• 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.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.460-468
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• 2023

Developmental Dysplasia of the Hip (DDH) is a pathological condition commonly occurring during the growth phase of infants. It acts as one of the factors that can disrupt an infant's growth and trigger potential complications. Therefore, it is critically important to detect and treat this condition early. The traditional diagnostic methods for DDH involve palpation techniques and diagnosis methods based on the detection of keypoints in the hip joint using X-ray or ultrasound imaging. However, there exist limitations in objectivity and productivity during keypoint detection in the hip joint. This study proposes a deep learning model-based keypoint detection method using X-ray and ultrasound imaging and analyzes the performance of keypoint detection using various deep learning models. Additionally, the study introduces and evaluates various data augmentation techniques to compensate the lack of medical data. This research demonstrated the highest keypoint detection performance when applying the residual network 152 (ResNet152) model with simple & complex augmentation techniques, with average Object Keypoint Similarity (OKS) of approximately 95.33 % and 81.21 % in X-ray and ultrasound images, respectively. These results demonstrate that the application of deep learning models to ultrasound and X-ray images to detect the keypoints in the hip joint could enhance the objectivity and productivity in DDH diagnosis.

• The Journal of Korean Physical Therapy
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• v.31 no.4
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• pp.212-215
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• 2019

Purpose: This study examined whether there is a difference in motor learning through short-term repetitive movement practice in stroke survivors with a unilateral brain injury compared to normal elderly participants. Methods: Twenty-six subjects who were divided into a stroke group (n=13) or sex-aged matched normal elder group (n=13) participated in this study. To evaluate the effects of motor learning, the participants conducted a tracking task for visuomotor coordination. The accuracy index was calculated for each trial. Both groups received repetitive tracking task training of metacarpophalangeal joint for 50 trials. The stroke group performed a tracking task in the upper extremity insi-lesional to the damaged hemisphere, and the normal elder group performed the upper extremity matched for the same side. Results: Two-way repetitive ANOVA revealed a significant difference in the interactions ($time{\times}group$) and time effects. These results indicated that the motor skill improved in both the stroke and normal elder group with a tracking task. On the other hand, the stroke group showed lesser motor learning skill than the normal elder group, in comparison with the amount of motor learning improvement. Conclusion: These results provide novel evidence that stroke survivors with unilateral brain damage might have difficulty in performing ipsilateral movement as well as in motor learning with the ipsilateral upper limb, compared to normal elderly participants.

• Journal of Korean Orthopaedic Sports Medicine
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• v.7 no.1
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• pp.8-14
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• 2008

Elbow joint injuries during exercise mostly occur by repeated stress to the joint than direct trauma. A pitcher who uses his arm above his head is most likely to be injured. So learning the right way to exercise and gaining the strength by maturating the body are essential for diminishing the chance of injury. On lateral ulnar tendon injury, which is most commonly injured area on elbow joint, pitchers generally complain of pain in arm movement above head and reduction of velocity, accuracy, and number of pitching. When there is pain on upper arm in harsh using, the stress fracture must be thought and epicondylar physis fracture of medial arm can occur by repeated abduction stress and contraction of flexors on forearm on children with immature skeleton. Osteochondritis dissecans of capitullum occur in young athletes who use there upper limb continuously lifting weights and gym work. And stress of abduction-extension includes damage of soft tissue and bone components, post medial crush syndrome, lateral ulnar ligament injury, extensor-abductor injury, stress of radius- capitullum are in this category.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.101-106
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• 1997

This paper proposes a stable adaptive neural network control(NNC) for fixable joint manipulators. For designing the stable adaptive NNC, the flexible system dynamics is separated into fast and slow subdynamics according to singular perturbation concept. For the slow subdynamics, an adaptive NNC is designed to warrant the system stability and NN learning by lyapunov stability criterion. And to stabilize the fast dynamics, derivative control loop is installed. Through numerical simulation, the performance of the proposed NNC was compared to that of an adaptive controller designed based on the knowledge of the system dynamics. The proposed NNC shows much improvement over the conventional adaptive controller.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.1-6
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• 1992

A principle of "orthogonalization" is proposed as an extended notion of hybrid (force and position) control for robot manipulators under geometric endpoint constraints. The principle realizes the hybrid control in a strict sense by letting position and velocity feedback signals be orthogonal in joint space to the contact force vector whose components are exerted at corresponding joints. This orthogonalization is executed via a projection matrix computed in real-time from a gradient of the equation of the surface in joint coordinates and hence both projected position and velocity feedback signals become perpendicular to the force vector that is normal to the surface at the contact point in joint space. To show the important role of the principle in control of robot manipulators, three basic problems are analyzed, the first is a hybrid trajectory tracking problem by means of a "modified hybrid computed torque method", the second is a model-based adaptive control problem for robot manipulators under geometric endpoint constraints, and the third is an iterative learning control problem. It is shown that the passivity of residual error dynamics of robots follows from the orthogonalization principle and it plays a crucial role in convergence properties of both positional and force error signals.force error signals.

• Journal of Engineering Education Research
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• v.22 no.5
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• pp.3-12
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• 2019

Due to the expansion of rolling admissions and diversification of college admissions, the widening gap between the level of scholastic ability and academic performance is the reality of college education. Thus, based on the incoming class of College A, this study analyzes the correlation between incoming students who enrolled in a college mathematics course during their first semester. Through this analysis, this study searches for a way to efficiently instruct students from various learning backgrounds when enrolled in the same course. Also, this study searches for a solution to lower the deviation of college mathematics' academic performance among engineering majors by examining the efficiency of a joint managed college mathematics curriculum.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.347-353
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• 2022

In this paper, we propose a method that performs a text-video retrieval model by replacing video properties using captions. In general, the exisiting embedding-based models consist of both joint embedding space construction and the CNN-based video encoding process, which requires a lot of computation in the training as well as the inference process. To overcome this problem, we introduce a video-captioning module to replace the visual property of video with captions generated by the video-captioning module. To be specific, we adopt the caption generator that converts candidate videos into captions in the inference process, thereby enabling direct comparison between the text given as a query and candidate videos without joint embedding space. Through the experiment, the proposed model successfully reduces the amount of computation and inference time by skipping the visual processing process and joint embedding space construction on two benchmark dataset, MSR-VTT and VATEX.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.86-94
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• 1997

This paper proposes the robot inverse calibration method using a neural networks. A high-order networks called Pi-Sigma networks has been used. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the diff- erence of joint variables only between measuring value and analytic value about the desired pose(position, orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness, the SCARA type direct drive robot(4-DOF) and anthropomorphic robot(6-DOF) are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from .+-. 5 .deg. to .+-. 0.1 .deg. .

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.862-868
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• 2007

The planar motion of the index finger in general human hands is usually implemented by the actuation of three joints. This task requires a technique to determine the joint combination for each fingertip position which is well-known as the inverse kinematics problem in robotics. Especially, it is an essential work for grasping and manipulation tasks by robotic and humanoid fingers. In this paper, an intelligent neural learning scheme for solving such inverse kinematics is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed for fast learning. Also, a bio-mimetic feature of general human fingers is incorporated to the learning scheme. The usefulness of the proposed approach is verified by simulations.