of SNP data is a potential tool for chronic hepatitis susceptibility.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.42-52
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• 2022

Bus Information System (BIS) services are expanding nationwide to small and medium-sized cities, including large cities, and user satisfaction is continuously improving. In addition, technology development related to improving reliability of bus arrival time and improvement research to minimize errors continue, and above all, the importance of information accuracy is emerging. In this study, accuracy performance was evaluated using LSTM, a machine learning method, and compared with existing methodologies such as Kalman filter and neural network. As a result of analyzing the standard error for the actual travel time and predicted values, it was analyzed that the LSTM machine learning method has about 1% higher accuracy and the standard error is about 10 seconds lower than the existing algorithm. On the other hand, 109 out of 162 sections (67.3%) were analyzed to be excellent, indicating that the LSTM method was not entirely excellent. It is judged that further improved accuracy prediction will be possible when algorithms are fused through section characteristic analysis.