• Journal of the Korean Orthopaedic Association
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• v.55 no.2
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• pp.143-153
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• 2020

Purpose: To evaluate the timing of the improvement in surgical skills of the direct anterior approach for hip arthroplasty through an analysis of the clinical features and learning curve in 58 cases. Materials and Methods: From November 2016 to November 2018, 58 patients, who were divided into an early half and late half, and underwent hip arthroplasty by the direct anterior approach, were enrolled in this retrospective study. The operation time and complications (fracture, lateral femoral cutaneous nerve injury, heterotopic ossification, infection, and dislocation) were assessed using a chi-square test, paired t-test, and cumulative sum (CUSUM) test. Results: The mean operation times in total hip arthroplasty (26 cases) and bipolar hemi-arthroplasty were 132.1 minutes and 79.7 minutes, respectively, demonstrating a significant difference between the two groups. CUSUM analysis based on the results revealed breakthrough points of the operation time, decreasing to less than the mean operation time because of the 16th case in total hip arthroplasty and 14th case in bipolar hemiarthroplasty. Complications were encountered in the early phase and late phase: five cases of fractures in the early phase, no case in the late phase; eight and two cases of lateral femoral cutaneous nerve injury, respectively; three and two cases of heterotopic ossification, respectively; and one case of dislocation, one case of infection and three cases of others in the early phase. The CUSUM chart for the fracture rate during operation in the early phase revealed the following: five cases fracture (17.2%) in the early phase and no case in the late phase (0%). This highlights the learning curve and the need for monitoring the inadequacy of operation based on the complications. Conclusion: Hip arthroplasty performed by the direct anterior approach based on an anatomical understanding makes it difficult to observe the surgical field and requires a learning curve of at least 30 cases.

• Journal of the military operations research society of Korea
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• v.36 no.2
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• pp.1-10
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• 2010

Learning rate is generally applied to estimate an appropriate production labor cost. Learning effect is obtained from repetitive work during the production period under 3 assumptions ; homogeneous production, same producer, quantity measure in continuous unit. However, production breaks occur frequently in Korean defense industry environment because of budget constraint and annual requirements. In this case previous learning effect can not be applied due to learning loss. This paper proposed the application of learning rate when a production break occurs in Korea defense industry. To obtain a learning loss, we surveyed various learning loss factors for different production breaks(6, 12, 18 months) from 4 defense industry companies. Then, we estimate the first unit labor hours in re-production phase after production break using Anderlohr method and Retrograde method with the result of the survey. This work is the first attempt to show a method which defines and evaluates the learning loss factors in Korean defense industry environment.

• Smart Media Journal
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• v.5 no.3
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• pp.35-41
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• 2016

In this paper, we propose hand-gesture signal recognition based on EPS(Electronic Potential Sensor) using Deep learning model. Extracted signals which from Electronic field based sensor, EPS have much of the noise, so it must remove in pre-processing. After the noise are removed with filter using frequency feature, the signals are reconstructed with dimensional transformation to overcome limit which have just one-dimension feature with voltage value for using convolution operation. Then, the reconstructed signal data is finally classified and recognized using multiple learning layers model based on deep learning. Since the statistical model based on probability is sensitive to initial parameters, the result can change after training in modeling phase. Deep learning model can overcome this problem because of several layers in training phase. In experiment, we used two different deep learning structures, Convolutional neural networks and Recurrent Neural Network and compared with statistical model algorithm with four kinds of gestures. The recognition result of method using convolutional neural network is better than other algorithms in EPS gesture signal recognition.

• Structural Engineering and Mechanics
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• v.91 no.2
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• pp.163-175
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• 2024

The objective of this research is to improve public safety in civil engineering by recognizing fractures in concrete structures quickly and correctly. The study offers a new crack detection method based on advanced image processing and machine learning techniques, specifically transfer learning with convolutional neural networks (CNNs). Four pre-trained models (VGG16, AlexNet, ResNet18, and DenseNet161) were fine-tuned to detect fractures in concrete surfaces. These models constantly produced accuracy rates greater than 80%, showing their ability to automate fracture identification and potentially reduce structural failure costs. Furthermore, the study expands its scope beyond crack detection to identify concrete health, using a dataset with a wide range of surface defects and anomalies including cracks. Notably, using VGG16, which was chosen as the most effective network architecture from the first phase, the study achieves excellent accuracy in classifying concrete health, demonstrating the model's satisfactorily performance even in more complex scenarios.

• Therapeutic Science for Rehabilitation
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• v.4 no.2
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• pp.73-83
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• 2015

Objective: The purpose of this study was to examine the effect of the Spaced Retrieval Training (SRT) with Errorless learning on the elderly with Mild Cognitive Impairment (MCI)'s memory, Instrumental Activities Daily Living, Depression symptom. Methods: A single subject experimental research with ABA design was conducted in this study on the 78-years-old person who was enrolled in day-care center. The total experimental sessions were 16 which composed of 3 sessions for baseline, 10 sessions for intervention and 3 sessions for second baseline. K-Auditory Verbal Learning Test (K-AVLT) was measured for the memory each session. For the measurement of cognitive function, IADL, depression Symptom, Korean version of Montreal Cognitive Assessment (MoCA-K), Philadelphia Geriatric Center Instrumental Activities Daily Living (PGC IADL), Geriatric Depression Scale Korean Version (GDS-K) was measured at pre-post test. Results: Memory at the phase B was improved than Phase A. At the phase B, the scores trend was ascending, but after the intervention at the phase A', the scores trend was descending. The scores of MoCA-K were improved, PGC IADL were maintained, GDS-K were decreased. Conclusion: This results support the evidence of the SRT with EL on the elderly with MCI in the clinical setting. In the future, the correlation researches about MCI's memory and other functional factors will be needed for effective occupational therapy service.

• 교육공학연구
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• v.33 no.4
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• pp.1001-1024
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• 2017

Flipped classroom has used widely in university in that its unique structure can facilitate learners' higher-thinking skills and promote competencies. Learners are expected to extend knowledge through performing online and offline, but they have difficulty in understanding their roles and specific behaviors to achieve the learning goals in the flipped learning. Therefore, a guidance for students has been required to support learners' mastery learning. The purpose of this study is to identify successful learners' characteristics in terms of "competency". For this, three-phased competency modeling was employed. In Phase I, Behavioral Event Interviews were conducted with eight learners of the flipped classroom. In Phase II for identifying competencies and developing a competency model, the data was coded, followed by testing reliability of the coding. Based on the meaning codes, competencies and behavioral indexes were developed. The final competencies consist of learning orientation, learning management, feedback seeking, peer interaction, and knowledge extension. In Phase III, validation of the competency model was conducted by explanatory factor analysis. As last, competencies were aligned by the two-phase of the flipped classroom. The finding will be used as the guidance for the learners and instructors in the flipped classroom.

• Biomolecules & Therapeutics
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• v.16 no.4
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• pp.320-327
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• 2008

Several lines of evidence indicate that adenosine $A_{2A}$ agonist disrupts spatial working memory. However, it is unclear which stages of learning and memory are affected by the stimulation of adenosine $A_{2A}$ receptor. To clarify these points, we employed CV-1808 as adenosine $A_{2A}$ agonist and investigated its effects on acquisition, consolidation, and retrieval phases of learning and memory using passive avoidance and the Morris water maze tasks. During the acquisition phase, CV-1808 (2-phenylaminoadenosine, 1 and 2 mg/kg, i.p.) decreased the latency time in passive avoidance task and the mean savings in the Morris water maze task, respectively. During the consolidation and retrieval phase tests, CV-1808 did not exhibited any effects on latency time in passive avoidance task and the mean savings in the Morris water maze task. These results suggest that CV-1808 as an adenosine $A_{2A}$ agonist impairs memory acquisition but not consolidation or retrieval.

• Korean Journal of Hydrosciences
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• v.4
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• pp.1-9
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• 1993

It is a major objective for the management and operation of water resources system to forecast streamflows. The applicability of artificial neural network model to hydrologic system is analyzed and the performance is compared by statistical method with observed. Multi-layered perception was used to model rainfall-runoff process at Pyung Chang River Basin in Korea. The neural network model has the function of learning the process which can be trained with the error backpropagation (EBP) algorithm in two phases; (1) learning phase permits to find the best parameters(weight matrix) between input and output. (2) adaptive phase use the EBP algorithm in order to learn from the provided data. The generalization results have been obtained on forecasting the daily and hourly streamflows by assuming them with the structure of ARMA model. The results show validities in applying to hydrologic forecasting system.

• Research in Mathematical Education
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• v.25 no.1
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• pp.21-43
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• 2022

Investigating the relationship between intrinsic and extrinsic motivation and their effects on affective mathematics engagement in a cultural context is critical for determining which types of motivation promote affective mathematics engagement and the relationship with cultural affordance. The investigation in the current study is comprised of two dependent studies. The results from Phase 1 indicate that attitude and emotion are better explained by extrinsic motivation, while self-acknowledgment and value are better explained by intrinsic motivation. The results of Phase 2 indicate that the Korean sample has greater extrinsic motivation, attitude, and emotion, while the U.S. sample has greater intrinsic motivation, self-acknowledgment, and value. The key outcome for this research is that disentangling cultural affordance from the emotional and cognitive structures is impossible.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.10
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• pp.934-940
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• 1991

A modified quantizing method is introduced to teach single layer learning algorithm, which is implemented optically. The proposed optical system consists of input masks, holographic diffraction grating. LCD and CCD camera. The 2 dimensional interconnections between input neurons and output neurons are realized using holographic phase grating, which is fabricated for equal intensity distribution of diffraction orders. The two gray levels of LCD act as binary weights for each interconnection. The weights are compensated according to the learning algorithm in which the amount of weights to be compensated is determined by comparing the output patterns with target patterns. The learning process is iterated until the predetermined conditions are satisfied. Optical experiments are performed for two learning rates, 0.5 and 0.9 and the experimental results show that the proposed system is useful for optical neural networks.