• Food Quality and Culture
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• v.3 no.1
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• pp.1-5
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• 2009

The objective of this study was to develop a reliable and reproducible descriptive analysis procedure for Korean style sweet pumpkin gruel (Hobakjuk). The sensory attributes of the sweet pumpkin gruel were developed and defined, the sample preparation method was standardized, and the sensory evaluation procedure for a sample was established. Seven types of sweet pumpkin gruel (five ready-to-eat type vs. two ready-to-heat type) were selected to be analyzed. Panel training and descriptive analysis were carried out with these 7 samples. A total of 12 sensory attributes (2 aroma/odor, 5 taste/flavor, 4 texture/mouthfeel, and 1 aftertaste attributes) were developed to describe the sensory characteristics of the sweet pumpkin gruel. The definition and reference standards for each sensory attribute were determined to clearly understand each attribute. In the main experiment, trained panelists evaluated the sensory characteristics of the 7 gruel samples based on a fifteen-point intensity scale using the developed attributes. The results were statistically analyzed by analysis of variance, principal component analysis, and cluster analysis. The results showed that the 7 sweet pumpkin gruel samples significantly differed in their intensities of all attributes except for sweet pumpkin aroma and viscosity. The ready-to-eat style samples were distinctly characterized by their sweet pumpkin aroma and flavor, whereas the ready-to-heat style samples were markedly characterized by their low intensity of gelatinized starch and pumpkin flavor retention.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.1-7
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• 2016

As new information and communication technologies evolve, security threats are also becoming increasingly intelligent and advanced. In this paper, we analyze the time series data continuously entered through a series of periods from the network device or lightweight IoT (Internet of Things) devices by using the statistical technique and propose a system to detect abnormal behaviors of the device or abnormality based on the analysis results. The proposed system performs the first level abnormal detection by using previously entered data set, thereafter performs the second level anomaly detection according to the trust bound configured by using stored time series data based on time attribute or group attribute. Multi-level analysis is able to improve reliability and to reduce false positives as well through a variety of decision data set.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.131-140
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• 2020

The purpose of this study is to derive various clues for the establishment of marketing strategies for companies that produce and sell high-involvement products for Millennials who are not the subject of current consumption but who will lead future consumption. For this purpose, this study aimed to derive 17 factors of high-involvement product selection attributes through FGI, and its relationship on repurchase intention after make a variable through PCA. A total of 158 valid questionnaires were used, and IPA, independent sample t-test, regression analysis, and hierarchical controlled regression analysis were performed. The results showed that overall, external and internal selection factors had a positive influence on repurchase intentions, and in particular, appealing to internal and external selection factors in order to promote repurchase intention. Meanwhile, the Millennials were found to have no gender difference. Therefore, the company producing and selling high-involvement products suggests that it is necessary to make a priority effort to secure brand awareness, trust in product producers, store trust, and product self-reputation as components of internal selection factors. It was also concluded that more strategic efforts were needed to focus on and appeal to the characteristics of the Millennial itself rather than to consider gender differences.

• Korean Journal of Organic Agriculture
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• v.17 no.4
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• pp.413-439
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• 2009

This study conducts a survey and test to understand U.S. public's perception about multifunctionality. The questionnaire suggests seven alternative way of providing questions about intangible benefits provided by agriculture in the U.S. The final questionnaire was administered as an e-mail survey in June 2008 to a nationally representative household panel maintained in the U.S. by the Ipsos Observer. Data analysis shows that 64 percent of respondents considered the multifunctionality of agriculiture as an important issue and 45 percent of respondents were in favor of increasing government expenditure to support farmland preservation. Using Fishbein's multi-attribute model as a theoretical background, this paper develops an empirical model to assess and attributes of multifunctionality. For the analysis, bivariate orderd probit model was set up to reflect respondent's attitude. Regression analyses show that two questions (how much you agree with agriculture's intangible benefit and increasing government expenditure to support agriculture) are shaped by different sets of facts.

• Journal of Korean Society for Quality Management
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• v.26 no.2
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• pp.93-105
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• 1998

Many large scale discount stores try to enter the market in newly developed city areas. In order to sucessfully increase the market share, it is essential for them to understand customers' needs. In doing so, various forms of survey methods are often used and survey forms can influence respondents' decision. The main objective of this paper is to check consistency of different survey methods in terms of deriving the expected market share. In this paper, we a, pp.y two survey forms for both AHP and conjoint analyses using a randomized complete block design. Using AHP, we compare Kim's club, Carf, E-mart and Macro in terms of the following four customer attributes: parking facility, size of store, business hours, and special pricing policy. In conjoint analysis, we estimate the part-worth of each level of the customer's attribute so that one can design the best store which would optimizethe customer's attribute so that one can design the best store which would optimize the customer's utility. Empirical comparison results indicate very low consistency between the two methods. It implies the importance of verification methods of survey. We also analyze the impact of special pricing policy using a meta analysis. It turns out that older customers are a, pp.rently more sensitive to pricing policy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.221-228
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• 2020

This study suggests an alternative to the conventional collaborative filtering method for predicting consumer choice, using case-based reasoning. The algorithm of case-based reasoning determines the similarity between the alternative sets that each subject chooses. Case-based reasoning uses the inverse of the normalized Euclidian distance as a similarity measurement. This normalized distance is calculated by the ratio of difference between each attribute level relative to the maximum range between the lowest and highest level. The alternative case-based reasoning based on similarity predicts a target subject's choice by applying the utility values of the subjects most similar to the target subject to calculate the utility of the profiles that the target subject chooses. This approach assumes that subjects who deliberate in a similar alternative set may have similar preferences for each attribute level in decision making. The result shows the similarity between comparable alternatives the consumers consider buying is a significant factor to predict the consumer choice. Also the interaction effect has a positive influence on the predictive accuracy. This implies the consumers who looked into the same alternatives can probably pick up the same product at the end. The suggested alternative requires fewer predictors than conjoint analysis for predicting customer choices.

• The Korean Journal of Community Living Science
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• v.22 no.3
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• pp.327-339
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• 2011

This study aims to investigate relationships between benefit sought and selection attributes of green tea consumers. For data collection, a total of 595 copies of questionnaires were collected by convenience sampling in the Seoul and Gyeonggi-do area. The data were analyzed by using SPSS 15.0. The factor analysis identified four dimensions of the benefit sought : health benefit, sensory, sociality, and self-esteem. Six dimensions of selection attributes were identified as manufacturing, design, sensory appeal, recommendation, utility and brand. The results of the canonical correlation analysis indicated that health benefit, sensory, sociality of benefit sought and manufacturing, design, sensory appeal, recommendation, utility, brand of selection attributes were highly correlated, and the self-esteem of benefit sought and recommendation of selection attribute were highly correlated. This means it is important to place an emphasis on safety production, package design, sensory characteristics, product description, utility and brand for consumers who seek health benefit, flavor and sociality. It is also important to place an emphasis on product description for consumers who pursue self-esteem benefits. Green tea marketers should consider benefit sought aspects as the most important factors affecting selection attributes on green tea purchasing.

• Journal of Korean Port Research
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• v.7 no.1
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• pp.13-24
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• 1993

The rapid progress of the intermodal freight transportation in recent years has induced fierce competition among the adjacent hub ports for container transport. This brings increased attention to the evaluation of the port competitive ability. But it is not easy to evaluate the port competitive ability because this belongs to ill-defined system which is composed of ambiguous interacting attributes. Paying attention to this point, this paper deals the competitive ability of container port in Far-East Asia by fuzzy integral evaluation which is adequate to interacting ambiguous attribute problem. For this, the proposed fuzzy evaluation algorithm is applied to the real problem, based on the factors such as cargo volumes, costs, services, infrastructure and geographical sites These are extracted from the precedent study of port competitive ability, etc. The results show that the port evaluation factors come in following order ; services, costs, infrastructure, geographical sites and cargo volumes. There are some interactions(interaction coefficient, ${\lambda}=-0.664$ between evaluation attributes. The port competitive ability comes in following order : Singapore, Hongkong, Kobe, Kaoshiung and Busan. According to the sensitivity analysis, the rank between Busan and Kaoshiung changes when ${\lambda}=0.7$. From the analysis of the results, we confirmed that the proposed fuzzy evaluation algorithm is very effective in the complex-fuzzy problem which is composed of hierarchical structure with interacting attributes.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.1
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• pp.47-58
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• 2019

The paper presents the results of a multi-criteria comparative evaluation of 12 feasible Russian nuclear energy deployment scenarios with thermal and fast reactors in a closed nuclear fuel cycle. The comparative evaluation was performed based on 6 performance indicators and 5 different MCDA methods (Simple Scoring Model, MAVT / MAUT, AHP, TOPSIS, PROMETHEE) in accordance with the recommendations elaborated by the IAEA/INPRO section. It is shown that the use of different MCDA methods to compare the nuclear energy deployment scenarios, despite some differences in the rankings, leads to well-coordinated and similar results. Taking into account the uncertainties in the weights within a multi-attribute model, it was possible to rank the scenarios in the absence of information regarding the relative importance of performance indicators and determine the preference probability for a certain nuclear energy deployment scenario. Based on the results of the uncertainty/sensitivity analysis and additional analysis of alternatives as well as the whole set of graphical and attribute data, it was possible to identify the most promising nuclear energy deployment scenario under the assumptions made.