• Journal of Information Processing Systems
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• v.17 no.3
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• pp.518-536
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• 2021

Studies and applications related to unidirectional flow are gaining attention from researchers across disciplines in the recent years. Flow can be viewed as a concept, where the material, fluid, people, air, and electricity are moving from one node to another over a transportation network, water network, or through electricity distribution systems. Unlike other networks such as computer networks, most of the flow networks are visible and have strong material existence and are responsible for the flow of materials with definite shape and volume. The flow of electricity is also unidirectional, and also share similar features as of flow of materials such as liquids and air. Generally, in a flow network, every node in the network participates and contributes to the efficiency of the network. In this survey paper, we would like to evaluate and analyze the depth and application of the acyclic nature of unidirectional flow in several domains such as industry, biology, medicine, and electricity. This survey also provides, how the unidirectional flow and flow networks play an important role in multiple disciplines. The study includes all the major developments in the past years describing the key attributes of unidirectional flow networks, including their applications, scope, and routing methods.

• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.105-112
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• 2015

Product Lifecycle Management (PLM) ranges from design concepts of products to disposal. In this paper, we focus on the production planning phase in PLM, which is related to process planning and production scheduling and so on. In this study, key decisions for the creation of production plans are defined as production-planning attributes. Production-planning attributes correlate complexly in production-planning problems. Traditionally, the production-planning problem splits sub-problems based on experiences, because of the complexity. In addition, the orders in which to solve each sub-problem are determined by priorities between sub-problems. However, such approaches make solution space over-restricted and make it difficult to find a better solution. We have proposed a representation of combinations of alternatives in production-planning attributes by using Zero-Suppressed Binary Decision Diagrams. The ZDD represents only feasible combinations of alternatives that satisfy constraints in the production planning. Moreover, we have developed a solution search method that solves production-planning problems with ZDDs. In this paper, we propose an approach for managing solution candidates by ZDDs' network for addressing larger production-planning problems. The network can be created by linkages of ZDDs that express constraints in individual sub-problems and between sub-problems. The benefit of this approach is that it represents solution space, satisfying whole constraints in the production planning. This case study shows that the validity of the proposed approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.441-448
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• 2014

USN(Ubiquitous Sensor Network) application software has a characteristic that it controls a variety of sensor nodes based on the various target operating systems. Accordingly, many researches for efficient development of USN application software are being performed. In this paper, the attributes design technique to support attribute-based development of USN node software for multi-platform is proposed. In the proposed technique, the method to design attributes for modeling Platform Independent Model and Platform Specific Model is presented. When using the proposed technique, productivity of software development will be increased because node software design for multi-platform is easily performed by selecting values of attributes. Also, maintainability of software will be increased because node software is easily regenerated by changing attributes according to the changes of operating systems.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.3
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• pp.492-507
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• 2008

Many classical network analysis methods approach networks in aspatial perspectives. Measuring network reliability and finding critical nodes in particular, the analyses consider only network connection topology ignoring spatial components in the network such as node attributes and edge distances. Using local network autocorrelation measure, this study handles the problem. By quantifying similarity or clustering of individual objects' attributes in space, local autocorrelation measures can indicate significance of individual nodes in a network. As an application, this study analyzed internet backbone networks in the United States using both classical disjoint product method and Getis-Ord local G statistics. In the process, two variables (population size and reliability) were applied as node attributes. The results showed that local network autocorrelation measures could provide local clusters of critical nodes enabling more empirical and realistic analysis particularly when research interests were local network ranges or impacts.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• Asian Journal for Public Opinion Research
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• v.1 no.2
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• pp.103-127
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• 2014

This study reviews existing research on the measurement of and the relationship between political diversity and political participation. It addresses the inconsistency in the arguments of existing studies researching the influence of political diversity on political participation. It attempts to find the cause in the variety of approaches to conceptualize and operationalize the two variables. As the measure of political diversity, political network heterogeneity and network attributes are discussed in detail in specific relation to political participation. As for political participation, an in-depth analysis of various ways to understand different forms of political involvement is presented. Implications for public opinion research are discussed.

• Asia pacific journal of information systems
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• v.30 no.1
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• pp.94-119
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• 2020

Digital platforms characterized by network effects enable provisioning of various types of services and provide a mechanism for linking producers and consumers. Identifying the key Quality of Service attributes of such platforms is vital for their continued success and growth. In this paper, a set of quality attributes for platforms is first extracted from different extant quality models. Then actual user feedback data from three platform providers are analysed and mapped against the set of quality attributes to determine the key attributes that are relevant. These findings are corroborated with qualitative data from interviews of different stakeholders. The results show that service quality characteristics are important to the success of platforms. Functional characteristics of platforms assume importance where the digital contributions of the platform is higher. Apart from these, 'fitness for use' as a major determinant of quality is also important in digital platforms.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.814-817
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• 2002

High-dimensional data with two or more attributes are considered. A typical example of such data is face images of various individuals and expressions. In these cases, collecting a complete data set is often difficult since the number of combinations can be large. In the present study, we propose a method to interpolate data of missing combinations from other data. If this becomes possible, robust recognition of multiple attributes is expectable. The key of this subject is appropriate extraction of the similarity that the face images of same individual or same expression have. Bilinear model ［1］has been proposed as a solution of this subjcet. However, experiments on application of bilinear model to classification of face images resulted in low performance ［2］. In order to overcome the limit of bilinear model, in this research, a nonlinear model on a neural network is adopted and usefulness of this model is experimentally confirmed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.780-783
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• 2002

This paper proposes an approximate LCA methodology fur the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes Into impact driver (ID) index. The relationship Is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then an artificial neural network model is developed to predict an approximate LCA of grouping products in conceptual design stage. The training is generalized by using identified product attributes for an ID In a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give an approximate LCA results for design concepts.

• Asia pacific journal of information systems
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• v.1 no.1
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• pp.49-62
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• 1991

This paper examines the problems of database partitioning and file allocation in a fixed topology distributed computer network. The design objective is to make files as collections of attributes and to allocate these files to network nodes so that a minimum total transmission cost is achieved subject to storage capacity constraints. A mathematical model for solving the problem is formulated and, the resulting optimization problem is shown to fall in a class of NP-complete problems. A new heuristic algorithm is developed which uses the idea of allocating attributes according to the transaction requirements at each computer node and then making files using the allocated attributes. Numerical results indicate that the heuristic algorithm yields practicable low cost solutions in comparison with the existing methods which deal with the file allocation problems and database partitioning problems independently.