• Korean Journal of Acupuncture
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• v.32 no.3
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• pp.124-129
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• 2015

Objectives : The purpose of this study is to understand truncus area conception to be explained in the pyobon and geungyul (Root-Stem and Origin-End relationships; PG) of meridian theory as to do the macro organic spatial conception in bio-mechanic system for human body. Methods : The extremity areas and truncus area to be explained in PG theories were classified spatially, and the reason was discussed that human body was classified as the extremities and truncus areas for the people of old times. It was considered the structural characteristics, exercise species and movement characteristics of truncus area in human body also. Results : The truncus area were made of cranium, vertebrae, sternum, ribs, clavicle, scapula, sacrum and the hipbone. It was the hollow structure and classified as cranial cavity, pleural cavity(thoracic cavity) and pelvic cavity. These cavities had mutually organic relationships, and because of cavity structures in the truncus and full structures in extremities would act in different direction about gravity with each other for movements. So it would be estimated that the old peoples had to need to separate the truncus and the extremities. Conclusions : In this study, the truncus area and the extremity areas in the PG of meridian theory could be recognized as the spatial conception of bio-mechanic system in human body. Although this study was the theoretical study not to be proven, it will be used to understand for the meridian theory as basic reference for spatial conception and bio-mechanic system.

• Journal of Biosystems Engineering
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• v.32 no.1 s.120
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• pp.44-49
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• 2007

A stereo vision system was developed for robot milking system (RMS) using two monochromatic cameras. An algorithm for inverse perspective transformation was developed for the 3-D information acquisition of all teats. To verify performance of the algorithm in the stereo vision system, indoor tests were carried out using a test-board and model teats. A real cow and a model cow were used to measure distance errors. The maximum distance errors of test-board, model teats and real teats were 0.5 mm, 4.9 mm and 6 mm, respectively. The average distance errors of model teats and real teats were 2.9 mm and 4.43 mm, respectively. Therefore, it was concluded that this algorithm was sufficient for the RMS to be applied.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1935-1941
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• 2021

In order to develop an algorithm using deep learning, which has been recently applied to various fields, it is necessary to have rich, high-quality learning data. In this paper, we propose an acquisition system for biological signals that simultaneously collects bio-signal data such as optical videos, thermal videos, and voices, which are mainly used in developing deep learning algorithms and useful in derivation of information, and transmit them to the server. To increase the portability of the collector, it was made based on Raspberry Pi, and the collected data is transmitted to the server through the wireless Internet. To enable simultaneous data collection from multiple collectors, an ID for login was assigned to each subject, and this was reflected in the database to facilitate data management. By presenting an example of biological data collection for fatigue measurement, we prove the application of the proposed acquisition system.