• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.06a
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• pp.61-70
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• 2000

This paper described the current status of machine tool technology and its future trends with a particular emphasis on high-speed machining. People in machine tool industry have continuously sought to serve fast-changing manufacturing industry with economical machining solutins. At presents, it appears that more productivity gain is demanded to shorten time-to-market and machining requirements become more stringent. In this regard, this paper firstly addressed a high-speed spindle as a key element for the next-generation machine tools. The sequel to it apparently went to high-speed feed axes and final discussion included the problem of how to optimize overall system including servo function. Lastly a brief look to NC technology including machine intelligence was taken.

• Parasites, Hosts and Diseases
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• v.54 no.6
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• pp.751-758
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• 2016

This study aimed at constructing a draft genome of the adult female worm Toxocara canis using next-generation sequencing (NGS) and de novo assembly, as well as to find new genes after annotation using functional genomics tools. Using an NGS machine, we produced DNA read data of T. canis. The de novo assembly of the read data was performed using SOAPdenovo. RNA read data were assembled using Trinity. Structural annotation, homology search, functional annotation, classification of protein domains, and KEGG pathway analysis were carried out. Besides them, recently developed tools such as MAKER, PASA, Evidence Modeler, and Blast2GO were used. The scaffold DNA was obtained, the N50 was 108,950 bp, and the overall length was 341,776,187 bp. The N50 of the transcriptome was 940 bp, and its length was 53,046,952 bp. The GC content of the entire genome was 39.3%. The total number of genes was 20,178, and the total number of protein sequences was 22,358. Of the 22,358 protein sequences, 4,992 were newly observed in T. canis. Following proteins previously unknown were found: E3 ubiquitin-protein ligase cbl-b and antigen T-cell receptor, zeta chain for T-cell and B-cell regulation; endoprotease bli-4 for cuticle metabolism; mucin 12Ea and polymorphic mucin variant C6/1/40r2.1 for mucin production; tropomodulin-family protein and ryanodine receptor calcium release channels for muscle movement. We were able to find new hypothetical polypeptides sequences unique to T. canis, and the findings of this study are capable of serving as a basis for extending our biological understanding of T. canis.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.487-496
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• 2020

For the purpose of next generation technology for robot perfomances, a RAoRA (Robot Actor on Robot Arm) structure was proposed using a robot arm joined with a humanoid robot actor. Mechanical analysis, machine design and fabrication were performed for motions combined with the robot arm and the humanoid robot actor. Kinematical analysis for 3D model, spline interpolation of positions, motion control algorithm and control devices were developed for movements of the robot actor. Preliminary visualization, simulation tools and integrated operation of consoles were constructed for the non-professionals to produce intuitive and safe contents. Air walk was applied to test the developed platform. The air walk is a natural walk close to a floor or slow ascension to the air. The RAoRA also executed a performance with 5 minute-running time. Finally, the proposed platform of robot performance presented intensive and live motions which was impossible in conventional robot performances.