Korea Institute of Science and Technology Information

About: Korea Institute of Science and Technology Information is a facility organization based out in Daejeon, South Korea. It is known for research contribution in the topics: Gravitational wave & LIGO. The organization has 1152 authors who have published 2319 publications receiving 93849 citations. The organization is also known as: Korea Institute of Science and Technology Information & KISTI.

Cellular-Automaton-Based Node Scheduling Control for Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) generally consist of densely deployed sensor nodes that depend on batteries for energy. Having a large number of densely deployed sensor nodes causes energy waste and high redundancy in sensor data transmissions. The problems of power limitation and high redundancy in sensing coverage can be solved by appropriate scheduling of node activity among sensor nodes. In this paper, we propose a cellular automata (CA)-based node scheduling algorithm for prolonging network lifetime with a balance of energy savings among nodes while achieving high coverage quality. Based on a CA framework, we propose a new mathematical model for the node scheduling algorithm. The proposed algorithm uses local interaction based on environmental state signaling for making scheduling decisions. We analyze the system behavior and derive steady states of the proposed system. Simulation results show that the proposed algorithm outperforms existing protocols by providing energy balance with significant energy savings while maintaining sensing coverage quality.

The PRAGMA Testbed - Building a Multi-Application International Grid

Abstract: This practices and experience paper describes the coordination, design, implementation, availability, and performance of the Pacific Rim Applications and Grid Middleware Assembly (PRAGMA) Grid Testbed. Applications in high-energy physics, genome annotation, quantum computational chemistry, wildfire simulation, and protein sequence alignment have driven the middleware requirements, and the testbed provides a mechanism for international users to share software beyond the essential, de facto standard Globus core. In this paper, we describe how human factors, resource availability and performance issues have affected the middleware, applications and the testbed design. We also describe how middleware components in grid monitoring, grid accounting, grid Remote Procedure Calls, grid-aware file systems, and grid-based optimization have dealt with some of the major characteristics of our testbed. We also briefly describe a number of mechanisms that we have employed to make software more easily available to testbed administrators.

Generation of assembly models from kinematic constraints

Abstract: In this paper, we propose a new joint-based assembly modeling method. In joint-based assembly modeling, the assembly constraints are specified on the components, but not on the geometric elements of the components. The proposed method generates assembly models from kinematic joint constraints by applying three procedures (1) to extract all feasible JMFs (Joint Mating Feature) for each mating component using information on joint constraints, (2) to derive mating alternatives for each pair of mating components after reducing the number of JMFs using the pruning criteria, and (3) to generate an assembly model by choosing the intended one from the mating alternatives for each pair of mating components and solving the JMF constraints. Since the joint constraints are expressed in terms of the relations between components rather than relations between geometric elements, the proposed method is more intuitive and natural for assembly modeling and supports modeling activities effectively by minimizing user interactions. By using joint mating constraints for assembly modeling, moreover, the kinematic behaviors of assemblies determined in the conceptual design stage can be directly applied and consistently maintained up to the detailed design stage. In the proposed method, it is also not necessary to re-input the mating constraints even when the component topology is changed.

Graph kernel based measure for evaluating the influence of patents in a patent citation network

Abstract: A new kernel based influence measure for evaluating patent influence is proposed.We use the difference in kernel matrix norms as a measure of node influence.Node with largest difference in matrix norm is considered as most influential node.Von Neumann kernel can be used to account for both direct and indirect citations.Experiments show that our proposed approach performs better than existing measures. Identifying important patents helps to drive business growth and focus investment. In the past, centrality measures such as degree centrality and betweenness centrality have been applied to identify influential or important patents in patent citation networks. How such a complex process like technological change can be analyzed is an important research topic. However, no existing centrality measure leverages the powerful graph kernels for this end. This paper presents a new centrality measure based on the change of the node similarity matrix after leveraging graph kernels. The proposed approach provides a more robust understanding of the identification of influential nodes, since it focuses on graph structure information by considering direct and indirect patent citations. This study begins with the premise that the change of similarity matrix that results from removing a given node indicates the importance of the node within its network, since each node makes a contribution to the similarity matrix among nodes. We calculate the change of the similarity matrix norms for a given node after we calculate the singular values for the case of the existence and the case of nonexistence of that node within the network. Then, the node resulting in the largest change (i.e., decrease) in the similarity matrix norm is considered to be the most influential node. We compare the performance of our proposed approach with other widely-used centrality measures using artificial data and real-life U.S. patent data. Experimental results show that our proposed approach performs better than existing methods.