A rough set based approach to patent development with the consideration of resource allocation

Patent strategy is the overriding mechanism that helps direct investment, resource allocation, expectations, and policy development within an organization. Much studies of patent, for example, patent classification, patent analysis, patent management, patent strategy planning have been made. Due to the high cost of devoting to the research and development for a new patent application, it is essential for a company to develop the patent portfolio based on analyzing related information for fitting with cost constraint and maximizing the benefit. However, only few research attempts to develop new patents with the consideration of resource allocation, for example, optimizing budget utilization. In addition, the previous studies did not derive significant technologies and induct rules for resource allocation through patent analysis. In some cases, the patent analysis may process qualitative information that is difficult to analyze by standard statistical techniques. The rough set approach, which is suitable for processing qualitative information, is required to induct decision rules to derive critical technologies of patents. In this paper, a systematic approach to analyze existing patent information based on rough set theory with the consideration of resource allocation is developed. A case study is presented to demonstrate the contribution of the proposed approach which assists on decision-making in patent reform or invention with constraint resource.     

Characterization of closed-loop measurement accuracy in precision CNC milling

This study investigates the closed-loop measurement error in computer numerical controlled (CNC) milling as they relate to the different inspection techniques. The on-line inspection of machining accuracy using a spindle probe has an inherent shortcoming because the same machine-produced parts are used for inspection. In order to use the spindle probe measurement as a means of correcting deviations in machining, the magnitude of measurement errors needs to be quantified. The empirical verification was made by conducting three sets of cutting experiments, followed by a design of experiment with three levels and three factors on a state-of-the-art CNC machining center. Three different material types and parameter settings were selected to simulate a diverse cutting condition. During the cutting, the cutting force and spindle vibration sensor signals were collected and a tool wear was recorded using a computer vision system. The bore tolerance was gauged by a spindle probe as well as a coordinate-measuring machine. The difference between the two measurements was defined as a closed-loop measurement error and the subsequent analysis was performed to determine the significant factors affecting the errors. The analysis results showed the potential of improving production efficiency and improved part quality.     

Enhanced dielectric properties of three phase dielectric MWCNTs/BaTiO3/PVDF nanocomposites for energy storage using fused deposition modeling 3D printing

This research studied the effect of fused deposition modeling (FDM) 3D printing on three phase dielectric nanocomposites using poly(vinylidene) fluoride (PVDF), BaTiO₃ (BT), and multiwall carbon nanotubes (CNTs). PVDF polymer and BT ceramics are piezo-, pyro- and di-electric materials extensively used for sensor and energy storage/harvesting applications due to their unique characteristic of dipole polarization. To increase dielectric property, CNTs have been recently utilized for uniform dispersion of BT nanoparticles, ultrahigh polarization density, and local micro-capacitor among matrix. It was proved that 3D printing process provides homogeneous dispersion of nanoparticles, alleviating agglomeration of nanoparticles and reducing micro-crack/voids in matrix which can potentially enhance their dielectric property than traditional methods. In this research, these three-phase nanocomposites are fabricated through FDM 3D printing process and characterized for dielectric property. Increasing both BT and CNT nanoparticles improves dielectric properties, while CNTs have a percolation threshold near 1.7?wt%. The most desirable combination of dielectric constant and loss properties (118 and 0.11 at 1?kHz) is achieved with nanocomposites containing 1.7?wt%-CNT/45?wt%-BT/PVDF. These results provide not only a technique to 3D print dielectric nanocomposites with improved dielectric property but also large-scale electronic device manufacturing possibility with freedom of design, low cost, and faster process.     

Behavior of oblong and rectangular bridge columns with conventional tie and multi-spiral transverse reinforcement under combined axial and flexural loads

Abstract

This study investigates the behavior of transversely reinforced oblong and rectangular bridge columns under combined axial and flexural loading, including eccentric compression and lateral cyclic loading under constant axial load. The transverse reinforcement schemes include conventional tie and multi-spiral reinforcement. The multi-spiral reinforcement for the oblong column comprises two interlocking spirals and, for rectangular columns, comprises two interlocking large central spirals interlocked with four small spirals at the corners. The amount of transverse reinforcement for all of the columns conforms to the current seismic bridge design specifications. Test results indicate that all of the columns exhibit ductile behavior with ductility capacities significantly higher than the ductility capacity required by the design code. The oblong spiral column with an amount of transverse reinforcement 43% that of the corresponding tied column shows strength, ductility, energy dissipation, and over-strength similar to the tied column. Additionally, the rectangular spiral column with an amount of transverse reinforcement 59% that of the corresponding tied column exhibits strength, ductility, energy dissipation and over-strength superior to the tied column. Moreover, the code P-M interaction analysis method can provide a conservative means of estimating nominal moment strength. The two code methods to determine the maximum probable moment strengths may not provide conservative estimates. Results of this study demonstrate that the maximum probable moment of the columns examined can be estimated conservatively as 1.4 times the nominal moment strength.     

Capitalizing on knowledge: a novel approach to crucial-knowledge determination

Corporation managers make informed decisions based upon a combination of judgment and knowledge from various departments such as marketing, sales, research, development, manufacturing, and finance. Ideally, all relevant knowledge should be brought together before judgment is exercised. However, determining the knowledge requirements and obtaining pertinent, consistent, and up-to-date knowledge across a large company is a complex process. Crucial-knowledge determination is a complex process: specifically the identification of crucial knowledge and knowledge requirements. In this paper, a methodology is developed for modeling the knowledge requirements and the associated tasks for collecting the knowledge simultaneously. This methodology provides a valuable contribution in knowledge-management systems by defining a major plan for discovering issues-oriented knowledge. One procedure and one heuristic algorithm are illustrated with numerical examples.     

The panpositionable panconnectedness of augmented cubes

A graph G is panconnected if, for any two distinct vertices x and y of G, it contains an [x, y]-path of length l for each integer l satisfying d_G(x, y) ? l ? ∣V(G)∣ − 1, where d_G(x, y) denotes the distance between vertices x and y in G, and V(G) denotes the vertex set of G. For insight into the concept of panconnectedness, we propose a more refined property, namely panpositionable panconnectedness. Let x, y, and z be any three distinct vertices in a graph G. Then G is said to be panpositionably panconnected if for any d_G(x, z) ? l₁ ? ∣V(G)∣ − d_G(y, z) − 1, it contains a path P such that x is the beginning vertex of P, z is the (l₁ + 1)th vertex of P, and y is the (l₁ + l₂ + 1)th vertex of P for any integer l₂ satisfying d_G(y, z) ? l₂ ? ∣V(G)∣ − l₁ − 1. The augmented cube, proposed by Choudum and Sunitha [6] to be an enhancement of the n-cube Q_n, not only retains some attractive characteristics of Q_n but also possesses many distinguishing properties of which Q_n lacks. In this paper, we investigate the panpositionable panconnectedness with respect to the class of augmented cubes. As a consequence, many topological properties related to cycle and path embedding in augmented cubes, such as pancyclicity, panconnectedness, and panpositionable Hamiltonicity, can be drawn from our results.     

Discovering business intelligence from online product reviews: A rule-induction framework

Online product reviews are a major source of business intelligence (BI) that helps managers and marketers understand customers’ concerns and interests. The large volume of review data makes it difficult to manually analyze customers’ concerns. Automated tools have emerged to facilitate this analysis, however most lack the capability of extracting the relationships between the reviews’ rich expressions and the customer ratings. Managers and marketers often resort to manually read through voluminous reviews to find the relationships. To address these challenges, we propose the development of a new class of BI systems based on rough set theory, inductive rule learning, and information retrieval methods. We developed a new framework for designing BI systems that extract the relationship between the customer ratings and their reviews. Using reviews of different products from Amazon.com, we conducted both qualitative and quantitative experiments to evaluate the performance of a BI system developed based on the framework. The results indicate that the system achieved high accuracy and coverage related to rule quality, and produced interesting and informative rules with high support and confidence values. The findings have important implications for market sentiment analysis and e-commerce reputation management.