• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.6
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• pp.1-7
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• 2001

It is very important to test circular motion accuracy of NC machine tools as it affects all other machines machined by them in industries. In this paper, it has become possible to detect errors of linear displacement of radial directions for circle tar motion accuracy test using newly assembled magnetic type of linear scale so called Magnescale ball-bar system. It has also organized computer program systems using tick pulses come out from computer for getting error motion data at colt start time interval in circular motion test on NC lathe. Error data gotten from test is expressed to plots and analysed to numerics by various statistical treatments.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.387-392
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• 2002

It is very important to measure linear cycle plane positioning accuracy of NC lathes as they affect those of all other machines machined by them in industries. For example, if the linear cycle plane positioning accuracy of each axes directions is bad, the accuracy of works will be wrong and the change-ability will be bad in the assembly of machine parts. In this paper, computer software systems are organized to measure linear displacements of ATC(Automatic tool changer) of NC lathes using linear scale and time pulses comming out from computer in order to get data at constant time intervals from the sensors. And each sets of error data gotten from the test is expressed to plots by computer treatment and the results of linear cycle plane positioning error motion estimated to numerics by statistical treatments.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.9
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• pp.127-134
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• 2009

Laser-assisted machining uses primarily laser power to heat the local area before the material is removed. It not only efficiently reduces the cutting force during the manufacturing process but also improves the machining characteristics and accuracy with regard to difficult-to-machine materials. The prediction of relative deformations between the cutting tool and workpiece is important to improve the accuracy of machined components. This paper presents the deformation errors caused for a cylindrical workpiece by thermal effects in the laser-assisted machine tool using finite element method. The results can be used to increase the cutting accuracy by compensating thermal distortion prior to laser-assisted machining.

• Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.128-139
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• 2014

This study investigates the effects of machining parameters as they relate to the quality characteristics of machined features. Two most important quality characteristics are set as the dimensional accuracy and the surface roughness. Before any newly acquired machine tool is put to use for production, it is important to test the machine in a systematic way to find out how different parameter settings affect machining quality. The empirical verification was made by conducting a Design of Experiment (DOE) with 3 levels and 3 factors on a state-of-the-art Cincinnati Hawk Arrow 750 Vertical Machining Center (VMC). Data analysis revealed that the significant factor was the Hardness of the material and the significant interaction effect was the Hardness + Feed for dimensional accuracy, while the significant factor was Speed for surface roughness. Since the equally important thing is the capability of the instruments from which the quality characteristics are being measured, a comparison was made between the VMC touch probe readings and the measurements from a Mi-tutoyo coordinate measuring machine (CMM) on bore diameters. A machine mounted touch probe has gained a wide acceptance in recent years, as it is more suitable for the modern manufacturing environment. The data vindicated that the VMC touch probe has the capability that is suitable for the production environment. The test results can be incorporated in the process plan to help maintain the machining quality in the subsequent runs.

• Journal of Sasang Constitutional Medicine
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• v.20 no.1
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• pp.42-47
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• 2008

1. Objectives The Body Shape and Feature is one of the important standard for classification of Sasang Constitutions. In order to evaluate one's Body Shape and Feature objectively we have been developing the Body Measuring Machine. Now we develop the 3D Automatic Body Measuring Machine(3D-ABMM). So we make an analysis of the 3D-ABMM's Accuracy. 2. Methods By using the 3D-ABMM and Vivid 9i(3D laser scanner, Konica Minolta) we have a surface scan of the three objects which are the upper body of the female and male Manikin and a male model. We overlap each scan data using the RapidForm2006 (3D scan data solution, INUS Technology) and calculate the average distance and standard deviation between the same point of each scan data. 3. Results and Conclusions In the female Manikin, the average distance is 0.84mm and the standard deviation is 1.16mm and the maximum distance is 10.68mm. In the male Manikin, the average distance is 1.12mm and the standard deviation is 1.19mm and the maximum distance is 12.00mm. In the male model, the average distance is 3.26mm and the standard deviation is 2.59mm and the maximum distance is 12.75mm. From the results, 3D-ABMM has good accuracy for scanning body and will be a usable hardware of the 3D Automatic Body Analysis Machine.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.83-91
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• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1567-1577
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• 2020

Urban land cover classification is role in urban planning and management. So, it's important to improve classification accuracy on urban location. In this paper, machine learning model, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are proposed for urban land cover classification based on high resolution satellite imagery (KOMPSAT-3A). Satellite image was trained based on 25 m rectangle grid to create training data, and training models used for classifying test area. During the validation process, we presented confusion matrix for each result with 250 Ground Truth Points (GTP). Of the four SVM kernels and the two activation functions ANN, the SVM Polynomial kernel model had the highest accuracy of 86%. In the process of comparing the SVM and ANN using GTP, the SVM model was more effective than the ANN model for KOMPSAT-3A classification. Among the four classes (building, road, vegetation, and bare-soil), building class showed the lowest classification accuracy due to the shadow caused by the high rise building.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.32-43
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• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.57-64
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• 2024

This paper aims to enhance the accuracy of music genre classification for music tracks where genre information is not provided, by utilizing machine learning to classify a large amount of music data. The paper proposes collecting and preprocessing data instead of using the commonly employed GTZAN dataset in previous research for genre classification in music. To create a dataset with superior classification performance compared to the GTZAN dataset, we extract specific segments with the highest energy level of the onset. We utilize 57 features as the main characteristics of the music data used for training, including Mel Frequency Cepstral Coefficients (MFCC). We achieved a training accuracy of 85% and a testing accuracy of 71% using the Support Vector Machine (SVM) model to classify into Classical, Jazz, Country, Disco, Soul, Rock, Metal, and Hiphop genres based on preprocessed data.