• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.265-273
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• 2021

The aim of this study is to analyze the correlation between concept perceptions and people's attitudes in halal tourism, development perceptions and people's attitudes, attitude and people's interest, concept perceptions and people's interest, development perceptions and people's interest, concept perceptions and people's interest, and development perceptions and people's interest. The method used in this research is SEM (Structural Equation Modeling) method. The population in this study was all Banten people. The samples in this study were respondents in five districts/cities in Banten, namely Tangerang (127 respondents), Serang (63 respondents), Pandeglang (97 respondents), Lebak (69 respondents), and Tangerang City (62 respondents). The sampling technique used is cluster random sampling. The data collection method used by researchers is a survey through filling out an online questionnaire. Based on regression test results shows concept perceptions has a significant effect on people's attitudes Development perceptions has no significant effect on people's attitudes, the attitude has no significant effect on people's interest, concept perceptions have no significant effect on people's interest, development perceptions have no significant effect on people's interest. Concept perceptions have no significant effect on people's interests through people's attitudes. Development perceptions have no significant effect on people's interests through people's attitudes.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• Steel and Composite Structures
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• v.45 no.3
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• pp.425-436
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• 2022

The mechanical behaviors of the prefabricated steel-concrete composite beams are usually affected by the strength and the number of shear studs. Furthermore, the discrete degree of the arrangement for shear stud clusters, being defined as the clustering degree of shear stud λ in this paper, is an important factor for the mechanical properties of composite beams, even if the shear connection degree is unchanged. This paper uses an experimental and calculation method to investigate the influence of λ on the mechanical behavior of the composite beam. Five specimens (with different λ but having the same shear connection degree) of prefabricated composite beams are designed to study the ultimate supporting capacity, deformation, slip and shearing stiffness of composite beams. Experimental results are compared with the conventional slip calculation method (based on the influence of λ) of prefabricated composite beams. The results showed that the stiffness in the elastoplastic stage is reduced when λ is greater than 0.333, while the supporting capacity of beams has little affected by the change in λ. The slip distribution along the beam length tends to be zig-zagged due to the clustering of studs, and the slip difference increases with the increase of λ.

• Annual Conference of KIPS
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• 2007.05a
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• pp.870-872
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• 2007

Sensor networks have been widely applied for data collection. Due to the energy limitation of the sensor nodes and the most energy consuming data transmission, we should allocate as much work as possible to the sensors, such as data compression and aggregation, to reduce data transmission and save energy. Querying extreme values is a general query type in wireless sensor networks. In this paper, we propose a novel querying method called Two-Level Filter (TLF) for querying extreme values in wireless sensor networks. We first divide the whole sensor network into domains using the Distributed Data Aggregation Model (DDAM). The sensor nodes report their data to the cluster heads using push method. The advantages of two-level filter lie in two aspects. When querying extreme values, the number of pull operations has the lower boundary. And the query results are less affected by the topology changes of the wireless sensor network. Through this method, the sensors preprocess the data to share the burden of the base station and it combines push and pull to be more energy efficient.

• Smart Structures and Systems
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• v.33 no.6
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• pp.465-482
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• 2024

In this paper, a robust wireless sensor network configuration design method is proposed to develop the optimal configuration under the consideration of sensor failure and energy consumption. A malfunctioned sensor in a wireless sensor network may lead to data transmission failure of the entire sensing cluster, inducing severe deterioration in system identification performance. The proposed method determines a wireless sensor network configuration that is robust against sensor failure. By utilizing Bayesian inference, we introduce a robust indicator to evaluate the impact on estimation accuracy of sensor configurations with various malfunctioned sensors. Moreover, a network formation strategy is proposed to optimize the energy efficiency of the wireless sensor network configuration. Therefore, the resultant robust wireless sensor network configuration can operate with the minimum energy consumption while the measurement information of the sensor network with malfunctioned sensors can be guaranteed. The proposed method is illustrated by designing the robust wireless sensor network configurations of a truss model and a bridge model.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.1-21
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• 2021

As the global business environment changes, uncertainties in technology development and market needs increase, and competition among companies intensifies, interests and demands for R&D activities of individual companies are increasing. In order to cope with these environmental changes, R&D companies are strengthening R&D investment as one of the means to enhance the qualitative competitiveness of R&D while paying more attention to facility investment. As a result, facilities or R&D investment elements are inevitably a burden for R&D companies to bear future uncertainties. It is true that the management strategy of increasing investment in R&D as a means of enhancing R&D capability is highly uncertain in terms of corporate performance. In this study, the structural factors that influence the R&D capabilities of companies are explored in terms of technology management capabilities, R&D capabilities, and corporate classification attributes by utilizing data mining techniques, and the characteristics these individual factors present according to the level of R&D capabilities are analyzed. This study also showed cluster analysis and experimental results based on evidence data for all domestic R&D companies, and is expected to provide important implications for corporate management strategies to enhance R&D capabilities of individual companies. For each of the three viewpoints, detailed evaluation indexes were composed of 7, 2, and 4, respectively, to quantitatively measure individual levels in the corresponding area. In the case of technology management capability and R&D capability, the sub-item evaluation indexes that are being used by current domestic technology evaluation agencies were referenced, and the final detailed evaluation index was newly constructed in consideration of whether data could be obtained quantitatively. In the case of corporate classification attributes, the most basic corporate classification profile information is considered. In particular, in order to grasp the homogeneity of the R&D competency level, a comprehensive score for each company was given using detailed evaluation indicators of technology management capability and R&D capability, and the competency level was classified into five grades and compared with the cluster analysis results. In order to give the meaning according to the comparative evaluation between the analyzed cluster and the competency level grade, the clusters with high and low trends in R&D competency level were searched for each cluster. Afterwards, characteristics according to detailed evaluation indicators were analyzed in the cluster. Through this method of conducting research, two groups with high R&D competency and one with low level of R&D competency were analyzed, and the remaining two clusters were similar with almost high incidence. As a result, in this study, individual characteristics according to detailed evaluation indexes were analyzed for two clusters with high competency level and one cluster with low competency level. The implications of the results of this study are that the faster the replacement cycle of professional managers who can effectively respond to changes in technology and market demand, the more likely they will contribute to enhancing R&D capabilities. In the case of a private company, it is necessary to increase the intensity of input of R&D capabilities by enhancing the sense of belonging of R&D personnel to the company through conversion to a corporate company, and to provide the accuracy of responsibility and authority through the organization of the team unit. Since the number of technical commercialization achievements and technology certifications are occurring both in the case of contributing to capacity improvement and in case of not, it was confirmed that there is a limit in reviewing it as an important factor for enhancing R&D capacity from the perspective of management. Lastly, the experience of utility model filing was identified as a factor that has an important influence on R&D capability, and it was confirmed the need to provide motivation to encourage utility model filings in order to enhance R&D capability. As such, the results of this study are expected to provide important implications for corporate management strategies to enhance individual companies' R&D capabilities.

• Korean Journal of Acupuncture
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• v.34 no.2
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• pp.88-99
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• 2017

Objectives : Saam acupuncture initiated by Saamdoin in $17^{th}$ century is one of the most widely adopted acupuncture techniques used by Korean medical doctors in clinic. Our study aimed to analyze the application of the Saam acupuncture method to pain diseases based on the literature data. Methods : Based on the contents described in "(Do Hae Kyo Kam) Saam's acupuncture method", the texts related to pain condition were analyzed. The frequency of prescription of Saam acupuncture method was analyzed, and then the relationships between each acupoint were visualized by network analysis and hierarchical cluster analysis for the quantitative aspect. Results and conclusions: In our study, Lung tonifying and Liver tonifying acupuncture were the most frequently used method for the treatment of pain disease. As the acupoints, BL66 and SI5 were used the most frequently. It was found that visceral pattern identification was considered as the most important factor in the selection of the Saam acupuncture method. Network analysis and hierarchical clustering analysis showed that each acupoint was closely related to other acupoints, and most of them were connected more closely according to the method of Saam acupuncture operation. The experiential prescriptions of Saam acupuncture were classified as an independent group. In the future, fundamental research on the principle of Saam acupuncture method is needed for the various diseases, and research for the clinical efficacy and the mechanism of Saam acupuncture method should be preceded.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.4
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• pp.586-597
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• 2009

The purpose of this study is to identify the moderating effect of Internet lifestyle among relational benefits effect, customer satisfaction and customer loyalty on the Internet shopping mall. For the study, utilizing the convenient sampling method, the sample of study was composed of female and male adults aged between 20 and 30 living in Seoul metropolitan area who had experienced purchase of fashion products on the web. Of 350 distributed, 311 useful questionnaires were returned. The survey research design was employed with structured questionnaire. For data analysis, descriptive statistics, factor analysis, reliability analysis, cluster analysis and multiple-regression analysis were used. The results of this study are as follows: The results of Internet lifestyle were regarding 4 cluster of Internet lifestyle. 1) information seeking type, 2) shopping maniac type, 3) social gathering type and 4) passive follower type were obtained. The interaction of psychological benefits and the type of Internet lifestyle affected customer satisfaction positively. The interaction of customer satisfaction and Internet lifestyle reinforced customer loyalty. Especially interaction of shopping maniac type among Internet lifestyle types and customer satisfaction affected customer loyalty strongly.

• Journal of the Korean Chemical Society
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• v.34 no.5
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• pp.396-403
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• 1990

We describe how to design and construct a LPCVD (Low Pressure Chemical Vapor Deposition) apparatus which can be applicable to the study of reaction mechanism in general CVD experiments. With this apparatus we have attempted to make diamond like carbon films on the p-type (111) Si wafer from (H$_2$ ＋ CH$_4$) gas mixtures. Two different methods have been tried to get products. (1)The experiment was carried out in the reactor with two different inlet gas tubes. One coated with phosphoric acid was used for supplying microwave discharged hydrogen gas stream, and methane has been passed through the other tube without the microwave discharge. In this method we got only amorphous carbon cluster products. (2) The gas mixture (H$_2$ ＋ CH$_4$) has been passed through the discharge tube with the Si wafer located in and/or near the microwave plasma. In this case diamond-like carbon products could be obtained.