• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• PNF and Movement
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• v.19 no.3
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• pp.391-399
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• 2021

Purpose: As laptop use increases throughout the COVID-19 pandemic and its use outside of traditional workstations proliferates, it is imperative to expand the limited research available regarding ergonomic exposure. This study aimed to identify the effects of a laptop supporter on biomechanical characteristics in the wrist joint muscles of healthy young adults. Methods: This was a cross-sectional observational study design with thirty-four healthy young adults as participants. They conducted a typing exercise performed under two different conditions, which were with and without a notebook computer supporter. This study measured the biomechanical characteristics of the muscles of the wrist joints including the flexor carpi ulnaris (FCU), the flexor carpi radialis (FCR), the extensor carpi radialis longus (ECRL), and the extensor carpi ulnaris (ECU). Measurements were taken three times: before typing, immediately after typing for 30 minutes with a supporter, and immediately after typing for 30 minutes without a supporter. The statistical method to compare the three different measurement conditions was the repeated measures ANOVA. Results: The participants showed significantly different levels of dynamic stiffness in both the FCU before typing and immediately after 30 minutes of typing with a supporter, and showed significantly different levels of dynamic stiffness in the FCR before typing and immediately after 30 minutes of typing with a supporter. The dynamic stiffness level immediately after 30 minutes of typing without a supporter was significantly different than that immediately after 30 minutes of typing with a supporter. However, the muscle tone was not significantly different among the three different conditions. Conclusion: The results of this study revealed that a laptop supporter used to correct the eye level of the electronic screen increases the dynamic stiffness of the wrist joint flexors, so it is necessary to consider the neutral position of the wrist joint during typing.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.307-316
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• 2021

This study aims to explore the effects of deductive FFI and inductive FFI in L2 learners' grammatical achievement and their reaction to the grammar instruction. 84 students were placed into three groups: 29 given deductive FFI(DG), 28 receiving inductive FFI(IG), and 27 with traditional instruction(CG). All students completed pre/post tests and questionnaires, and took a delayed post test 9 weeks after the treatment. For statistical anlayses of all the quantitative data, a one-way ANOVA, paired samples T-test, and repeated measures ANOVA were performed. The results indicated that both deductive and inductive FFI affected learners' grammatical achievement and their achievement was sustained over time. Deductive FFI was more effective than inductive FFI, whereas the IG students more positively changed their attitudes and perceptions to the grammar instruction. These findings of the study imply that FFI should be valued in an Korean EFL classroom, which would contribute to further longitudinal research for its sustainability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.98-105
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• 2021

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

• The Journal of Advanced Prosthodontics
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• v.10 no.6
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• pp.395-400
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• 2018

PURPOSE. This study tried to find the most significant factors predicting implant prognosis using machine learning methods. MATERIALS AND METHODS. The data used in this study was based on a systematic search of chart files at Seoul National University Bundang Hospital for one year. In this period, oral and maxillofacial surgeons inserted 667 implants in 198 patients after consultation with a prosthodontist. The traditional statistical methods were inappropriate in this study, which analyzed the data of a small sample size to find a factor affecting the prognosis. The machine learning methods were used in this study, since these methods have analyzing power for a small sample size and are able to find a new factor that has been unknown to have an effect on the result. A decision tree model and a support vector machine were used for the analysis. RESULTS. The results identified mesio-distal position of the inserted implant as the most significant factor determining its prognosis. Both of the machine learning methods, the decision tree model and support vector machine, yielded the similar results. CONCLUSION. Dental clinicians should be careful in locating implants in the patient's mouths, especially mesio-distally, to minimize the negative complications against implant survival.

• Annals of Rehabilitation Medicine
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• v.42 no.6
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• pp.846-853
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• 2018

Objective To compare the effects of low intensity ultrasound (LIUS), traditional exercise therapy (TET), low level laser therapy (LLLT) and TET on temporomandibular joint (TMJ) pain and trismus following recovery from head and neck cancer (HNC). Methods Sixty participants following, who had experienced HNC, were randomly allocated to three groups of 20 people each. Each group received different therapy. Group A received LIUS and TET; group B received LLLT and TET; while group C received TET. All 60 participants were evaluated under the visual analog scale (VAS), the University of Washington Quality of Life questionnaire (UW-QOL) and the Vernier caliper scale (VCS) at the beginning of the therapies and after 4 weeks. Results ANOVA test revealed significant improvements across all three groups with outcomes of p<0.05. The results of the UW-QOL questionnaire showed a significant difference between groups A, B and C in favor of group A (p<0.05). The VAS results showed a more improvement in group A as compared to group B (p<0.05), while there was no statistical difference between groups B and C (p>0.05). The VCS results showed more improvement for the individuals in group B as compared to those in group C (p<0.05), while there was minimal difference between groups A and B (p>0.05). Conclusion The LIUS and TET are more effective than LLLT and/or TET in reducing TMJ pain and trismus following HNC.

• Journal of Korean Academy of Oral Health
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• v.41 no.4
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• pp.262-266
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• 2017

Objectives: The aim of this study was to compare Plaque Percent Index (PPI), calculated by Patient Hygiene Performance Index (PHPI), Rustogi's modification of the Navy Plaque Index (RMNPI), and the Quigley & Hein Plaque Index (QHPI), with visual assessment. Methods: Ninety-six subjects, aged between 30-65 years, were examined; twenty subjects were included in the final analysis. The subjects' teeth were stained and photographed. Dental coloring and intraoral camera photography were performed by a single examiner. The oral images obtained were analyzed using Image J to measure the area of dental plaque. The values of PHPI, RMNPI, and QHPI were calculated twice. Statistical analyses were performed using descriptive statistics, chi-square test, and Pearson's correlation coefficient. Results: The results of the correlation analyses of PPI with PHPI, QHPI, and RMNPI were as follows: for PHPI, the correlation coefficient (r)=0.584; for QHPI, r=0.689; and for RMNPI, r=0.729. Further, the kappa indices of PHPI, QHPI, and RMNPI were 0.810, 0.677, and 0.590 respectively. Conclusions: Among RMNPI, QHPI, and PHPI dental plaque indices, RMNPI and QHPI showed a high degree of correlation with the actual stained dental plaque area; on the other hand, PHPI showed the highest kappa index.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.41-68
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• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.