998 resultados para Fuzzy bi-residuation


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Large communities built around social media on the Internet offer an opportunity to augment analytical customer relationship management (CRM) strategies. The purpose of this paper is to provide direction to advance the conceptual design of business intelligence (BI) systems for implementing CRM strategies. After introducing social CRM and social BI as emerging fields of research, the authors match CRM strategies with a re-engineered conceptual data model of Facebook in order to illustrate the strategic value of these data. Subsequently, the authors design a multi-dimensional data model for social BI and demonstrate its applicability by designing management reports in a retail scenario. Building on the service blueprinting framework, the authors propose a structured research agenda for the emerging field of social BI.

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Increasing global competition, rapid technological changes, advances in manufacturing and information technology and discerning customers are forcing supply chains to adopt improvement practices that enable them to deliver high quality products at a lower cost and in a shorter period of time. A lean initiative is one of the most effective approaches toward achieving this goal. In the lean improvement process, it is critical to measure current and desired performance level in order to clearly evaluate the lean implementation efforts. Many attempts have tried to measure supply chain performance incorporating both quantitative and qualitative measures but failed to provide an effective method of measuring improvements in performances for dynamic lean supply chain situations. Therefore, the necessity of appropriate measurement of lean supply chain performance has become imperative. There are many lean tools available for supply chains; however, effectiveness of a lean tool depends on the type of the product and supply chain. One tool may be highly effective for a supply chain involved in high volume products but may not be effective for low volume products. There is currently no systematic methodology available for selecting appropriate lean strategies based on the type of supply chain and market strategy This thesis develops an effective method to measure the performance of supply chain consisting of both quantitative and qualitative metrics and investigates the effects of product types and lean tool selection on the supply chain performance Supply chain performance matrices and the effects of various lean tools over performance metrics mentioned in the SCOR framework have been investigated. A lean supply chain model based on the SCOR metric framework is then developed where non- lean and lean as well as quantitative and qualitative metrics are incorporated in appropriate metrics. The values of appropriate metrics are converted into triangular fuzzy numbers using similarity rules and heuristic methods. Data have been collected from an apparel manufacturing company for multiple supply chain products and then a fuzzy based method is applied to measure the performance improvements in supply chains. Using the fuzzy TOPSIS method, which chooses an optimum alternative to maximise similarities with positive ideal solutions and to minimise similarities with negative ideal solutions, the performances of lean and non- lean supply chain situations for three different apparel products have been evaluated. To address the research questions related to effective performance evaluation method and the effects of lean tools over different types of supply chains; a conceptual framework and two hypotheses are investigated. Empirical results show that implementation of lean tools have significant effects over performance improvements in terms of time, quality and flexibility. Fuzzy TOPSIS based method developed is able to integrate multiple supply chain matrices onto a single performance measure while lean supply chain model incorporates qualitative and quantitative metrics. It can therefore effectively measure the improvements for supply chain after implementing lean tools. It is demonstrated that product types involved in the supply chain and ability to select right lean tools have significant effect on lean supply chain performance. Future study can conduct multiple case studies in different contexts.

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High temperature superconductor precursor billets (feed rods) have been developed for loading into silver tubes. The billets are loaded prior to rolling or drawing operations in PIT wire manufacture. Investigations have shown that wires and tapes prepared from feed rod loaded tubes show enhanced uniformity of electrical transport properties when compared with conventional powder packing, especially in wires drawn to long lengths. Analysis on production feed rods have shown carbon content to be as low as 110 ppm. © 1999 IEEE.

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Bi-2212 thick film on silver tapes are seen as a simple and low cost alternative to high temperature superconducting wires produced by the Powder In Thbe (PIT) technique, particularly in react and wind applications. A rig for the continuous production of Bi-2212 tapes for use in react and wind component manufacture has been developed and commissioned. The rig consists of several sections, each fully automatic, for task specific duties in the production of HTS tape. The major sections are: tape coating, sintering and annealing. High temperature superconducting tapes with engineering critical current densities of 10 kA/cm2 (77 K, self field), and lengths of up to 100 m have been produced using the rig. Properties of the finished tape are discussed and results are presented for current density versus bend radius and applied strain. Depending on tape content and thickness, irreversible strain tirrm varies between 0.04 and 0.1 %. Cyclic bending tests when applied strain does not exceed Eirrm showed negligible reduction in J c along the length of the tape.

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Bi-2212 tapes were fabricated using a powder-in-tube method and their superconducting properties were measured as a function of heat treatment. The tapes were heated to temperature, T1 (884-915 °C), and kept at that temperature for 20 min to induce partial (incongruent) melting. The samples were cooled to T2 with a ramp rate of 120 °C h-1 and then slowly cooled to T3 with a cooling rate, R2, and from T3 to T4 with a cooling rate, R3. The tapes were kept at the temperature T4 for P1 hours and then cooled to room temperature. Both R1 and R2 were chosen between 2 and 8 °C h-1. It was found that the structure and Jc of the tapes depend on the sintering conditions, i.e. T1-4, R1-3 and P1. The highest Jc of 5800 Å cm-2 was obtained at 77 K in a self-field with heat treatment where T1 = 894 and 899 °C, R1 = R2 = 5 °C h-1 and P1 = 6 h were employed. When 0.7% of bend strain, which is equivalent to a bend radius of 5 mm, was applied to the tape, 80% of the initial Jc was sustained.

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Multi-Objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the thermoeconomic and Environmental aspects have been considered, simultaneously. The environmental objective function has been defined and expressed in cost terms. One of the most suitable optimization techniques developed using a particular class of search algorithms known as; Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been used here. This approach has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of fuzzy decision-making with the aid of Bellman-Zadeh approach has been presented and a final optimal solution has been introduced.

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Most civil engineering structures are formed using a number of materials that are bonded to each other with their surface-to-surface interaction playing key role on the overall response of the structure. Unfortunately these interactions are extremely variable; simplified and extremely detailed models trialed to date prove quite complex. Models that assume perfect interaction, on the other hand, predict unsafe behavior. In this paper a damage mechanics based interaction between two materials of different softening properties is developed using homogenisation approach. This paper describes the process of developing a bi-material representative volume element (RVE) using damaged homogenisation approach. The novelty in this paper is the development of non-local transient damage identification algorithm. Numerical examples prove the stability of the approach for a simplified RVE and encourage application to other shapes of RVEs.

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Because cartilage and bone tissues have different lineage-specific biological properties, it is challenging to fabricate a single type of scaffold that can biologically fulfill the requirements for regeneration of these two lineages simultaneously within osteochondral defects. To overcome this challenge, a lithium-containing mesoporous bioglass (Li-MBG) scaffold is developed. The efficacy and mechanism of Li-MBG for regeneration of osteochondral defects are systematically investigated. Histological and micro-CT results show that Li-MBG scaffolds significantly enhance the regeneration of subchondral bone and hyaline cartilage-like tissues as compared to pure MBG scaffolds, upon implantation in rabbit osteochondral defects for 8 and 16 weeks. Further investigation demonstrates that the released Li+ ions from the Li-MBG scaffolds may play a key role in stimulating the regeneration of osteochondral defects. The corresponding mechanistic pathways involve Li+ ions enhancing the proliferation and osteogenic differentiation of bone mesenchymal stem cells (BMSCs) through activation of the Wnt signalling pathway, as well as Li+ ions protecting chondrocytes and cartilage tissues from the inflammatory osteoarthritis (OA) environment through activation of autophagy. These findings suggest that the incorporation of Li+ ions into bioactive MBG scaffolds is a viable strategy for fabricating bi-lineage conducive scaffolds that enhance regeneration of osteochondral defects.

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Cryptosystems based on the hardness of lattice problems have recently acquired much importance due to their average-case to worst-case equivalence, their conjectured resistance to quantum cryptanalysis, their ease of implementation and increasing practicality, and, lately, their promising potential as a platform for constructing advanced functionalities. In this work, we construct “Fuzzy” Identity Based Encryption from the hardness of the Learning With Errors (LWE) problem. We note that for our parameters, the underlying lattice problems (such as gapSVP or SIVP) are assumed to be hard to approximate within supexponential factors for adversaries running in subexponential time. We give CPA and CCA secure variants of our construction, for small and large universes of attributes. All our constructions are secure against selective-identity attacks in the standard model. Our construction is made possible by observing certain special properties that secret sharing schemes need to satisfy in order to be useful for Fuzzy IBE. We also discuss some obstacles towards realizing lattice-based attribute-based encryption (ABE).

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At Eurocrypt’04, Freedman, Nissim and Pinkas introduced a fuzzy private matching problem. The problem is defined as follows. Given two parties, each of them having a set of vectors where each vector has T integer components, the fuzzy private matching is to securely test if each vector of one set matches any vector of another set for at least t components where t < T. In the conclusion of their paper, they asked whether it was possible to design a fuzzy private matching protocol without incurring a communication complexity with the factor (T t ) . We answer their question in the affirmative by presenting a protocol based on homomorphic encryption, combined with the novel notion of a share-hiding error-correcting secret sharing scheme, which we show how to implement with efficient decoding using interleaved Reed-Solomon codes. This scheme may be of independent interest. Our protocol is provably secure against passive adversaries, and has better efficiency than previous protocols for certain parameter values.

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This paper presents a novel three-phase to single-phase matrix converter (TSMC) based bi-directional inductive power transfer (IPT) system for vehicle-to-grid (V2G) applications. In contrast to existing techniques, the proposed technique which employs a TSMC to drive an 8th order high frequency resonant network, requires only a single-stage power conversion process to facilitate bi-directional power transfer between electric vehicles (EVs) and a three-phase utility power supply. A mathematical model is presented to demonstrate that both magnitude and direction of power flow can be controlled by regulating either relative phase angles or magnitudes of voltages generated by converters. The viability of the proposed mathematical model is verified using simulated results of a 10 kW bi-directional IPT system and the results suggest that the proposed system is efficient, reliable and is suitable for high power applications which require contactless power transfer.

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Matrix converter (MC) based bi-directional inductive power transfer (BD-IPT) systems are gaining popularity as an efficient and reliable technique with single stage grid integration as opposed to two stage grid integration of conventional grid connected BD-IPT systems. However MCs are invariably rich in harmonics and thus affect both power quality and power factor on the grid side. This paper proposes a mathematical model through which the grid side harmonics of MC based BD-IPT systems can accurately be estimated. The validity of the proposed mathematical model is verified using simulated results of a 3 kW BD-IPT system and results suggest that the MC based BD-IPT systems have a better power factor with higher power quality over conventional grid connected rectifier based systems.

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This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.