• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.181-197
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• 2003

Notion Compensation(MC) technique using Sub-Band Coding with the hierarchical structure is efficient to estimate real motion. In the hierarchical pyramid method, low-band MC pyramid method is popular, where the upper layer estimate the glover motion and next lower layer estimate the local motion. The low-band MC pyramid scheme has two problems. First, because the quantization errors at lower layer are accumulated when using coding and quantizing, it is impossible to search the exact Motion Vector(MV) Second, because of the top-down search problem in the hierarchical structure, MV mismatch in upper layer causes serious MV in lower layer So. we propose new hierarchical MC pyramid method based on edge classification. In this Paper, we show that the performance of proposed Pass-band motion compensation pyramid technique is better than low-band motion compensation pyramid. Also, in the pyramid motion estimation, we propose initial MV estimation scheme based on the edge-pattern classification. As a result, we find that PSNR was increased.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.492-494
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• 2002

We established the job classification method that a classification standard is clear, and can subdivide job by using the raw data of time-and-motion study performed to analyze the medical staffs job elements. The final target of this study is to optimize job allocation and calculate human cost of medical staffs in hospitals.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.551-560
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• 2023

Recently, there has been significant research into the recognition of human activities using three-dimensional sequential skeleton data captured by the Kinect depth sensor. Many of these studies employ deep learning models. This study introduces a novel feature selection method for this data and analyzes it using machine learning models. Due to the high-dimensional nature of the original Kinect data, effective feature extraction methods are required to address the classification challenge. In this research, we propose using the first four moments as predictors to represent the distribution of joint sequences and evaluate their effectiveness using two datasets: The exergame dataset, consisting of three activities, and the MSR daily activity dataset, composed of ten activities. The results show that the accuracy of our approach outperforms existing methods on average across different classifiers.

• International Journal of Contents
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• v.7 no.1
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• pp.14-22
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• 2011

Temporal Sign Language Recognition (TSLR) from hand motion is an active area of gesture recognition research in facilitating efficient communication with deaf people. TSLR systems consist of two stages: a motion sensing step which extracts useful features from signers' motion and a classification process which classifies these features as a performed sign. This work focuses on two of the research problems, namely unknown time varying signal of sign languages in feature extraction stage and computing complexity and time consumption in classification stage due to a very large sign sequences database. In this paper, we propose a combination of Dynamic Time Warping (DTW) and application of the Single hidden Layer Feedforward Neural networks (SLFNs) trained by Extreme Learning Machine (ELM) to cope the limitations. DTW has several advantages over other approaches in that it can align the length of the time series data to a same prior size, while ELM is a useful technique for classifying these warped features. Our experiment demonstrates the efficiency of the proposed method with the recognition accuracy up to 98.67%. The proposed approach can be generalized to more detailed measurements so as to recognize hand gestures, body motion and facial expression.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.186-192
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• 2021

In this paper, we evaluated prediction accuracy of Euler angle spectrograph classification method using a convolutional neural networks (CNN) for hand gesture recognition in augmented reality (AR) cognitive rehabilitation system based on Leap Motion Controller (LMC). Hand gesture recognition methods using a conventional support vector machine (SVM) show 91.3% accuracy in multiple motions. In this paper, five hand gestures ("Promise", "Bunny", "Close", "Victory", and "Thumb") are selected and measured 100 times for testing the utility of spectral classification techniques. Validation results for the five hand gestures were able to be correctly predicted 100% of the time, indicating superior recognition accuracy than those of conventional SVM methods. The hand motion recognition using CNN meant to be applied more useful to AR cognitive rehabilitation training systems based on LMC than sign language recognition using SVM.

• Journal of Biomedical Engineering Research
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• v.33 no.2
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• pp.65-71
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• 2012

In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.4
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• pp.65-77
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• 2007

Site response analyses were performed based on equivalent linear technique using shear wave velocity profiles of 162 sites collected around the Korean peninsula. The site characteristics, particularly the shear wave velocities and the depth to the bedrock, are compared to those in the western United States. The results show that the site-response coefficients based on the mean shear velocity of the top 30m ($V_{S30}$) suggested in the current code underestimates the motion in short-period ranges and overestimates the motion in mid-period ranges. The current Korean code based on UBC is required to be modified considering site characteristics in Korea for the reliable estimation of site amplification. From the results of numerical estimations, new regression curves were derived between site coefficients ($F_{a}\;and\;F_{v}$) and the fundamental site periods, and site coefficients were grouped based on site periods with reasonable standard deviations compared to site classification based on $V_{S30}$. Finally, new site classification system and modification of design response spectra are recommended considering geotechnical characteristics in Korea.

• Korean Journal of Applied Biomechanics
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• v.26 no.1
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• pp.51-69
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• 2016

Objective: The purpose of this study is to suggest new terminology for the ninety-five hand techniques based on the significance of their angular momentum, determined by analyzing each technique's influence or impact on the compartmentalized angular momentum of the trunk, upper arm, and forearm in the Taekwondo Poomsae. Method: An athlete who won the 2014 World Taekwondo Poomsae championship was selected and agreed to participate in the data collection phase of our investigation. The video data was collected using eight infrared cameras (Oqus 300, Qualysis, Sweden) and the Qualisys Track Manager software (Qualisys, Sweden). The angular momentum of each movement was then calculated using the Matlab R2009a software (The Mathworks, Inc., USA). Results: The classification of the ninety-five hand techniques in the Taekwondo Poomsae based on the significance of each segment's momentum is as follows. Makgi (blocking) is classified into fourteen categories, jireugi (punching) is classified into three categories, chigi (hitting) was classified into six categories, palgupchigi (elbow hitting) was classified into four categories, and jjireugi (thrusting) was classified two categories. Conclusion: This study offers a new approach, based on a biomechanical method, to the classification of the hand techniques that reflect kinesthetic motions in the Taekwondo Poomsae.

• Special Issue of the Society of Naval Architects of Korea
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• 2005.06a
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• pp.15-21
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• 2005

In this paper, the procedure of generation and application of nonlinear wave loads for structural design of large container carrier was described. Ship motion and wave load was calculated by modified strip method. Pressure acting on wetted hull surface was calculated taking into account of relative hull motion to the wave. Design wave height was determined based on the most sensitive wave length considering rule vertical wave bending moment at head sea or fellowing sea condition. And the enforced heeling angie concept which was introduced by Germanischer Lloyd (GL) classification had been used to simulate high torsional moment in way of fore hold parts similar to actual sea going condition. Using wave load generated from this dynamic load calculation, FE analyses were performed. With this result, yielding, buckling, hatch diagonal deflection and fatigue strength of hatch corners were reviewed based on the requirement of GL classification. The results of FE analysis show good compatibility with GL classification.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.