• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6B
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• pp.579-585
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• 2002

The investigation into the effect of various feedback errors on system performance can help the robust feedback channel design and transmission of exact feedback channel information as well. In this paper, we address the algorithm that determines space combining weight vector maximizing received signal power at mobile on frequency flat fading channel and investigate the performance degradation by feedback channel delay in the FDD/CDMA systems employing transmit beamforming. We observe the effect of feedback channel delay corresponding to the number of transmit antennas and the temporal/spatial correlation of channel. The results show that performance is more sensitive to feedback delay with the larger number of antennas when fadings at transmit antennas are not spatially correlated.

• The Research Journal of the Costume Culture
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• v.13 no.4 s.57
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• pp.550-563
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• 2005

These days, photoshop and illustrator program can make a fashion illustration express easily and speedily, And they can also express a feeling and sensitivity of fashion illustrator by a tool and effect more than a manual work's media. The purpose of this study was to analyze a visual expression and characteristics in fashion illustration using 2D graphics. The method of this study was to analyze the fashion illustration books using 2D graphics. The visual expressions in fashion illustration using 2D graphics were as follows: Fashion illustration was based on a sketch or photography, and used a composition, drawing, mapping, painting, and manual work's re-touching. Characteristics of visual expression were as follows: First, a image composition was showed discontinuity and heterogeneity of image, new context and composition, and allowance of reality. Second, image transform was showed image overlap, body transformation by image recomposition, and deformed transformation by vector drawing. Third, hyper-real was showed precise touching, mechanical and neutral image, omission of background and focus of an object's characteristic and information. Fourth, following a realistic expression was showed simplified color, shading, dominant view point of fashion illustrator by omission, and daily lift style. Fifth, following a pictorial expression was showed non-fixed and irregular line, natural painting, and drawing and painting by conventional pictorial media. In conclusion, a photoshop and illustrator in 2D graphics will serve as a new media far fashion illustration with a manual work. And they will not only intensify a capacity as a commercial role of fashion illustration but also present a positive motive for students learning a fashion illustration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1682-1688
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• 2008

The Counterpropagation algorithm(CP) is a combination of Kohonen competition network as a hidden layer and the outstar structure of Grossberg as an output layer. CP has been used in many real applications for pattern matching, classification, data compression and statistical analysis since its learning speed is faster than other network models. However, due to the Kohonen layer's winner-takes-all strategy, it often causes instable learning and/or incorrect pattern classification when patterns are relatively diverse. Also, it is often criticized by the sensitivity of performance on the learning rate. In this paper, we propose an enhanced CP that has multiple Kohonen layers and dynamic controlling facility of learning rate using the frequency of winner neurons and the difference between input vector and the representative of winner neurons for stable learning and momentum learning for controlling weights of output links. A real world application experiment - pattern recognition from passport information - is designed for the performance evaluation of this enhanced CP and it shows that our proposed algorithm improves the conventional CP in learning and recognition performance.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Neurospine
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• v.15 no.4
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.903-909
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• 2019

Periodontal disease is observed in many adult persons. However we has not clear know the molecular mechanism and how to treat the disease at the molecular levels. Here, we investigated the molecular differences between periodontal disease and normal controls using gene expression data. In particular, we checked whether the periodontal disease and normal tissues would be classified by machine learning algorithms using gene expression data. Moreover, we revealed the differentially expression genes and their function. As a result, we revealed that the periodontal disease and normal control samples were clearly clustered. In addition, by applying several classification algorithms, such as decision trees, random forests, support vector machines, the two samples were classified well with high accuracy, sensitivity and specificity, even though the dataset was imbalanced. Finally, we found that the genes which were related to inflammation and immune response, were usually have distinct patterns between the two classes.

• Journal of the Korean Society for Environmental Technology
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• v.19 no.6
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• pp.528-535
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• 2018

In this study, the effect of obstacle aspect ratio on vortex in building canyon was numerically investigated using a computational fluid dynamics(CFD) model. The sensitivity experiments were performed in the cases of increasing building length(L) and height(H) by the width(W) of building canyon. The wind vector fields and secondary reverse vortex in building canyon were discussed in this study. For the horizontal vortex, the vortex zone increased as the building length increases, but the vectors at the middle of building canyon began to change in the case of L/W=2.5. In the case of L/W=3.0, the smaller primary vortex was presented with the secondary reverse vortex. For the vertical vortex, the vortex zone increased as the building height increases, but the direction of vectors at the bottom of building canyon began to change in the case of H/W=2.5. In the case of H/W=3.5, the smaller primary vortex was presented with the secondary reverse vortex.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.143-148
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• 2021

A systematic study of Bloch surface wave (BSW), which is created by guided mode resonance (GMR) of dielectric multilayer structures with a grating profile, is presented to analyze the sensing performance of bio-sensors. The effect of structural parameters on optical behavior is evaluated by using Babinet's principle and modal transmission-line theory. The sensitivity of designed bio-sensors is proportional to the grating constant at wavelength spectrum, and inversely proportional to the normal wave vector of incident electromagnetic wave at angular spectrum. Numerical results for two devices with SiO/SiO₂ and TiO₂/SiO₂ multilayer dielectric stacks are presented, showing that BSW can be exploited for the realization of efficient diffraction-based bio-sensors from infrared to visible-band range.

• Physical Therapy Korea
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• v.28 no.2
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• pp.123-131
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• 2021

Background: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture). Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects: The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features. Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee ("knee together") or the mat ("knee on mat"), or neither the other knee nor the mat was contacted by the impacting-side knee ("free knee"). Falls involved "backward initial fall direction" or "free knee" were defined as "injurious falls" as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls. Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion: Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.