Browsing by Author "Gil-Lopez, Sergio"
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Item Design and implementation of an extended corporate crm database system with big data analytical functionalities(2015-07-25) Torre-Bastida, Ana I.; Villar-Rodriguez, Esther; Gil-Lopez, Sergio; Del Ser, Javier; HPA; Quantum; IAThe amount of open information available on-line from heterogeneous sources and domains is growing at an extremely fast pace, and constitutes an important knowledge base for the consideration of industries and companies. In this context, two relevant data providers can be highlighted: the “Linked Open Data” (LOD) and “Social Media” (SM) paradigms. The fusion of these data sources – structured the former, and raw data the latter –, along with the information contained in structured corporate databases within the organizations themselves, may unveil significant business opportunities and competitive advantage to those who are able to understand and leverage their value. In this paper, we present two complementary use cases, illustrating the potential of using the open data in the business domain. The first represents the creation of an existing and potential customer knowledge base, exploiting social and linked open data based on which any given organization might infer valuable information as a support for decision making. The second focuses on the classification of organizations and enterprises aiming at detecting potential competitors and/or allies via the analysis of the conceptual similarity between their participated projects. To this end, a solution based on the synergy of Big Data and semantic technologies will be designed and developed. The first will be used to implement the tasks of collection, data fusion and classification supported by natural language processing (NLP) techniques, whereas the latter will deal with semantic aggregation, persistence, reasoning and information retrieval, as well as with the triggering of alerts based on the semantized information.Item An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments(Springer, 2020-10-27) Mendia, Izaskun; Gil-Lopez, Sergio; Del Ser, Javier; Grau, Iñaki; Lejarazu, Adelaida; Maqueda, Erik; Perea, Eugenio; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; Tecnalia Research & Innovation; IA; DIGITAL ENERGYThe concern of the industrial sector about the increase of energy costs has stimulated the development of new strategies for the effective management of energy consumption in industrial setups. Along with this growth, the irruption and continuous development of digital technologies have generated increasingly complex industrial ecosystems. These ecosystems are supported by a large number of variables and procedures for the operation and control of industrial processes and assets. This heterogeneous technological scenario has made industries difficult to manage by traditional means. In this context, the disruptive potential of cyber physical systems is beginning to be considered in the automation and improvement of industrial services. Particularly, intelligent data-driven approaches relying on the combination of Energy Management Systems (EMS), Manufacturing Execution Systems (MES), Internet of Things (IoT) and Data Analytics provide the intelligence needed to optimally operate these complex industrial environments. The work presented in this manuscript contributes to the definition of the aforementioned intelligent data-driven approaches, defining a systematic, intelligent procedure for the energy efficiency diagnosis and improvement of industrial plants. This data-based diagnostic procedure hinges on the analysis of data collected from industrial plants, aimed at minimizing energy costs through the continuous assessment of the production-consumption ratio of the plant (i.e. energy per piece or kg produced). The proposed methodology aims to support managers and energy-efficiency technicians to minimize the plant’s energy consumption without affecting the production and therefore, increase its competitiveness. The data used in the design of this methodology are real data from a company dedicated to the design and manufacture of automotive components and one of the main manufacturers in the automotive sector worldwide. The present methodology is under the pending patent application EU19382002.4-120.Item A novel approach for the detection of anomalous energy consumption patterns in industrial cyber-physical systems(2024-02) Mendia, Izaskun; Gil-Lopez, Sergio; Grau, Iñaki; Del Ser, Javier; Gil‐Lopez, Sergio; Tecnalia Research & Innovation; IAMost scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber-physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI-based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real-world industrial solutions has not so far developed at the same pace. Currently, 90% of AI-based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI-based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non-supervised, human-understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real-world case study from the automotive sector, comparing it to an extensive benchmark comprising state-of-the-art unsupervised and semi-supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI-based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection.Item On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification(2022-07) Barredo Arrieta, Alejandro; Gil-Lopez, Sergio; Laña, Ibai; Bilbao, Miren Nekane; Del Ser, Javier; Tecnalia Research & Innovation; IASince their inception, learning techniques under the reservoir computing paradigm have shown a great modeling capability for recurrent systems without the computing overheads required for other approaches, specially deep neural networks. Among them, different flavors of echo state networks have attracted many stares through time, mainly due to the simplicity and computational efficiency of their learning algorithm. However, these advantages do not compensate for the fact that echo state networks remain as black-box models whose decisions cannot be easily explained to the general audience. This issue is even more involved for multi-layered (also referred to as deep) echo state networks, whose more complex hierarchical structure hinders even further the explainability of their internals to users without expertise in machine learning or even computer science. This lack of explainability can jeopardize the widespread adoption of these models in certain domains where accountability and understandability of machine learning models is a must (e.g., medical diagnosis, social politics). This work addresses this issue by conducting an explainability study of echo state networks when applied to learning tasks with time series, image and video data. Among these tasks, we stress on the latter one (video classification) which, to the best of our knowledge, has never been tackled before with echo state networks in the related literature. Specifically, the study proposes three different techniques capable of eliciting understandable information about the knowledge grasped by these recurrent models, namely potential memory, temporal patterns and pixel absence effect. Potential memory addresses questions related to the effect of the reservoir size in the capability of the model to store temporal information, whereas temporal patterns unveil the recurrent relationships captured by the model over time. Finally, pixel absence effect attempts at evaluating the effect of the absence of a given pixel when the echo state network model is used for image and video classification. The benefits of the proposed suite of techniques are showcased over three different domains of applicability: time series modeling, image and, for the first time in the related literature, video classification. The obtained results reveal that the proposed techniques not only allow for an informed understanding of the way these models work, but also serve as diagnostic tools capable of detecting issues inherited from data (e.g., presence of hidden bias).Item Semantic Information Fusion of Linked Open Data and Social Big Data for the Creation of an Extended Corporate CRM Database(2015-01-01) Torre-Bastida, Ana I.; Villar-Rodriguez, Esther; Del Ser, Javier; Gil-Lopez, Sergio; Tecnalia Research & Innovation; HPA; Quantum; IAThe amount of on-line available open information from heterogeneous sources and domains is growing at an extremely fast pace, and constitutes an important knowledge base for the consideration of industries and companies. In this context, two relevant data providers can be highlighted: the “Linked Open Data” and “Social Media” paradigms. The fusion of these data sources – structured the former, and raw data the latter –, along with the information contained in structured corporate databases within the organizations themselves, may unveil significant business opportunities and competitive advantage to those who are able to understand and leverage their value. In this paper, we present a use case that represents the creation of an existing and potential customer knowledge base, exploiting social and linked open data based on which any given organization might infer valuable information as a support for decision making. In order to achieve this a solution based on the synergy of big data and semantic technologies will be designed and developed. The first will be used to implement the tasks of collection and initial data fusion based on natural language processing techniques, whereas the latter will perform semantic aggregation, persistence, reasoning and retrieval of information, as well as the triggering of alerts over the semantized information.