• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.2
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• pp.30-34
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• 2011

Variable precision rough set models have been successfully applied to problems whose domains are discrete values. However, there are many situations where discrete data is not available. When it comes to the problems with interval values, no variable precision rough set model has been proposed. In this paper, we propose a variable precision rough set model for interval values in which classification errors are allowed in determining if two intervals are same. To build the model, we define equivalence class, upper approximation, lower approximation, and boundary region. Then, we check if each of 11 characteristics on approximation that works in Pawlak's rough set model is valid for the proposed model or not.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.189-193
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• 1996

Piezoelectric actuator is widely used in precision positioning applications due to its excellent positioning resolution. However, serious hysteresis nonlinearity of the actuator deteriorates its open loop positioning capability. Generally, a nonlinear hysteresis model is used in feedforward loop to improve positioning accuracy. In this study, however, a simple lead compensator is proposed as a substitution for a complex nonlinear hysteresis model and tested through experiments for precision position control.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.259-266
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• 2023

It is very difficult to measure the performance of the machine learning model in the business service stage. Therefore, managing the performance of the model through the operational department is not done effectively. Academically, various studies have been conducted on the concept drift detection method to determine whether the model status is appropriate. The operational department wants to know quantitatively the performance of the operating model, but concept drift can only detect the state of the model in relation to the data, it cannot estimate the quantitative performance of the model. In this study, we propose a performance prediction model (PPM) that quantitatively estimates precision through the statistics of concept drift. The proposed model induces artificial drift in the sampling data extracted from the training data, measures the precision of the sampling data, creates a dataset of drift and precision, and learns it. Then, the difference between the actual precision and the predicted precision is compared through the test data to correct the error of the performance prediction model. The proposed PPM was applied to two models, a loan underwriting model and a credit card fraud detection model that can be used in real business. It was confirmed that the precision was effectively predicted.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.179-184
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• 2021

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.35-36
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• 2011

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.131-134
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• 2001

The three-dimensional precision measurement technology for industry product of middle and/or large scale has been developed. Theodolite measurement system which is one of the technology is widely used in aerospace industry. This paper describes measurement method and results for spacecraft structure test model by using the measurement system. And structural stability for STM is desribed through the comparison between design values and measured values.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.335-339
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• 2001

City gas is one of the most important necessities of daily city life and social infrastructures. City gas is delivered to every user through a pipeline network. The gas pressure in the pipeline is regulated by gas regulator. In the pressure control system, characteristics of gas pipeline is as important as characteristics of regulator. There are many reports about the transfer function model of the fluid pipeline. But suitable model about the gas transmission pipeline is not known. In this paper, as the transfer function model of the gas pipeline, new model considering the heat transfer between pipe wall and gas and temperature change of gas is proposed. To evaluate this model, frequency response tests are used. As the result, the proposed model shows a better agreement when compared with the experimental result than conventional models. The results show the effectiveness of the model.

• Journal of Radiation Industry
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• v.18 no.3
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• pp.177-182
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• 2024

The purpose of this study is to develop a CNN based deep learning model that can effectively detect pneumonia by analyzing chest X-ray images of adults over the age of 20 and compare it with VUNO, LUNIT a commercialized AI model. The data of chest X-ray image was evaluate based on accuracy, precision, recall, F1 score, and AUC score. The CNN model recored an accuracy of 82%, precision 76%, recall 99%, F1 score 86%, and AUC score 0.7937. The VUNO model recordded an accuracy of 84%, precision 81%, recall 94%, F1 score 87%, and AUC score 0.8233. The LUNIT model recorded an accuracy of 77%, precision 72%, recall 96%, F1 score 83%, and AUC score 0.7436. As a result of the Confusion Matrix analysis, the CNN model showe FN (3), showing the highest recall rate (99%) in the diagnosis of pneumonia. The VUNO model showed excellent overall perfomance with high accuracy (84%) and AUC score (0.8233), and the LUNIT model showed high recall rate (96%) but the accuracy and precision showed relatively low results. This study will be able to provide basic data useful for the development of a pneumonia diagnosis system by comprehensively considers the perfomance of the medel is necessary to effectively discriminate between penumonia and normal groups.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.375-378
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• 2005

Voice Coil Motor is used linear motion actuator system that require precision positioning control. In order to control precision positioning of voice coil motor, Mathematical model of voice coil motor is needed. Mathematical model is obtained by combining voice coil motor's equation of motion with the equation of circuit and characteristic of voice coil motor. The induced model can predict output displacement according to duty ratio and amplitude. The model is verified by experimental test. Simulated results have tracking errors of less than 10 percent of experimental results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.624-628
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• 1996

Unsteady-state temperature distributions in the high precision spindle system with built-in motor are studied. For the analysis, three dimensional model is built for the high precision spindle. The three dimensional model includes the estimation on the amount of heat generation of bearing and built-in motor and the thermal characteristic values such as heat transfer coefficient. Temperature distributions are computed using the finite element method. Analysis results are compared with the measured data. Analysis shows that temperature distributions of high precision spindle system can be estimated resonably using the three dimensional model through the finite element method.