• Journal of Communications and Networks
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• v.9 no.4
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• pp.473-481
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• 2007

Energy efficiency is one of the most critical concerns for wireless sensor networks. By allowing sensor nodes in close proximity to cooperate in transmission to form a virtual multiple-input multiple-output(MIMO) system, recent progress in wireless MIMO communications can be exploited to boost the system throughput, or equivalently reduce the energy consumption for the same throughput and BER target. However, these cooperative transmission strategies may incur additional energy cost and system overhead. In this paper, assuming that data collectors are equipped with antenna arrays and superior processing capability, energy efficiency of relevant traditional and cooperative transmission strategies: Single-input-multiple-output(SIMO), space-time block coding(STBC), and spatial multiplexing(SM) are studied. Analysis in the wideband regime reveals that, while receive diversity introduces significant improvement in both energy efficiency and spectral efficiency, further improvement due to the transmit diversity of STBC is limited, as opposed to the superiority of the SM scheme especially for non-trivial spectral efficiency. These observations are further confirmed in our analysis of more realistic systems with limited bandwidth, finite constellation sizes, and a target error rate. Based on this analysis, general guidelines are presented for optimal transmission strategy selection in system level and link level, aiming at minimum energy consumption while meeting different requirements. The proposed selection rules, especially those based on system-level metrics, are easy to implement for sensor applications. The framework provided here may also be readily extended to other scenarios or applications.

• Journal of the East Asian Society of Dietary Life
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• v.24 no.4
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• pp.457-464
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• 2014

This study investigated the effects and interrelationship on sensual behavior and wine information sources in selection attributes of wine. Selection attributes of wine were categorized into four variables: sensual behavior, recommend information, label information, and wine information. The study showed that for sensual behavior variable, "taste" was the most influential item as compared to "food harmony", "mood harmony" and "partner's choice". For recommendation information, label information and wine information, "specialist", "grape varieties" and "brand" had the most significant effects. The study performed factor analysis on sensual behavior and wine information sources. The cumulative variance was 74.197%, implying that all four variables incorporated appropriate items. In the reliability analysis, all four variables showed Cronbach's ${\alpha}$ values above 0.6. In the analysis of the causal relations using a structural model, the effects of customers' sensual behavior on wine information sources was further investigated. The model verified that taste, food harmony, mood harmony and partner's choice, which are items of sensual behavior, had significant impacts when choosing wine. Sensual behavior influenced all wine information sources, which customers utilize in decision-making. Among these sources, sensual behavior had the biggest effects on recommendation information, followed by wine information and label information.

• The Research Journal of the Costume Culture
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• v.18 no.4
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• pp.717-730
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• 2010

The purpose of this study was to investigate the effects of narcissistic personality and self-esteem on the appearance management behaviors(weight, skin care, makeup, hair care, clothing selection) of female college students. The questionnaires were administrated to 362 female college students living in Gwang-ju city, Korea. For analysis of data, descriptive statistics, factor analysis, Cronbach'$\alpha$, regression analysis were applied. The results were summarized as follows. First. the female college students' narcissistic personality was categorized into four factors, need for administration, leadership/self-confidence, need for power/entitlement, and superiority. Second, narcissistic personality significantly influenced appearance management behaviors. The further examination of the effects showed that need for administration appeared to affect clothing selection, hair care, skin care, makeup, and weight. Third, self-esteem turned out to have positive effects on overall appearance management behaviors. The further examination of the effects showed that self-esteem appeared to affect clothing selection, skin care, hair care, makeup, and weight. The results indicated that female college students' narcissistic personality and self-esteem were important factors to their appearance management behaviors and marketing programs for fashion industries.

• Journal of Pharmaceutical Investigation
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• v.37 no.6
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• pp.377-395
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• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.

• Journal of Navigation and Port Research
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• v.31 no.2
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• pp.133-139
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• 2007

Relative importance of maritime transport that takes charge of main current of freight in country' economy is very large. Especially, port and facility carry out important role which treats freight of import and export smoothly and improves international trade as turning point, to achieve key role on connection and association between sea and land. For such reason, enlargement of port facilities or development of port needs to grasp exactly the utilization of port, attributes and selective factors of shipper. On the other hand, the amounts of physical distribution on Mokpo port located in Korean west coast are increasing, with fast economic growth of East Asian including China. This study uses discrete choice model that is measuring to analyze attribute and characteristic of Mokpo port, and analyzes port selection by decision factors of shipper. This paper composed a questionnaire using the result of preceding research, to decide port selection factor among competitive ports. Through factor analysis on a basis of the questionnaire' result, five principal components were extracted. These are resorted out by Logit model, to grasp competitive elements of port. This research fin present direction which raises competitive power of ports in west coast of Korea, especially on alternative and concentration of middle-class port as Mokpo may be useful.

• The Korean Journal of Food And Nutrition
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• v.27 no.1
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• pp.128-135
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• 2014

The purpose of this study is to analyze the gap in perceived importance-satisfaction rates of foreign Chinese students regarding the university foodservice selection attributes. All statistical analyses are conducted by the SPSS package program (ver 20.0). The results of the statistical analyses are as follows: The validity of the 22 food service selection attributes is being evaluated via the exploratory factor analysis and then five factors are extracted. The five factors are: 'Factor 1. Cleanness and service quality', 'Factor 2. Food quality and price', 'Factor 3. Physical environment', 'Factor 4. Convenience', and 'Factor 5. Service environment'. According to the results of one-way ANOVA, physical environment showed that significant differences across the periods of residence in Korea and the eating frequency at on campus foodservices. On the other hand, the food quality and price, convenience, and service environment showed that significant differences across the periods of residence in Korea. In addition, according to the Importance-Satisfaction Analysis results, 'ventilation of dining room' is the key aspect that university food service managers should reinforce. In conclusion, in order to increase the customer satisfaction rates, the food service managers should not only improve the quality of food and service but also the physical environments of the food service facility.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Journal of Distribution Science
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• v.21 no.11
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• pp.103-111
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• 2023

Purpose: This research aims to study the selection attributes influencing the purchasing decisions of the MZ generation in online luxury stores and explores the moderating effects of consumer value. The research aims to validate the impact of reasonable pricing, brand reliability, product variety, comprehensive product information, and user-friendly interfaces on customers' decision to purchase products from online luxury stores. Research design, data and methodology: A survey was conducted with 101 participants, and data analysis included exploratory and confirmatory factor analysis, as well as covariance structure model analysis. Results: The findings reveal that brand trust, product variety, and information sufficiency significantly influence brand affect, which in turn influences purchase intention. Additionally, the study identifies that consumers prioritizing hedonic value are more influenced by brand trust and information, while those prioritizing utilitarian value are more responsive to factors like reasonable price, product variety, and ease of use. Conclusions: The study provides insights into the preferences and behaviors of the MZ generation, highlighting their digital proficiency, mobile-centric lifestyle, desire for product variety, price-consciousness, social media influence, and the availability of personalized shopping experiences as factors contributing to their preference for online luxury stores. These findings contribute to understanding consumer behavior and decision-making processes in the context of online luxury shopping.

• The Journal of Fisheries Business Administration
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• v.55 no.1
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• pp.37-53
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• 2024

In the post-COVID-19, the food industry is rapidly reshaping its market structure toward online distribution. Rapid delivery system driven by large distribution platforms has ushered in an era of online distribution of fresh seafood that was previously limited. This study surveyed 1,000 consumers nationwide to determine their online seafood purchasing behaviors. The research methodology used factor analysis of consumer lifestyle and Heckman's ordered probit sample-selection model. The main results of the analysis are as follows. First, quality, freshness, selling price, product reviews from other buyers, and convenience are particularly important considerations when consumers purchase seafood from online shopping. Second, online retailers and the government must prepare measures to expand seafood consumption by considering household characteristics and consumer lifestyles. Third, it was analyzed that consumers trust the quality and safety of seafood distributed online platforms. It is not possible to provide purchase incentives to consumers who consider value consumption important, so improvement measures are needed. The results of this study are expected to provide implications on consumer preferences to online platforms, seafood companies, and producers, and can be used to establish future marketing strategies.

• Smart Media Journal
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• v.1 no.1
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• pp.36-41
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• 2012

Feature selection is the important technique of selecting a subset of relevant features for building robust pattern recognition systems. Various methods have been studied for feature selection from sequential search algorithms to stochastic algorithms. In this work, we adopted a Quantum-inspired Genetic Algorithm (QGA) which is based on the concept and principles of quantum computing such as Q-bits and superposition of state for feature selection. The performance of QGA is compared to that of the Conventional Genetic Algorithm (CGA) with respect to the classification rates and the number of selected features. The experimental result using UCI data sets shows that QGA is superior to CGA.