• Knee surgery & related research
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• v.30 no.4
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• pp.303-310
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• 2018

Purpose: The purpose of this study was to investigate whether surgical experience could improve surgical competency in medial open wedge high tibial osteotomy (MOWHTO). Materials and Methods: One hundred consecutive cases of MOWHTO were performed with preoperative planning using the Miniaci method. Surgical errors were defined as under- or overcorrection, excessive posterior slope change, or the presence of a lateral hinge fracture. Each of these treatment failures was separately evaluated using the cumulative summation test for learning curve (LC-CUSUM). Results: The LC-CUSUM showed competency in prevention of undercorrection, excessive posterior slope change, and lateral hinge fracture after 27, 47, and 42 procedures, respectively. However, the LC-CUSUM did not signal achievement of competency in prevention of overcorrection after 100 procedures. Furthermore, the failure rate for overcorrection showed an increasing tendency as surgical experience increased. Conclusions: Surgical experience may improve the surgeon's competency in prevention of undercorrection, excessive posterior slope change, and lateral hinge fracture. However, it may not help reduce the incidence of overcorrection even after performance of 100 cases of MOWHTO over a period of 6 years.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.470-473
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• 1996

This paper presents an iterative learning control scheme for industrial manipulators. Based upon the frequency-domain analysis, the input update law of the learning controller is given together with a sufficient condition for the convergence of the iterative process in the frequency domain. The proposed learning control scheme is structurally simple and computationally efficient since it is independent joint control depending only on locally measured variables and it does not involve the computation of complicated nonlinear manipulator dynamics. Moreover, it is capable of canceling the unmodeled dynamics of the manipulator without even the parametric model. Several important aspects of the learning scheme inherent in the frequency-domain design are discussed and the control performance is demonstrated through computer simulations.

• Research in Mathematical Education
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• v.14 no.1
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• pp.11-18
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• 2010

The basic idea of cooperative learning focuses on team reward, equal opportunities for success, cooperation within team and competition among teams, and emphasizes share of sense of achievement through joint efforts so as to realize specific learning objectives. The main strategies of engineering mathematics teaching based on cooperative learning are to establish favorable team and design reasonable team activity plan. During the period of team establishment, attention shall be given to team structure including such elements as team status, role, norm and authority. Team activity plan includes team activity series and team activity task. Team activity task shall be designed to be a chain of questions following a certain principle.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.243-248
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• 2019

Multi-Task Learning(MTL) is a training method that trains a single neural network with multiple tasks influences each other. In this paper, we compare performance of MTL Named entity recognition(NER) model trained with Korean traditional culture corpus and other NER model. In training process, each Bi-LSTM layer of Part of speech tagging(POS-tagging) and NER are propagated from a Bi-LSTM layer to obtain the joint loss. As a result, the MTL based Bi-LSTM model shows 1.1%~4.6% performance improvement compared to single Bi-LSTM models.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.95-104
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• 2008

The purpose of this study was to investigate kinematic coordination and control of lower segments in skill process. For the investigation, we examined the difference of resultant linear velocity of segments and angle vs angle graph. Novice subjects were 9 male middle school students who has never been experienced a taekwondo and expert subjects were 7 university taekwondo players. We analyzed kinematic variables of Dollyochagi motion through videographical analysis and the conclusion were as follows. 1. Examining the graph of novice subjects' maximal resultant linear velocity of the thigh, shank, and foot segment, as it gets closer to the end of the training, the maximal resultant linear velocity in each segment increased. Statistical analysis showed the following results; thigh segment caused the increase of speed, using the trunk segment's momentum in the latter term of learning, while the shank segment utilized the momentum of the adjacent proximal segment at the beginning of learning, and the foot segment in the middle of learning. 2. Until the point where the knee joint angle is minimum, as the novice group learn the skill, the flexion of knee and hip joints has changed into the form of coordination pattern in phase. On the other hand, the expert group showed continual coordination pattern in phase that the movement sequences were smooth. From the knee joint maximal flexion to impact timing, all novice and expert groups showed coordination pattern out of phase. 3. From the knee joint maximal flexion to impact timing, the ankle joint was fixed and the knee joint was extended to all the novice stages and expert subjects.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.393-398
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• 2022

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint(TMJ) magnetic resonance imaging (MRI) protocol. Materials and Methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement(ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement(ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.357-363
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• 2005

The non-minimum phase characteristic of a flexible manipulator makes tracking control of its tip difficult. The level of the tip tracking performance of a flexible manipulator is significantly affected by the characteristics of the tip reference trajectory as well as the characteristics of the flexible manipulator system. This paper addresses the question of how to best specify a reference trajectory for the tip of a flexible manipulator to follow in order to achieve the objectives of reducing : tip tracking error, residual tip vibration, and the required actuation effort at the manipulator joint. A novel method of tip-based learning controller for the flexible manipulator system is proposed in the paper, where a model of the flexible manipulator system with a command shaping filter is used to generate a smooth and realizable tip reference trajectory for a tip-based learning controller.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.345-354
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• 2021

Recently, deep learning-based image recognition systems have been adopted to various surveillance environments, but most of them are still picture-type object recognition methods, which are insufficient for the long term temporal analysis and high-dimensional situation management. Therefore, we propose a method recognizing the specific dangerous situation generated by human in real-time, and utilizing deep learning-based object analysis techniques. The proposed method uses deep learning-based object detection and tracking algorithms in order to recognize the situations such as 'trespassing', 'loitering', and so on. In addition, human's joint pose data are extracted and analyzed for the emergent awareness function such as 'falling down' to notify not only in the security but also in the emergency environmental utilizations.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.2
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• pp.1-8
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• 2024

The expansion joint is installed to offset the expansion of the superstructure and must ensure sufficient gap during its service life. In detailed guideline of safety inspection and precise safety diagnosis for bridge, damage due to lack or excessive gap is specified, but there are insufficient standards for determining the abnormal behavior of superstructures. In this study, a data-based maintenance was proposed by continuously monitoring the expansion-gap data of the same expansion joint. A total of 2,756 data were collected from 689 expansion joint, taking into account the effects of season. We have developed a method to evaluate changes in the expansion joint-gap that can analyze the thermal movement through four or more data at the same location, and classified the factors that affect the superstructure behavior and analyze the influence of each factor through deep learning and explainable artificial intelligence(AI). Abnormal behavior of the superstructure was classified into narrowing and functional failure through the expansion joint-gap evaluation graph. The influence factor analysis using deep learning and explainable AI is considered to be reliable because the results can be explained by the existing expansion gap calculation formula and bridge design.