743 research outputs found
Design and Planning of Manufacturing Networks for Mass Customisation and Personalisation: Challenges and Outlook
AbstractManufacturers and service providers are called to design, plan and operate globalized manufacturing networks, addressing to challenges such as ever-decreasing lifecycles and increased product complexity. These factors, caused primarily by mass customisation and demand volatility, generate a number of issues related to the design and planning of manufacturing systems and networks, which are not holistically tackled in industrial and academic practices. The mapping of production performance requirements to process and production planning requires automated closed-loop control systems, which current systems fail to deliver. Technology-based business approaches are an enabler for increased enterprise performance. Towards that end, the issues discussed in this paper focus on challenges in the design and planning of manufacturing networks in a mass customization and personalization landscape. The development of methods and tools for supporting the dynamic configuration and optimal routing of manufacturing networks and facilities under cost, time, complexity and environmental constraints to support product-service personalization are promoted
Fully automated urban traffic system
The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible
Speaker independent isolated word recognition
The work presented in this thesis concerns the recognition of
isolated words using a pattern matching approach. In such a system,
an unknown speech utterance, which is to be identified, is
transformed into a pattern of characteristic features. These
features are then compared with a set of pre-stored reference
patterns that were generated from the vocabulary words. The unknown
word is identified as that vocabulary word for which the reference
pattern gives the best match.
One of the major difficul ties in the pattern comparison process is
that speech patterns, obtained from the same word, exhibit non-linear
temporal fluctuations and thus a high degree of redundancy. The
initial part of this thesis considers various dynamic time warping
techniques used for normalizing the temporal differences between
speech patterns. Redundancy removal methods are also considered, and
their effect on the recognition accuracy is assessed.
Although the use of dynamic time warping algorithms provide
considerable improvement in the accuracy of isolated word recognition
schemes, the performance is ultimately limited by their poor ability
to discriminate between acoustically similar words. Methods for
enhancing the identification rate among acoustically similar words,
by using common pattern features for similar sounding regions, are
investigated.
Pattern matching based, speaker independent systems, can only operate
with a high recognition rate, by using multiple reference patterns
for each of the words included in the vocabulary. These patterns are
obtained from the utterances of a group of speakers. The use of
multiple reference patterns, not only leads to a large increase in
the memory requirements of the recognizer, but also an increase in
the computational load. A recognition system is proposed in this
thesis, which overcomes these difficulties by (i) employing vector
quantization techniques to reduce the storage of reference patterns,
and (ii) eliminating the need for dynamic time warping which reduces
the computational complexity of the system.
Finally, a method of identifying the acoustic structure of an
utterance in terms of voiced, unvoiced, and silence segments by using
fuzzy set theory is proposed. The acoustic structure is then
employed to enhance the recognition accuracy of a conventional
isolated word recognizer
Robust Anomaly Detection with Applications to Acoustics and Graphs
Our goal is to develop a robust anomaly detector that can be incorporated into pattern recognition systems that may need to learn, but will never be shunned for making egregious errors. The ability to know what we do not know is a concept often overlooked when developing classifiers to discriminate between different types of normal data in controlled experiments. We believe that an anomaly detector should be used to produce warnings in real applications when operating conditions change dramatically, especially when other classifiers only have a fixed set of bad candidates from which to choose.
Our approach to distributional anomaly detection is to gather local information using features tailored to the domain, aggregate all such evidence to form a global density estimate, and then compare it to a model of normal data. A good match to a recognizable distribution is not required. By design, this process can detect the "unknown unknowns" [1] and properly react to the "black swan events" [2] that can have devastating effects on other systems. We demonstrate that our system is robust to anomalies that may not be well-defined or well-understood even if they have contaminated the training data that is assumed to be non-anomalous.
In order to develop a more robust speech activity detector, we reformulate the problem to include acoustic anomaly detection and demonstrate state-of-the-art performance using simple distribution modeling techniques that can be used at incredibly high speed. We begin by demonstrating our approach when training on purely normal conversational speech and then remove all annotation from our training data and demonstrate that our techniques can robustly accommodate anomalous training data contamination. When comparing continuous distributions in higher dimensions, we develop a novel method of discarding portions of a semi-parametric model to form a robust estimate of the Kullback-Leibler divergence. Finally, we demonstrate the generality of our approach by using the divergence between distributions of vertex invariants as a graph distance metric and achieve state-of-the-art performance when detecting graph anomalies with neighborhoods of excessive or negligible connectivity.
[1] D. Rumsfeld. (2002) Transcript: DoD news briefing - Secretary Rumsfeld and Gen. Myers.
[2] N. N. Taleb, The Black Swan: The Impact of the Highly Improbable. Random House, 2007
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