There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

https://www.robots.ox.ac.uk/~davidc/pubs/NDreview2014.pdf

Review: A review of novelty detection

Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

A low-cost open source hardware in control education. case study: arduino-feedback ms-150

Communication with resource-constrained devices through MQTT for control education

Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes

Three-way analysis of structural health monitoring data

Education in technological disciplines requires students to be always in contact with real systems, where they can apply their theoretical knowledge. These systems tend to be expensive and the high initial investment is returned after a long time, as the resource can only be used by a limited number of students during the on-site practical classes. The use of remote laboratories, which allow students to access the system through the Internet, optimizes resources by providing access to a larger number of users at any time. In this article, we present a remote laboratory of a quadruple-tank industrial scale model, with real industrial equipment. The students carry out control activities on the system through the Internet as they would do in a laboratory classroom. The remote laboratory architecture is based on a three layer (physical layer–server layer–client layer) whose middle layer consists of four servers: Web Server, Proxy Server, Database Server, and Control Server. In the Control server, a link application based on OPC (Ole for Process Control) standard to select different industrial controllers which are connected to the scale model simultaneously has been implemented. Since the application is based on a standard, this structure can be expanded easily with other industrial controllers from other manufacturers. The remote laboratory is used in automatic and control subjects from different Spanish universities. Surveys conducted among the students about the use of Laboratory show that they perceive an improvement of their learning using the lab. © 2011 Wiley Periodicals, Inc. Comput Appl Eng Educ 22:375–386, 2014; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.10.1002/cae.20562

Miguel A. Prada

Papers

A low-cost open source hardware in control education. case study: arduino-feedback ms-150

Communication with resource-constrained devices through MQTT for control education

Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes

Three-way analysis of structural health monitoring data

Remote laboratory of a quadruple tank process for learning in control engineering using different industrial controllers