• Journal of Distribution Research
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• v.10 no.3
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• pp.87-101
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• 2005

Market segmentation has been an important issue in marketing for a long time. Many models and statistical methods have been developed by many scholars. The purpose of this research provides one insight for market segmentation based on clustering technique in channel benefit sought. We proposed a sequential approach in market segmentation. A sequential approach means that we do market segmentation by multi-stage method based on the benefits sought in marketing channel. To achieve this approach, we divided the main benefits sought into subcategories. That is to say, after dividing each benefit sought into more detailed concepts, we did market segmentation sequentially.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• Journal of Families and Better Life
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• v.6 no.1
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• pp.95-116
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• 1988

this study has dual purposes; one is to develope a new theoretical framework in consumer behavior area by applying the human ecological approach, and the other is to test the theory empirically area by applying the human ecological approach, and the other is to test the theory empirically by examining prepurchasing behavior of housing. Research methods adopted in this study are library search and survey research with self-administered questionnaires. The statistical methods used in the survey research are factor analysis, chi square test, and multivariate analysis with crosstablulations. According to the human ecological approach, ecological environments are important sources of consumer needs which , in turn, are satisfied by purchasing behavior in the market. Within this theoretical framework, consumers con improve the quality to life by perceving clearly what their needs are thereby making the most possible efficient purchasing decision making. The major findings of the empirical research on the basis of the theoretical framework are as follows; 1) Housing needs significantly vary with different ecological environment. 2) consumer information search behavior does not differ significantly by housing needs. 3) Housing needs turn out to be an intervening variable between ecological environments and consumer information search behavior. the results of this study show that the human ecological approach is useful in consumer behavior studies. The empirical result that consumer needs are not significantly satisfied by consumer behavior suggests a now direction in consumer education.

• ETRI Journal
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• v.39 no.5
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• pp.718-728
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• 2017

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal-to-noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first-order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.

• Computers and Concrete
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• v.23 no.6
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• pp.377-398
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• 2019

Behavior of RC beam-column joint is very complex as the composite material behaves differently in elastic and inelastic range. The approaches generally used for predicting joint shear strength are either based on theoretical, strut-and-tie or empirical methods. These approaches are incapable of predicting the accurate response of the joint for entire range of loading. In the present study a new generalized RC beam-column joint shear strength model based on hybrid approach i.e. combined strut-and-tie and empirical approach has been proposed. The contribution of governing parameters affecting the joint shear strength under compression has been derived from compressive strut approach whereas; the governing parameters active under tension has been extracted from empirical approach. The proposed model is applicable for various conditions such as, joints reinforced either with or without shear reinforcement, joints with wide beam or wide column, joints with transverse beams and slab, joints reinforced with X-bars, different anchorage of beam bar, and column subjected to various axial loading conditions. The joint shear strength prediction of the proposed model has been compared with 435 experimental results and with eleven popular models from literature. In comparison to other eleven models the prediction of the proposed model is found closest to the experimental results. Moreover, from statistical analysis of the results, the proposed model has the least coefficient of variation. The proposed model is simple in application and can be effectively used by designers.

• International Area Studies Review
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• v.20 no.3
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• pp.3-22
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• 2016

This paper uses Dynamic OLS(DOLS) approach to estimate the long-run energy demand functions. The results are compared with those of standard cointegration approach. Cointegration tests verify that there is a cointegration among energy consumption, real GDP, and energy price in China. Johansen approach and DOLS approach are more appropriate to estimate for the long-run energy demand function than Engle-Granger Cointegration approach. DOLS provided significant negative sign of price while Engle-Granger did not. Based on the DOLS results, the elasticities of real GDP and energy price on energy consumption are 0.83 and -0.45 respectively, and their statistical significances are high.

• Structural Engineering and Mechanics
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• v.18 no.3
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• pp.287-301
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• 2004

Structural acceleration regulation is a means of managing structural response energy and enhancing the performance of civil structures undergoing large seismic events. A quadratic output regulator that minimizes a measure including the total structural acceleration energy is developed and tested on a realistic non-linear, semi-active structural control case study. Suites of large scaled earthquakes are used to statistically quantify the impact of this type of control in terms of changes in the statistical distribution of controlled structural response. This approach includes the impulses due to control inputs and is shown to be more effective than a typical displacement focused control approach, by providing equivalent or better performance in terms of displacement and hysteretic energy reductions, while also significantly reducing peak story accelerations and the associated damage and occupant injury. For earthquake engineers faced with the dilemma of balancing displacement and acceleration demands this control approach can significantly reduce that concern, reducing structural damage and improving occupant safety.

• The Korean Journal of Applied Statistics
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• v.4 no.2
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• pp.195-207
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• 1991

We consider the development of simple regression-like diagnostics for assessing the sensitivity of an optimal design to deviations from the assumptions of constant error variance. This contains a review of Cook's local influence approach and an application of local influence aproach to D-optimal experimental design. The method is applied in a number of simple examples in Section 3. Conclusions and directions for further research follow in Section 4.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1673-1679
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• 2010

A photovoltaic (PV) generator is significantly regarded as one important alternative of renewable energy systems recently. Fault detection and diagnosis of engineering dynamic systems is a fundamental issue to timely prevent unexpected damages in industry fields. This paper presents an intelligent monitoring approach and fault detection technique for PV generator systems by means of artificial neural network and statistical signal detection theory. We devise a multi-Fourier neural network model for representing dynamics of PV systems and apply a general likelihood ratio test (GLRT) approach for investigating our decision making algorithm in fault detection and diagnosis. We make use of a test-bed of ubiquitous sensor network (USN) based PV monitoring systems for testing our proposed fault detection methodology. Lastly, a real-time experiment is accomplished for demonstrating its reliability and practicability.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1386-1396
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• 2017

In this paper, a new approach is proposed to select the optimal sitting and sizing of distributed solar photovoltaic generation (SPVG) in a radial electrical distribution systems (EDS) considering load/generation uncertainties. Here, distributed generations (DGs) allocation problem is modeled as optimization problem with network loss based objective function under various equality and inequality constrains in an uncertain environment. A boundary power flow is utilized to address the uncertainties in load/generation forecasts. This approach facilitates the consideration of random uncertainties in forecast having no statistical history. Uncertain solar irradiance is modeled by beta distribution function (BDF). The resulted optimization problem is solved by a new Dynamic Harmony Search Algorithm (DHSA). Dynamic band width (DBW) based DHSA is proposed to enhance the search space and dynamically adjust the exploitation near the optimal solution. Proposed approach is demonstrated for two standard IEEE radial distribution systems under different scenarios.