• IE interfaces
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• v.24 no.2
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• pp.105-111
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• 2011

Measurement protocols for hand anthropometry have been studied for ergonomic product design. The present study developed a 3D semi-automatic measurement protocol (3D-SAMP) which semi-automatically measures various hand dimensions using a 3D scanner. The 3D-SAMP was compared with the conventional direct measurement method (DMM) to examine its effectiveness. The 3D-SAMP consists of (1) fabricating a plaster cast of the hand, (2) placing landmarks on the plaster hand, (3) scanning the plaster hand with a 3D scanner, (4) identifying automatically the positions of the landmarks on the digital hand, and (5) extracting automatically hand anthropometric measurements (lengths, widths, thicknesses, and circumferences). An evaluation experiment conducted in the study found the 3D-SAMP preferred to the DMM in terms of reliability (the number of dimensions exceeding the variability criteria SD=2 mm and CV=5% : 3D-SAMP =2 and DMM=24) and ease of measurement (3D-SAMP=5.2 and DMM=4.3 out of 7). The 3D-SAMP can be applied to ergonomic design of a hand-held product.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.20-25
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• 2012

The oscillometric method and Korotkoff sound method are the most common ways to measure the blood pressure. A new automatic blood pressure measurement device, which uses both oscillometric method and Korotkoff method, was developed. A pressure sensor was used to obtain cuff pressure and oscillation signal, and a microphone was used to detect Korotkoff sounds. Forty-five measurements from fifteen subjects were used for analysis. Correlation coefficients between the traditional auscultatory method and Korotkoff sound method were 0.9820 and 0.9721 for the systolic and diastolic blood pressure values, respectively. Standard deviations of differences for the systolic and diastolic blood pressure values were 1.3019 and 1.4495, respectively. Correspondingly, correlation coefficients between the traditional auscultatory method and oscillometric method using newly developed algorithm were 0.9651 and 0.9136 for the systolic and diastolic blood pressure values, with the standard deviations of 1.42 and 1.73, respectively. The results showed that the newly developed algorithm for oscillometirc method provide accurate blood pressure values, moreover, Korotkoff sound method using microphone provides even higher accuracy. Therefore, a new automatic device which utilizes both oscillometric method and Korotkoff sound method would provide the accurate and reliable blood pressure values.

• 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.

• Korean Journal of Radiology
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• v.22 no.2
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• pp.168-178
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• 2021

Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). Materials and Methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). Conclusion: The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.50-58
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• 2024

Body temperature plays an important role in medicine, some diseases are characterized by changes in human body temperature. Monitoring body temperature also allows doctors to monitor the effectiveness of medical treatments. Accurate body temperature measurement is key to detecting fevers, especially fevers related to infection with the SARS-CoV-2 virus that caused the recent Covid-19 pandemic in the world. The solution of measuring body temperature using a thermal camera is fast but has a high cost and is not suitable for some organizations with difficult economic conditions today. Use a medical thermometer to measure body temperature directly for a slow rate, making it easier to spread disease from person to person. In this paper, we propose a completely automatic body temperature measurement system that can adjust the height according to the person taking the measurement, has a measurement logging system and is monitored via the internet. Experimental results show that the proposed method has successfully created a fully automatic human body measurement system. Furthermore, this research also helps the school's scientists and students gain more knowledge and experience to apply Internet of Things technology in real life.

• Journal of Advanced Navigation Technology
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• v.22 no.2
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• pp.64-69
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• 2018

Automatic test equipment for field maintenance of aircraft mounted equipment is effective for integrated design when operating a small number of aircraft for special purposes. The integrated automatic test equipment identifies commonly used interfaces and is used for branching or generating routes for each unit under test specific inspection. High-precision signals such as RTD, TC, and analog voltage can cause measurement errors due to conduction resistance during signal branching and connection when generating branches and paths. The measurement error caused by the resistance of the wire leads to a lot of restrictions in designing the equipment to be inspected. In this paper, we propose a method of calibrating highly accurate signals of an integrated automatic inspection equipment that minimizes measurement errors of analog voltage and high - precision signals.

• Journal of the Semiconductor & Display Technology
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• v.13 no.1
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• pp.51-55
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• 2014

In this paper, an hybrid automatic focusing method has been proposed for speedy and reliable measurement and inspection in industry. It can improve reliability of focusing position by using not a focusing measure but the hybrid one that is incorporated with sobel operator and auto-correlation. Also, it can not only reduce control time of focusing position using adaptive step size, but also improve accuracy of focusing position by gaussian interpolation. Its performance is verified by experiments. It is expected that it can apply to optical system for measurement and inspection in industry fields.

• Journal of Environmental Science International
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• v.11 no.10
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• pp.1055-1060
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• 2002

A new method of automatic recording raingauge is developed to measure rainfall 1200mm full scale with high accuracy and resolution. The principle of new instrument is to detect a weight change of a buoyant weight according to a change in water level of raingauge measured by the use of a strain gauge load cell. This method has the advantage of increasing measurement accuracy, since no moving equipment is used. Laboratory test of the instrument was recorded 0.4% error of 190mm rainfall amount. The validity of new instrument was examined by comparing its measured values with values recorded by automatic weather station on June 24 to 25 2001 at Daegu Meteorological Station, when there is 148.3mm rainfall amount. In spite of much rainfall there is only 0.77mm difference of total rainfall amount. This instrument was accomplished high accuracy and resolution at field test in much rainy day.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1826-1835
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• 2007

In this paper, we present a new approach for automatic detection and tracking for multiple targets. We combine a highest probability data association(HPDA) algorithm for target detection with a particle filter for multiple target tracking. The proposed approach evaluates the probabilities of one-to-one assignments of measurement-to-track and the measurement with the highest probability is selected to be target- originated, and the measurement is used for probabilistic weight update of particle filtering. The performance of the proposed algorithm for target tracking in clutter is compared with the existing clustering algorithm and the sequential monte carlo method for probability hypothesis density(SMC PHD) algorithm for multi-target detection and tracking. Computer simulation studies demonstrate that the HPDA algorithm is robust in performing automatic detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.371-375
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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.