• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1188-1202
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• 2012

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.101-107
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• 2010

The aim of this paper is to come up with a methodology of constructing an efficient model for multiple representations which can manage and reconcile real-time data about large-scale roads in Vector Domain. In other words, we suggested framework based on a bottom-up approach, which is allowed to integrate data from the network of the lowest level sequentially and perform automated matching in order to produce variable-scale map. Finally, we applied designed multi-LoD model to in-vehicle application.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.644-654
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• 2024

After the Tohoku earthquake and tsunami (Japan, 2011), regulatory efforts to mitigate external hazards have increased both the safety requirements and the total capital cost of nuclear power plants (NPPs). In these circumstances, identifying not only disaster robustness but also cost-effective capacity setting of NPPs has become one of the most important tasks for the nuclear power industry. A few studies have been performed to relocate the seismic capacity of NPPs, yet the effects of multiple hazards have not been accounted for in NPP capacity optimization. The major challenges in extending this problem to the multihazard dimension are (1) the high computational costs for both multihazard risk quantification and system-level optimization and (2) the lack of capital cost databases of NPPs. To resolve these issues, this paper proposes an effective method that identifies the optimal multihazard capacity of NPPs using a multi-objective genetic algorithm and the two-stage direct quantification of fault trees using Monte Carlo simulation method, called the two-stage DQFM. Also, a capacity-based indirect capital cost measure is proposed. Such a proposed method enables NPP to achieve safety and cost-effectiveness against multi-hazard simultaneously within the computationally efficient platform. The proposed multihazard capacity optimization framework is demonstrated and tested with an earthquake-tsunami example.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.382-387
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• 2002

In this paper, we propose a geostatistical approach incorporated to the Bayesian data fusion technique for supervised classification of multi-sensor remote sensing data. Traditional spectral based classification cannot account for the spatial information and may result in unrealistic classification results. To obtain accurate spatial/contextual information, the indicator kriging that allows one to estimate the probability of occurrence of classes on the basis of surrounding observations is incorporated into the Bayesian framework. This approach has its merit incorporating both the spectral information and spatial information and improves the confidence level in the final data fusion task. To illustrate the proposed scheme, supervised classification of multi-sensor test remote sensing data set was carried out.

• Asia pacific journal of information systems
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• v.5 no.1
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• pp.52-85
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• 1995

This paper develops a new approach to dealing with qualitative reasoning processes involved in managerial decisions, drawing upon choice strategies that have been developed within the general framework of multi-criteria decision making. Issues such as choices under uncertainty and preference formulation are addressed. An MCDM DSS intended to assist in high-level management decisions must focus on helping the decision maker to properly define the problem by providing a structure to it and to dynamically evaluate the alternative courses of action. A conceptual architecture is developed and presented to propose a general model for designing decision support systems specifically designed to assist in MCDM in a managerial context. A commercial loan approval judgment case is described to illustrate the real-world situation where decisions are made under fuzziness and usually require a high degree of intuition and subjective judgment. Development of a prototype system intended to partially represent application of the architecture is described.

• Ocean and Polar Research
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• v.37 no.3
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• pp.211-223
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• 2015

Korean society has been recently promoting the restoration of coastal wetlands. These efforts might become the basis of a policy framework that compensates for the limitations of a regulation-oriented policy such as the designation of Marine Protected Areas (MPAs). The shift in government policy could contribute to strengthening the socioeconomic infrastructure of coastal development through the accumulation of ecological capital. Although our scientific efforts and social demands in regard to the ecological restoration of the coastal wetlands have increased during the past years, the bases for restoration in Korea requires that scientific, technological, financial, social and legal aspects be enhanced. The present study re-examined the concept and attitudes behind coastal wetland restoration in the light of changing circumstances in Korea. Herein, we first defined coastal wetland restoration as "An act of recovering the functions of the ecosystem of coastal wetlands to a state that resembles conditions prior to being damaged." Next, this study discussed the limitations and future directions of such restoration efforts based on the descriptive analyses of recent restoration practices from social, economic, and technological aspects. Finally, we suggest future policy directions regarding coastal wetland restoration on the basis of a PFST (Policy, Financial, Social, and Technological) analysis; 1) re-arranging legal mechanisms, 2) setting multi-dimensional restoration goals, 3) establishing a multi-discipline- and convergence based R&D system, 4) linking spatial management and local development to the restoration, 5) building restoration governance at the local level, 6) implementing an ecosystem service payment system, and 7) applying test-bed projects in accordance with proper directions.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• The Journal of Information Systems
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• v.13 no.2
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• pp.225-240
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• 2004

The most critical problem of MIS evaluation is the lack of the systematic framework to cover various factors and viewpoints. To solve this problem, this study takes the multi-level and contingent approach to performance evaluation, composed of three levels: evaluating the contribution of MIS to an organization [strategy level]; evaluating the activities of MIS department or MIS function as an organizational sub-function through the overall MIS lifecycle [function level]; and evaluating the quality or productivity of the application systems as MIS outputs [system level]. Ideal MIS evaluation should include all three levels of the hierarchy with balanced importance. However, MIS evaluationcanbedividedintothreetypes,suchasstrategy-oriented, function-oriented and system-oriented evaluation, depending on the focus and emphasis of evaluation. The usage pattern of each evaluation type is analyzed according to contingent variables of MIS evaluation such as MIS maturity, information intensity and firm size, and top management's intent. It is also found that the firms of higher MIS maturity and top management's intent use the strategy-oriented evaluation type, and the firms with strategy-oriented evaluation type show a higher MIS performance. Further, MIS maturity and top management's intent show contingent effects between evaluation type and MIS performance. Some managerial implications can be drawn based on the results of the study. First, strategy-oriented evaluation of MIS is more important as many firms more often use information technology as a strategic weapon. Second, MIS performance varies with evaluation type. Therefore, the design of MIS evaluation framework should be done carefully in the strategic and managerial contexts. Third, firms are recommend to use a different evaluation type according to organizational characteristics such as MIS maturity and information intensity.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.1-20
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• 2021

As the Great Reset is discussed at the World Economic Forum due to the COVID-19 pandemic, artificial intelligence, the driving force of the 4th industrial revolution, is also in the spotlight. However, corporate research in the field of artificial intelligence is still scarce. Since 2000, related research has focused on how to create value by applying artificial intelligence to existing companies, and research on how startups seize opportunities and enter among existing businesses to create new value can hardly be found. Therefore, this study analyzed the cases of startups using the comprehensive framework of the multi-level perspective with the research question of how artificial intelligence based startups, a sub-industry of software, have different innovation patterns from the existing software industry. The target firms are gazelle firms that have been certified as venture firms in South Korea, as start-ups within 7 years of age, specializing in machine learning modeling purposively sampled in the medical, finance, marketing/advertising, e-commerce, and manufacturing fields. As a result of the analysis, existing software companies have achieved process innovation from an enterprise-wide integration perspective, in contrast machine learning technology based startups identified unit processes that were difficult to automate or create value by dismantling existing processes, and automate and optimize those processes based on data. The contribution of this study is to analyse the birth of artificial intelligence-based startups and their innovation patterns while validating the framework of an integrated multi-level perspective. In addition, since innovation is driven based on data, the ability to respond to data-related regulations is emphasized even for start-ups, and the government needs to eliminate the uncertainty in related systems to create a predictable and flexible business environment.