• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.24 no.2
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• pp.37-42
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• 2018

Background: The Purpose of this study was to evaluate the value of Spencer technique on the range of motion (ROM), Pain, function in patients with shoulder adhesive capsulitis. Methods: subjects consisted of 30patients who were diagnosed shoulder adhesive capsulitis. All subjects are randomly assigned to 2groups: Spencer technique (ST) group (n=15), self assistive ROM exercise(S-A ROM E) group (n=15). The subjects performed an intervention program 30 minuets per day and was repeated 3 times a week for 4 weeks a total of 12 times. ROM of flexion, abduction, external rotation, internal rotation were measured using a goniometer. The visual analog scale (VAS), Shoulder pain and disability index (SPADI) were used to measure pain, functional ability. Results: In the intergroup comparisons after the intervention, ROM of flexion, abduction, internal rotation, VAS, SPADI were significantly different(p<.05). Spencer technique was more effective for improving ROM, pain, functional ability than self assistive ROM exercise. Conclusions: Our study suggest that considering Spencer technique for the patient with shoulder adhesive capsulitis. Further studies on Spencer technique are needed in the future.

• Smart Structures and Systems
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• v.4 no.6
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• pp.809-828
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• 2008

Real-time hybrid testing is an attractive method to evaluate the response of structures under earthquake loads. The method is a variation of the pseudodynamic testing technique in which the experiment is executed in real time, thus allowing investigation of structural systems with time-dependent components. Real-time hybrid testing is challenging because it requires performance of all calculations, application of displacements, and acquisition of measured forces, within a very small increment of time. Furthermore, unless appropriate compensation for time delays and actuator time lag is implemented, stability problems are likely to occur during the experiment. This paper presents an approach for real-time hybrid testing in which time delay/lag compensation is implemented using model-based response prediction. The efficacy of the proposed strategy is verified by conducting substructure real-time hybrid testing of a steel frame under earthquake loads. For the initial set of experiments, a specimen with linear-elastic behavior is used. Experimental results agree well with the analytical solution and show that the proposed approach and testing system are capable of achieving a time-scale expansion factor of one (i.e., real time). Additionally, the proposed method allows accurate testing of structures with larger frequencies than when using conventional time delay compensation methods, thus extending the capabilities of the real-time hybrid testing technique. The method is then used to test a structure with a rate-dependent energy dissipation device, a magnetorheological damper. Results show good agreement with the predicted responses, demonstrating the effectiveness of the method to test rate-dependent components.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.1022-1028
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• 2020

One of the possible options for spent-fuel management in Korea is pyroprocessing, which is a process for electrochemical recycling of spent nuclear fuel. Nuclear material accountancy is considered to be a safeguards measure of fundamental importance, for the purposes of which, the amount of nuclear material in the input and output materials should be measured as accurately as possible by means of chemical analysis and/or non-destructive assay. In the present study, a neutron measurement system based on the fast-neutron energy multiplication (FNEM) and passive neutron albedo reactivity (PNAR) techniques was designed for nuclear material accountancy of a spent-fuel assembly (i.e., the input accountancy of a pyroprocessing facility). Various parameters including inter-detector distance, source-to-detector distance, neutron-reflector material, the structure of a cadmium sleeve around the close detectors, and an air cavity in the moderator were investigated by MCNP6 Monte Carlo simulations in order to maximize its performance. Then, the detector responses with the optimized geometry were estimated for the fresh-fuel assemblies with different ²³⁵U enrichments and a spent-fuel assembly. It was found that the measurement technique investigated here has the potential to measure changes in neutron multiplication and, in turn, amount of fissile material.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.