Supply network integration in multi-organisational network systems

This paper explores supply network integration in complex product service systems involving close collaboration between primes. Four case study networks are studied (aerospace, naval, power and telecoms), each involving equipment manufacture and service provision. Factors that support network integration, identified from the literature and refined in the in-depth pilot case, were used to explore which processes support integration of the extended enterprise. Results suggests that a select set of processes support integration of the extended enterprise and that the absence of a shared view on these critical enabling processes results from contextual complexity of the network rather than from competing commercial interests. Copyright © 2011 Inderscience Enterprises Ltd.

Diversification strategies in emerging industries: A supply network perspective

The sustainable translation of scientific and technical innovation into global products and services is key to capturing value from emerging industries. For industrial practitioners, choosing the appropriate entry mode into these industries will often determine their level of success in sharing in this value capture. © 2011 IEEE.

Managing global network operations along the engineering value chain

Purpose: This paper aims to improve understanding of how to manage global network operations from an engineering perspective. Design/methodology/approach: This research adopted a theory building approach based on case studies. Grounded in the existing literature, the theoretical framework was refined and enriched through nine in-depth case studies in the industry sectors of aerospace, automotives, defence and electrics and electronics. Findings: This paper demonstrates the main value creation mechanisms of global network operations along the engineering value chain. Typical organisational features to support the value creation mechanisms are captured, and the key issues in engineering network design and operations are presented with an overall framework. Practical implications: Evidenced by a series of pilot applications, outputs of this research can help companies to improve the performance of their current engineering networks and design new engineering networks to better support their global businesses and customers in a systematic way. Originality/value: Issues about the design and operations of global engineering networks (GEN) are poorly understood in the existing literature in contrast to their apparent importance in value creation and realisation. To address this knowledge gap, this paper introduces the concept of engineering value chain to highlight the potential of a value chain approach to the exploration of engineering activities in a complex business context. At the same time, it develops an overall framework for managing GEN along the engineering value chain. This improves our understanding of engineering in industrial value chains and extends the theoretical understanding of GEN through integrating the engineering network theories and the value chain concepts. © Emerald Group Publishing Limited.

An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Improving LVCSR system combination using neural network language model cross adaptation

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. The standard approach involves only cross adapting acoustic models. To fully exploit the complimentary features among sub-systems, language model (LM) cross adaptation techniques can be used. Previous research on multi-level n-gram LM cross adaptation is extended to further include the cross adaptation of neural network LMs in this paper. Using this improved LM cross adaptation framework, significant error rate gains of 4.0%-7.1% relative were obtained over acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. Copyright © 2011 ISCA.

Making a shallow network deep: Conversion of a boosting classifier into a decision tree by boolean optimisation

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimization problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit-a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems. © 2011 Springer Science+Business Media, LLC.

Paraphrastic language models and combination with neural network language models

In natural languages multiple word sequences can represent the same underlying meaning. Only modelling the observed surface word sequence can result in poor context coverage, for example, when using n-gram language models (LM). To handle this issue, paraphrastic LMs were proposed in previous research and successfully applied to a US English conversational telephone speech transcription task. In order to exploit the complementary characteristics of paraphrastic LMs and neural network LMs (NNLM), the combination between the two is investigated in this paper. To investigate paraphrastic LMs' generalization ability to other languages, experiments are conducted on a Mandarin Chinese broadcast speech transcription task. Using a paraphrastic multi-level LM modelling both word and phrase sequences, significant error rate reductions of 0.9% absolute (9% relative) and 0.5% absolute (5% relative) were obtained over the baseline n-gram and NNLM systems respectively, after a combination with word and phrase level NNLMs. © 2013 IEEE.

Injection locked Fabry-Perot laser diodes for WDM passive optical network spare function

In this paper, a protection scheme for transmitters in wavelength-division-multiplexing passive optical network (WDM-PON) has been proposed and demonstrated. If any downstream transmitter encounters problems at the central office (CO), the interrupted communication can be restored immediately by injecting a Fabry-Perot laser diode (FP-LD) with the upstream lightwave corresponding to the failure transmitter. Compared with the conventional methods, this proposed architecture provides a cost-effective and reliable protection scheme employing a common FP-LD. In the experiment, a 1 36 protection capability was implemented with a 2.5 Gbit/s downstream transmission capability. (C) 2009 Elsevier B.V. All rights reserved.

Packing LUT clusters with network flow programming

This paper describes a two-step packing algorithm for LUT clusters of which the LUT input multipliers are depopulated. In the first step, a greedy algorithm is used to search for BLE locations and cluster inputs. If the greedy algorithm fails, the second step with network flow programming algorithm is employed. Numerical results illustrate that our two-step packing algorithm obtains better packing density than one-step greedy packing algorithm.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79 . 1.45 . 1.18-competitive separately for traditional timeout PM . adaptive predictive PM and stochastic PM.

adaptive location update mechanism for network-constrained moving objects in changeful traffic conditions

Natl Chiao Tung Univ, Dept Comp Sci