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Review of Product Recovery, Sensor-Embedded Products, Warranty and Maintainability
Published in Ammar Y. Alqahtani, Surendra M. Gupta, Warranty and Preventive Maintenance for Remanufactured Products, 2018
Ammar Y. Alqahtani, Surendra M. Gupta
Disassembly-to-order systems (DTOs) are undertaken to determine the optimal lot-sizes of EOLPs to disassemble to satisfy the demand for the components therein from a mix of various product types that have components and/or modules in common (Lambert & Gupta, 2002). Lambert & Gupta (2008) employed heuristics developed under the assumption of deterministic disassembly yield to develop a method called the tree network model. The tree network model was developed by modifying the disassembly graph method for a multi-product-demand-driven disassembly system with commonality and multiplicity. Kongar & Gupta (2002) proposed a single period integer goal programming model for a DTO system to identify the optimal combination for multiple products to selectively disassemble. The selective disassembly was undertaken to meet the demand for items and materials under physical, financial and environmental constraints and goals (Kongar & Gupta, 2006a).
Dominating Set Theory and Algorithms
Published in Jiguo Yu, Xiuzhen Cheng, Honglu Jiang, Dongxiao Yu, Hierarchical Topology Control for Wireless Networks, 2018
Jiguo Yu, Xiuzhen Cheng, Honglu Jiang, Dongxiao Yu
Along with the in-depth research of the MIS problem, the design of the MIS algorithm has been ripe. MIS technology is applied extensively in the design of wireless network protocol, especially in the aspects of the CDS and coloring. In Reference [10], Kuhn et al. proposed an MIS deterministic construction algorithm in a bounded growth graph with time complexity as O(log Δ · log* n), where Δ is the maximum degree of the node in the graph; n is the total number of nodes in the graph. The bounded growth graph includes the common network model graph such as the well-known UDG. If all r-neighbors of an arbitrary node have a limited number of independent nodes, the graph is regarded as a bounded growth graph. The algorithm assumes that it does not need any information of location and distance, but only needs connection information for the node and its neighbors. This kind of assumption is also suitable for other regular graphs.
Location Awareness and Navigation in Location-Based Systems
Published in Krzysztof W. Kolodziej, Johan Hjelm, Local Positioning Systems, 2017
Krzysztof W. Kolodziej, Johan Hjelm
The fundamental principle of a three-dimensional indoor geocoding method employs a traditional address-matching technology developed for location positioning of outdoor phenomena. A TIGER-type reference database model can be comprised of the three-dimensional geometric network model (GNM). An edge in the three-dimensional GNM, a hallway line segment, contains indoor address (room number) range attribute data, which are the low (F_RA_l) and high (T_RA_l) room numbers on the edge’s left side and the low (F_RA_r) and high (T_RA_r) room numbers on its right side. Unlike a street address, the indoor address for a room or suite is not always assigned as a number in sequence, or is named as the “Liberty Room.” In order to standardize the indoor address (using a sequential numbering), translation tables can be used.
Implementing blockchain in information systems: a review
Published in Enterprise Information Systems, 2022
The two steps of the process of establishing consensus among nodes in the system are abstracted: each node processes the data saved by each node and uses the network to communicate with other nodes to update the data. The consensus of a distributed system mainly depends on three factors: node reliability, node location, and network communication status. Errors caused by node downtime, crash, or failure are called non-Byzantine errors; errors caused by the malicious sending or non-sending of messages of opponent nodes are called Byzantine errors. The network communication model (also called the time model) in the distributed system is mainly divided into three types: the synchronous network model, the asynchronous network model, and the weak network model (Liu et al. 2017; Sankar, Sindhu, and Sethumadhavan 2017).