• Computers and Concrete
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• 제18권6호
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• pp.1065-1082
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• 2016

The main aim of this study is to predict the compressive and flexural strengths of self-compacting mortar (SCM) containing $nano-SiO_2$, $nano-Fe_2O_3$ and nano-CuO using wavelet-based weighted least squares-support vector machines (WLS-SVM) approach which is called WWLS-SVM. The WWLS-SVM regression model is a relatively new metamodel has been successfully introduced as an excellent machine learning algorithm to engineering problems and has yielded encouraging results. In order to achieve the aim of this study, first, the WLS-SVM and WWLS-SVM models are developed based on a database. In the database, nine variables which consist of cement, sand, NS, NF, NC, superplasticizer dosage, slump flow diameter and V-funnel flow time are considered as the input parameters of the models. The compressive and flexural strengths of SCM are also chosen as the output parameters of the models. Finally, a statistical analysis is performed to demonstrate the generality performance of the models for predicting the compressive and flexural strengths. The numerical results show that both of these metamodels have good performance in the desirable accuracy and applicability. Furthermore, by adopting these predicting metamodels, the considerable cost and time-consuming laboratory tests can be eliminated.

• 복식문화연구
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• 제21권5호
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• pp.639-654
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• 2013

Understanding consumers' being well and stylishly dressed is a key for marketers' success in ever changing fashion industry. The purpose of this study is to identify the antecedents of dressing style. As antecedents, personal values and clothing-related variables were considered: this study included physical appearance, materialism, and individualism as personal values and quality conscious, price conscious, and brand conscious as clothing related variables. It was hypothesized that personal values influence dressing style both directly and indirectly through clothing related variables. Data were gathered by surveying university students in Seoul, using convenience sampling. Three hundred eleven questionnaires were used in the statistical analysis, exploratory factor analysis using SPSS and confirmatory factor analysis and path analysis using structural equation modeling. The results showed that all the fit statistics for the variable measures were quite acceptable. In addition, the overall fits of the model suggest that the model fits the data well. The hypothesized relationship test also showed that individualism among personal values directly influences dressing style and that only price consciousness among clothing-related variables influences dressing style. With respect to the relative importance, individualism showed the largest standardized regression weight. The results suggest effective product, price, and promotion strategies for marketers whose target market is style conscious consumers.

• Smart Structures and Systems
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• 제14권2호
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.

• Industrial Engineering and Management Systems
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• 제15권1호
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• pp.110-121
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• 2016

With the growing interest in skincare and maintenance, there are increasing numbers of studies on the classification of skin type and the factors influencing each type. This study presents a novel methodology by using data mining, for the determination of the relationships between skin type, lifestyle, and patterns of cosmetic utilization. Eight skin-specific factors, which are moisture, sebum in U-zone (both cheeks), sebum in T-zone (forehead, nose, and chin), pore, melanin, wrinkle, acne, hemoglobin, were measured in 1,246 subjects living in South Korea, in conjunction with a questionnaire survey analyzing their lifestyles and pattern of cosmetic utilization. Using various multivariate statistical methods and data mining techniques, we classified the skin types based on the skin-specific values, determined the relationship between skin type and lifestyle, and accordingly sorted the subjects into clusters. Logistic regression analysis revealed gender-related differences in the skin; therefore, separate analyses were performed for males and females. Using the Gaussian Mixture Modeling (GMM) technique, we classified the subjects based on skin type (two male and four female). Using the ANOVA and decision tree techniques, we attempted to characterize the relationship between each skin type and the lifestyles of the subjects. Menstruation, eating habits, stress, and smoking were identified as the major factors affecting the skin.

• 한국항해항만학회지
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• 제31권1호
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• pp.107-113
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• 2007

교통의 흐름에 있어서의 트럭 및 콘테이너 차량 등 대형차량의 혼입은 차체의 크기에 기인한 넓은 공간의 점유 및 승용차에 비해 상대적으로 떨어지는 차량운행 능력 등의 특성 때문에 도로의 용량을 감소시키는 중요한 요인이 된다. 도로의 교통용량산정 시 이러한 대형차량의 혼입에 의한 용량의 감소를 나타내는 척도로서의 승용차 환산계수의 개념은 1965년 도로용량편람에서 처음으로 도입되었으며, 이후 구미의 많은 학자들에 의해 연구되었다. 본 연구에서는 차량배열 형태에 따른 차두시간의 분석을 통해 환산계수의 도출을 시도하였다. 분석을 위한 교통자료의 수집은 통행차량 차종의 대부분이 승용차와 대형차로 구성되어 있으며 비교적 관측이 용이한 부산시 도시고속도로의 평지구간에서 첨두시간과 비 첨두시간으로 나누어 실시되었다. 본 연구에서의 환산계수 산정방법은 차두시간의 분석을 통한 미시적 접근방법인바, 동방법의 정확성 및 효율적 검증을 위해서는 거시적 접근 방법에 의한 산정방법과의 비교분석이 요구된다.

• 한국표면공학회지
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• 제43권3호
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• pp.159-164
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• 2010

Surface roughness of deposited or etched film strongly depends on ion bombardment. Relationships between ion bombardment variables and surface roughness are too complicated to model analytically. To overcome this, an empirical neural network model was constructed and applied to a deposition process of silicon nitride (SiN) films. The films were deposited by using a pulsed plasma enhanced chemical vapor deposition system in $SiH_4$-$NH_4$ plasma. Radio frequency source power and duty ratio were varied in the range of 200-800 W and 40-100%. A total of 20 experiments were conducted. A non-invasive ion energy analyzer was used to collect ion energy distribution. The diagnostic variables examined include high (or) low ion energy and high (or low) ion energy flux. Mean surface roughness was measured by using atomic force microscopy. A neural network model relating the diagnostic variables to the surface roughness was constructed and its prediction performance was optimized by using a genetic algorithm. The optimized model yielded an improved performance of about 58% over statistical regression model. The model revealed very interesting features useful for optimization of surface roughness. This includes a reduction in surface roughness either by an increase in ion energy flux at lower ion energy or by an increase in higher ion energy at lower ion energy flux.

• International Journal of Advanced Culture Technology
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• 제7권1호
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• pp.89-95
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• 2019

One benefit of education for self-management of chronic diseases is to increase the use of cognitive techniques for coping with symptoms. Unfortunately, that benefit can deteriorate over time, and that phenomenon, which is sometimes called "decay of impact", has been studied only rarely. This study was done to understand the decay of impact with regard to the use of cognitive techniques for coping with symptoms, and especially to understand how that decay might be predicted. Data were analyzed from 381 adults suffering from chronic medical conditions, all of whom were involved in education to improve their self-management of their chronic condition(s). During the first year after the educational program, coping was measured four times. Variables associated with the decay of impact were found using statistical modeling (logistic regression). Decay of impact was found in almost half of the participants. The analysis provided moderately good predictions regarding the decay of impact. Given this new information, interventions to further improve coping with symptoms can be appropriately targeted to the people for whom they will be most beneficial.

• International Journal of Internet, Broadcasting and Communication
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• 제13권4호
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• pp.121-128
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• 2021

In order to design a real time big data collection and analysis system of manufacturing data in a smart factory, it is important to establish an appropriate wired/wireless communication system and protocol. This paper introduces the latest communication protocol, OPC-UA (Open Platform Communication Unified Architecture) based client/server function, applied user interface technology to configure a network for real-time data collection through IoT Integration. Then, Database is designed in MES (Manufacturing Execution System) based on the analysis table that reflects the user's requirements among the data extracted from the new cutting process automation process, bush inner diameter indentation measurement system and tool monitoring/inspection system. In summary, big data analysis system introduced in this paper performs SPC (statistical Process Control) analysis and visualization analysis with interface of OPC-UA-based wired/wireless communication. Through AI learning modeling with XGBoost (eXtream Gradient Boosting) and LR (Linear Regression) algorithm, quality and visualization analysis is carried out the storage and connection to the cloud.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1565-1575
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• 2020

This study examines the game balance of the MOBA game genre, which was selected as a model item for the Asian Games. The "bird-eye view" was used for a more efficient representation of 3D modeling. Based on that, statistical analysis was conducted to present appropriate game camera settings and camera balance to match the competitive structure of the MOBA game. A review of the game camera settings reveals that 64° to 70° is the angle that minimizes the difference in vision between the two-player teams the most. Through a one-way ANOVA analysis, we found that the user ranking level and SVB value are closely related. Therefore, the factor of the regression equation using the SVB value must have a user ranking level. As a result of the optimized camera focus analysis of , the camera setting methods were classified into 3 types. For main action games, the recommended camera angle is 64°~66°, and the recommended camera focus is 11.2 mm~19.3 mm. For action and strategy games, the camera angle is 66°~68°, camera focus - 19.3 mm~27.3 mm. And lastly, for the main strategy game, the recommended camera angle is 68°~70°, and the camera focus is 27.3 mm~35.3 mm.

• 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.