• Smart Structures and Systems
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• v.14 no.2
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• pp.129-144
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• 2014

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• Journal of Science Education
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• v.44 no.1
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• pp.84-91
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• 2020

In this study, we investigated the elements of knowledge and information processing competence of science inquiry activities in grades 3-6 science textbooks. For this purpose, the elements of knowledge and information processing competence were extracted and a framework for analysis was created. Analysis of eight science textbooks for grades 3-6 in elementary school was carried out using the analysis framework. The following results were obtained by the analysis framework: First, we divided the knowledge and information processing competence into five elements: 'collection,' 'analysis,' 'expression,' 'selection,' and 'evaluation.' Second, the elements of knowledge and information processing competence of science inquiry activity of elementary science textbooks were not evenly composed. The science inquiry activities of elementary science textbooks focused on 'collection' and 'analysis' among the five elements of the knowledge and information processing competence, followed by 'expression,' 'selection,' and 'evaluation.' As a result, we found that a science inquiry model or a science instruction model is needed to develop the knowledge and information processing competence in elementary school science curriculum.

• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.67-82
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• 2007

The purpose of this paper is to imply for the assessing framework of next generation Enterprise Portals'(EPs) function through the lens of organizational knowledge creation. As the emerging "suite" products such as Smart Enterprise Suite (SES) converges EPs with collaboration and content management, EPs are no longer just a gateway to information and content. EPs are emerging as the center of convergence for many complementary technologies. Considering that this trend is common and knowledge creation is one of key goals of EPs, it is important to assess EPs' function through the knowledge creation process. This paper reviews literatures to set initial assessing elements of EPs' function, then adopts Delphi Technique to prove and categorize the elements according to the knowledge creation process. The samples are consisted of 7 professional knowledge management consultants at Entrue Consulting Partners, LG CNS. This paper may be significant in providing a new vision to the evaluation of EPs by combining a theoretical model with a practical situation.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1598-1608
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• 1999

Performance testing for ATM network deals with evaluation of maximal throughput of network by measuring and analyzing of various performance parameters. However, because of the absence of the methodology and framework for performance testing, the results of performance measurements for same implementation under test were much relied on tester. In this paper, the concept of ATM performance testing is described in both cell and frame level and a framework of ATM performance testing is suggested by describing of testing environment for performance measurement and requirements of performance evaluation. For this, we define performance measuring method and parameters for each case.

• KIISE Transactions on Computing Practices
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• v.21 no.5
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• pp.379-386
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• 2015

We propose a novel user-perceived performance optimization framework for the Android platform that takes advantage of the user-centric response time analysis. To this end, we propose a new definition of response time, which we call the user-centric response time, as a metric for the quality of user-perceived performance of the smartphone application. In this paper, we describe the design and implementation of an on-line user-centric response time analyzer for Android-based smartphones, which provides smartphone application developers with valuable insight for user-perceived performance optimization. We implemented the user-centric response time analyzer on the Android platform, version 4.0.4 (ICS) running on a Galaxy Nexus smartphone. From our experimental results, the proposed user-centric response time analyzer accurately estimates user-centric response times with an accuracy of 92.0% compared to manually measured times with less than 1% performance penalty. In order to evaluate the efficiency of the proposed framework, we were able to reduce the user-centric response time of the target application by up to 16.4% based on the evaluation results by the proposed framework.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.595-607
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• 2021

Purpose: This study introduces a systematic framework to evaluate service satisfaction of cosmetic brands through online review analysis utilizing Text-Mining technique. Methods: The framework assumes that the service satisfaction is evaluated by positive comments from online reviews. That is, the service satisfaction of a cosmetic brand is evaluated higher as more positive opinions are commented in the online reviews. This study focuses on two approaches. First, it collects online review comments from the top 50 global cosmetic brands and evaluates customer service satisfaction for each cosmetic brands by applying Sentimental Analysis and Latent Dirichlet Allocation. Second, it analyzes the determinants that induce or influence service satisfaction and suggests the guidelines for cosmetic brands with low satisfaction to improve their service satisfaction. Results: For the satisfaction evaluation, online review data were extracted from the top 50 global cosmetic brands in the world based on 2018 sales announced by Brand Finance in the UK. As a result of the satisfaction analysis, it was found that overall there were more positive opinions than negative opinions and the averages for polarity, subjectivity, positive ratio, and negative ratio were calculated as 0.50, 0.76, 0.57, and 0.19, respectively. Polarity, subjectivity and positive ratio showed the opposite pattern to negative ratio, and although there was a slight difference in fluctuation range and ranking between them, the patterns are almost same. Conclusion: The usefulness of the proposed framework was verified through case study. Although some studies have suggested a method to analyze online reviews, they didn't deal with the satisfaction evaluation among competitors and cause analysis. This study is different from previous studies in that it evaluates service satisfaction from a relative point of view among cosmetic brands and analyze determinants.

• Asia pacific journal of information systems
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• v.30 no.2
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• pp.397-419
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• 2020

Business analytics is a management tool for achieving significant business performance improvements. Many organizations fail to or only partially achieve their business objectives and goals from business analytics. Business analytics adoption is a multi-stage complex activity consisting of evaluation, adoption, and assimilation stages. Several research papers have been published in the field of business analytics, but the research on multi-stage BA adoption is fewer in number. This study contributes to the scant literature on the multi-stage adoption model by identifying the critical themes for evaluation, adoption, and assimilation stages of business analytics. This study uses the thematic content analysis of peer-reviewed published academic papers as a research technique to explore the key themes of business analytics adoption. This study links the critical themes with the popular theoretical foundations: Resource-Based View (RBV), Dynamic Capabilities, Diffusion of Innovations, and Technology-Organizational-Environmental (TOE) framework. The study identifies twelve major factors categorized into three key themes: organizational characteristics, innovation characteristics, and environmental characteristics. The main organizational factors are top management support, organization data environment, centralized analytics structure, perceived cost, employee skills, and data-based decision making culture. The major innovation characteristics are perceived benefits, complexity, and compatibility, and information technology assets. The environmental factors influencing BA adoption stages are competition and industry pressure. A conceptual framework for the multi-stage BA adoption model is proposed in this study. The findings of this study can assist the practicing managers in developing a stage-wise operational strategy for business analytics adoption. Future research can also attempt to validate the conceptual model proposed in this study.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.121-131
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• 2024

This research explores the potential of leveraging unstructured data from construction supervision documents, which contain detailed inspection insights from independent third-party monitors of building construction processes. With the evolution of analytical methodologies, such unstructured data has been recognized as a valuable source of information, offering diverse insights. The study introduces a framework designed to assess cost performance by applying advanced analytical methods to the unstructured data found in final construction supervision reports. Specifically, key phrases were identified using text mining and social network analysis techniques, and these phrases were then analyzed through binomial logistic regression to assess cost performance. The study found that predictions of cost performance based on unstructured data from supervision documents achieved an accuracy rate of approximately 73%. The findings of this research are anticipated to serve as a foundational resource for analyzing various forms of unstructured data generated within the construction sector in future projects.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.792-797
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• 2015

Effective profiling diagnoses the failure of the system and informs risk. If a failure in the target system occurs, it is impossible to diagnose more than one of the exiting tools. In this respect, monitoring of the system based on virtualization is useful. We present in this paper a monitoring framework that uses the characteristics of hardware virtualization to prevent side-effects from a target guest, and uses dynamic binary instrumentation with instruction-level trapping based on hardware virtualization to achieve efficiency and flexibility. We also present examples of some applications that use this framework. The framework provides tracing of guest kernel function, memory dump, and debugging that uses GDB stub with GDB remote protocol. The experimental evaluation of our prototype shows that the monitoring framework incurs at most 2% write memory performance overhead for end users.