Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks

Pattern classification is one of the major components for the design and development of a computerized pattern recognition system. Focused on computational intelligence models, this thesis describes in-depth investigations on two possible directions to design robust and flexible pattern classification models with high performance. Firstly is by enhancing the learning algorithm of a neural-fuzzy network; and secondly by devising an ensemble model to combine the predictions from multiple neural-fuzzy networks using an agent-based framework. Owing to a number of salient features which include the ability of learning incrementally and establishing nonlinear decision boundary with hyperboxes, the Fuzzy Min-Max (FMM) network is selected as the backbone for designing useful and usable pattern classification models in this research. Two enhanced FMM variants, i.e. EFMM and EFMM2, are proposed to address a number of limitations in the original FMM learning algorithm. In EFMM, three heuristic rules are introduced to improve the hyperbox expansion, overlap test, and contraction processes. The network complexity and noise tolerance issues are undertaken in EFMM2. In addition, an agent-based framework is capitalized as a robust ensemble model to house multiple EFMM-based networks. A useful trust measurement method known as Certified Belief in Strength (CBS) is developed and incorporated into the ensemble model for exploiting the predictive performances of different EFMM-based networks

Charge exchange contribution to the decay of the ring current, measured by energetic neutral atoms (ENAs)

In this paper we calculate the contribution of charge exchange to the decay of the ring current. Past works have suggested that charge exchange of ring current protons is primarily responsible for the decay of the ring current during the late recovery phase, but there is still much debate about the fast decay of the early recovery phase. We use energetic neutral atom (ENA) measurements from Polar to calculate the total ENA energy escape. To get the total ENA escape we apply a forward modeling technique, and to estimate the total ring current energy escape we use the Dessler-Parker-Sckopke relationship. We find that during the late recovery phase of the March 10, 1998 storm ENAs with energies greater than 17.5 keV can account for 75% of the estimated energy loss from the ring current. During the fast recovery the measured ENAs can only account for a small portion of the total energy loss. We also find that the lifetime of the trapped ions is significantly shorter during the fast recovery phase than during the late recovery phase, suggesting that different processes are operating during the two phases

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

A simulation Approach to Assess Partners Selected for a Collaborative Network

[EN] Manufacturing enterprises are increasingly more aware of the importance of establishing collaborative relationships with their network partners, due to the advantages associated to collaboration. Nevertheless, the participation in a collaborative network (CN) comes with associated challenges, namely the need to reduce the potential for conflicts among partners. A CN consists of heterogeneous partners, each one defining its own objectives and activating its own strategies. In this context, the ability to quickly identify partners with aligned strategies is crucial for smooth operation of the CN. The main aim of this paper is to address the partners' selection problem in the context of Virtual organizations Breeding Environments (VBE) that facilitate and enable the creation of Virtual Organisations (VO), as one type of CN. In a first stage, the sets of enterprises, characterised by having the required competencies to create the VO, are identified among different potential candidates within the VBE. In a second stage, the strategies alignment approach, based on the system dynamics simulation method, is used for the partners' selection process, identifying the best set of enterprises. In this paper, the final stage of partners' selection process is addressed by obtaining the degree of alignment of the business strategies formulated by each set of enterprises. In the light of this, a system dynamics-simulation model, in AnyLogic, is presented to obtain the set of enterprises that have higher levels of alignment in its strategies. The proposed system dynamics-simulation model is applied to a case in the building industry, to deal with the partners' selection problem in a VBE with the aim of forming a stable and sustainable VO.This work has been funded in part by Programa Val i+d para investigadores en formación (ACIF 2012) and by the Uninova–Center of Technology and Systems and the Portuguese FCT-PEST program UID/EEA/00066/2013.Andres, B.; Poler, R.; Camarinha-Matos, L.; Afsarmanesh, H. (2017). A simulation Approach to Assess Partners Selected for a Collaborative Network. International Journal of Simulation Modelling. 16(3):399-411. https://doi.org/10.2507/IJSIMM16(3)3.382S39941116

Smart Buildings

This talk presents an efficient cyberphysical platform for the smart management of smart buildings http://www.deepint.net. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart building is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study at Salamanca - Ecocasa. This platform could enable smart building to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques

Smart territories

The concept of smart cities is relatively new in research. Thanks to the colossal advances in Artificial Intelligence that took place over the last decade we are able to do all that that we once thought impossible; we build cities driven by information and technologies. In this keynote, we are going to look at the success stories of smart city-related projects and analyse the factors that led them to success. The development of interactive, reliable and secure systems, both connectionist and symbolic, is often a time-consuming process in which numerous experts are involved. However, intuitive and automated tools like “Deep Intelligence” developed by DCSc and BISITE, facilitate this process. Furthermore, in this talk we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems, as well as the use of edge platforms or fog computing

Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information

We consider variants of trust-region and cubic regularization methods for non-convex optimization, in which the Hessian matrix is approximated. Under mild conditions on the inexact Hessian, and using approximate solution of the corresponding sub-problems, we provide iteration complexity to achieve $\epsilon$-approximate second-order optimality which have shown to be tight. Our Hessian approximation conditions constitute a major relaxation over the existing ones in the literature. Consequently, we are able to show that such mild conditions allow for the construction of the approximate Hessian through various random sampling methods. In this light, we consider the canonical problem of finite-sum minimization, provide appropriate uniform and non-uniform sub-sampling strategies to construct such Hessian approximations, and obtain optimal iteration complexity for the corresponding sub-sampled trust-region and cubic regularization methods.Comment: 32 page

Integrated intelligent systems for industrial automation: the challenges of Industry 4.0, information granulation and understanding agents .

The objective of the paper consists in considering the challenges of new automation paradigm Industry 4.0 and reviewing the-state-of-the-art in the field of its enabling information and communication technologies, including Cyberphysical Systems, Cloud Computing, Internet of Things and Big Data. Some ways of multi-dimensional, multi-faceted industrial Big Data representation and analysis are suggested. The fundamentals of Big Data processing with using Granular Computing techniques have been developed. The problem of constructing special cognitive tools to build artificial understanding agents for Integrated Intelligent Enterprises has been faced