• Progress in Medical Physics
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• v.14 no.2
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• pp.131-140
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• 2003

As the importance of accuracy in measurings of 3-D anatomical structures continues to be stressed, an objective and quantitative of assessing image quality and accuracy of 3-D volume-rendered images is required. The purpose of this study was to evaluate the quantitative accuracy of 3-D rendered images obtained with MDCT, scanned at various scanning parameters (scan modes, slice thicknesses and reconstruction slice thickness). Twelve clinically significant points that play an important role for the craniofacial bone in plastic surgery and dentistry were marked on the surface of a dry human skull. The direct distances between the reference points were defined as gold standards to assess the measuring errors of 3-D images. Then, we scanned the specimen with acquisition parameters of 300 mA, In kVp, and 1.0 sec scan time in axial and helical scan modes (pitch 3:1 and 6:1) at 1,25 mm, 2.50 mm, 3.75 mm and 5.00 mm slice thicknesses. We performed 3-D visualizations and distance measurements with volumetric analysis software and statistically evaluated the quantitative accuracy of distance measurements. The accuracy of distance measurements on the 3-D images acquired with 1.25, 2.50, 3,75 and 5.00 mm slice thickness were 48%, 33%, 23%, 14%, respectively, and those of the reconstructed 1.25 mm were 53%, 41%, 43%, 36% respectively. Meanwhile, there were insignificant statistical differences (P-value<0.05) in the accuracy of the distance measurements of 3-D images reconstructed with 1.25 mm thickness. In conclusion, slice thickness, rather than scan mode, influenced the quantitative accuracy of distance measurements in 3-D rendered images with MDCT. The quantitative analysis of distance measurements may be a useful tool for evaluating the accuracy of 3-D rendered images used in diagnosis, surgical planning, and radiotherapeutic treatment.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.1
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• pp.19-29
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• 2000

Because the previous simulation tool for attitude control of satellite was designed for the modeling of rigid body and PD controller, the attitude error can be made more than the limitation value for keeping for communication link, and then the communication link can be lost at moving of satellite. So, for rapid attitude restoration and design of stable and modernized controller, the modelling of rigid body and flexible body structure for moving GEO and LEO satellites were performed. Also the minimum time controller is designed for the rapid restoration of attitude error at communication broken and to minimize the disconnection period from ground communication system during the satellite stationkeeping. The linear regulator is designed using the space state vector that is better than accuracy and stability of PD controller. Firstly the simulation was performed for comparison of the rigid and stability of PD controller. Firstly the simulation was performed for comparison of the rigid and flexible models using PD controller and the case of the pitch angle changing by ground command, and the case of the periodic north-south stationkeeping are performed for the analysis of response characteristics of each controller when the attitude is changed. As a result, the flexible body model represents more sililar results of real situation than the rigid body model. The minimum time controller can restore 7 times rapidly than PD controller for its lost attitude. The linear regulator has several merits for capability of adaptation against the external disturbance, stability and response time. In future, we can check the estimated results using this satellite model and controller for real operation. Futhermore the development of new controller and training can be supported.

• The korean journal of orthodontics
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• v.41 no.1
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• pp.25-35
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• 2011

Objective: The purpose of this study was to optimize the thread pattern of orthodontic microimplants. Methods: In search of an optimal thread for orthodontic microimplants, an objective function stability quotient (SQ) was built and solved which will help increase the stability and torsional strength of microimplants while reducing the bone damage during insertion. Selecting the AbsoAnchor SH1312-7 microimplant (Dentos Inc., Daegu, Korea) as a control, and using the thread height (h) and pitch (p) as design parameters, new thread designs with optimal combination of hand p combination were developed. Design soundness of the new threads were examined through insertion strain analyses using 3D finite element simulation, torque test, and clinical test. Results: Solving the function SQ, four new models with optimized thread designs were developed (h200p6, h225p7, h250p8, and h275p8). Finite element analysis has shown that these new designs may cause less bone damage during insertion. The torsional strength of two models h200p6 and h225p7 were significantly higher than the control. On the other hand, clinical test of models h200p6 and h250p8 had similar success rates when compared to the control. Conclusion: Overall, the new thread designs exhibited better performance than the control which indicated that the optimization methodology may be a useful tool when designing orthodontic microimplant threads.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.457-460
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• 2002

The image quality of three-dimensional (3D) images has been widely investigated by the qualitative analysis method. A need remains for an objective and quantitative method to assess the image quality of 3D volume-rendered images. The purpose of this study was to evaluate the quantitative accuracy of distance measurements on 3D volume-rendered images of a dry human skull by using multi-detector computed tomography (MDCT). A radiologist measured five times the twenty-one direct measurement line items composed among twelve reference points on the skull surface with a digital vernier caliper. The water filled skull specimen was scanned with a MDCT according to the section thicknesses of 1.25, 2.50, 3.75, and 5.00 mm for helical (high quality; pitch 3:1) scan mode. MDCT data were reconstructed with its acquisition section thickness and with 1.25 mm section thickness for all scans. An observer also measured seven times the corresponding items on 3D volume-rendered images with measuring tools provided by volumetric analysis software. The quantitative accuracy of distance measurements on the 3D volume-rendered images was statistically evaluated (p-value < 0.05) by comparatively analyzing these measurements with the direct distance measurements. The accuracy of distance measurements on the 3D volume-rendered MDCT images acquired with 1.25, 2.50, 3,75 and 5.00 mm section thickness and reconstructed with its section thickness were 48%, 33%, 23%, and 14%, respectively. Meanwhile, there were insignificant statistical differences in accuracy of distance measurements among 3D volume-rendered images reconstructed with 1.25 mm section thickness for the each acquisition section thickness. MDCT images acquired with thick section thickness and reconstructed with thin section thickness in helical scan mode should be effectively used in medical planning of 3D volume-rendered images. The quantitative analysis of distance measurement may be a useful tool for evaluating the quantitative accuracy and the defining optimal parameters of 3D volume-rendered CT images.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.2
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• pp.89-96
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• 2022

Background and Objectives The quality of life after thyroidectomy, such as voice change, is considered to be as important as control of the disease. In this study, we aimed to evaluate changes in both subjective and objective voice parameters after thyroidectomy resulting in normal morbidity of the vocal cords. Materials and Method In this prospective cohort study, 204 patients who underwent thyroidectomy with or without central neck dissection at a single referral center from Feb 2015 to Aug 2016 were enrolled. All patients underwent prospective voice evaluations including both subjective and objective assessments preoperatively and then at 2 weeks, 3, 6, and 12 months postoperatively. Temporal changes of the voice parameters were analyzed. Results Values of the subjective assessment tool worsened during the early postoperative follow-up period and did not recover to the preoperative values at 12 months postoperatively. The maximal phonation time gradually decreased, whereas most objective parameters, including maximal vocal pitch (MVP), reached preoperative values at 3-6 months postoperatively. The initial decrease in MVP was significantly greater in patients undergoing total thyroidectomy, and their MVP recovery time was faster than that of patients undergoing lobectomy (p=0.001). Patients whose external branch of the superior laryngeal nerve was confirmed intact by electroidentification showed no difference in recovery speed compared with patients without electroindentification (p=0.102), although the initial decrease in MVP was lower with electroidentification. Conclusion Subjective assessment in voice quality and maximal phonation time after thyroidectomy did not show recovery to preoperative values. Aggravation of MVP was associated with surgical extent and electroidentification.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.729-735
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• 2021

Recently Artificial Intelligence(AI) has been developed and used in various fields. Especially AI recognition technology can perceive and distinguish images so it should plays a significant role in quality inspection process. For stability of autonomous driving technology, semiconductors inside automobiles must be protected from external electromagnetic wave(EM wave). As a shield film, a thin polymeric material with hole shaped micro-patterns created by a laser processing could be used for the protection. The shielding efficiency of the film can be increased by the hole structure with appropriate pitch and size. However, since the sensitivity of micro-machining for some parameters, the shape of every single hole can not be same, even it is possible to make defective patterns during process. And it is absolutely time consuming way to inspect all patterns by just using optical microscope. In this paper, we introduce a AI inspection system which is based on web site AI tool. And we evaluate the usefulness of AI model by calculate Area Under ROC curve(Receiver Operating Characteristics). The AI system can classify the micro-patterns into normal or abnormal ones displaying the text of the result on real-time images and save them as image files respectively. Furthermore, pressing the running button, the Hardware of robot arm with two Arduino motors move the film on the optical microscopy stage in order for raster scanning. So this AI system can inspect the entire micro-patterns of a film automatically. If our system could collect much more identified data, it is believed that this system should be a more precise and accurate process for the efficiency of the AI inspection. Also this one could be applied to image-based inspection process of other products.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.