• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4293-4304
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• 2014

In order to ensure the wireless connectivity and seamless service to mobile users, the next generation network system will be an integration of multiple wireless access networks. In a heterogeneous wireless access system, bandwidth allocation becomes crucial for load balancing to avoid network congestion and improve system utilization efficiency. In this article, we propose a new dynamic bandwidth allocation scheme for hierarchical wireless network systems. First, we derive a multi-objective decision criterion for each access point. Second, a bargaining strategy selection algorithm is developed for the dynamic bandwidth re-allocation. Based on the intervenient Stackelberg game model, the proposed scheme effectively formulates the competitive interaction situation between several access points. The system performance of proposed scheme is evaluated by using extensive simulations. With a simulation study, it is confirmed that the proposed scheme can achieve better performance than other existing schemes under widely diverse network environments.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.469-470
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• 2007

We used the non-reversible hierarchical scheme and the fixed channel assignment scheme for the strategy of call processing at hierarchical cellular system. We analyzed the performance of system according to the number of channel assigned at macrocell out of the usable channel at global system. Microcell is assigned the channel excepted the channel assigned at macrocell. The more the channel was aigned at macrocell, the more decreased the blocking probability became. The blocking probability of microcell had similar trend.

• Asia pacific journal of information systems
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• v.16 no.3
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• pp.49-65
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• 2006

This study proposes a model for customer segmentation using the psychological characteristics of Internet banking customers. The model was developed through two phased clustering method, called SONN-HC by integrating self-organizing neural networks (SONN) and hierarchical clustering (HC) method. We applied the SONN-HC method to internet banking customer segmentation and performed an empirical analysis with 845 cases. The results of our empirical analysis show the psychological characteristics of Internet banking customers have significant differences among four clusters of the customers created by SONN-HC. From these results, we found that the psychological characteristics of Internet banking customers had an important role of planning a strategy for customer segmentation in a financial institution.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.820-831
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• 2020

Extractive document summarization aims to select a few sentences while preserving its main information on a given document, but the current extractive methods do not consider the sentence-information repeat problem especially for news document summarization. In view of the importance and redundancy of news text information, in this paper, we propose a neural extractive summarization approach with joint sentence semantic clipping and selection, which can effectively solve the problem of news text summary sentence repetition. Specifically, a hierarchical selective encoding network is constructed for both sentence-level and document-level document representations, and data containing important information is extracted on news text; a sentence extractor strategy is then adopted for joint scoring and redundant information clipping. This way, our model strikes a balance between important information extraction and redundant information filtering. Experimental results on both CNN/Daily Mail dataset and Court Public Opinion News dataset we built are presented to show the effectiveness of our proposed approach in terms of ROUGE metrics, especially for redundant information filtering.

• Journal of Information Technology Applications and Management
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• v.22 no.2
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• pp.149-169
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• 2015

This study tried to find out how to overcome multicollinearity problems in the structural equation model by creating a hierarchical construct model about the repurchase intention of social commerce. This study selected, as independent variables, price, quality, service, and social influence, based on literature review about social commerce, and then, as detailed variables of independent variables, selected system quality, information quality, transaction safety, order fulfillment and after-sales service, communication, subjective norms, and reputation. As results of empirical analysis about hierarchical construct model, all the independent variables were accepted having a significant impact on repurchase intention of social commerce. Next, this study analyzed the competition model that eight independent variables of price, system quality, information quality, transaction safety, order fulfillment and after-sales service, communication, subjective norm, and reputation directly influence the repurchase intention of social commerce. As results of empirical analysis, system quality, information quality, transaction safety, communication appeared to be insignificant. This study showed that hierarchical construct model is useful to overcome the multicollinearity problem in structural equational model and to increase explanatory power.

• ETRI Journal
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• v.42 no.6
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• pp.922-931
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• 2020

A hierarchical iterative algorithm for the canonical polyadic decomposition (CPD) of tensors is proposed by improving the traditional conjugate gradient least squares (CGLS) method. Methods based on algebraic operations are investigated with the objective of estimating the direction of arrival (DoA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector-sensors. The proposed algorithm adopts a hierarchical iterative strategy, which enables the algorithm to obtain a fast recovery for the highly collinear factor matrix. Moreover, considering the same accuracy threshold, the proposed algorithm can achieve faster convergence compared with the alternating least squares (ALS) algorithm wherein the highly collinear factor matrix is absent. The results reveal that the proposed algorithm can achieve better performance under the condition of fewer snapshots, compared with the ALS-based algorithm and the algorithm based on generalized eigenvalue decomposition (GEVD). Furthermore, with regard to an array with a small number of sensors, the observed advantage in estimating the DoA and polarization parameters of the signal is notable.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.93-115
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• 2017

The ability of smartphones to facilitate various services like mobile banking, e-commerce and mobile payments has made them part of consumers' lives. Conjoint analysis (CA) is a marketing research approach used to assess how consumers' preferences for products or services develop. The potential applications of CA are numerous in consumer electronics, banking and insurance services, job selection and workplace loyalty, consumer packaged goods, and travel and tourism. Choice-Based Conjoint (CBC) analysis is the most commonly used CA approach in marketing research. The purpose of this study is to utilise CBC analysis to investigate the relative importance of smartphone attributes that influence consumer smartphone preference. An experiment was designed using Sawtooth CBC Software. 326 students attempted the online survey. Utility values were derived by Hierarchical Bayes (HB) estimation and used to explain consumers' smartphone preferences. All the six attributes used for the study were found to significantly influence smartphone preference. Smartphone brand was the most important, followed by the price, camera, RAM, battery life, and storage. This study is one of the first to use Sawtooth CBC analysis to assess consumer smartphone preference based on the six attributes. We provide implications for the development of new smartphones based on attributes.

• Management Science and Financial Engineering
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• v.13 no.1
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• pp.73-91
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• 2007

We investigate factors that influence the choice of high-share brands(HSBs) vs. low-share brands(LSBs) among various product and consumer characteristics related to brand-share perceptions. Specifically, using 8 product categories varying in terms of purchase decision involvement, we show how the influencing factors vary across the categories. At the general level that cover all the 8 categories, our hierarchical Bayesian regressions analysis shows that factors that favor high-share brands are purchase decision involvement, search goods, experience goods, price-quality relationship, positive network externalities, and price-prestige beliefs. Conversely, consumers who value variety seeking and need for uniqueness favor low-share brands. The effects of these factors, however, vary across product categories. The identification of these characteristics can help brand managers establish a more effective brand-share strategy in such areas as setting an optimal market share goal, extending a brand, and developing ad copy. Furthermore, our consumer segmentation analysis demonstrates the general market has two distinct segments - (1) a segment composed of HSB buyers(86%) and (2) a segment composed of LSB buyers(14%). The two segments are also shown to have different significant factors that explain their brand choice. Our segmentation analysis can help marketers establish a marketing strategy that targets a specific segment of interest.

• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.19-45
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• 2010

This research has analyzed the differentiated influence of organizational variables(an organization's learning culture and organizational support networks) and personal variables(the individual's motivation to learn and self-efficacy) on the process of gaining and using of knowledge. These two variables have been regarded as the major variables for the successful learning of 6Sigma, according to Social Cognitive Theory. In addition, this research has proven the role structure of the abovementioned two variables through a suitable methodology(Hierarchical Linear Model). In regard to this methodology, the different hierarchical level of the personal variable and organizational variable was especially focused on, and the effect of interaction between the high level and the low level was considered in detail. Considering the current situation, in that the importance of organizational factor and personal factor has been emphasized but the accurate role of each variable has not been verified, the research model is thought to help to establish an effective strategy to implement 6 Sigma.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.576-588
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• 2014

The superiority of CMI (Cascaded Multilevel Inverter) is unparalleled in high power and high voltage STATCOM (Static Synchronous Compensator). However, the parameters and operating conditions of each individual power unit composing the cascaded STATCOM differ from unit to unit, causing unit voltage disequilibrium on the DC side. This phenomenon seriously impairs the operation performance of STATCOM, and thus maintaining the DC voltage balance and stability becomes critical for cascaded STATCOM. This paper analyzes the case of voltage disequilibrium, combines the operation characteristics of the cascaded STATCOM, and proposes a new DC voltage control scheme with the advantages of good control performance and stability. This hierarchical control method uses software to achieve the total active power control and also uses chopper controllers to enable that the imbalance power can flow among the capacitors in order to keep DC capacitor voltages balance. The operating principle of the chopper controllers is analyzed and the implementation is presented. The major advantages of the proposed control strategy are that the number of PI regulators has been decreased remarkably and accordingly the blindness of system design and debugging also reduces obviously. The simulation reveals that the proposed control scheme can achieve the satisfactory control goals.