• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.310-328
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• 2022

Internet of things (IoT) has emerged as the most popular technique that facilitates enhancing humans' quality of life. However, most time sensitive IoT applications require quick response time. So, processing these IoT applications in cloud servers may not be effective. Therefore, fog computing has emerged as a promising solution that addresses the problem of managing large data bandwidth requirements of devices and quick response time. This technology has resulted in processing a large amount of data near the data source compared to the cloud. However, efficient management of computing resources involving balancing workload, allocating resources, provisioning resources, and scheduling tasks is one primary consideration for effective computing-based solutions, specifically for time-sensitive applications. This paper provides a comprehensive review of the source management strategies considering resource limitations, heterogeneity, unpredicted traffic in the fog computing environment. It presents recent developments in the resource management field of the fog computing environment. It also presents significant management issues such as resource allocation, resource provisioning, resource scheduling, task offloading, etc. Related studies are compared indifferent mentions to provide promising directions of future research by fellow researchers in the field.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.151-152
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• 2001

A common problem of the model simulations of the land surface - atmosphere interaction is to choose the appropriate spatial scale and resolution at which the simulations are to be performed. The accuracy of energy and water exchange predictions between the land surface and the atmosphere in regional and global scale atmospheric models is mainly influenced by: model simplifications applied to describe the spatial heterogeneity of land surface properties within individual grid cells; ignoring the variability of sub-grid properties (e.g. relief, vegetation, soils), and; lacks of necessary input meteorological and biophysical data.(omitted)

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11B
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• pp.689-697
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• 2005

The IETF has created the v6ops Working Group to assist IPv6 transition and propose technical solutions to achieve it. But it's quite problem which security consideration for a stage of IPv4/IPv6 transition and co-existence. There are new security problem threat that it caused by the characteristics of heterogeneity. In this paper, we describe IPv6 transition mechanisms and analyze security problem for IPv6 transition mechanism. also we propose security consideration and new security mechanism. We analyzed DoS and DRDoS in 6to4 environment and presented a address sanity check as a solution. We also showed an attack of address exhaustion in address allocation server. To solve this problem, we proposed challenge-response mechanism in DSTM.

• International Journal of CAD/CAM
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• v.5 no.1
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• pp.99-109
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• 2005

This paper introduces an approach to an ontology-based multi-level knowledge framework for a knowledge management system for discrete-product development. Participants in a product life cycle want to share comprehensive product knowledge without any ambiguity and heterogeneity. However, previous knowledge management approaches are limited in providing those aspects: therefore, we suggest an ontology-based multi-level knowledge framework (OBMKF). The bottom level, the axiom, specifies the semantics of concepts and relations of knowledge so ambiguity can be alleviated. The middle level is a product development knowledge map; it defines the concepts and the relations of the product domain knowledge and guides the engineer to process their engineering decisions. The middle level is then classified further into more detailed levels, such as generic product level, specific product level, product version level, and manufactured item level, according to the various viewpoints. The top level is specialized knowledge for a specific domain that gives the solution of a specific task or problem. It is classified into three knowledge types: expert knowledge, engineering function knowledge, and data-analysis-based knowledge. This proposed framework is based on ontology to accommodate a comprehensive range of knowledge and is represented with first-order logic to maintain a uniform representation.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.27-40
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• 2019

The study examines the effects of growth opportunities, debt maturity and liquidity risk on leverage, making use of a large panel of Chinese listed firms. Research on capital structure has broadened its scope from a single capital structure decision (the debt/equity choice) to various attributes of the debt in firms' capital structure. We use the system Generalized Method of Moments estimator to control for unobserved heterogeneity and the potential endogeneity of regressors. We find a negative relationship between growth opportunities and leverage. Further, we find that while the proportion of short-term debt attenuates the negative effect of growth opportunities on leverage, it negatively affects leverage as predicted by the liquidity risk hypothesis. When we distinguish between state owned firms and private controlled firms, we find evidence that these effects are only relevant to private controlled firms. However, our analysis indicates that the economic implication of liquidity risk effect is much lower for Chinese firms than that observed in the literature for US firms. Our study suggests that these differences can be explained by differences in the institutional environment in which firms operate. This finding related to Diamond's (1991) liquidity risk hypothesis extends our understanding of the relationship between liquidity risk and the debt maturity choice.

• Journal of Korean Academy of Nursing
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• v.43 no.5
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• pp.579-586
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• 2013

Purpose: This article provides an update and overview of a nursing research program focused on understanding the pathophysiology and management of irritable bowel syndrome (IBS). Methods: This review includes English language papers from the United States, Europe, and Asia (e.g., South Korea) from 1999 to 2013. We addressed IBS as a health problem, emerging etiologies, diagnostic and treatment approaches and the importance of a biopsychosocial model. Results: IBS is a chronic, functional gastrointestinal disorder characterized by recurrent episodes of abdominal pain and alterations in bowel habit (diarrhea, constipation, mixed). It is a condition for which adults, particularly women ages 20-45, seek health care services in both the United States and South Korea. Clinically, nurses play key roles in symptom prevention and management including designing and implementing approaches to enhance the patients' self-management strategies. Multiple mechanisms are believed to participate in the development and maintenance of IBS symptoms including autonomic nervous system dysregulation, intestinal inflammation, intestinal dysbiosis, dietary intolerances, alterations in emotion regulation, heightened visceral pain sensitivity, hypothalamic-pituitary-adrenal dysregulation, and dysmotility. Because IBS tends to occur in families, genetic factors may also contribute to the pathophysiology. Patients with IBS often report a number of co-morbid disorders and/or symptoms including poor sleep. Conclusion: The key to planning effective management strategies is to understand the heterogeneity of this disorder. Interventions for IBS include non-pharmacological strategies such as cognitive behavior therapy, relaxation strategies, and exclusion diets.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.3 s.180
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• pp.22-31
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• 2006

This paper introduces an approach to an ontology-based knowledge framework for product life cycle management (PLM). Participants in a product life cycle want to share comprehensive product knowledge without any ambiguity and heterogeneity. However, previous knowledge management approaches are limited in providing those aspects. Therefore, we suggest an ontology-based knowledge framework including knowledge maps, axioms and specific knowledge far domain. The bottom level, the axiom, specifies the semantics of concepts and relations of knowledge so that ambiguity of the semantics can be alleviated. The middle level is a product development knowledge map; it defines the concepts and the relations of the product domain common knowledge and guides engineers to process their engineering decisions. The middle level is then classified further into more detailed levels, such as generic product level, specific product level, product version level, and product item level for PLM. The top level is specialized knowledge fer a specific domain that gives the solution of a specific task or problem. It is classified into three knowledge types: expert knowledge, engineering function knowledge, and data-analysis-based knowledge. This proposed framework is based on ontology to accommodate a comprehensive range of unambiguous knowledge for PLM and is represented with first-order logic to maintain a uniform representation.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.848-855
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• 2016

Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.2
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• pp.53-64
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• 1993

Successful completion of a visual task in a predetermined time is very crucial to many operations such as piloting an aircraft. Although existing ergonomic interface models often provide a function of vision tests, it determines only the visibility at any given location. To complement this problem in existing models, the isoresponse time region considering the factors related to visual tasks is presented. Using a multiple regression model, equal response time regions were obtained within which mean response time is expected to be the same and is asymmetrical in shape. Among the factors considered, expectancy significantly decreased response time, and when cued, the effects of field heterogeneity, target uncertainty, density, size contrast and peripheral position on search time were less significant than those in unexpected cases. Response time and error rate, gender and visual acuity were not significantly correlated, and response time and age was positively correlated. These results are expected to be directly applicable to designing various visual tasks in real-life situations.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.120-126
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• 2020

Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.