Industrial relations in the privatised coal industry: continuity, change and contradictions.

This study seeks both to describe and account for the patterns of industrial relations which have emerged in the UK coal industry since privatisation in 1994. In doing so, it also aims to address some of the wider questions concerning the relationship between ownership and industrial relations. A series of hypotheses are advanced concerning how changes in ownership might affect industrial relations within the industry, and whether such changes would have positive or negative implications for organised labour. A case study approach is utilised to analyse labour relations developments at a number of collieries, and it is shown that the industrial relations strategies adopted by management within the new coal enterprises have had a determining effect upon the patterns of labour relations within the privati sed industry. This study also demonstrates that the emergent pattern of labour relations in the privatised industry is characterised by both continuity and change. However, whilst continuity with the patterns of labour relations established during the final decade of public ownership is shown to have had negative implications for organised labour within the industry, the changes associated with privatisation are demonstrated to have been a more ambivalent force. Change has, in different contexts, had some positive implications for organised labour, but in the majority of cases, the implications for labour have been negative. Overall, therefore, this study concludes that privatisation has had a significant influence upon industrial relations within the coal industry, and that organised labour has been detrimentally affected by these developments.

The effect of the product cost factor on error handling in industrial robots

N.W. Hardy and M.H. Lee. The effect of the product cost factor on error handling in industrial robots. In Maria Gini, editor, Detecting and Resolving Errors in Manufacturing Systems. Papers from the 1994 AAAI Spring Symposium Series, pages 59-64, Menlo Park, CA, March 1994. The AAAI Press. Technical Report SS-94-04, ISBN 0-929280-60-1.

Experiences with a knowledge engineering toolkit: an assessment in industrial robotics

V. Robinson, N. W. Hardy, D. P. Barnes, C. J. Price, M. H. Lee. Experiences with a knowledge engineering toolkit: an assessment in industrial robotics. Knowledge Engineering Review, 2 (1):43-54, 1987.

Knowledge based error recovery in industrial robots

M. H. Lee, D. P. Barnes, and N. W. Hardy. Knowledge based error recovery in industrial robots. In Proc. 8th. Int. Joint Conf. Artificial Intelligence, pages 824-826, Karlsruhe, FDR., 1983.

A control and monitoring system for multiple-sensor industrial robots

Barnes, D. P., Lee, M. H., Hardy, N. W. (1983). A control and monitoring system for multiple-sensor industrial robots. In Proc. 3rd. Int. Conf. Robot Vision and Sensory Controls, Cambridge, MA. USA., 471-479.

Learning Qualitative Models in the Presence of Noise

G. M. Coghill, S. M. Garrett and R. D. King (2002), Learning Qualitative Models in the Presence of Noise, QR'02 Workshop on Qualitative Reasoning

A importância da marca no processo de tomada de decisão de compra industrial

Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências da Comunicação, com especialização em Marketing e Comunicação Estratégica.

Asistencia técnica de ingeniería y diseño industrial. Adecuación de equipos y herramientas.

27 hojas : ilustraciones, planos.

Muestras de tejidos con fibras de fique e hilo de algodón natural o industrial con las técnicas del crochet y de bordados, y sombrereras y coronas ornamentales utilizadas por las mujeres Wayúu Guajira.

15 hojas : ilustraciones, fotografías a color.

Exponential sensitivity of noise-driven switching in genetic networks

Cells are known to utilize biochemical noise to probabilistically switch between distinct gene expression states. We demonstrate that such noise-driven switching is dominated by tails of probability distributions and is therefore exponentially sensitive to changes in physiological parameters such as transcription and translation rates. However, provided mRNA lifetimes are short, switching can still be accurately simulated using protein-only models of gene expression. Exponential sensitivity limits the robustness of noise-driven switching, suggesting cells may use other mechanisms in order to switch reliably.

Statistical Properties of Single and Competing Non-Linear Fast-Slow Oscillators in Noise

Statistical properties offast-slow Ellias-Grossberg oscillators are studied in response to deterministic and noisy inputs. Oscillatory responses remain stable in noise due to the slow inhibitory variable, which establishes an adaptation level that centers the oscillatory responses of the fast excitatory variable to deterministic and noisy inputs. Competitive interactions between oscillators improve the stability in noise. Although individual oscillation amplitudes decrease with input amplitude, the average to'tal activity increases with input amplitude, thereby suggesting that oscillator output is evaluated by a slow process at downstream network sites.

Extracted Memory from Temporal Patterns Using Adaptive Resonance and Recurrent Network

We can recognize objects through receiving continuously huge temporal information including redundancy and noise, and can memorize them. This paper proposes a neural network model which extracts pre-recognized patterns from temporally sequential patterns which include redundancy, and memorizes the patterns temporarily. This model consists of an adaptive resonance system and a recurrent time-delay network. The extraction is executed by the matching mechanism of the adaptive resonance system, and the temporal information is processed and stored by the recurrent network. Simple simulations are examined to exemplify the property of extraction.

A study of the RF characteristics for wireless sensor deployment in building environment

In this paper, The radio Frequency (RF) Monitoring and Measurement of the Environmental Research Institute (ERI) located in Cork city will be monitored and analyzed in both the Zigbee (2.44 GHz) and the industrial, scientific and medical (ISM 433 MHz). The main objective of this survey is to confirm what the noise and interferences threat signals exist in these bands. It was agreed that the surveys would be carried out in 5 different rooms and areas that are candidates for the Wireless Sensors deployments. Based on the carried on study, A Zigbee standard Wireless Sensor Network (WSN) will be developed employing a number of motes for sensing number of signals like temperature, light and humidity beside the RSSI and battery voltage monitoring. Such system will be used later on to control and improve indoor building climate at reduced costs, remove the need for cabling and both installation and operational costs are significantly reduced.

Sequential anomaly detection in the presence of noise and limited feedback

This paper describes a methodology for detecting anomalies from sequentially observed and potentially noisy data. The proposed approach consists of two main elements: 1) filtering, or assigning a belief or likelihood to each successive measurement based upon our ability to predict it from previous noisy observations and 2) hedging, or flagging potential anomalies by comparing the current belief against a time-varying and data-adaptive threshold. The threshold is adjusted based on the available feedback from an end user. Our algorithms, which combine universal prediction with recent work on online convex programming, do not require computing posterior distributions given all current observations and involve simple primal-dual parameter updates. At the heart of the proposed approach lie exponential-family models which can be used in a wide variety of contexts and applications, and which yield methods that achieve sublinear per-round regret against both static and slowly varying product distributions with marginals drawn from the same exponential family. Moreover, the regret against static distributions coincides with the minimax value of the corresponding online strongly convex game. We also prove bounds on the number of mistakes made during the hedging step relative to the best offline choice of the threshold with access to all estimated beliefs and feedback signals. We validate the theory on synthetic data drawn from a time-varying distribution over binary vectors of high dimensionality, as well as on the Enron email dataset. © 1963-2012 IEEE.

High-fidelity, broadband stimulated-Brillouin-scattering-based slow light using fast noise modulation.

We demonstrate a 5-GHz-broadband tunable slow-light device based on stimulated Brillouin scattering in a standard highly-nonlinear optical fiber pumped by a noise-current-modulated laser beam. The noisemodulation waveform uses an optimized pseudo-random distribution of the laser drive voltage to obtain an optimal flat-topped gain profile, which minimizes the pulse distortion and maximizes pulse delay for a given pump power. In comparison with a previous slow-modulation method, eye-diagram and signal-to-noise ratio (SNR) analysis show that this broadband slow-light technique significantly increases the fidelity of a delayed data sequence, while maintaining the delay performance. A fractional delay of 0.81 with a SNR of 5.2 is achieved at the pump power of 350 mW using a 2-km-long highly nonlinear fiber with the fast noise-modulation method, demonstrating a 50% increase in eye-opening and a 36% increase in SNR in the comparison.