Energetical analysis of two different configurations of a liquid-gas compressed energy storage

In order to enhance the spreading of renewable energy sources in the Italian electric power market, as well as to promote self-production and to decrease the phase delay between energy production and consumption, energy storage solutions are catching on. Nowadays, in general, small size electric storage batteries represent a quite diffuse technology, while air liquid-compressed energy storage solutions are used for high size. The goal of this paper is the development of a numerical model for small size storage, environmentally sustainable, to exploit the higher efficiency of the liquid pumping to compress air. Two different solutions were analyzed, to improve the system efficiency and to exploit the heat produced by the compression phase of the gas. The study was performed with a numerical model implemented in Matlab, by analyzing the variation of hermodynamical parameters during the compression and the expansion phases, making an energetic assessment for the whole system. The results show a good global efficiency, thus making the system competitive with the smallest size storage batteries

Performance evaluation of Attribute-Based Encryption on constrained IoT devices

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. This raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e., smartphones or embedded systems, has been demonstrated in recent works. In this paper, we consider IoT devices characterized by strong limitations in terms of computing, storage, and power. Specifically, we assess the performance of ABE in typical IoT constrained devices. We evaluate the performance of three representative ABE schemes configured considering the worst-case scenario on two popular IoT platforms, namely ESP32 and RE-Mote. Our results show that, if we assume to employ up to 10 attributes in ciphertexts and to leverage hardware cryptographic acceleration, then ABE can indeed be adopted on devices with very limited memory and computing power, while obtaining a satisfactory battery lifetime. In our experiments, as also performed in other works in the literature, we consider only the worst-case configuration, which, however, might not be completely representative of the real working conditions of sensors employing ABE. For this reason, we complete our evaluation by proposing a novel benchmark method that we used to complement the experiments by evaluating the average performance. We show that by always considering the worst case, the current literature significantly overestimates the processing time and the energy consumption

Efficient energy storage in residential buildings integrated with RESHeat system

The Renewable Energy System for Residential Building Heating and Electricity Production (RESHeat) system has been realized for heating and cooling residential buildings. The main components of the RESHeat system are a heat pump, photovoltaic modules, sun-tracking solar collectors and photovoltaic/thermal modules, an under-ground thermal energy storage unit, and a ground heat exchanger. One of the main novelties of the RESHeat system is efficient ground regeneration due to the underground energy storage unit. During a heating season, a large amount of heat is taken from the ground. The underground energy storage unit allows the restoration of ground heating capability and the heat pump's coefficient of performance (COP) to be kept high as possible for consecutive years. The paper presents an energy analysis for a residential building that is a RESHeat system demo site, along with integrating the RESHeat system with the building. The experimentally validated components coupled with the building model to achieve the system performance in TRNSYS software. The results show that the yearly average COP of the heat pump is 4.85 due to the underground energy storage unit. The RESHeat system is able to fully cover the heating demand of the building using renewable energy sources and an efficient underground energy storage system

Comparison of different heating generator systems to reduce energy consumption in social housing in a Mediterranean climate

This study analyses the energy consumption of a social housing built in the 80's. This building typology is deteriorating over time with increased energy consumption for air conditioning and indoor comfort that is well below the standard. This typology is also widely diffused in the city's building stock, especially in its suburbs. Thus, the energy efficiency of public social housing represents a major concern for the Italian national scene, and its improvement represents an effort of critical importance. However, public funding is significantly reduced compared to the past and. In addition, it is often difficult to act on passive systems, such as installing thermal insulation, or replacing terminal units inside apartments. In these cases, as an energy retrofit, it may be appropriate to evaluate the possibility of preserving as much of the existing distribution and supply system as possible, while modifying the thermal energy generation system. In general, where the boiler is not obsolescent, the idea is to propose a hybrid generation system with the inclusion of a heat pump (HHP), which could be implemented with renewable energy equipment, properly installed in the building. The main goal of the present work was to evaluate through dynamic analysis different HVAC scenarios, to assess the optimal configuration of the system for residential use. The results show that a hybrid system can lower the primary energy consumption up to 28%, thus allowing the employment of renewable energies within the social housing building stock

Planning Machine Activity Between Manufacturing Operations: Maintaining Accuracy While Reducing Energy Consumption

There has recently been an increased emphasis on reducing energy consumption in manufacturing. This is largely because of fluctuations in energy costs causing uncertainty. The increased competition between manufacturers means that even a slight change in energy consumption can have implications on their profit margin or competitiveness of quote. Furthermore, there is a drive from policy-makers to audit the environmental impact of manufactured goods from cradle-to-grave. The understanding, and potential reduction of machine tool energy consumption has therefore received significant interest as they require large amounts of energy to perform either subtractive or additive manufacturing tasks. One area that has received relatively little interest, yet could harness great potential, is reducing energy consumption by optimally planning machine activities while the machine is not in operation. The intuitive option is to turn off all non-essential energy-consuming processes. However, manufacturing processes such as milling often release large amounts of heat into the machine's structure causing deformation, which results in deviation of the machine tool's actual cutting position from that which was commanded, a phenomenon known as thermal deformation. A rapid change in temperature can increase the deformation, which can deteriorate the machine's manufacturing capability, potentially producing scrap parts with the associated commercial and environmental repercussions. It is therefore necessary to consider the relationship between energy consumption, thermal deformation, machining accuracy and time, when planning the machine's activity when idle, or about to resume machining. In this paper, we investigate the exploitability of automated planning techniques for planning machine activities between subtractive manufacturing operations, while being sufficiently broad to be extended to additive processes. The aim is to reduce energy consumption but maintain machine accuracy. Specifically, a novel domain model is presented where the machine's energy consumption, thermal stability, and their relationship to the overall machine's accuracy is encoded. Experimental analysis then demonstrates the effectiveness of the proposed approach using a case study which considers real-world dat

Optimized energy and air quality management of shared smart buildings in the covid-19 scenario

Worldwide increasing awareness of energy sustainability issues has been the main driver in developing the concepts of (Nearly) Zero Energy Buildings, where the reduced energy consumptions are (nearly) fully covered by power locally generated by renewable sources. At the same time, recent advances in Internet of Things technologies are among the main enablers of Smart Homes and Buildings. The transition of conventional buildings into active environments that process, elaborate and react to online measured environmental quantities is being accelerated by the aspects related to COVID-19, most notably in terms of air exchange and the monitoring of the density of occupants. In this paper, we address the problem of maximizing the energy efficiency and comfort perceived by occupants, defined in terms of thermal comfort, visual comfort and air quality. The case study of the University of Pisa is considered as a practical example to show preliminary results of the aggregation of environmental data

Possible use of Digital Variance Angiography in Liver Transarterial Chemoembolization: A Retrospective Observational Study

Purpose Digital variance angiography (DVA), a recently developed image processing technology, provided higher contrast-to-noise ratio (CNR) and better image quality (IQ) during lower limb interventions than digital subtraction angiography (DSA). Our aim was to investigate whether this quality improvement can be observed also during liver transarterial chemoembolization (TACE).Materials and MethodsWe retrospectively compared the CNR and IQ parameters of DSA and DVA images from 25 patients (65% male, mean +/- SD age: 67.5 +/- 11.2 years) underwent TACE intervention at our institute. CNR was calculated on 50 images. IQ of every image set was evaluated by 5 experts using 4-grade Likert scales. Both single image evaluation and paired image comparison were performed in a blinded and randomized manner. The diagnostic value was evaluated based on the possibility to identify lesions and feeding arteries.ResultsDVA provided significantly higher CNR (mean CNRDVA/CNRDSA was 1.33). DVA images received significantly higher individual Likert score (mean +/- SEM 3.34 +/- 0,08 vs. 2.89 +/- 0.11, Wilcoxon signed-rank p < 0.001) and proved to be superior also in paired comparisons (median comparison score 1.60 [IQR:2.40], one sample Wilcoxon p < 0.001 compared to equal quality level). DSA could not detect lesion and feeding artery in 28 and 36% of cases, and allowed clear detection only in 22% and 16%, respectively. In contrast, DVA failed only in 8 and 18% and clearly revealed lesions and feeding arteries in 32 and 26%, respectively.ConclusionIn our study, DVA provided higher quality images and better diagnostic insight than DSA; therefore, DVA could represent a useful tool in liver TACE interventions

Automated Planning Techniques for Robot Manipulation Tasks Involving Articulated Objects

The goal-oriented manipulation of articulated objects plays an important role in real-world robot tasks. Current approaches typically pose a number of simplifying assumptions to reason upon how to obtain an articulated object’s goal configuration, and exploit ad hoc algorithms. The consequence is two-fold: firstly, it is difficult to generalise obtained solutions (in terms of actions a robot can execute) to different target object’s configurations and, in a broad sense, to different object’s physical characteristics; secondly, the representation and the reasoning layers are tightly coupled and inter-dependent. In this paper we investigate the use of automated planning techniques for dealing with articulated objects manipulation tasks. Such techniques allow for a clear separation between knowledge and reasoning, as advocated in Knowledge Engineering. We introduce two PDDL formulations of the task, which rely on conceptually different representations of the orientation of the objects. Experiments involving several planners and increasing size objects demonstrate the effectiveness of the proposed models, and confirm its exploitability when embedded in a real-world robot software architecture

Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10462-016-9505-7.The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration.I thank the organisers of the AEPIA Summer School On Artificial Intelligence, held in September 2014, for giving me the opportunity to give a lecture on 'AI Evaluation'. This paper was born out of and evolved through that lecture. The information about many benchmarks and competitions discussed in this paper have been contrasted with information from and discussions with many people: M. Bedia, A. Cangelosi, C. Dimitrakakis, I. GarcIa-Varea, Katja Hofmann, W. Langdon, E. Messina, S. Mueller, M. Siebers and C. Soares. Figure 4 is courtesy of F. Martinez-Plumed. Finally, I thank the anonymous reviewers, whose comments have helped to significantly improve the balance and coverage of the paper. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grants TIN 2013-45732-C4-1-P, TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEOII2015/013.José Hernández-Orallo (2016). Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement. 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