• 인터넷정보학회논문지
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• 제10권2호
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• pp.85-98
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• 2009

최근 들어 SaaS기반 이동형 개인 맞춤 소프트웨어 플랫폼에 관한 연구 및 개발이 활발해지고 있다. 이러한 플랫폼들은 다양한 사용자의 OS를 만족시키기 위해 최적화된 가상머신이 필수적으로 요구된다. 또한 다양한 Host OS에서 내부, 외부 네트워크 간에 빠르고 안정적인 서비스를 지원 함으로서 사용자의 작업환경 이동성을 보장해야 한다. 이를 위해 가상머신은 다양한 관점에서의 성능 측정이 필요하다. 하지만 기존 연구에서 가상머신의 성능 측정은 Host 컴퓨터에 설치된 VM에 Guest OS를 설치하여 이를 하나의 컴퓨터로 간주하고, 외부의 클라이언트에서 네트워크 성능을 측정한다. 이는 이동형 개인 맞춤 소프트웨어 플랫폼을 위한 가상머신 성능 측정에 적합하지 않다. 본 논문은 SaaS기반 이동형 개인 맞춤 소프트웨어 플랫폼을 위한 최적화된 가상머신을 선정하기 위해서 네트워크 성능 측정 방법과 측정된 결과 분석을 통한 최적화된 가상머신을 제안한다.

• 한국공작기계학회논문집
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• 제11권2호
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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.

• 한국공작기계학회논문집
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• 제12권5호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 추계학술대회 논문집
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 춘계학술대회 논문집
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• pp.231-232
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• 2009

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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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.

• 한국정밀공학회지
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• 제11권2호
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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.

• 센서학회지
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• 제31권2호
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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.