• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1557-1563
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• 2016

According to recent studies, poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. We proposed a strategy for classification of sitting posture using machine learning. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject's neck in 5-types of sitting posture. 6 subjects without any spinal disorder were participated in this experiment. Acceleration data were transformed to the feature vectors of principle component analysis. Support vector machine (SVM) and K-means clustering were used to classify sitting posture with the transformed feature vectors. To evaluate performance, we calculated the correct rate for each classification strategy. Although the correct rate of SVM in sitting back arch was lower than that of K-means clustering by 2.0%, SVM's correct rate was higher by 1.3%, 5.2%, 16.6%, 7.1% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 94.5% and 88.84% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1729-1738
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• 2021

Machine learning has a close relationship with cognitive psychology and brain science and is developing together. This paper analyzes the OASIS-3 dataset using machine learning techniques and proposes a model for predicting dementia. Dimensional reduction through PCA (Principal Component Analysis) is performed on the data quantifying the volume of each area among OASIS-3 data, and only important elements (features) are extracted and then various machine learning including gradient boosting and stacking Apply the models and compare the performance of each. Unlike previous studies, the proposed technique has a great differentiation because it uses not only the brain biometric data, but also basic information data such as the participant's gender and medical information data of the participant. In addition, it was shown that the proposed technique through various performance evaluations is a model that can better predict dementia by finding features that are more related to dementia among various numerical data.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.1
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• pp.1196-1204
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• 2001

In this paper, a new architecture of operating software associated with the component-based method is proposed. The proposed architecture comprises 문 execution module and a decision-making module. In order to make effective development and maintenance, the execution module is divided into three components. The components are referred to as Symbol, Gateway, and Control, respectively: The symbol component is for the GUI environments and the standard interfaces; the gateway component is for the network communication and the structure of asynchronous processes; the control component is for the asynchronous processing and machine setting or operations. In order to verify the proposed architecture, and off-line version of operating software is made, and its steps are as follows; I) Make virtual execution modules for the manufacturing devices such as dual-arm robot, handling robot, CNC, and sensor; ii) Make decision-making module; iii) Integrate the modules and GUI using a well-known development tools such as Microsofts Visual Basic; iv) Execute the overall operating software to validate the proposed architecture. The proposed software architecture in this paper has the advantages such as independent development of each module, easy development of network communication, and distributed processing of resources, and so on.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.2
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• pp.198-209
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• 1999

This study reports the selection of dependent variables for momentum equations in general curvilinear coordinates. Catesian, covariant and contravariant velocity components were examined for the dependent variable. The focus of present study is confined to staggered grid system Each dependent variable selected for momentum equations are tested for several flow fields. Results show that the selection of Cartesian and covariant velocity components intrinsically can not satisfy mass conservation of control volume unless additional converting processes ore used. Also, Cartesian component can only be used for the flow field in which main-flow direction does not change significantly. Convergence rate for the selection of covariant velocity component decreases quickly as with the increase of non-orthogonality of grid system. But the selection of contravariant velocity component reduces the total mass residual of discretized equations rapidly to the limit of machine accuracy and the solutions are insensitive to the main-flow direction.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.8-8
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• 2000

PICARD (Port-Interface Component Architecture for Real-time system Design) is a software architecture and environment, which is aimed to reduce development time and cost of real-time, control system. With PICARD, a control engineer can construct a control system software by assembling pre-built software components us ing interact ive graphical development environment. PICARD consists of PVM(Picard Virtual Machine) , a component library, and PICE(PIcard Configuration Editor). PVM is a real-time engine of the PICARD system which runs control tasks on a real-time operating system. The component library is composed of components which are called task blocks. PICE is a visual editor which can configure control tasks by creating data-flow diagrams of task blocks or Ladder diagrams for sequential logics. For the communication between PVM on a target system and PICE on a host computer, a simple protocol and tools for stub generation was dove]oped because RPC or CORBA is difficult to be applied for the embedded system. New features such as a byte-code based run time system and a simple and easy MMI builder are also introduced.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.55-59
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• 2008

For effective mobility control of UGV(unmanned ground vehicle), the terrain cover classification is an important component as well as terrain geometry recognition and obstacle detection. The vision based terrain cover classification algorithm consists of pre-processing, feature extraction, classification and post-processing. In this paper, we present a method to classify terrain covers based on the color and texture information. The color space conversion is performed for the pre-processing, the wavelet transform is applied for feature extraction, and the SVM(support vector machine) is applied for the classifier. Experimental results show that the proposed algorithm has a promising classification performance.

• Journal of the Earthquake Engineering Society of Korea
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• v.2 no.2
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• pp.1-12
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• 1998

The seismic behaviour of rotating machine-foundation systems subjected to six-component nonstationary earthquake ground accelerations is analyzed. The rotating machine-foundation system is idealized by using discs, rotating shaft, fluid-film journal bearings, pedestals, and space frame foundation. Thus, governing equations of motion for the rotating machine-foundation system are obtained by considering Gyroscopic effect, Coriolis effect, dynamic characteristics of fluid-film journal bearings, and translational and rotational motions of seismic rigid base. The influences due to Gyroscopic effects, Coriolis effects, and rotational motions of seismic base on the overall structural response are demonstrated by a numerical example. The results show that the inclusion of base rotations and Gyroscopic effects contributes significantly to the system response.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.