Miguel Castaño Arranz

Featured Publications

1. 

   An application software for visualization and control configuration selection of interconnected processes

   Wolfgang Birk, Miguel Castaño Arranz, and Andreas Johansson

   Control Engineering Practice 2014

   Abstract Bib HTML PDF Code

   This paper presents a new application software for control configuration selection of interconnected industrial processes, called ProMoVis. Moreover, ProMoVis is able to visualize process models and process layout at the physical level together with the control system dynamics. The software consists of a builder part where the visual representation of the interconnected process is created and an analyzer part where the process is analyzed using different control configuration selection tools. The conceptual idea of the software is presented and the subsequent design and implementation of ProMoVis are discussed. The implemented analysis methods are briefly described including their usage and implementation aspects. The use of ProMoVis is demonstrated by an application study on the stock preparation process at SCA Obbola AB, Sweden. The results of this study are compared with the currently used control strategy. The study indicates that ProMoVis introduces a systematic and comprehensive way to perform control configuration selection. ProMoVis has been released under the Apache Open Source license.

   @article{birk2014application,
     title = {An application software for visualization and control configuration selection of interconnected processes},
     author = {Birk, Wolfgang and Casta{\~n}o Arranz, Miguel and Johansson, Andreas},
     journal = {Control Engineering Practice},
     volume = {26},
     number = {0},
     pages = {188--200},
     year = {2014},
     publisher = {Elsevier},
   }

2. 

   On control structure design for a walking beam furnace

   Hedyeh Jafari, Miguel Castaño Arranz, Thomas Gustafsson, and 1 more author

   In 25th Mediterranean Conference on Control and Automation (MED) 2017

   Abstract Bib HTML PDF

   The aim of this article is to introduce a novel sparse controller design for the temperature control of an experimental walking beam furnace in steel industry. Adequate tracking of temperature references is essential for the quality of the heated slabs. However, the design of the temperature control is hindered by the multivariable (non-square) dynamic behavior of the furnace. These dynamics include significant loop interactions and time delays. Furthermore, a novel datadriven model, based on real life experimental data that relies on a subspace state representation in a closed loop approach is introduced. In the sequel, the derived model is utilized to investigate the controller’s structure. By applying the relative gain array approach a decentralized feedback controller is designed. However, in spite of the optimal and sparse design of the controller, there exists interaction between loops. By analyzing the interaction between the inputs-outputs with the σ 2 Gramian-based interaction methodology, a decoupled multi-variable controller is implied. The simulation result, based on the experimental modeling of the furnace, shows that the controller can successfully decrease the interaction between the loops and track the reference temperature set-points.

   @inproceedings{jafari2017control,
     title = {On control structure design for a walking beam furnace},
     author = {Jafari, Hedyeh and Casta{\~n}o Arranz, Miguel and Gustafsson, Thomas and Nikolakopoulos, George},
     booktitle = {25th Mediterranean Conference on Control and Automation (MED)},
     pages = {1355--1360},
     year = {2017},
     organization = {IEEE}
   }

3. 

   Cloud computing for big data analytics in the Process Control Industry

   E Goldin, D Feldman, Georgios Georgoulas, and 2 more authors

   In 25th Mediterranean Conference on Control and Automation (MED) 2017

   Abstract Bib HTML PDF

   The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0.

   @inproceedings{goldin2017cloud,
     title = {Cloud computing for big data analytics in the Process Control Industry},
     author = {Goldin, E and Feldman, D and Georgoulas, Georgios and Castaño, Miguel and Nikolakopoulos, George},
     booktitle = {25th Mediterranean Conference on Control and Automation (MED)},
     pages = {1373-1378},
     year = {2017},
     organization = {IEEE},
   }

4. 

   Autonomous MAV navigation in underground mines using darkness contours detection

   Sina Sharif Mansouri, Miguel Castaño Arranz, Christoforos Kanellakis, and 1 more author

   In International conference on computer vision systems 2019

   Abstract Bib HTML Video PDF dataset

   This article considers a low-cost and light weight platform for the task of autonomous flying for inspection in underground mine tunnels. The main contribution of this paper is integrating simple, effi- cient and well-established methods in the computer vision community in a state of the art vision-based system for Micro Aerial Vehicle (MAV) navigation in dark tunnels. These methods include Otsu’s threshold and Moore-Neighborhood object tracing. The vision system can detect the position of low-illuminated tunnels in image frame by exploiting the in- herent darkness in the longitudinal direction. In the sequel, it is converted from the pixel coordinates to the heading rate command of the MAV for adjusting the heading towards the center of the tunnel. The efficacy of the proposed framework has been evaluated in multiple experimental field trials in an underground mine in Sweden, thus demonstrating the capability of low-cost and resource-constrained aerial vehicles to fly au- tonomously through tunnel confined spaces

   @inproceedings{mansouri2019autonomous,
     title = {Autonomous {MAV} navigation in underground mines using darkness contours detection},
     author = {Mansouri, Sina Sharif and Casta{\~n}o Arranz, Miguel and Kanellakis, Christoforos and Nikolakopoulos, George},
     booktitle = {International conference on computer vision systems},
     pages = {164--174},
     year = {2019},
     organization = {Springer, Cham},
   }

5. 

   A generic framework for data quality analytics

   Miguel Castaño Arranz, Anna Gustafson, and Hussan Al Chalabi

   COMADEM International 2020

   Abstract Bib PDF

   The challenge of generalizing Data Quality assessment is hindered by the fact that Data Quality requisites depend on the purpose for which the data will be used and on the subjectivity of the data consumer. The approach proposed in this paper to address this challenge is to employ a semi-automated user-guided Data Quality assessment. This paper introduces a generic framework for data quality analytics which is mainly composed by a set of software units to perform semi-automated Data Quality analytics and a set of Graphical User Interfaces to enable the user to guide the Data Quality assessment. The framework has been implemented and can be customized according to the needs of the purpose and of the consumer. The framework has been instantiated in a case study on Long-hole drill rigs, where several Data Quality issues have been discovered and their root cause investigated

   @article{castano2020generic,
     title = {A generic framework for data quality analytics},
     author = {Casta{\~n}o Arranz, Miguel and Gustafson, Anna and Al Chalabi, Hussan},
     journal = {COMADEM International},
     volume = {23},
     number = {1},
     year = {2020},
     publisher = {COMADEM}
   }

6. 

   Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

   Sina Sharif Mansouri, Farhad Pourkamali Anaraki, Miguel Castaño Arranz, and 3 more authors

   In 2020 28th Mediterranean Conference on Control and Automation (MED) 2020

   Abstract Bib HTML PDF Slides

   This article proposes a novel unsupervised learn- ing framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology.

   @inproceedings{mansouri2020unsupervised,
     title = {Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud},
     author = {Mansouri, Sina Sharif and Pourkamali Anaraki, Farhad and Castaño Arranz, Miguel and Agha-Mohammadi, Ali-Akbar and Burdick, Joel and Nikolakopoulos, George},
     booktitle = {2020 28th Mediterranean Conference on Control and Automation (MED)},
     pages = {802--807},
     year = {2020},
     organization = {IEEE},
   }

7. 

   Graph-Based Knowledge Representation and Algorithms for Air and Maintenance Operations

   Ella Olsson, Olov Candell, Peter Funk, and 4 more authors

   33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022

   Abstract Bib PDF

   This work presents an approach for information exchange between adjacent air operations domains by means of graph technologies. The approach has the ability to leverage interoperability and collaboration between air- and ground-based systems and stakeholders in respective domains. In its foundation, it provides a means for relevant actors to access and assess relevant data, information and knowledge, and thus provide input in terms of viable action alternatives in a complex and dynamic operational context. As a proof-of-concept, we have utilized a full-stack application framework to implement a decision support demonstrator for operational aircraft maintenance. Our solution facilitates a lightweight and dynamic representation of relevant domain knowledge, readily available for exploitation by graph algorithms, adapted to our domain. We have based our implementation on the full-stack application framework Grand-Stack, which is an architecture designed to exploit the power of graphs throughout its stack.

   @article{OlssonICAS2022,
     title = {Graph-Based Knowledge Representation and Algorithms for Air and
     Maintenance Operations},
     author = {Olsson, Ella and Candell, Olov and Funk, Peter and Sohlberg, Rickard and Castaño Arranz, Miguel and Gustafsson, Mats A. and Bladh, Peter},
     journal = {33rd Congress of the International Council of the Aeronautical Sciences, ICAS},
     year = {2022},
   }

8. 

   Modeling pet behavior during unsupervised walks using a GPS tracker on a domestic cat

   Miguel Castaño Arranz

   2022

   Abstract PDF

   A method for modeling the behavior of a domestic animal in an unsupervised walk is presented. The subject of studies has been a domestic cat. GPS technology has been used to track the animal and determine a set of locations which the animal usually visits during unsupervised walks. The transition between these locations has been modeled as a discrete Markov chain

9. 

   A Human-Centric Model for Sustainable Asset Management in Railway: A Case Study

   Ravdeep Kour, Miguel Castaño Arranz, Ramin Karim, and 3 more authors

   Sustainability 2022

   Abstract Bib HTML PDF

   The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway.

   @article{kour2022human,
     title = {A Human-Centric Model for Sustainable Asset Management in Railway: A Case Study},
     author = {Kour, Ravdeep and Casta{\~n}o Arranz, Miguel and Karim, Ramin and Patwardhan, Amit and Kumar, Manish and Granstr{\"o}m, Rikard},
     journal = {Sustainability},
     volume = {14},
     number = {2},
     pages = {936},
     year = {2022},
     publisher = {MDPI},
   }