• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.697-715
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• 2004

Component-Based Development (CBD) is a reuse technology Providing enhancement in productivity through using the unit of component which is larger-grained than an object. However, reference model defining the elements and semantics of CBD component is standardized neither internationally nor in industrial. This yields interoperability and portability problem between CBD platforms, and presents burden of choosing appropriate model to developers. In this paper, we define meta-models for representative component reference models, and identify advantages, disadvantages, and features of each model through technical comparison of meta-models. Besides, through a proposal of essential component model containing common and essential elements that all component models must conform and a extended component model containing maximum elements and mechanisms, we can precisely assess candidate component models in practice.

• Journal of Periodontal and Implant Science
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• v.52 no.6
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• pp.479-495
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• 2022

Purpose: Rodent models have emerged as an alternative to established larger animal models for peri-implantitis research. However, the construct validity of rodent models is controversial due to a lack of consensus regarding their histological, morphological, and biochemical characteristics. This systematic review sought to validate rodent models by characterizing their morphological changes, particularly marginal bone loss (MBL), a hallmark of peri-implantitis. Methods: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A literature search was performed electronically using MEDLINE (PubMed), and Embase, identifying pre-clinical studies reporting MBL after experimental peri-implantitis induction in rodents. Each study's risk of bias was assessed using the Systematic Review Center for Laboratory animal Experimentation (SYRCLE) risk of bias tool. A meta-analysis was performed for the difference in MBL, comparing healthy implants to those with experimental peri-implantitis. Results: Of the 1,014 unique records retrieved, 23 studies that met the eligibility criteria were included. Peri-implantitis was induced using 4 methods: ligatures, lipopolysaccharide, microbial infection, and titanium particles. Studies presented high to unclear risks of bias. During the osseointegration phase, 11.6% and 6.4%-11.3% of implants inserted in mice and rats, respectively, had failed to osseointegrate. Twelve studies were included in the meta-analysis of the linear MBL measured using micro-computed tomography. Following experimental peri-implantitis, the MBL was estimated to be 0.25 mm (95% confidence interval [CI], 0.14-0.36 mm) in mice and 0.26 mm (95% CI, 0.19-0.34 mm) in rats. The resulting peri-implant MBL was circumferential, consisting of supra- and infrabony components. Conclusions: Experimental peri-implantitis in rodent models results in circumferential MBL, with morphology consistent with the clinical presentation of peri-implantitis. While rodent models are promising, there is still a need to further characterize their healing potentials, standardize experiment protocols, and improve the reporting of results and methodology.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.98-107
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• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.5-12
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• 2000

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution and it is shown to be useful in such Bayesian meta-analysis. A general class of skewed elliptical distribution is reviewed and developed. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierarchical selection model and use Markov chain Monte Carlo methods to develop inference for the parameters of interest.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.299-319
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• 2016

This study first introduces platform services and their business models. On the basis of the concepts of business ecosystem, we present a framework for distinguishing types of the platform service business models. Two key characteristics of business ecosystems-ecosystem configuration and value production process-are employed as fundamental dimensions for constructing typology. In particular, we also present the notion of value ecosystem, where not a single platform provider but a federation of platforms constitutes a virtual platform and completes a service system. The value ecosystem represents two distinct types of platform service business models : meta-platform ecosystem and platform coalition ecosystem. They show different governance structure in the platform federation and service flows across the ecosystem. We present detailed analyses of these two value ecosystems focusing on relevant cases of e-payment FinTech : Apple Pay as an example of meta-platform and Kakao Pay for platform coalition. Our conceptual typology contributes to platforms' proper strategy formulation and presents policy implications to, for example, platform neutrality.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.75-85
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• 2003

This study develops the System(MetaGene) to create meta-data for learning object based on SCORM including meta-data of Assets. SCO, Contents Aggregation and metadata of Contents Package. API function cocle is embeded in Learning Object for interfacing API adopter in LMS to support SCORM and for tracking on learning process based on data models. Also, the learning objects are packaged the PIF(Packaged Interchange File) to transmit with LMS. MetaGene is verified by $SCORM^{(TM)}$ Conformance TestSuite for meta-data of learning objects, manifest file of Contents Packaging.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 2003.11a
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• pp.127-133
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• 2003

The data models which express all types of video information physically and logically. and the definition of spatiotemporal relationship of video data objects In This paper, we classifies meta-model for efficient management on spatiotemporal relationship between two objects in video image data, suggests meta-models based on Rambaugh's OMT technique, and expanded user model to apply the adaptive model, established from hyper-media or web agent to VOD. The proposed meta-model uses data's special physical feature: the effects of camera's and editing effects of shot, and 17 spatial relations on Allen's 13 temporal relations, topology and direction to include logical presentation of spatiotemporal relation for possible spatiotemporal reference and having unspecified applied mediocrity.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.2 s.17
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• pp.65-73
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• 2004

In general, weapon system design/configuration data consist of system structure information which is linked to Part information, documents and drawings. For configuration management, version and revision control are necessary and access control of users to information should be managed for information security. Configuration data of weapon systems have various kinds of different meta data which are contained in the structure as well as attributes of parts and documents information. If neutral types of meta data models be used for building configuration management system, they can be applied to many different kinds of weapon systems with a little customization. In this paper, five meta data models are supposed and implementation results of them by using CBD(component based design) methodology are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2067-2081
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• 2024

Recently, image analysis research has been actively conducted due to the accumulation of big image data and the development of deep learning. Image analytics research has different characteristics from other data such as data size, real-time, image quality diversity, structural complexity, and security issues. In addition, a large amount of data is required to effectively analyze images with deep-learning models. However, in many fields, the data that can be collected is limited, so there is a need for meta learning based image analysis technology that can effectively train models with a small amount of data. This paper presents a comprehensive survey of meta-learning-based object-tracking techniques. This approach comprehensively explores object tracking methods and research that can achieve high performance in data-limited situations, including key challenges and future directions. It provides useful information for researchers in the field and can provide insights into future research directions.