• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.85-98
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• 2009

Recently, there are a number of researches and developments for the personalized software platform for mobility based on SaaS. The platform requires an optimal virtual machine in order to satisfy the operating systems of various users for the software. In addition, the platform must guarantee the mobility of the users' working environments by supporting fast and secure services between internal and external networks in the platform operating systems. In order to verify the optimal behaviors of virtual machines for the platform, the performance of the virtual machines must be measured and analyzed in various perspectives. In the previous research, unfortunately, the performance of a virtual machine were conducted in the condition that a guest operating system was installed on the virtual machine and considered as a computer, by measuring the network traffic between the guest operating system and an external client operating system. This performance measurement was not suitable for a virtual machine for the platform since a number of different software must be handled in the virtual machine. In order to overcome this limitation, this paper presents a measurement method for network performance and proposes the most optimal virtual machine by the method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.24-29
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• 2002

A study for the circularity of machine tool is classified into two ways. One is to measure the circularity of machine tool by a existing measuring device. The other is to measure the circularity by remodeling the existing measuring device. We will improve on measurement of circularity using circular test method by a two-dimensional probe and a master ring for machining tools and investigate influence of trigger method, acceleration data sampling.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.5
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• pp.15-23
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• 2003

A feature-based inspection planning system is proposed in this research to develop more efficient measuring methodology for the OMM(On-Machine Measurement) or CMM(coordinate Measuring Machine) for complicated workpiece having many primitive form features. This paper is proposed solution that optimum inspection sequence of the objective features. The sequences are determined by analyzing the feature information such as the nearest relationship and the possible probe-approach direction(PAD) of the features, and forming feature groups. A series of heuristic rules are developed to accomplish it. Also, each feature is decomposed into its constituent geometric elements for inspection process, and then the number of sampling points, location of the measuring points, optimum probing path are determined.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.345-350
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• 1993

This paper presents a new method based upon the light scattering concept for on-the-machine measurement of roughness of ground surfaces. The present method utilizes fluxes of scattered lights condensed through lenses aligned along the specular direction. A theorctical analysis is preformed for the purpose of investigating the possibility of the method as well as determining the experimental condition. Experiment is also performed to show the effectiveness of the proposed method. The theoretical and experimental results show that the proposed method has a potential to identify a wide range of surface roughness and is robust enough to be useful in on-the-machine measurement of roughness of ground surfaces.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.232-236
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• 1996

This paper deals with an on-the-machine measurement method for roughness of ground surface by using flux ratio of scattered lights. A sensor and control unit is developed so as to e applied to surface grinding processes. The performance of the sensor is compared with that of stylus. The experimental investigation shows that not onlythe sensor has good performance as a surface roughness sensor but alsothe sensor is very useful for monitoring grinding condition in order to detect ill-conditioned grinding or dressing time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.231-232
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.911-914
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• 1995

Measurement and inspection of freeform surface are required in reverse design process. In the case of surface measurement using a touch probe, probe radius compensation affects measuring accuracy But current industrial practice depends upon an operator's experience to compensate for probe radius. In this paper, an on-the-machine measuring and inspection system for freeform surfface was developed. Probe radius compensation methodology was studied via modeling of B-spline surfaces based on digitized data. The accuracy and reliability of the measurement system was confirmed through various kinds of experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.445-449
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• 2001

The tool wear that is developed by long-term machining in mold manufacturing with machining center makes a severe influence to the accuracy and the surface roughness. In this reason, tool-wear supervising system which has guaranteed high accuracy and high speed is needed to improve the measurement quality. Touching probe and touch sensor are widely used to measure the tool profile at on-machine measurement. In this paper, using the newly developed electric touch point measuring system, the Automatic Tool Compensation System is developed to correct the error of tool diameter resulted from the wear, and the operating method of this system is also provided.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.2
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• pp.149-163
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• 1994

A novel method of dimensional measurement using machine vision, which is called Landmark Tracking System, has been developed. Its advantages come form tracking only the bright, standard shaped "landmarks" which are made from retroreflective sheets. In the design of the LTS, it is essential to know the relationship between optical parameters and their influence on system performance. Such optical parameters include the brightness of landmark image, the illumination system design, and the choice of imaging optics. And the performance of retroreflective material also plays important role in the LTS performances. Influences of such optical parameters on LTS's dimensional measurement characteristics are investigated, with respect to the retroreflective material, the imaging optics, and the illumination system. Measuremtn errors due to parameter variations are also analyzed. Experiments are performed with a LTS prototype. Retroreflective characteristics are verified, and the LTS's measurement performances are measured in the form of repeatability and accuracy. Experimental results shgow that the LTS has repeatability better than 1/30,000 of a field of view(30 degrees), and accuracy better tha 1/3,000 of a field fo view.d fo view.

• 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.