Advanced Engineering Mathematics

Enterprises evaluate intellectual property rights and the quality of patent documents in order to develop innovative products and discover state-of-the-art technology trends. The product technologies covered by patent claims are protected by law, and the quality of the patent insures against infringement by competitors while increasing the worth of the invention. Thus, patent quality analysis provides a means by which companies determine whether or not to customize and manufacture innovative products. Since patents provide significant financial protection for businesses, the number of patents filed is increasing at a fast pace. Companies which cannot process patent information or fail to protect their innovations by filing patents lose market competitiveness. Current patent research is needed to estimate the quality of patent documents. The purpose of this research is to improve the analysis and ranking of patent quality. The first step of the proposed methodology is to collect technology specific patents and to extract relevant patent quality performance indicators. The second step is to identify the key impact factors using principal component analysis. These factors are then used as the input parameters for a back-propagation neural network model. Patent transactions help judge patent quality and patents which are licensed or sold with intellectual property usage rights are considered high quality patents. This research collected 283 patents sold or licensed from the news of patent transactions and 116 patents which were unsold but belong to the technology specific domains of interest. After training the patent quality model, 36 historical patents are used to verify the performance of the trained model. The match between the analytical results and the actual trading status reached an 85% level of accuracy. Thus, the proposed patent quality methodology evaluates the quality of patents automatically and effectively as a preliminary screening solution. The approach saves domain experts valuable time targeting high value patents for R&D commercialization and mass customization of products.

This paper reports on a laboratory study that uses Virtual Design Construction (VDC) modeling and Extreme Collaboration (XC) for early phase building project design. The process creates a specific project ontology that forms the initial design of a building project. Our project ontology explicitly represents the functional requirements, designed forms, and predicted and observed behaviors of the product, organization and process of the project. With collateral drawings, models, analyses and explanations, it becomes the schematic and initial detailed design for the project. We observed a group of six designers (experienced graduate students) developing a first draft project proposal bid for a real project scenario. They created a high-level project model containing about 40 product, organization and process objects that represented their solution to the problem specification. They created and documented a design in one day at a level of detail that often takes engineers weeks to establish using traditional processes. For simplicity, we represented the evolving ontology in Excel and projected it and the corresponding product, organization and process models for all to see in the design meeting. While large generic building project ontologies exist, e.g., the Industry Foundation Classes, we find that our ontology is so simple that teams using any design tools and methods can use it today, and it is helpful. We interpret our results as early evidence that the Tripod engineering process of VDC, XC and use of a generic project ontology together enable a design team to build an ontology for a new project design very rapidly. It can then be used to support downstream detailed design and construction planning and also reuse in other engineering projects.

This paper presents the application of path planning in construction sites according to multiple objectives. It quantitatively evaluates the performance of three optimisation algorithms namely: Dijkstra, A p , and Genetic algorithms that are used to find multi-criteria paths in construction sites based on transportation and safety-related cost. During a construction project, site planners need to select paths for site operatives and vehicles, which are characterised by short distance, low risks and high visibility. These path evaluation criteria are combined using a multi-objective approach. The criteria can be optimised to present site planners with the shortest path, the safest path, the most visible path or a path that reflects a combination of short distance, low risk and high visibility. The accuracy of the path solutions and the time complexities of the optimisation algorithms are compared and critically analysed. q

Building activities are inadequately monitored during construction projects when attempting to satisfy the need for information, especially in those cases of unforeseen events and conditions on-site. Many IT-supported methods have been introduced, but so far none has been able to deliver satisfactory and reliable information. This paper presents a combined method, consisting of three components: an automated activity tracking subsystem based on image recognition, an automated material tracking subsystem, and a mobile computing supported communication environment. All subsystems are based on the same 4D model of a building.

We study the problems associated with allocating berths for containerships in the port of Seville. It is the only inland port in Spain and it is located on the Guadalquivir River. This paper addresses the berth allocation planning problems using simulation and optimisation with Arena software. We propose a mathematical model and develop a heuristic procedure based on genetic algorithm to solve non-linear problems. Allocation planning aims to minimise the total service time for each ship and considers a first-come-first-served allocation strategy. We conduct a large amount of computational experiments which show that the proposed model improves the current berth management strategy.