1,721,116 research outputs found
FIT MANUFACTURING: PRODUCTION FITNESS AS THE MEASURE OF PRODUCTION OPERATIONS PERFORMANCE
No full textRapid changes in market demands have resulted in manufacturing companies having to remain competitive in order to survive. Therefore, a combination of manufacturing capabilities, such as leanness, flexibility, agility, responsiveness, and sustainability, is essential to manufacturing companies. However, the performance of manufacturing capabilities has not yet been measured through integrated manufacturing concepts. Consequently, the thesis presents a model for evaluating operational performance from the specific viewpoint of production capability, termed Production Fitness. In this respect, determination of Production Fitness refers to Fit Manufacturing systems in general. An assessment of Production Fitness is developed based on the concept of multidimensional performances through integration of three distinctive concepts: (i) Lean Manufacturing (leanness), (ii) Agile Manufacturing (agility), (iii) Sustainability.
The aim is to provide an index for Production Fitness, determined through a simpler, more useful, and objective system of assessment. In this way, the Production Fitness measures can be used as a decision support tool for production and marketing (e.g., Production Waste Index ( PWI), Production Profitability Index ( PAI), Production Stability Index (PSI), and Production Fitness Index ( PFI), as well as providing a means of avoiding common conflict between these two areas.
The Production Fitness measures were applied to six case studies of micro-SMEs with batch manufacturing processes in various industries. Results from the six case studies show that it is crucial for manufacturing companies to sustain an ideal PFI, which can be achieved through maximum PPI, consistent PAI, and ideal PSI. In the meantime, it is also important for manufacturing companies to achieve a higher PFI, especially in highly competitive market environments. Factors influencing the fitness indices are indentified from the aspect of company and production characteristics. SWOT analysis results indicate that the PFI can be affected by company strengths, weakness, opportunities, and threats. Suggestions for improving Production Fitness are made using empirical evidence from previous studies on relevant aspects.
This thesis concludes that the Production Fitness measures can be applied to batch process types in various manufacturing industries where common production and sales data are applied
Application of Spiking Neural Networks and the Bees Algorithm to Control Chart Pattern Recognition
No full textStatistical process control (SPC) is a method for improving the quality of products. Control charting plays the most important role in SPC. A control chart can be used to indicate whether a manufacturing process is under control. Unnatural patterns in control charts mean that there are some unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. In recent years, neural network techniques have increasingly been applied to pattern recognition. Spiking Neural Networks (SNNs) are the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. Latest research has shown SNNs to be computationally more powerful than other types of artificial neural networks. This PhD Thesis proposes the application of SNN techniques to control chart pattern recognition. The thesis work focuses on the architecture and the learning procedure of the network. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies
FRACTAL DEMENSION FOR CLUSTERING AND UNSUPERVISED AND SUPERVISED FEATURE SELECTION
No full textData mining refers to the automation of data analysis to extract patterns from large amounts of data. A major breakthrough in modelling natural patterns is the recognition that nature is fractal, not Euclidean. Fractals are capable of modeling self-similarity, infinite details, infinite length and the absence of smoothness.
This research was aimed at simplifying the discovery and detection of groups in data using fractal dimension. These data mining tasks were addressed efficiently. The first task defines groups of instances (clustering), the second selects useful features from non-defined (unsupervised) groups of instances and the third selects useful features from pre-defined (supervised) groups of instances. Improvements are shown on two data mining classification models: hierarchical clustering and Artificial Neural Networks (ANN).
For clustering tasks, a new two-phase clustering algorithm based on the Fractal Dimension (FD), compactness and closeness of clusters is presented. The proposed method, uses self-similarity properties of the data, first divides the data into sufficiently large sub-clusters with high compactness. In the second stage, the algorithm merges the sub-clusters that are close to each other and have similar complexity. The final clusters are obtained through a very natural and fully deterministic way.
The selection of different feature subspaces leads to different cluster interpretations. An unsupervised embedded feature selection algorithm, able to detect relevant and redundant features, is presented. The algorithm is based on the concept to fractal dimension. The level of relevance in the features is quantified using a new proposed entropy measure, which is less complex than the current state-of-the-art technology. The proposed algorithm is able to maintain and in some cases improve the quality of the clusters in reduced feature spaces.
For supervised feature selection, for classification purposes, a new algorithm is proposed that maximizes the relevance and minimizes the redundancy of the features simultaneously. This algorithm makes use of the FD and the Mutual Information (MI) techniques, and combines them to create a new measure of feature usefulness and to produce a simpler and non-heuristic algorithm. The similar nature of the two techniques, FD and MI, makes the proposed algorithm suitable for a straightforward global analysis of the data
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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