• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.9-16
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• 2023

In the IoT environment, various objects mutually interactive, and various services can be composed based on this environment. In the previous study, we have developed a resource collaboration system to provide services by substituting limited resources in the user's personal device using resource collaboration. However, in the preceding system, when the number of resources and situations increases, the inference time increases exponentially. To solve this problem, this study proposes a method of classifying users and resources by applying the BIG5 user type classification model. In this paper, we propose a method to reduce the inference time by filtering the user's preferred resources through BIG5 type-based preprocessing and using the filtered resources as an input to the recommendation system. We implement the proposed method as a prototype system and show the validation of our approach through performance and user satisfaction evaluation.

• Journal of Information Technology Services
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• v.10 no.4
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• pp.149-169
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• 2011

As the number of smart phone users increases, many organizations begin to adopt social media rapidly to diversify communication channels with customers. Specifically, twitter, which supports instant and two-way communications between users and between organizations and users, has been adopted by many organizations as an efficient way not only to identify new customers but also to retain existing customers. However, little attention has been given to the issue on how organizations can effectively use twitter to improve customer satisfaction. To explore the issue, this study proposes two major dimensions, customer participation and organization resource utilization, which should be considered in building a utilization strategy for twitter in organizations. We then develop four different combinations along with these dimensions-follow, mention, retweet, and review types. Based on case studies of 27 organizations that use twitter, we evaluate the degrees of customer participation, resource utilization, and customer satisfaction, and examine matching or mismatching of the adoption purpose of twitter and its actual utilization. The study results reveal that organizations in the matching group show higher customer satisfaction that those in the mismatching group. This study sheds new light on twitter research by developing a new conceptual framework and using a case study approach to explore the relationship between the utilization strategy of twitter and customer satisfaction.

• Journal of the Korean Home Economics Association
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• v.44 no.4 s.218
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• pp.23-38
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• 2006

The purpose of this study was to investigate the strengths of single-parent families and the related variables based on the family strength approach. The major findings were as follows. First, good communication, family bond, children's adjustment and coping ability were characteristics of single-parent family strengths. Secondly, strengths of single-parent family were related to income and period of becoming a single-parent family. Thirdly, single parents who had a higher level of personal resources such as high self-esteem and economic stability perceived a higher level of family strengths. Fourth, single parents who had a higher level of social support perceived a higher level of family strength. Fifth, there was no interaction effect between personal resource and social resource on single-parent family strengths. Last, both personal and social resources had effects on the strength of single- parent family. Especially personal resource and self-esteem were the most important variables and had a strong influence on single-parent family strengths. The results of this study have important implications for theory, research and practice. Research on the strengths of the Korean single-parent family is new and more extensive investigation is required.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.89-98
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• 1998

The enhanced resource-allocating network(ERAN) that adaptively generates hidden units of radial basis function(RBF) network for systems modeling has been proposed. The ERAN is an improved version of the resource-allocating network(RAN) that allocates new hidden units based on the novelty of observation data. The learning process of the ERAN involves allocation of new hidden units and adjusting the network parameters. The network starts with no hidden units. As observation data are received, the network adds a hidden units only if the three network growth criteria are satisfied. The network parameters are adjusted by the LMS algorithm. The performance of the ERAN is compared with the RAN for nonlinear static systems modeling problem with sequential and random learning. For two simulations, the ERAN has been shown to realize RBF networks with better accuracy with fewer hidden units.

• Asia pacific journal of information systems
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• v.15 no.1
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• pp.115-133
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• 2005

This study is to develop an information security management model(ISMM) for small and medium sized enterprises(SMEs). Based on extensive literature review, a five-pillar twelve-component reference ISMM is developed. The five pillars of SME's information security are: centralized decision making, ease of management, flexibility, agility and expandability. Twelve components are: scope & organization, security policy, resource assessment, risk assessment, implementation planning, control development, awareness training, monitoring, change management, auditing, maintenance and accident management. Subsequent survey designed and administered to expose experts' perception on the importance of these twelve components revealed that five out of tweleve components require relatively immediate attention than others, especially in SME's context. These five components are: scope and organization, resource assessment, auditing, change management, and incident management. Other seven components are policy, risk assessment, implementation planning, control development, awareness training, monitoring, and maintenance. It seems that resource limitation of SMEs directs their attention to ISMM activities that may not require a lot of resources. On the basis of these findings, a three-phase approach is developed and proposed here as an SME ISMM. Three phases are (1) foundation and promotion, (2) management and expansion, and (3) maturity. Implications of the model are discussed and suggestions are made for further research.

• Environmental Engineering Research
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• v.24 no.1
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• pp.74-81
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• 2019

Upcycling generally refers to the conversion of waste materials to something useful or valuable and is a useful concept that can be applied not only to the waste design industry but also to waste recycling and resource circulation. Our study highlights upcycling as the key concept for improving the value of waste by redefining the concept as "the recycling of waste materials and discarded products in ways that enhance their value." Four upcycling strategies are linked to material flow analyses conducted on waste electronic and electrical equipment, specifically waste refrigerators and waste computers, to examine the technologies available for implementation and suggest guidelines for the promotion of upcycling. The amount of waste refrigerators collected by the formal sector was 121,642 tons/y and the informal sector, 63,823 tons/y. The current recycling ratio of waste refrigerators was estimated as 88.53%. A total of 7,585 tons/y of waste computers were collected by the formal sector and 3,807 tons/y by the informal sector after discharge. Meanwhile, the current recycling ratio of waste computers was estimated as 77.43%. We found that it is possible to introduce 28 upcycling technologies in the case of refrigerators, and 15 technologies are available to promote upcycling in the case of computers. By refining the broad concept of upcycling and looking at the stages of material flow, our approach presents universally applicable directions for incorporating upcycling in resource recovery and recirculation plans.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.43-51
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• 2023

In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.1 s.24
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• pp.39-44
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• 2003

Recently, GIS has been increasing its applicability in water resource field. The GIS based modeling process can generally be used for extracting channel network and watershed delineation. Through the overlay analysis, the extracted channel network can be overlayed with topographic and landuse maps to generate the input files for running a hydrologic model. This lead to consider GIS as a tool which can include subjective factors of the model designers in hydrologic analysis. Therefore, this study has compared GIS based HEC-GEOHMS with the classical approach. In general, both approaches have similar results, however, HEC-GROHMS has showed some errors. Based on the results, a GIS based approach could be more effective method with better credibility to obtain input parameters from topographic information as subsequent efforts were made to lessen the errors.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.7
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• pp.219-230
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• 2014

In the cloud computing environment, cloud service users purchase and use the virtualized resources from cloud resource providers on a pay as you go manner. Typically, there are two billing plans for computing resource allocation adopted by large cloud resource providers such as Amazon, Gogrid, and Microsoft, on-demand and reserved plans. Reserved Virtual Machine(VM) instance is provided to users based on the lengthy allocation with the cheaper price than the one of on-demand VM instance which is based on shortly allocation. With the proper mixture allocation of reserved and on-demand VM corresponding to users' requests, cloud service providers are able to reduce the resource allocation cost. To do this, prior researches about VM allocation scheme have been focused on the optimization approach with the users' request prediction techniques. However, it is difficult to predict the expected demands exactly because there are various cloud service users and the their request patterns are heavily fluctuated in reality. Moreover, the previous optimization processing techniques might require unacceptable huge time so it is hard to apply them to the current cloud computing system. In this paper, we propose the cloud brokering system with the adaptive VM allocation schemes called A3R(Adaptive 3 Resource allocation schemes) that do not need any optimization processes and kinds of prediction techniques. By using A3R, the VM instances are allocated to users in response to their service demands adaptively. We demonstrate that our proposed schemes are able to reduce the resource use cost significantly while maintaining the acceptable Quality of Service(QoS) of cloud service users through the evaluation results.