• Journal of the Society of Cosmetic Scientists of Korea
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• v.42 no.1
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• pp.45-55
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• 2016

Light shows various optical behaviors such as reflection, absorption, and scattering on skin for individuals. In particular, reflection of light from the skin has been widely used as the brightness index of the skin of individuals through the measurement of the physical quantity of spectral reflectance. Therefore, the study of light behavior on skin would be useful for the preparation of new evaluation method in the development stage of make-up products. In this study, multi-dimensional analysis for spectral reflectance behavior of light on individual skin was performed using Kubelka-Munk model. Also, we analyzed the contribution of skin parameters such as skin thickness and hemoglobin, which could affect the spectral reflectance, using above model and literature information. Base on this, we calculated the theoretical reflectance of normal women for visual light, which showed good agreement with the measured reflectance. Our study of light propagation in skin based on Kubelka-Munk model provides useful insight for the development of personalized cosmetic in the near future.

• Journal of the Korean Geotechnical Society
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• v.15 no.2
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• pp.29-42
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• 1999

The SASW (Spectral-Analysis-of-Surface-Waves) method, which evaluates the stiffness structure of the subsurface and structures nonintrusively and nondestructively, has been successfully used in the civil engineering applications. However, the SASW method assumes that the subsurface or structures consist of horizontal multi-layers, so that the method has some difficulty in continuously evaluating the integrity of a tunnel lining and a pavement system. This difficulty prevents the SASW method from being used to generate a tomographic image of stiffness for the subsurface or structures. Recently, the GPR technique which has the advantage of continuously evaluating integrity of the subsurface and structures has been popular. This advantage of GPR technique initiated the efforts to make the SASW method, which is superior to GPR and other nondestructive testing methods due to its capability of evaluating stiffness and modulus, be able to do continuous evaluation of stiffness structure, and the efforts finally lead to the development of \ulcornerTomographic SASW Technique.\ulcorner Tomographic SASW technique is a variation of the SASW method, and can generate a tomographic image of stiffness structure along the measurement line. The tomographic SASW technique was applied to the investigation of lateral variability of a sand box placed by the raining method for the purpose of verifying its effectiveness. Tomographic SASW measurements on the sand box revealed that the investigated sand box has different shear stiffness along the measurement line, which gave a clue of how to make a better raining device.

• The Journal of Bigdata
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• v.5 no.1
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• pp.11-27
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• 2020

In this paper, we developed a big data platform to store, process, and analyze effectively on such education longitudinal study data. And it was applied to the Seoul Education Longitudinal Study(SELS) to confirm its usefulness. The developed platform consists of data preprocessing unit and data analysis unit. The data preprocessing unit 1) masking, 2) converts each item into a factor 3) normalizes / creates dummy variables 4) data derivation, and 5) data warehousing. The data analysis unit consists of OLAP and data mining(DM). In the multidimensional analysis, OLAP is performed after selecting a measure and designing a schema. The DM process involves variable selection, research model selection, data modification, parameter tuning, model training, model evaluation, and interpretation of the results. The data warehouse created through the preprocessing process on this platform can be shared by various researchers, and the continuous accumulation of data sets makes further analysis easier for subsequent researchers. In addition, policy-makers can access the SELS data warehouse directly and analyze it online through multi-dimensional analysis, enabling scientific decision making. To prove the usefulness of the developed platform, SELS data was built on the platform and OLAP and DM were performed by selecting the mathematics academic achievement as a measure, and various factors affecting the measurements were analyzed using DM techniques. This enabled us to quickly and effectively derive implications for data-based education policies.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.166-171
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• 2022

Background and Objectives Vocal fold (VF) scar is known to be the most common cause of dysphonia after laryngeal microsurgery (LMS). Steroids reduce postoperative scar formation by inhibiting inflammation and collagen deposition. However, the clinical evidence of whether steroids are helpful in reducing VF scar formation after LMS is still lacking. The purpose of this study is to determine whether intralesional VF steroid injection after LMS helps to reduce postoperative scar formation and voice quality. Materials and Method This study was conducted on 80 patients who underwent LMS for VF polyp, Reinke's edema, and leukoplakia. Among them, 40 patients who underwent VF steroid injection after LMS were set as the injection group, and patients who had similar sex, age, and lesion size and who underwent LMS alone were set as the control group. In each group, stroboscopy, multi-dimensional voice program, Aerophone II, and voice handicap index (VHI) were performed before and 1 month after surgery, and the results were statistically analyzed. Results There were no statistically significant differences in the distribution of sex, age, symptom duration, occupation and smoking status between each group. Both groups consisted of VF polyp (n=21), Reinke's edema (n=11), and leukoplakia (n=9). On stroboscopy, the lesion disappeared after surgery, and the amplitude and mucosal wave were symmetrical on both sides of the VFs in all patients. Acoustic parameters and VHI significantly improved after surgery in all patients. However, there was no significant difference between the injection and control group in most of the results. Conclusion There was no significant difference in the results of stroboscopy, acoustic, aerodynamic, and subjective evaluation before and after surgery in the injection group and the control group.

• Phonetics and Speech Sciences
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• v.11 no.3
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• pp.69-76
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• 2019

This study aimed to investigate the effects of vocal aerobic treatment (VAT) on the improvement of voice in patients with voice disorders. Twenty patients (13 males, 7 females) were diagnosed with voice disorders on the basis of videostroboscopy and voice evaluations. Acoustic evaluation was performed with the Multidimensional voice program (MDVP) and Voice Range Profile (VRP) of Computerized Speech Lab (CSL), and aerodynamic evaluation with PAS (Phonatory Aerodynamic System). The changes in F0, Jitter, Shimmer, and NHR before and after treatment were measured by MDVP. F0 range and Energy range were measured with VRP before and after treatment, and the changes in Expiratory Volume (FVC), Phonation Time (PHOT), Mean Expiratory Airflow (MEAF), Mean Peak Air Pressure (MPAP), and Aerodynamic Efficiency (AEFF) with PAS. Videostroboscopy was performed to evaluate the regularity, symmetry, mucosal wave, and amplitude changes of both vocal cords before and after treatment. Voice therapy was performed once a week for each patient using the VAT program in a holistic voice therapy approach. The average number of treatments per patient was 6.5. In the MDVP, Jitter, Shimmer, and NHR showed statistically significant decreases (p < .001, p < .01, p < .05). VRP results showed that Hz and semitones in the frequency range improved significantly after treatment (p < .01, p < .05), as did PAS, FVC, and PHOT (p < .01, p < .001). The results for videostroboscopy, functional voice disorder, laryngopharyngeal reflux, and benign vocal fold lesions were normal. Thus, the VAT program was found to be effective in improving the acoustic and aerodynamic aspects of the voice of patients with voice disorders. In future studies, the effect of VAT on the same group of voice disorders should be studied. It is also necessary to investigate subjective voice improvement and objective voice improvement. Furthermore, it is necessary to examine the effects of VAT in professional voice users.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.811-827
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• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.

• 대한공업교육학회지
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• v.38 no.1
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• pp.49-67
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• 2013

The purpose of this study is to analyze the external systems and the units 'problem solving and invention' of the middle school technology and home economics 1 textbooks of the revised 2011 national curriculum in an effort to provide some information on the content system of invention education in technology class, as invention education was provided as part of a regular subject for the first time. The findings of the study were as follows: First, 'Technology and Inventions' chapter of Technology and Home Economics 1 Textbooks occupied 10-18% share, with the subchapter of 'Problem Solving and Invention' unit taking up 6.7-29% of the textbooks. Second, for most textbooks, 'Technological Problem Solving', 'Idea Generation' 'Multi-dimensional Projection Method', 'Expansive Thought-Processing Methodology', 'Converging Thought Methodology' and 'Invention in Everyday Lives' were included as main contents based on the accomplishment criteria presented in education process interpretation documents. Third, the detailed structures were generally made up as follows: Introduction (Broad Chapter Title, Subchapter Table of Contents, Introduction, Subchapter Title, Study Objectives, Open Thinking); Development (Unit Title, Thinking Ahead, Core Terms, Main Text, Study Helper, Activities, Research Exercises, Supplemental Readings, In-depth Study Topics, Technology in Everyday Lives, Reading Topics, Discussion Topics, and Career Helpers); and Summary (Subchapter Summary, Study Summary, Terms Summary, Writing Follow-up, Self Review, Broad Chapter Evaluation). Fourth, based on the analysis of figures included, photographs had the largest share, followed by figures, tables, and graphs. The photos were used to illustrate various inventions, invention methodologies, and exercise activities, while figures were included to depict the contents included in the main text, and the tables to assist to preparation of process diagrams or materials lists. Fifth, based on the analysis of content weights, greater weights were placed on 'Inventions and Thoughts', and 'Invention Experiment Activities,' while 'Understanding Inventions' and 'Invention and Patents' chapters did not have a lot of texts involved. Sixth, based on the analysis of content presentation methods, most textbooks combined figures, tables, illustrations and texts to discuss the topics. Based on the above study results, we suggest the following: First, a consistent education curriculum should be developed over the topic of invention; and second, more precise and systematic analysis of textbooks would need to be performed.

• Journal of Korean Society of Forest Science
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• v.90 no.3
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• pp.380-387
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• 2001

The characteristics of ecological niche, breadth and overlap, for seventeen major tree species were evaluated in the natural deciduous forest in Mt. Chumbong area. Employed by the plot sampling method, the environmental gradient for vertical niche was based on the intensity of light within the forest, and that for horizontal niche was based on multi-dimensional resources in distribution pattern. The result showed that Fraxinus rhynchophylla had the highest value of vertical niche breadth and Maackia amurensis had the lowest, and Acer pseudo-sieboldianum had the highest value of horizontal niche breadth and Betula costata had the lowest. There was no significant correlation between both measures of niche breadth. However, the tolerance index for each species was positively correlated to the values of niche breadth. Spearman's rank correlation coefficients were applied to test the correlationship between the species ranks of tolerance index and those of two ecological niche breadths. The coefficient of $r_s=0.432$ ($P{\leq}0.1$) was not enough to support significant correlationship between the tolerance index and vertical niche breadth at the 95% probability. If Carpinus cordata, rarely reach canopy of the forest due to its own growth form, are excluded from the analysis, coefficient was calculated as $r_s=0.650$ ($P{\leq}0.01$), resulting in highly significant correlationship. The Spearman's rank correlation coefficient was $r_s=0.797$ ($P{\leq}0.01$) for tolerance indices and the values of horizontal niche breadth, indicating highly significant. Four distinctive species groups, produced by cluster analysis on the basis of ecological niche overlap for each pair of species, were in considerable accord with the positively associated species constellation pattern created by the inter-species association analysis.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.