3,774 research outputs found

    Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

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    Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

    Mobile Device Background Sensors: Authentication vs Privacy

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    The increasing number of mobile devices in recent years has caused the collection of a large amount of personal information that needs to be protected. To this aim, behavioural biometrics has become very popular. But, what is the discriminative power of mobile behavioural biometrics in real scenarios? With the success of Deep Learning (DL), architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), have shown improvements compared to traditional machine learning methods. However, these DL architectures still have limitations that need to be addressed. In response, new DL architectures like Transformers have emerged. The question is, can these new Transformers outperform previous biometric approaches? To answers to these questions, this thesis focuses on behavioural biometric authentication with data acquired from mobile background sensors (i.e., accelerometers and gyroscopes). In addition, to the best of our knowledge, this is the first thesis that explores and proposes novel behavioural biometric systems based on Transformers, achieving state-of-the-art results in gait, swipe, and keystroke biometrics. The adoption of biometrics requires a balance between security and privacy. Biometric modalities provide a unique and inherently personal approach for authentication. Nevertheless, biometrics also give rise to concerns regarding the invasion of personal privacy. According to the General Data Protection Regulation (GDPR) introduced by the European Union, personal data such as biometric data are sensitive and must be used and protected properly. This thesis analyses the impact of sensitive data in the performance of biometric systems and proposes a novel unsupervised privacy-preserving approach. The research conducted in this thesis makes significant contributions, including: i) a comprehensive review of the privacy vulnerabilities of mobile device sensors, covering metrics for quantifying privacy in relation to sensitive data, along with protection methods for safeguarding sensitive information; ii) an analysis of authentication systems for behavioural biometrics on mobile devices (i.e., gait, swipe, and keystroke), being the first thesis that explores the potential of Transformers for behavioural biometrics, introducing novel architectures that outperform the state of the art; and iii) a novel privacy-preserving approach for mobile biometric gait verification using unsupervised learning techniques, ensuring the protection of sensitive data during the verification process

    An agent-based approach for energy-efficient sensor networks in logistics

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    As part of the fourth industrial revolution, logistics processes are augmented with connected information systems to improve their reliability and sustainability. Above all, customers can analyse process data obtained from the networked logistics operations to reduce costs and increase margins. The logistics of managing liquid goods is particularly challenging due to the strict transport temperature requirements involving monitoring via sensors attached to containers. However, these sensors transmit much redundant information that, at times, does not provide additional value to the customer, while consuming the limited energy stored in the sensor batteries. This paper aims to explore and study alternative approaches for location tracking and state monitoring in the context of liquid goods logistics. This problem is addressed by using a combination of data-driven sensing and agent-based modelling techniques. The simulation results show that the longest life span of batteries is achieved when most sensors are put into sleep mode yielding an increase of Ă—21.7 and Ă—3.7 for two typical routing scenarios. However, to allow for situations in which high quality sensor data is required to make decisions, agents need to be made aware of the life cycle phase of individual containers. Key contributions include (1) an agent-based approach for modelling the dynamics of liquid goods logistics to enable monitoring and detect inefficiencies (2) the development and analysis of three sensor usage strategies for reducing the energy consumption, and (3) an evaluation of the trade-offs between energy consumption and location tracking precision for timely decision making in resource constrained monitoring systems

    Multi-objective particle swarm optimization for optimal scheduling of household microgrids

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    Addressing the challenge of household loads and the concentrated power consumption of electric vehicles during periods of low electricity prices is critical to mitigate impacts on the utility grid. In this study, we propose a multi-objective particle swarm algorithm-based optimal scheduling method for household microgrids. A household microgrid optimization model is formulated, taking into account time-sharing tariffs and users’ travel patterns with electric vehicles. The model focuses on optimizing daily household electricity costs and minimizing grid-side energy supply variances. Specifically, the mathematical model incorporates the actual input and output power of each distributed energy source within the microgrid as optimization variables. Furthermore, it integrates an analysis of capacity variations for energy storage batteries and electric vehicle batteries. Through arithmetic simulation within the Pareto optimal solution set, the model identifies the optimal solution that effectively mitigates fluctuations in energy input and output on the utility side. Simulation results confirm the effectiveness of this strategy in reducing daily household electricity costs. The proposed optimization approach not only improves the overall quality of electricity consumption but also demonstrates its economic and practical feasibility, highlighting its potential for broader application and impact

    Heuristic antenna selection and precoding for a massive MIMO system

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    Sixth Generation (6G) transceivers are envisioned to feature massively large antenna arrays compared to its predecessor. This will result in even higher spectral efficiency (SE) and multiplexing gains. However, immense concerns remain about the energy efficiency (EE) of such transceivers. This work focuses on partially connected hybrid architectures, with the primary aim of enhancing the EE of the system. To achieve this objective, the study proposes a combined approach of joint antenna selection and precoding, which holds the potential to further optimize the system’s EE while maintaining a satisfactory SE performance levels. The proposed approach incorporates antenna selection based on a meta-heuristic cyclic binary particle swarm optimization algorithm along with successive interference cancellation-based precoding. The results indicate that the proposed solution, in terms of SE and EE, performs very close to the optimal exhaustive search algorithm. This study also investigates the trade-off between SE and EE in a low and high signal-to-noise ratio (SNR) regimes. The robustness of the proposed scheme is also demonstrated when the channel state information is imperfect. In conclusion, this work presents a lower complexity approach to enhance EE in 6G transceivers while maintaining SE performance and along with a reduction in power consumption

    Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting : a comparative analysis of Grad-CAM and SHAP

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    DATA AVAILABILITY: Datasets related to this article can be found at [63], an open-source online data repository hosted at Mendeley Data.This study investigates the efficacy of Explainable Artificial Intelligence (XAI) methods, specifically Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP), in the feature selection process for national demand forecasting. Utilising a multi-headed Convolutional Neural Network (CNN), both XAI methods exhibit capabilities in enhancing forecasting accuracy and model efficiency by identifying and eliminating irrelevant features. Comparative analysis revealed Grad-CAM’s exceptional computational efficiency in high-dimensional applications and SHAP’s superior ability in revealing features that degrade forecast accuracy. However, limitations are found in both methods, with Grad-CAM including features that decrease model stability, and SHAP inaccurately ranking significant features. Future research should focus on refining these XAI methods to overcome these limitations and further probe into other XAI methods’ applicability within the time-series forecasting domain. This study underscores the potential of XAI in improving load forecasting, which can contribute significantly to the development of more interpretative, accurate and efficient forecasting models.National Key R&D Program of China, National Natural Science Foundation of China, National Research Foundation China/South Africa Research Cooperation Programme, China/South Africa Bilateral, and Royal Academy of Engineering Transforming Systems through Partnership.http://www.elsevier.com/locate/apenergyElectrical, Electronic and Computer Engineerin

    Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

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    The research landscape on the applications of advanced computational tools (ACTs) such as machine/deep learning and neural network algorithms for energy and power generation (EPG) was critically examined through publication trends and bibliometrics data analysis. The Elsevier Scopus database and the PRISMA methodology were employed to identify and screen the published documents, whereas the bibliometric analysis software VOSviewer was used to analyse the co-authorships, citations, and keyword occurrences. The results showed that 152 documents have been published on the topic comprising conference proceedings (58.6%) and articles (41.4%) between 2004 and 2022. Publication trends analysis revealed the number of publications increased from 1 to 31 or by 3,000% over the same period, which was ascribed to the growing scientific interest and research impact of the topic. Stakeholder analysis revealed the top authors/researchers are Anvari M, Ghaderi SF and Saberi M, whereas the most prolific affiliation and nations actively engaged in the topic are the North China Electric Power University, and China, respectively. Conversely, the top funding agency actively backing research on the topic is the National Natural Science Foundation of China (NSFC). Co-authorship analysis revealed high levels of collaboration between researching nations compared to authors and affiliations. Hotspot analysis revealed three major thematic focus areas namely; Energy Grid Forecasting, Power Generation Control, and Intelligent Energy Optimization. In conclusion, the study showed that the application of ACTs in EPG is an active, multidisciplinary, and impact area of research with potential for more impactful contributions to research and society at large

    Nonlinear characteristics identification of an impact oscillator with a one-sided elastic constraint

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Data availability: Data will be made available on request.Impacting systems are widely used in many engineering applications, such as self-propelled robots, energy harvesting and percussive drilling, which exhibit rich and complex nonlinear phenomena. Among these applications, predicting nonlinearities and estimating system parameters are of great interest of nonlinear dynamics research community. Backbone curve is an analytical tool that captures the frequencyamplitude dependence of nonlinear systems. In this paper, we estimate the impacting stiffness of a single-degree-of-freedom non-smooth dynamical system qualitatively by using the backbone curve. It was found that an increase of the impacting stiffness may lead to lowering the backbone curve. An adaptive differential evolution algorithm with the Metropolis criterion is proposed to identify the parameters of the impacting system quantitatively using experimental data, which are consistent with our theoretical predictions. Finally, the identified parameters are verified, and the limitations of the backbone curve are drawn. The nonlinear characteristics identification method studied in this paper could be extended to other vibro-impact systems and is potentially applicable to structural health monitoring and robotic sensing.European Union’s Horizon 202

    Intelligent Solar Forecasts: Modern Machine Learning Models & TinyML Role for Improved Solar Energy Yield Predictions

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    The advancement of sustainable energy sources necessitates the development of robust forecasting tools for efficient energy management. A prominent player in this domain, solar power, heavily relies on accurate energy yield predictions to optimize production, minimize costs, and maintain grid stability. This paper explores an innovative application of tiny machine learning to provide real-time, low-cost forecasting of solar energy yield on resource-constrained edge internet of things devices, such as micro-controllers, for improved residential and industrial energy management. To further contribute to the domain, we conduct a comprehensive evaluation of four prominent machine learning models, namely unidirectional long short-term memory, bidirectional gated recurrent unit, bidirectional long short-term memory, and simple bidirectional recurrent neural network, for predicting solar farm energy yield. Our analysis delves into the impacts of tuning the machine learning model hyperparameters on the performance of these models, offering insights to improve prediction accuracy and stability. Additionally, we elaborate on the challenges and opportunities presented by the implementation of machine learning on low-cost energy management control systems, highlighting the benefits of reduced operational expenses and enhanced grid stability. The results derived from this study offer significant implications for energy management strategies at both household and industrial scales, contributing to a more sustainable future powered by accurate and efficient solar energy forecasting

    Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review

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    To deliver electricity to customers safely and economically, power companies encounter numerous economic and technical challenges in their operations. Power flow analysis, planning, and control of power systems stand out among these issues. Over the last several years, one of the most developing study topics in this vital and demanding discipline has been electricity short-term load forecasting (STLF). Power system dispatching, emergency analysis, power flow analysis, planning, and maintenance all require it. This study emphasizes new research on long short-term memory (LSTM) algorithms related to particle swarm optimization (PSO) inside this area of short-term load forecasting. The paper presents an in-depth overview of hybrid networks that combine LSTM and PSO and have been effectively used for STLF. In the future, the integration of LSTM and PSO in the development of comprehensive prediction methods and techniques for multi-heterogeneous models is expected to offer significant opportunities. With an increased dataset, the utilization of advanced multi-models for comprehensive power load prediction is anticipated to achieve higher accuracy
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