• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1041-1051
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• 2014

In this paper, we propose a new image alignment technique based on CUDA SURF in order to solve the initial image alignment problem that frequently occurs in machine vision applications. Machine vision systems using multi-spectral images have recently become more common for solving various decision problems that cannot be performed by the human vision system. These machine vision systems mostly use markers for the initial image alignment. However, there are some applications where the markers cannot be used and the alignment techniques have to be changed whenever their markers are changed. In order to solve these problems, we propose a new image alignment method for multi-spectral machine vision applications based on SURF extracting image features without depending on markers. In this paper, we propose an image alignment method that obtains a sufficient number of feature points from multi-spectral images using SURF and removes outlier iteratively based on a least squares method. We further propose an effective preliminary scheme for removing mismatched feature point pairs that may affect the overall performance of the alignment. In addition, we reduce the execution time by implementing the proposed method using CUDA based on GPGPU in order to guarantee real-time operation. Simulation results show that the proposed method is able to align images effectively in applications where markers cannot be used.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.795-801
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• 2013

Recently, support vector machine has been widely used in various application fields due to its superiority of classification performance comparing with decision tree and neural network. Since support vector machine is basically designed for the binary classification problem, output coding method to analyze the classification result of multiclass binary classifier is used for the application of support vector machine into the multiclass problem. However, previous feature selection method for output coding based support vector machine found the features to improve the overall classification accuracy instead of improving each classification accuracy of each classifier. In this paper, we propose the novel feature selection method to find the features for maximizing the classification accuracy of each binary classifier in output coding based support vector machine. Experimental result showed that proposed method significantly improved the classification accuracy comparing with previous feature selection method.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.1
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• pp.35-39
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• 2017

We used a construction method using a CNC milling machine, where free-formed molds were made by cutting EPS (Expanded PolyStyrene) foam with the CNC machine, to build free-formed buildings. CNC milling is off-the-shelf technology that can easily cut EPS foam; however its production cost is too high and the time to manufacture an EPS mold is too long. This paper proposes a novel cutting machine with a fast and cost effective mechanism to manufacture EPS concrete molds. Our machine comprises a cutter and Cartesian coordinate type moving mechanism, where the cutter cuts EPS foam using a hotwire in the shape of '$\sqcap$' and is capable of adjusting its cutting angle in real-time while keeping its cutting width. We proved through cutting experiments on the CNC machine that cutting time was greatly shortened compared to the conventional method and that the resulting concrete mold satisfied manufacturing precision.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1029-1030
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• 2007

This paper describes the dynamic analysis method and the characteristics of rotating type transverse flux motors excited by permanent magnets; the machine is called TFRM in here. A prototype of TFRM, made by combing soft magnetic composite (SMC) core, is introduced first, then the magneto static and dynamic analysis methods are explained. Analysis results are compared with measured results, and finally the effects of the proposed dynamic analysis method and the characteristics of TFRM are discussed.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.147-158
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• 2022

Cloud Computing offers flexible, on demand, ubiquitous resources for cloud users. Cloud users are provided computing resources in a virtualized environment. In order to meet the growing demands for computing resources, data centres contain a large number of physical machines accommodating multiple virtual machines. However, cloud data centres cannot utilize their computing resources to their total capacity. Several policies have been proposed for improving energy proficiency and computing resource utilization in cloud data centres. Virtual machine placement is an effective method involving efficient mapping of virtual machines to physical machines. However, the availability of many physical machines accommodating multiple virtual machines in a data centre has made the virtual machine placement problem a non deterministic polynomial time hard (NP hard) problem. Metaheuristic algorithms have been widely used to solve the NP hard problems of multiple and conflicting objectives, such as the virtual machine placement problem. In this context, we presented essential concepts regarding virtual machine placement and objective functions for optimizing different parameters. This paper provides a taxonomy of metaheuristic algorithms for the virtual machine placement method. It is followed by a review of prominent research of virtual machine placement methods using meta heuristic algorithms and comparing them. Finally, this paper provides a conclusion and future research directions in virtual machine placement of cloud computing.

• Journal of Platform Technology
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• v.11 no.4
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• pp.71-79
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• 2023

In this paper, we analyze the characteristics of machine learning workloads and, based on them, propose a distributed in-memory caching technique to improve the performance of machine learning workloads. The core of machine learning workload is model training, and model training is a computationally intensive task. Performing machine learning workloads in a Kubernetes-based cloud environment in which the computing framework and storage are separated can effectively allocate resources, but delays can occur because IO must be performed through network communication. In this paper, we propose a distributed in-memory caching technique to improve the performance of machine learning workloads performed in such an environment. In particular, we propose a new method of precaching data required for machine learning workloads into the distributed in-memory cache by considering Kubflow pipelines, a Kubernetes-based machine learning pipeline management tool.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3
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• pp.349-355
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• 2013

Recently, there is an increasing need to produce more precise products with small deviations from defined target values. Machine capability is the ability of a machine tool to produce parts within a tolerance interval. Capability indices are a statistical way of describing how well a product is machined compared to defined target values and tolerances. Today, there is no standardized way to acquire a machine capability value. This paper describes a method for evaluating machine capability indices in machining centers. After the machining of specimens, the straightness, roundness, and positioning accuracy were measured by using CMM (coordinate measuring machine). These measured values and defined tolerances were used to evaluate the machine capability indices. It will be useful for the industry to have standardized ways to choose and calculate machine capability indices.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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• pp.797-803
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• 2010

A method has been developed to tune the static stiffness at a rotation joint considering the whole machine tool system by interactive use of finite element method and experiment. This paper describes the procedure of this method and shows the results. The method uses the static experiment on measurement model which is set-up so that the effects of uncertain factors can be excluded. For FEM simulation, the rotation joint model is simplified using only spindle, bearing and spring. At the rotation joint, the damping coefficient is ignored, The spindle and bearing is connected by only spring. By static experiment, 500 N is forced to the front and behind portion of spindle and the deformation is measured by capacitive sensor. The deformation by FEM simulation is extracted with changing the static stiffness from the initial static stiffness considering only rotation joint. The tuning static stiffness is obtained by exploring the static stiffness directly trusting the deformation from the static experiment. Finally, the general tuning method of the static stiffness of machine tool joint is proposed using the force stream and the modal analysis of machine tool.

• Geomechanics and Engineering
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• v.1 no.2
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• pp.121-142
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• 2009

The paper deals with the applications of spectral finite element method to the dynamic analysis of framed foundations supporting high speed machines. Comparative performance of approximate dynamic stiffness methods formulated using static stiffness and lumped or consistent or average mass matrices with the exact spectral finite element for a three dimensional Euler-Bernoulli beam element is presented. The convergence of response computed using mode superposition method with the appropriate dynamic stiffness method as the number of modes increase is illustrated. Frequency proportional discretisation level required for mode superposition and approximate dynamic stiffness methods is outlined. It is reiterated that the results of exact dynamic stiffness method are invariant with reference to the discretisation level. The Eigen-frequencies of the system are evaluated using William-Wittrick algorithm and Sturm number generation in the $LDL^T$ decomposition of the real part of the dynamic stiffness matrix, as they cannot be explicitly evaluated. Major's method for dynamic analysis of machine supporting structures is modified and the plane frames are replaced with springs of exact dynamic stiffness and dynamically flexible longitudinal frames. Results of the analysis are compared with exact values. The possible simplifications that could be introduced for a typical machine induced excitation on a framed structure are illustrated and the developed program is modified to account for dynamic constraint equations with a master slave degree of freedom (DOF) option.