Neuromorphic electronics for real-time biomedical image processing

Published versio

Agents enabling cyber-physical production systems

In order to be prepared for future challenges facing the industrial production domain, Cyber-Physical Production Systems (CPPS) consisting of intelligent entities which collaborate and exchange information globally are being proclaimed recently as part of Industrie 4.0. In this article the requirements of CPPS and abilities of agents as enabling technology are discussed. The applicability of agents for realizing CPPS is exemplarily shown based on three selected use cases with different requirements regarding real-time and dependability. The paper finally concludes with opportunities and open research issues that need to be faced in order to achieve agent-based CPPSs.info:eu-repo/semantics/publishedVersio

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

UIDB/00066/2020The advent of the Industry 4.0 initiative has made it so that manufacturing environments are becoming more and more dynamic, connected but also inherently more complex, with additional inter-dependencies, uncertainties and large volumes of data being generated. Recent advances in Industrial Artificial Intelligence have showcased the potential of this technology to assist manufacturers in tackling the challenges associated with this digital transformation of Cyber-Physical Systems, through its data-driven predictive analytics and capacity to assist decision-making in highly complex, non-linear and often multistage environments. However, the industrial adoption of such solutions is still relatively low beyond the experimental pilot stage, as real environments provide unique and difficult challenges for which organizations are still unprepared. The aim of this paper is thus two-fold. First, a systematic review of current Industrial Artificial Intelligence literature is presented, focusing on its application in real manufacturing environments to identify the main enabling technologies and core design principles. Then, a set of key challenges and opportunities to be addressed by future research efforts are formulated along with a conceptual framework to bridge the gap between research in this field and the manufacturing industry, with the goal of promoting industrial adoption through a successful transition towards a digitized and data-driven company-wide culture. This paper is among the first to provide a clear definition and holistic view of Industrial Artificial Intelligence in the Industry 4.0 landscape, identifying and analysing its fundamental building blocks and ongoing trends. Its findings are expected to assist and empower researchers and manufacturers alike to better understand the requirements and steps necessary for a successful transition into Industry 4.0 supported by AI, as well as the challenges that may arise during this process.publishersversionepub_ahead_of_prin

Adaptive Dosing Control System Through ARIMA Model for Peristaltic Pumps

Peristaltic pumps play a crucial role in the pharmaceutical industry, offering advantages such as reduced cross-contamination risks and ease of use. However, their dosing precision often lags behind other devices like volumetric pumps. This study investigates the underlying causes of this phenomenon and proposes effective mitigation strategies to enhance accuracy. Notably, two novel aspects are explored: the underlying causes of dosing variation and compensation systems on precision filling. Through comprehensive analysis, the impact of product temperature on accuracy is unveiled, resulting mainly from variations that alter the elastic properties of the pipe material and lead to significant deviations in dosed volume. Therefore, temperature stabilization becomes imperative for optimal performance. Additionally, the Adaptive Dosing Control System (ADCS) based on time series prediction is introduced, enabling real-time compensation of volume delivery. The filling system is considered as a black box, allowing potential application of these findings on other similar industrial setups. Extensive experiments on state-of-the-art robotic production lines validate the ADCS’s stability and effectiveness, demonstrating up to a 30% improvement in accuracy. In conclusion, this research sheds light on the critical relationship between product temperature and peristaltic pump dosing, while the ADCS represents an advancement in precision filling technology. These results hold potential for enhancing precision, reducing wastage, and improving product quality in the pharmaceutical industry and other precision filling applications

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

Design and development of energy management system for smart homes and buildings

The smart grid, as the next generation of power grid, has redefined the positions of the homes and buildings in the contexts of a whole energy system. With the increasing installation of Distributed Energy Resources (DERs) and retention of Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs), the energy system of homes and buildings in power distribution network is becoming more and more complex. In order to find the efficient and effective way for managing the appliances and DERs in smart homes and buildings through the Energy Management System (EMS), the pathway of the thesis is to investigate the optimisation and control approaches of EMS from controlling the loads within home, to fully optimising the operation of both loads and DERs in smart home, and at last coordinating the EMSs in the buildings through the aggregator

Metodologia para desenvolvimento de base de conhecimento aplicada à manutenção baseada em condição de usinas hidrelétricas

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2008.Este trabalho apresenta uma metodologia para o desenvolvimento de bases de conhecimento aplicada à manutenção baseada em condição e implementação de um sistema inteligente de manutenção preditiva. A metodologia descreve um método detalhado que inclui desde a coleta dos dados até a implementação das regras. O trabalho começa com a coleta de dados, que resultou na catalogação das possíveis falhas e nas intervenções de manutenção adotadas para corrigir as falhas. Em seqüência foi definida a estrutura da base de conhecimento e finalmente a implementação das regras de produção. O sistema inteligente de manutenção preditiva foi projetado para usar a base de conhecimento como fonte de raciocínio, permitindo realizar manutenção preditiva de turbinas hidráulicas. A implementação permitiu: monitorar os dados dos sensores instalados nos equipamentos da usina; sugerir tomadas de decisão apontando intervenções de manutenção; aumentar a segurança do processo de geração de energia a partir da monitoração dos dados; e tratar e interpretar as informações adquiridas a partir dos sensores instalados nos equipamentos monitorados pelo sistema. _______________________________________________________________________________ ABSTRACTThis work presents a methodology for the development of knowledge bases applied to the condition based maintenance and implementation of an intelligent system. The methodology describes a detailed method that includes since the collection of the data until the implementation of the rules. The work starts with the data collection. The knowledge engineering identified possible failures and the actions adopted to correct those imperfections. After that the structure of the knowledge base was defined and finally the implementation of the production rules. The intelligent maintenance system was projected to use the knowledge base as a thinking source. This allowed the maintenance of hydraulic turbines using both ways, the specialist system and the knowledge base. The implementation of the system allowed to realize data evaluation from the sensors installed in the plant. With the system it is possible to suggest maintenance decision and increase the security of the process. The data aquired from the sensors can be processed and interpreted by the system

Multi-Agent Modelling of Industrial Cyber-Physical Systems for IEC 61499 Based Distributed Intelligent Automation

Traditional industrial automation systems developed under IEC 61131-3 in centralized architectures are statically programmed with determined procedures to perform predefined tasks in structured environments. Major challenges are that these systems designed under traditional engineering techniques and running on legacy automation platforms are unable to automatically discover alternative solutions, flexibly coordinate reconfigurable modules, and actively deploy corresponding functions, to quickly respond to frequent changes and intelligently adapt to evolving requirements in dynamic environments. The core objective of this research is to explore the design of multi-layer automation architectures to enable real-time adaptation at the device level and run-time intelligence throughout the whole system under a well-integrated modelling framework. Central to this goal is the research on the integration of multi-agent modelling and IEC 61499 function block modelling to form a new automation infrastructure for industrial cyber-physical systems. Multi-agent modelling uses autonomous and cooperative agents to achieve run-time intelligence in system design and module reconfiguration. IEC 61499 function block modelling applies object-oriented and event-driven function blocks to realize real-time adaption of automation logic and control algorithms. In this thesis, the design focuses on a two-layer self-manageable architecture modelling: a) the high-level cyber module designed as multi-agent computing model consisting of Monitoring Agent, Analysis Agent, Self-Learning Agent, Planning Agent, Execution Agent, and Knowledge Agent; and b) the low-level physical module designed as agent-embedded IEC 61499 function block model with Self-Manageable Service Execution Agent, Self-Configuration Agent, Self-Healing Agent, Self-Optimization Agent, and Self-Protection Agent. The design results in a new computing module for high-level multi-agent based automation architectures and a new design pattern for low-level function block modelled control solutions. The architecture modelling framework is demonstrated through various tests on the multi-agent simulation model developed in the agent modelling environment NetLogo and the experimental testbed designed on the Jetson Nano and Raspberry Pi platforms. The performance evaluation of regular execution time and adaptation time in two typical conditions for systems designed under three different architectures are also analyzed. The results demonstrate the ability of the proposed architecture to respond to major challenges in Industry 4.0

Cooperative Strategies for Management of Power Quality Problems in Voltage-Source Converter-based Microgrids

The development of cooperative control strategies for microgrids has become an area of increasing research interest in recent years, often a result of advances in other areas of control theory such as multi-agent systems and enabled by emerging wireless communications technology, machine learning techniques, and power electronics. While some possible applications of the cooperative control theory to microgrids have been described in the research literature, a comprehensive survey of this approach with respect to its limitations and wide-ranging potential applications has not yet been provided. In this regard, an important area of research into microgrids is developing intelligent cooperative operating strategies within and between microgrids which implement and allocate tasks at the local level, and do not rely on centralized command and control structures. Multi-agent techniques are one focus of this research, but have not been applied to the full range of power quality problems in microgrids. The ability for microgrid control systems to manage harmonics, unbalance, flicker, and black start capability are some examples of applications yet to be fully exploited. During islanded operation, the normal buffer against disturbances and power imbalances provided by the main grid coupling is removed, this together with the reduced inertia of the microgrid (MG), makes power quality (PQ) management a critical control function. This research will investigate new cooperative control techniques for solving power quality problems in voltage source converter (VSC)-based AC microgrids. A set of specific power quality problems have been selected for the application focus, based on a survey of relevant published literature, international standards, and electricity utility regulations. The control problems which will be addressed are voltage regulation, unbalance load sharing, and flicker mitigation. The thesis introduces novel approaches based on multi-agent consensus problems and differential games. It was decided to exclude the management of harmonics, which is a more challenging issue, and is the focus of future research. Rather than using model-based engineering design for optimization of controller parameters, the thesis describes a novel technique for controller synthesis using off-policy reinforcement learning. The thesis also addresses the topic of communication and control system co-design. In this regard, stability of secondary voltage control considering communication time-delays will be addressed, while a performance-oriented approach to rate allocation using a novel solution method is described based on convex optimization