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Energy efficiency financing is considered among the top priorities in the energy sector among several stakeholders. In this context, accurately estimating the energy savings achieved by energy efficiency actions before being approved and implemented is of major importance to ensure the optimal allocation of the available financial resources. This study aims to provide a machine-learningbased methodological framework for a priori predicting the energy savings of energy efficiency renovation actions. The proposed solution consists of three tree-based algorithms that exploit bagging and boosting as well as an additional ensembling level that further mitigates prediction uncertainty. The proposed models are empirically evaluated using a database of various, diverse energy efficiency renovation investments. Results indicate that the ensemble model outperforms the three individual models in terms of forecasting accuracy. Also, the generated predictions are relatively accurate for all the examined project categories, a finding that supports the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

Energy communities can support the energy transition, by engaging citizens through collective energy actions and generate positive economic, social and environmental outcomes. Renewable Energy Sources (RES) are gaining increasing share in the electricity mix as the economy decarbonises, with Photovoltaic (PV) plants to becoming more efficient and affordable. By incorporating Artificial Intelligence (AI) techniques, innovative applications can be developed to provide added value to energy communities. In this context, the scope of this paper is to compare Machine Learning (ML) and Deep Learning (DL) algorithms for the prediction of short-term production in a solar plant under an energy cooperative operation. Three different cases are considered, based on the data used as inputs for forecasting purposes. Lagged inputs are used to assess the historical data needed, and the algorithms' accuracy is tested for the next hour's PV production forecast. The comparative analysis between the proposed algorithms demonstrates the most accurate algorithm in each case, depending on the available data. For the highest performing algorithm, its performance accuracy in further forecasting horizons (3 hours, 6 hours and 24 hours) is also tested. [ABSTRACT FROM AUTHOR]

Seamless human–robot collaboration requires the equipping of robots with cognitive capabilities that enable their awareness of the environment, as well as the actions that take place inside the assembly cell. This paper proposes an AI-based system comprised of three modules that can capture the operator and environment status and process status, identify the tasks that are being executed by the operator using vision-based machine learning, and provide customized operator support from the robot side for shared tasks, automatically adapting to the operator's needs and preferences. Moreover, the proposed system is able to assess the ergonomics in human–robot shared tasks and adapt the robot pose to improve ergonomics using a heuristics-based search algorithm. An industrial case study derived from the elevator manufacturing sector using a high payload collaborative robot is presented to demonstrate that collaboration efficiency can be enhanced through the use of the discussed system. [ABSTRACT FROM AUTHOR]

Human–Robot Collaborative (HRC) workcells aim to elevate the conventional industrial lines, by enabling seamless interaction between operators and machines. Shifting away from standard (mainly static) workcells, with operators and machines following strictly defined limits and schedules, the new era of manufacturing introduces versatile workspaces, shared between the manufacturing resources. The non-deterministic workflow of such workspaces raises concerns over planning of task and actions among the resources, as the workcell is now a dynamic environment. Following these demands, this study introduces a solution in scheduling and assignment of assembly tasks to both human and robot resources, aiming for effective emergence of alternative task-resource assignment sequences. The proposed framework is optimized to provide a human-centered approach on the assembly line and alleviate human operators from exhaustive or unsafe tasks, while accounting for environment changes, such as positioning of resources and parts. This tool was used in a use case from the industrial modules manufacturing sector. © 2020 The Authors, Published by Elsevier B.V. Peer review under the responsibility of the scientific committee of CIRP [ABSTRACT FROM AUTHOR]

Nowadays, in order to cover the needs of market for product mass customization, industries have started to move to hybrid production cells, involving both robots and human operators. Research has been done during previous years to promote and improve the collaboration between humans and robots, trying to address topics such as safety, awareness and cognitive support in form of Augmented Reality based instructions. Results of previous research show bottlenecks related to the way of interaction of the operators with such supportive systems though. Direct interaction approach with the use of push buttons or indirect-gesture based interaction, which are most often adopted by the researchers, require operators to constantly occupy their hands performing the relevant button presses or gestures. Moreover, previous approaches are hardware dependent and need a lot of customization to work with different hardware. This work tries to address these bottlenecks proposing the usage of wearable devices enhanced with AI in order to support the interaction of human operators with robots in human-robot collaborative environments in a seamless and non-intrusive way, wrapped around a framework called "Operator Support Module" (OSM). Among others, OSM supports a variety of hardware to easily fit in various industrial scenarios. Two case studies will be presented to demonstrate the approach. [ABSTRACT FROM AUTHOR]

Over the last years both Research and Industry have tried to address the requirement for flexible production by introducing technologies that allow humans and robots to coexist and share production tasks. The focus has been to ensure the safety of humans while interacting with robots. Previous EU funded projects provided proof that humans and robots destiny is collaboration rather than competition. It has been revealed though that Human Robot Collaborative (HRC) applications present drawbacks that limit industrial adoption. SHERLOCK EU project aims to exploit the lessons learnt and the technical excellence, developing the first high payload collaborative robot (COMAU AURA), dynamically reconfigurable safety monitoring systems and smart Human Robot (HR) interfaces allowing the seamless integration of operators and robots in a common workflow. The aim is to introduce the latest safe robotic technologies including high payload collaborative arms, exoskeletons and mobile manipulators in diverse production environments, enhance them with smart mechatronics and AI based cognition and thus create efficient HRC stations that are designed to be safe and guarantee the acceptance and wellbeing of operators. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) technology is increasingly vital in various sectors, particularly for simulating real environments in training and teleoperation. However, it has primarily focused on static, controlled settings like indoor industrial shopfloors. This paper proposes a novel method for remotely controlling robots in hazardous environments safely, without compromising efficiency. Operators can execute tasks from remote locations ensuring continuity regardless of distance. Real‐time efficiency is achieved by updating the virtual environment from on‐site sensors and mirroring the real environment, utilizing 3D reconstruction, Google Images, and video streams. Communication between VR and the remote robot is facilitated through a remote robot operating system connection. The efficacy of this concept will be validated through real road maintenance interventions. [ABSTRACT FROM AUTHOR]

This paper presents a computer-aided approach for nodule delineation in thyroid ultrasound (US) images. The developed algorithm is based on a novel active contour model, named variable background active contour (VBAC), and incorporates the advantages of the level set region-based active contour without edges (ACWE) model, offering noise robustness and the ability to delineate multiple nodules. Unlike the classic active contour models that are sensitive in the presence of intensity inhomogeneities, the proposed VBAC model considers information of variable back-ground regions. VBAC has been evaluated on synthetic images, as well as on real thyroid US images. From the quantification of the results, two major impacts have been derived: 1) higher average accuracy in the delineation of hypoechoic thyroid nodules, which exceeds 91%; and 2) faster convergence when compared with the ACWE model. [ABSTRACT FROM AUTHOR]

Current climate change threats and increasing CO2 emissions, especially from the building stock, represent a context where action is required. It is necessary to provide efficient manners to manage energy demand in buildings and contribute to a decarbonised future. By combining new technologies, such as artificial intelligence, Internet of things, blockchain, and the exploitation of big data towards solving real life problems, the way could be paved towards smart and energy-aware buildings. In this context, the aim of this paper is to present a critical review and an in-detail definition of the big data value chain for the built environment in Europe, covering multiple needs and perspectives: "policy", "technology" and "business", in order to explore the main challenges and opportunities in this area. [ABSTRACT FROM AUTHOR]

Abstract: Advances in modern technologies and computers have enabled digital image processing to become a vital tool in conventional clinical practice, including mammography. However, the core problem of the clinical evaluation of mammographic tumors remains a highly demanding cognitive task. In order for these automated diagnostic systems to perform in levels of sensitivity and specificity similar to that of human experts, it is essential that a robust framework on problem-specific design parameters is formulated. This study is focused on identifying a robust set of clinical features that can be used as the base for designing the input of any computer-aided diagnosis system for automatic mammographic tumor evaluation. A thorough list of clinical features was constructed and the diagnostic value of each feature was verified against current clinical practices by an expert physician. These features were directly or indirectly related to the overall morphological properties of the mammographic tumor or the texture of the fine-scale tissue structures as they appear in the digitized image, while others contained external clinical data of outmost importance, like the patient''s age. The entire feature set was used as an annotation list for describing the clinical properties of mammographic tumor cases in a quantitative way, such that subsequent objective analyses were possible. For the purposes of this study, a mammographic image database was created, with complete clinical evaluation descriptions and positive histological verification for each case. All tumors contained in the database were characterized according to the identified clinical features’ set and the resulting dataset was used as input for discrimination and diagnostic value analysis for each one of these features. Specifically, several standard methodologies of statistical significance analysis were employed to create feature rankings according to their discriminating power. Moreover, three different classification models, namely linear classifiers, neural networks and support vector machines, were employed to investigate the true efficiency of each one of them, as well as the overall complexity of the diagnostic task of mammographic tumor characterization. Both the statistical and the classification results have proven the explicit correlation of all the selected features with the final diagnosis, qualifying them as an adequate input base for any type of similar automated diagnosis system. The underlying complexity of the diagnostic task has justified the high value of sophisticated pattern recognition architectures. [Copyright &y& Elsevier]