Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

A machine learning approach to pedestrian detection for autonomous vehicles using High-Definition 3D Range Data

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).This work was partially supported by ViSelTR (ref. TIN2012-39279) and cDrone (ref. TIN2013-45920-R) projects of the Spanish Government, and the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia—19895/GERM/15). 3D LIDAR has been funded by UPCA13-3E-1929 infrastructure projects of the Spanish Government. Diego Alonso wishes to thank the Spanish Ministerio de Educación, Cultura y Deporte, Subprograma Estatal de Movilidad, Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016 for grant CAS14/00238

Efficient intrusion detection scheme based on SVM

The network intrusion detection problem is the focus of current academic research. In this paper, we propose to use Support Vector Machine (SVM) model to identify and detect the network intrusion problem, and simultaneously introduce a new optimization search method, referred to as Improved Harmony Search (IHS) algorithm, to determine the parameters of the SVM model for better classification accuracy. Taking the general mechanism network system of a growing city in China between 2006 and 2012 as the sample, this study divides the mechanism into normal network system and crisis network system according to the harm extent of network intrusion. We consider a crisis network system coupled with two to three normal network systems as paired samples. Experimental results show that SVMs based on IHS have a high prediction accuracy which can perform prediction and classification of network intrusion detection and assist in guarding against network intrusion

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