• Journal of Sasang Constitutional Medicine
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• v.36 no.2
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• pp.39-48
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• 2024

Objectives The aim was to develop a method for detecting Sasang constitution based on the ratio of facial landmarks and provide an objective and reliable tool for Sasang constitution classification. Methods Facial images, KS-15 scores, and certainty scores were collected from subjects identified by Korean Medicine Data Center. Facial ratio landmarks were detected, yielding 2279 facial ratio features. Tree-based models were trained to classify Sasang constitution, and Shapley Additive Explanations (SHAP) analysis was employed to identify important facial features. Additionally, Body Mass Index (BMI) and personality questionnaire were incorporated as supplementary information to enhance model performance. Results Using the Tree-based models, the accuracy for classifying Taeeum, Soeum, and Soyang constitutions was 81.90%, 90.49%, and 81.90% respectively. SHAP analysis revealed important facial features, while the inclusion of BMI and personality questionnaire improved model performance. This demonstrates that facial ratio-based Sasang constitution analysis yields effective and accurate classification results. Conclusions Facial ratio-based Sasang constitution analysis provides rapid and objective results compared to traditional methods. This approach holds promise for enhancing personalized medicine in Korean traditional medicine.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.209-220
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• 2014

In this paper, we analyze the discriminability of features for the classification of transient signals with an ambient noise in a shallow water. For the classification of the transient signals, robust features for the variance of a noise are required due to a low SNR under a marine environment. In the modelling the ambient noise in shallow water, theoretical noise model, Wenz's observation data from the shallow water, and Yule-walker filter are used. Discrimination of each feature of the transient signals with an additive ambient noise is analyzed by utilizing a Fisher score. As the analysis of a classification accuracy about the transient signals of 24 classes using the selected features with a high discriminability, the features selected in the environment without a noise relatively have a good classification accuracy. From the analyzed results, we finally select a total 16 features out of 28 features. The recognition using the selected features results in the classification accuracy of 92% in SNR 20dB using Multi-class SVM.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.16-22
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• 2012

In this paper, we proposed a method of baseline correction for analysis of Raman spectra of platelets from Alzheimer's disease (AD) transgenic mice. Measured Raman spectra include the meaningful information and unnecessary noise which is composed of baseline and additive noise. The Raman spectrum is divided into the local region including several peaks and the spectrum of the region is modeled by curve fitting using Gaussian model. The additive noise is clearly removed from the process of replacing the original spectrum with the fitted model. The baseline correction after interpolating the local minima of the fitted model with linear, piecewise cubic Hermite and cubic spline algorithm. The baseline corrected models extract the feature with principal component analysis (PCA). The classification result of support vector machine (SVM) and maximum $a$ posteriori probability (MAP) using linear interpolation method showed the good performance about overall number of principal components, especially SVM gave the best performance which is about 97.3% true classification average rate in case of piecewise cubic Hermite algorithm and 5 principal components. In addition, it confirmed that the proposed baseline correction method compared with the previous research result could be effectively applied in the analysis of the Raman spectra of platelet.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.75.2-75.2
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• 2010

Polymer electrolyte fuel cells (PEFCs) have received a lot of attention as a power source for both stationary and mobile applications due to their attractive feature. In general, the performance of PEFCs is highly affected by the property of the electrodes. A PEFC electrode essentially consists of a gas diffusion layer and a catalyst layer. The gas difusion layer is highly porous and hydrophobicized with PTFE polymer. The catalyst layer usually contains electrocatalyst, proton conducting polymer, even PTFE as additive. Particularly, the proton conducting ionomer helps to increase the catalytic activity at three-phase boundary and catalyst utilization. Futhermore, it helps to retain moisture, resulting in preventing the electrodes from membrane dehydration. The most widely used proton conducting ionomer is perfluorinated sulfonic acid polymer, namely, Nafion from DuPont due to its high proton conductivity and good mechanical property. However, there are great demands for alternative ionomers based on non-fluorinated materials in terms of high temperature availability, environmental adaptability and production cost. In this study, the electrodes with the various content of the sulfonated poly(ether sulfone) ionomer in the catalyst layer were prepared. In addition, we evaluated electrochemical properties of the prepared electrodes containing the various amount of the ionomers by using the cyclic voltammetry and impedance spectroscopy to find an optimal ionomer composition in the catalyst layer.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2016.11a
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• pp.128-128
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• 2016

Cu 배선폭 미세화 기술은 반도체 디바이스의 성능 향상을 위한 핵심 기술이다. 현재 배선 기술은 lithography, deposition, planarization등 종합적인 공정 기술의 발전에 따라 10x nm scale까지 감소하였다. 하지만 지속적인 feature size 감소를 위하여 요구되는 높은 공정 기술 및 비용과 배선폭 미세화로 인한 재료의 물리적 한계로 인하여 배선폭 미세화를 통한 성능의 향상에는 한계가 있다. 배선폭 미세화를 통한 2차원적인 집적도 향상과는 별개로 chip들의 3차원 적층을 통하여 반도체 디바이스의 성능 향상이 가능하다. 칩들의 3차원 적층을 위해서는 별도의 3차원 배선 기술이 요구되는데, TSV(through-Si-via)방식은 Si기판을 관통하는 via를 통하여 chip간의 전기신호 교환이 최단거리에서 이루어지는 가장 진보된 형태의 3차원 배선 기술이다. Si 기판에 $50{\mu}m$이상 깊이의 via 및 seed layer를 형성 한 후 습식전해증착법을 이용하여 Cu 배선이 이루어지는데, via 내부 Cu ion 공급 한계로 인하여 일반적인 공정으로는 void와 같은 defect가 형성되어 배선 신뢰성에 문제를 발생시킨다. 이를 해결하기 위해 각종 유기 첨가제가 사용되는데, suppressor를 사용하여 Si 기판 상층부와 via 측면벽의 Cu 증착을 억제하고, accelerator를 사용하여 via 바닥면의 Cu 성장속도를 증가시켜 bottom-up TSV filling을 유도하는 방식이 일반적이다. 이론적으로, Bottom-up TSV filling은 sample 전체에서 Cu 성장을 억제하는 suppressor가 via bottom의 강한 potential로 인하여 국부적 탈착되고 via bottom에서만 Cu가 증착되어 되어 이루어지므로, accelerator가 없이도 void-free TSV filling이 가능하다. Accelerator가 Suppressor를 치환하여 오히려 bottom-up TSV filling을 방해한다는 보고도 있었다. 본 연구에서는 유기 첨가제의 치환으로 인한 TSV filling performance 저하를 방지하고, 유기 첨가제 조성을 단순화하여 용액 관리가 용이하도록 하기 위하여 suppressor만을 이용한 TSV filling 연구를 진행하였다. 먼저, suppressor의 흡착, 탈착 특성을 이해하기 위한 연구가 진행되었고, 이를 바탕으로 suppressor만을 이용한 bottom-up Cu TSV filling이 진행되었다. 최종적으로 $60{\mu}m$ 깊이의 TSV를 1000초 내에 void-free filling하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.892-898
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• 2008

3D rapid prototyping is the manufacturing technology to fabricate a prototype with the data stored in a computer, which differs from conventional casting technology in terms of an additive process. Various 3D rapid prototyping techniques such as stereolithograpy. fused deposition modeling. selective laser sintering, laminated object manufacturing have been developed but among them, 3D inkjet printing has a unique feature that materials could be jetted to directly form the body of a prototype, which could be a finished product functionally and structurally. However, this needs ink with a high solid content, which tends to increase the dynamic viscosity of ink. The increase of ink viscositytends to restrict the jettable range of ink and hence the jetting conditions should be optimized. The intrinsic speed of sound in a hot melt ink with ceramic nanoparticles dispersed is one of key components to determine the jettable range of ink. In this paper, the way to measure the intrinsic speed of sound in a hot melt ceramic ink is proposed and its influence on the jetting condition is discussed.

• Journal of the Korean Electrochemical Society
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• v.15 no.1
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• pp.19-26
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• 2012

SEI (solid electrolyte interphase) layers are generated on a graphite negative electrode from three different electrolytes and low-temperature ($-30^{\circ}C$) charge/discharge performance of the graphite electrode is examined. The electrolytes are prepared by adding 2 wt% of vinylene carbonate (VC) and fluoroethylene carbonate (FEC) into a standard electrolyte solution. The charge-discharge capacity of graphite electrode shows the following decreasing order; FEC-added one>standard>VC-added one. The polarization during a constant-current charging shows the reverse order. These observations illustrate that the SEI film resistance and charge transfer resistance differ according to the used additives. This feature has been confirmed by analyzing the chemical composition and thickness of three SEI layers. The SEI layer generated from the standard electrolyte is composed of polymeric carbon-oxygen species and the decomposition products ($Li_xPF_yO_z$) of lithium salt. The VC-derived surface film shows the largest resistance value even if the salt decomposition is not severe due to the presence of dense film comprising C-O species. The FEC-derived SEI layer shows the lowest resistance value as the C-O species are less populated and salt decomposition is not serious. In short, the FEC-added electrolyte generates the SEI layer of the smallest resistance to give the best low-temperature performance for the graphite negative electrode.