• 산업경영시스템학회지
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• 제43권4호
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• pp.107-115
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• 2020

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the ⲉ-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within ⲉ. In most studies, the ⲉ-insensitive loss function is used symmetrically, and it is of interest to determine the value of ⲉ. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of ⲉ and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of ⲉ. In this study, a new ε-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of ⲉ and the slope of the penalty using the parameters p1 and p2, respectively. Moreover, the figures show that the asymmetry of the width of ⲉ and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.

• Structural Engineering and Mechanics
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• 제85권3호
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• pp.315-335
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• 2023

Limited studies are available on the mathematical estimates of the compressive strength (CS) of glass fiber-embedded polymer (glass-FRP) compressive elements. The present study has endeavored to estimate the CS of glass-FRP normal strength concrete (NSTC) compression elements (glass-FRP-NSTC) employing two various methodologies; mathematical modeling and artificial neural networks (ANNs). The dataset of 288 glass-FRP-NSTC compression elements was constructed from the various testing investigations available in the literature. Diverse equations for CS of glass-FRP-NSTC compression elements suggested in the previous research studies were evaluated employing the constructed dataset to examine their correctness. A new mathematical equation for the CS of glass-FRP-NSTC compression elements was put forwarded employing the procedures of curve-fitting and general regression in MATLAB. The newly suggested ANN equation was calibrated for various hidden layers and neurons to secure the optimized estimates. The suggested equations reported a good correlation among themselves and presented precise estimates compared with the estimates of the equations available in the literature with R²= 0.769, and R² =0.9702 for the mathematical and ANN equations, respectively. The statistical comparison of diverse factors for the estimates of the projected equations also authenticated their high correctness for apprehending the CS of glass-FRP-NSTC compression elements. A broad parametric examination employing the projected ANN equation was also performed to examine the effect of diverse factors of the glass-FRP-NSTC compression elements.

• 품질경영학회지
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• 제50권3호
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• pp.305-332
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• 2022

Purpose: As the public interest in entrepreneurship has been highlighted and entrepreneurship policies have been generated, this study is to construct Entrepreneurship Ecosystem (EE) models which have a significant relationship to national entrepreneurship with quantitative analysis. It aims to provide implications to EE policymakers that which national components are effective in cultivating innovative entrepreneurship and validate its EE quality based on quantitative performance goals. Methods: This study utilizes secondary data, categorized under the PESTLE factor from credible international organizations (WB, UNDP, GEM, GEDI, and OECD) to determine significant factors in the quality of the entrepreneurial ecosystem. This paper uses the Multiple Linear Regression (MLR) analysis to select the significant variables contributing to entrepreneurship performance. Using the AUC-ROC performance evaluation method for machine learning MLR results, this paper evaluates the performance of EE models so that it can allow approving EE quality by predicting potential performance. Results: Among nine hypothesis models, MLR analysis examines that the number of the Unicorn company, Unicorn companies' economic value, and entrepreneurship measured as GEI can be reasonable dependent variables to indicate the performance derived from EE quality. Rather than government policies and regulations, the social, finance, technology, and economic variables are significant factors of EE quality determining its performance. By having high Area Under Curve values under AUC-ROC analysis, accepted MLR models are regarded as having high prediction accuracy. Conclusion: Superior EE contributes to the outstanding Unicorn companies, and improvement in macro-environmental components can enhance EE quality.

• Journal of Digestive Cancer Research
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• 제4권2호
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• pp.99-106
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• 2016

Minimally invasive surgery for gastric cancer has increased in popularity during the last two decades mainly in the Asia for patients with early-stage cancer. Nevertheless, the development of laparoscopic surgery for gastric cancers in the Western world has been slow because of the advanced stage at diagnosis for which LG is not yet considered an acceptable alternative to standard open surgery. RAG has been reported as a safe alternative to conventional surgery for treating of early gastric carcinoma. We assess the current status of robotic surgery in the treatment of gastric cancer focusing on the technical details, postoperative outcome, oncological considerations and future perspectives. In gastrectomy the biggest advantage of the robotic approach is the ease and reproducibility of lymphadenectomy. Reports also show that even the intra corporeal digestive restoration is facilitated by use of the robotic approach, particularly following TG. Additionally, the accuracy of robotic dissection is confirmed by decreased blood loss in comparison to conventional laparoscopy. The learning curve and technical reproducibility also appear to be shorter with robotic surgery and, consequently, robotics can help to standardize and diffuse minimally invasive surgery in the treatment of gastric cancer. While published reports have shown no significant differences in surgical morbidity, mortality, or oncological adequacy between robot-assisted and conventional gastrectomy. There are some advantages in terms of postoperative recovery of patients after robotic surgery. More studies are needed to assess the true indications and oncological effectiveness of robotic use in the treatment of gastric carcinoma.

• 대한의용생체공학회:의공학회지
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• 제44권5호
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• pp.354-361
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• 2023

Most of spinal diseases are diagnosed based on the subjective judgment of a specialist, so numerous studies have been conducted to find objectivity by automating the diagnosis process using deep learning. In this paper, we propose a method that combines segmentation and feature extraction, which are frequently used techniques for diagnosing spinal diseases. Four models, U-Net, U-Net++, DeepLabv3+, and M-Net were trained and compared using 1000 X-ray images, and key-points were derived using Douglas-Peucker algorithms. For evaluation, Dice Similarity Coefficient(DSC), Intersection over Union(IoU), precision, recall, and area under precision-recall curve evaluation metrics were used and U-Net++ showed the best performance in all metrics with an average DSC of 0.9724. For the average Euclidean distance between estimated key-points and ground truth, U-Net was the best, followed by U-Net++. However the difference in average distance was about 0.1 pixels, which is not significant. The results suggest that it is possible to extract key-points based on segmentation and that it can be used to accurately diagnose various spinal diseases, including spondylolisthesis, with consistent criteria.

• 한국음향학회지
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• 제25권1호
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• pp.14-20
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• 2006

본 논문에서는 지연혼합에서의 암묵신호분리를 위해 분리행렬의 초기 값을 설정하는 방법을 제안한다. 혼합신호의 상호상관행렬에 대한 고유분리를 분석한 후, 고유벡터의 지연정보를 이용하여 초기 값으로 설정한다. 제안하는 방법을 기존의 주파수영역 독립성분분석 (FDICA: Frequency domain independent component analysis)에 초기 값으로 설정하여 분리 성능을 향상시킨다. 컴퓨터 시뮬레이션을 통해 제안하는 방법이 신호대간섭비 (SIR: Signal to Interference Ratio)가 우수하고 학습곡선의 수렴속도가 개선됨을 보인다.

• 한국응용과학기술학회지
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• 제37권3호
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• pp.473-483
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• 2020

본 논문은 지각된 가치가 적용된 관광 행동의도 정보를 이용한 지능형 클라우드 환경에서의 관광추천시스템을 제안한다. 이 제안 시스템은 관광정보와 관광객의 지각적 가치가 행동의도에 반영되는 실증적 분석 정보를 와이드 앤 딥러닝 기술을 이용하여 관광추천시스템에 적용하였다. 본 제안 시스템은 다양하게 수집할 수 있는 관광 정보와 관광객이 평소에 지각하고 있던 가치와 사람의 행동에서 나타나는 의도를 수집 분석하여 관광 추천시스템에 적용하였다. 이는 기존에 활용되던 다양한 분야의 관광플랫폼에 관광 정보, 지각된 가치 및 행동의도에 대한 연관성을 분석하고 매핑하여, 실증적 정보를 제공한다. 그리고 관광정보와 관광객의 지각적 가치가 행동의도에 반영되는 실증적 분석 정보를 선형 모형 구성요소와 신경만 구성요소를 합께 학습하여 한 모형에서 암기 및 일반화 모두를 달성할 수 있는 와이드 앤 딥러닝 기술을 이용한 관광추천 시스템을 제시하였고, 파이프라인 동작 방법을 제시하였다. 본 논문에서 제시한 추천시스템은 와이드 앤 딥러닝 모형을 적용한 결과 관광관련 앱 스토어 방문 페이지 상의 앱 가입률이 대조군 대비 3.9% 향상했고, 다른 1% 그룹에 변수는 동일하고 신경망 구조의 깊은 쪽만 사용한 모형을 적용하여 결과 와이드 앤 딥러닝 모형은 깊은 쪽만 사용한 모형 대비해서 가입률을 1% 증가하였다. 또한, 데이터셋에 대해 수신자 조작 특성 곡선 아래 면적(AUC)을 측정하여, 오프라인 AUC 또한 와이드 앤 딥러닝 모형이 다소 높지만 온라인 트래픽에서 영향력이 더 강하다는 것을 도출하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.417-420
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• 2002

Recent works in the area of multimedia studies focus on a wide range of issues from the impact of multimedia on culture to its impact on economics and anything in between. The interconnectedness of the issues raised by this new practice is complicated by the fact that media are rapidly converging: in a very real way, multimedia is becoming a media prism that reflects the way in which media continually influence each other across disciplines and cultural borders. Thus, the impact of multimedia reflects a complicated crossroads where media, human experience, culture and technology converge. An effective design is generally based on shaping aesthetics for function and utility, with an emphasis on ease of use. However, in designing for cyberspace, it is possible to create narratives that challenge the interactor by encoding in the design an instructional aspect that teaches new approaches and forms. Such a design offers an equally aesthetic experience for the interactor as they explore the meaning of the work. This design approach has been used constructively in many applications. The crucial concern is to determine how little or how much information must be presented for the interactor to achieve a suitable level of cognition. This is always a balancing act: too much difficulty will result in interactor frustration and the abandonment of the activity and too little will result in boredom leading to the same negative result In addition, it can be anticipated that the interactor will bring her or his own level of experiential cognition and/or accretion, to the experience providing reflective cognition and/or restructure the learning curve. If the design of the application is outside their present experience, interactors will begin with established knowledge in order to explore the new work. Thus, it may be argued that the interactor explores, learns and cognates simultaneously based on primary experiential cognition. Learning is one of the most important keys to establishing a comfort level in a new media work. Once interactors have learned a new convention, they apply this cognitive knowledge to other new media experiences they may have. Pierre Levy would describe this process as a "new nomadism" that creates "an invisible space of understanding, knowledge, and intellectual power, within which new qualities of being and new ways of fashioning a society will flourish and mutate" (Levy xxv 1997). Thus, navigation itself of offers the interactors the opportunity to both apply and loam new cognitive skills. This suggests that new media narrative strategies are still in the process of developing unique conventions and, as a result, have not reached a level of coherent grammar. This paper intends to explore the cognitive aspects of new media design and in particular, will explore issues related to the design of new media interfaces. The paper will focus on the creation of narrative strategies that engage interactors through loaming curves thus enhancing interactivity.vity.

• Safety and Health at Work
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• 제10권4호
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• pp.452-460
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• 2019

Background: Monitoring and control of PM_2.5 are being recognized as key to address health issues attributed to PM_2.5. Availability of low-cost PM_2.5 sensors made it possible to introduce a number of portable PM_2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM_2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM_2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM_2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM_2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM_2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM_2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R²) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R² and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM_2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

• 디지털융복합연구
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• 제17권2호
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• pp.217-230
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• 2019

본 연구는 기존 동반질환을 이용한 중증도 보정 방법의 제한점을 보완하기 위해 급성심근경색증 환자의 맞춤형 중증도 보정방법을 개발하고, 이의 타당성을 평가하기 위해 수행되었다. 이를 위하여 질병관리본부에서 2006년부터 2015년까지 10년간 수집한 퇴원손상심층조사 자료 중 주진단이 급성심근경색증인 한국표준질병사인분류(KCD-7) 코드 I20.0~I20.9의 대상자를 추출하였고, 동반질환 중증도 보정 도구로는 기존 활용되고 있는 CCI(Charlson comorbidity index), ECI(Elixhauser comorbidity index)와 새로이 제안하는 CCS(Clinical Classification Software)를 사용하였다. 이에 대한 중증도 보정 사망예측모형 개발을 위하여 머신러닝 기법인 로지스틱 회귀분석, 의사결정나무, 신경망, 서포트 벡터 머신기법을 활용하여 비교하였고 각각의 AUC(Area Under Curve)를 이용하여 개발된 모형을 평가하였다. 이를 평가한 결과 중증도 보정도구로는 CCS 가 가장 우수한 것으로 나타났으며, 머신러닝 기법 중에서는 서포트 벡터 머신을 이용한 모형의 예측력이 가장 우수한 것으로 확인되었다. 이에 향후 의료서비스 결과평가 등 중증도 보정을 위한 연구에서는 본 연구에서 제시한 맞춤형 중증도 보정방법과 머신러닝 기법을 활용하도록 하는 것을 제안한다.