An artificial neural network for dimensions and cost modelling of internal micro-channels fabricated in PMMA using Nd:YVO4 laser

For micro-channel fabrication using laser micro-machining processing, estimation techniques are normally utilised to develop an approach for the system behaviour evaluation. Design of Experiments (DOE) and the Artificial Neural Networks (ANN) are two methodologies that can be used as estimation techniques. These techniques help in finding a set of laser processing parameters that provides the required micro-channel dimensions and in finding the optimal solutions in terms reducing the product development time, power consumption and of least cost. In this work, an integrated methodology is presented in which the ANN training experiments were obtained by the statistical software DoE to improve the developed models in ANN. A 33 factorial design of experiments (DoE) was used to get the experimental set. Laser power, P; pulse repetition frequency, PRF; and sample translation speed, U were the ANN inputs. The channel width and the produced micro-channel operating cost per metre were the measured responses. Four Artificial Neural Networks (ANNs) models were developed to be applied to internal micro-channels machined in PMMA using a Nd:YVO4 laser. These models were varied in terms of the selection and the quantity of training data set and constructed using a multi-layered, feed-forward structure with a the back-propagation algorithm. The responses were adequately estimated by the ANN models within the set micro-machining parameters limits. Moreover the effect of changing the selection and the quantity of training data on the approximation capability of the developed ANN model was discussed

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Rapid design of tool-wear condition monitoring systems for turning processes using novelty detection

Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the key technologies that provide the competitive advantage in many manufacturing environments. It is capable of providing an essential means to reduce cost, increase productivity, improve quality and prevent damage to the machine or workpiece. Turning operations are considered one of the most common manufacturing processes in industry. It is used to manufacture different round objects such as shafts, spindles and pins. Despite recent development and intensive engineering research, the development of tool wear monitoring systems in turning is still ongoing challenge. In this paper, force signals are used for monitoring tool-wear in a feature fusion model. A novel approach for the design of condition monitoring systems for turning operations using novelty detection algorithm is presented. The results found prove that the developed system can be used for rapid design of condition monitoring systems for turning operations to predict tool-wear

ANNz: estimating photometric redshifts using artificial neural networks

We introduce ANNz, a freely available software package for photometric redshift estimation using Artificial Neural Networks. ANNz learns the relation between photometry and redshift from an appropriate training set of galaxies for which the redshift is already known. Where a large and representative training set is available ANNz is a highly competitive tool when compared with traditional template-fitting methods. The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release 1, and for this particular data set the r.m.s. redshift error in the range 0 < z < 0.7 is 0.023. Non-ideal conditions (spectroscopic sets which are small, or which are brighter than the photometric set for which redshifts are required) are simulated and the impact on the photometric redshift accuracy assessed.Comment: 6 pages, 6 figures. Replaced to match version accepted by PASP (minor changes to original submission). The ANNz package may be obtained from http://www.ast.cam.ac.uk/~aa

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Evaluation of the effect of ND:YVO4 laser parameters on internal micro-channel fabrication in polycarbonate

This paper presents the development of Artificial Neural Network (ANN) models for the prediction of laser machined internal micro-channels’ dimensions and production costs. In this work, a pulsed Nd:YVO4 laser was used for machining micro-channels in polycarbonate material. Six ANN multi-layered, feed-forward, back-propagation models are presented which were developed on three different training data sets. The analysed data was obtained from a 33 factorial design of experiments (DoE). The controlled parameters were laser power, P; pulse repetition frequency, PRF; and sample translation speed; U. Measured responses were the micro-channel width and the micro-machining operating cost per metre of produced microchannel. The responses were sufficiently predicted within the set micro-machining parameters limits. Three carefully selected statistical criteria were used for comparing the performance of the ANN predictive models. The comparison showed that model which had the largest amount of training data provided the highest degree of predictability. However, in cases where only a limited amount of ANN training data was available, then training data taken from a Face Centred Cubic (FCC) model design provided the highest level of predictability compared with the other examined training data set

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig