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openaire: EC/FP7/600825/EU//RECONFIG In a multirobot system, using shared symbols for objects in the environment is a prerequisite for collaboration. Sharing symbols requires that each agent has anchored a symbol with an internal, sensor level representation, as well as that these symbols match between the agents. The problem can be solved easily when the internal representations can be communicated between the agents. However, with heterogeneous embodiments the available sensors are likely to differ, making it impossible to share the internal representations directly. We propose the use of pointing gestures to align symbols between a heterogeneous group of robots. We describe a planning framework that minimizes the required effort for anchoring representations across robots. The framework allows planning for both the gesturing and observing agents in a decentralized fashion. It considers both implicit sources of failure, such as ambiguous pointing, as well as costs required by actions. Simulation experiments demonstrate that the resulting planning problem has a complex solution structure with multiple local minima. Demonstration with a heterogeneous two-robot system shows the practical viability of this approach.

International audience; This article develops a comprehensive framework for stability analysis of a broad class of commonly used continuous-and discrete-time filters for stochastic dynamic systems with nonlinear state dynamics and linear measurements under certain strong assumptions. The class of filters encompasses the extended and unscented Kalman filters and most other Gaussian assumed density filters and their numerical integration approximations. The stability results are in the form of time-uniform mean square bounds and exponential concentration inequalities for the filtering error. In contrast to existing results, it is not always necessary for the model to be exponentially stable or fully observed. We review three classes of models that can be rigorously shown to satisfy the stringent assumptions of the stability theorems. Numerical experiments using synthetic data validate the derived error bounds.

Modeling the effect of multi-axial mechanical stress on the iron losses of an M400-50A grade non-oriented electrical steel sheet is studied. By utilizing the statistical loss theory, the total measured iron losses are first segregated to hysteresis, classical eddy current and excess losses. Then, the stress dependency of the excess losses is modeled by correlating them to the hysteresis losses under multi-axial stress. This correlation, coupled with a magneto-elastic invariant based loss model, can be used to predict the iron loss evolution with reasonable accuracy under multi-axial stress when only iron loss data under no applied stress at various excitation frequencies and under only two uniaxial stress levels at quasi-static excitation are available. Consequently, this approach significantly reduces the required measurement data for estimating the iron losses under multi-axial stress. acceptedVersion

openaire: EC/FP7/339380/EU//ALEM A detailed design of a new rotational single sheet tester device which allows comprehensive multiaxial magneto-mechanical analysis of ferromagnetic sheets is proposed. The challenges that arose during the mechanical and magnetic design phases are addressed. The applicability of the device is tested by performing magneto-mechanical measurements on an M400-50A electrical steel sheet. Results under several multiaxial magneto-mechanical loadings with circular and alternating magnetic flux densities are reported. It is shown that the effect of multiaxial stress on iron losses can be much more significant than that of uniaxial stress.

openaire: EC/FP7/339380/EU//ALEM The effect of multi-axial stress on the iron losses of a non-oriented electrical steel sheet under alternating magnetization is analyzed. Multi-axial magneto-mechanical measurements on a M400-50A grade non-oriented electrical steel sheet are performed by using a custom made single sheet tester device. The measured losses are separated into hysteresis, classical and excess loss components by using statistical loss theory, and the effect of various stress configurations on the hysteresis and the excess loss components is analyzed. By utilizing the statistical loss theory, an equivalent stress model and a magneto-elastic invariant based model are derived. These models can be used to predict the iron loss evolution under multi-axial stress even if only uniaxial stress dependent measurements are available. The accuracy of both models to predict the multi-axial stress dependent iron losses is found to be satisfactory when they are identified only from uniaxial stress dependent measurements. The invariant based model is shown to be slightly more accurate for the studied material.

openaire: EC/FP7/339380/EU//ALEM An equivalent strain/stress approach is proposed for modeling permeability change in ferromagnetic materials due to mechanical loadings. The model can be used for transforming complex multiaxial mechanical loadings into equivalent uniaxial loadings parallel to the magnetic field, such that the permeability can be predicted only based on uniaxial measurements. Contrary to earlier approaches, the new definition of the equivalent strain/stress also accounts for shear strain/stress with respect to the magnetic field. The results are shown to match well measurements under multiaxial stresses.

Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision and control tasks under uncertainty. Over the last decade, it has seen many successful applications, spanning localization and navigation, search and tracking, autonomous driving, multi-robot systems, manipulation, and human-robot interaction. This survey aims to bridge the gap between the development of POMDP models and algorithms at one end and application to diverse robot decision tasks at the other. It analyzes the characteristics of these tasks and connects them with the mathematical and algorithmic properties of the POMDP framework for effective modeling and solution. For practitioners, the survey provides some of the key task characteristics in deciding when and how to apply POMDPs to robot tasks successfully. For POMDP algorithm designers, the survey provides new insights into the unique challenges of applying POMDPs to robot systems and points to promising new directions for further research., 20 pages, 10 figures. Author version of paper. Accepted for publication in IEEE Transactions on Robotics

In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult. In this paper, we introduce DROPO, a novel method for estimating domain randomization distributions for safe sim-to-real transfer. Unlike prior work, DROPO only requires a limited, precollected offline dataset of trajectories, and explicitly models parameter uncertainty to match real data using a likelihood-based approach. We demonstrate that DROPO is capable of recovering dynamic parameter distributions in simulation and finding a distribution capable of compensating for an unmodeled phenomenon. We also evaluate the method in two zero-shot sim-to-real transfer scenarios, showing successful domain transfer and improved performance over prior methods., Comment: 16 pages, 21 figures

Funding Information: T.L. acknowledges funding by European Union’s Horizon 2020 research and innovation program H2020-FETPROACT-636 2018-01 (Grant 824070). E.P. acknowledges funding from the Academy of Finland (Grants 321996 and 328854) and Jane and Aatos Erkko Foundation. The authors acknowledge the provision of facilities and technical support by Aalto University at OtaNano─Nanomicroscopy Center (Aalto-NMC) and at Micronova Nanofabrication Centre. Publisher Copyright: © 2023 The Authors. Published by American Chemical Society. | openaire: EC/H2020/824070/EU//CONNECT One of the major challenges for in vivo electrochemical measurements of dopamine (DA) is to achieve selectivity in the presence of interferents, such as ascorbic acid (AA) and uric acid (UA). Complicated multimaterial structures and ill-defined pretreatments have been frequently utilized to enhance selectivity. The lack of control over the realized structures has prevented establishing associations between the achieved selectivity and the electrode structure. Owing to their easily tailorable structure, carbon nanofiber (CNF) electrodes have become promising materials for neurobiological applications. Here, a novel yet simple strategy to control the sensitivity and selectivity of CNF electrodes toward DA is reported. It consists of adjusting the lengths of CNF by modulating the growth phase during the fabrication process while keeping the surface chemistries similar. It was observed that the sensitivity of the CNF electrodes toward DA was enhanced with the increase in the fiber lengths. More importantly, the increase in the fiber length induced (i) an anodic shift in the DA oxidation peak and (ii) a cathodic shift in the AA oxidation peak. As the UA oxidation peak remained unaffected at high anodic potentials, the electrodes with long CNFs showed excellent selectivity. Electrodes without proper fibers showed only a single broad peak in the solution of AA, DA, and UA, completely lacking the ability to discriminate DA. Hence, the simple strategy of controlling CNF length without the need to carry out any complex chemical treatments provides us a feasible and robust route to fabricate electrode materials for neurotransmitter detection with excellent sensitivity and selectivity.

Publisher Copyright: Author In this paper, a single source five-level boost inverter has been proposed and analyzed. The proposed structure includes a five-level inverter and a single-input multi-output (SIMO) boost converter. In this structure, the DC link voltage has been controlled by controlling the implemented DC-DC boost converter. In addition, the inverter switching pattern has been obtained based on the pick current control (PCC) method. In order to test and prove the performance of the proposed structure, this system is tested with the local grid. Since the active and reactive powers have been controlled based on the PCC method, the generated energy in a renewable energy source can be transferred to the local grid with a controlled and high-quality current. In this paper, the proposed structure has been introduced and its operation in different modes has been analyzed completely. Furthermore, the design considerations of the required elements have been investigated. Also, the power loss analysis and the comparison results of the proposed converter with other similar structures have been presented. Finally, in order to test the practical performance of the proposed structure and prove the theoretical analysis, a 620 W laboratory prototype of this structure has been assembled and its performance with the local grid has been tested.

Publisher Copyright: Author A lightning strike is considered one of the most risky natural phenomena that can lead to human harmful and the surrounding soil layers. To tackle this issue, this article investigates the influence of direct lightning characteristics in terms of human body safety. Specifically, such investigation is carried out on the effect of resistivities of two-layer soils on human safety when lightning stroke hits the towers of the high voltage transmission lines (HVTLs). The merit of the proposed study is that the soil ionization phenomenon is taken into consideration. Further, the study focuses on the current passing through the human heart, when step and touch (contact) voltages are generated by grounding potential rise, caused by direct lightning strikes transmission tower and the produced potential rise that a person could be exposed. Also, studying the effects of peak current and time of lightning strokes are investigated. Additionally, the paper presents the effect of different reflection factors on human safety.For validation purposes, the ATP program is used in the simulation of the grounding system as well as the human body model. Numerous simulations were accomplished in order to examine the behavior of the current passing through with the human heart. Based on the simulation results, it was concluded that the soil characteristics have superior influences on the contact and step potentials and, accordingly, the survival threshold.

Publisher Copyright: Author In this paper, we describe how the up-to-date state of a digital twin, and its corresponding simulation model, can be used as a fitness function of an evolutionary algorithm for optimizing a large-scale industrial process. An ICT architecture is presented for solving the computational challenges that arise when the fitness function evaluation takes considerable amount of time. Parallel computation of the fitness function in a cloud computing environment is proposed and the evolutionary algorithm is connected to the computational environment using the Function-as-a-Service approach. A case-study was conducted on the district heating network of Espoo, the second largest city in Finland. The study shows that the architecture is suited for optimizing the operating costs of the large district heating network, with over 800 km of water pipes and over 14 heat producers, reaching a cost-saving of an average of 2%, and up-to 4%, over the current industrial state-of-the-art method in use at the city of Espoo.

Publisher Copyright: Author Anatomical human models have been widely used in the assessment of induced field strength for low-frequency (LF) electromagnetic field exposure. One bottleneck is the assignment of a single electrical conductivity to all the voxels of the corresponding tissue. This simplification is known to cause computational artifact; therefore, a large reduction factor was considered in international guidelines and standards. Recently, head models with nonuniform conductivities generated using deep learning networks were proposed, and the effect on the reduction of staircasing artifacts was demonstrated. If the effectiveness of the models is confirmed for different models and codes, it would be useful to derive the relationship between the internal and external field strengths needed for setting the exposure limit. The Subcommittee 6 of the IEEE International Committee on Electromagnetic Safety Technical Committee 95 launched a working group to conduct the first intercomparison study of the induced electric field in learning-based head models exposed to LF magnetic fields. Seven international research groups have cooperated in this joint study. The highest relative difference (RD) in averaged electric fields was 23%, which is attributable to the difference caused the by scalar potential finite difference (SPFD) method and finite element method. Except for one group, the RDs in the 100th and 99th percentile values of the averaged electric field using the SPFD method with different solvers and codes were below 1%, indicating that the uncertainty due to different codes is sufficiently small under the same exposure scenarios. The findings would be informative for future revision of exposure limits and reduction factors in the exposure standard, which is closely related to computational uncertainty.

openaire: EC/H2020/725513/EU//SuperRepel A myriad of natural surfaces such as plant leaves and insect wings can repel water and remain unwetted inspiring scientists and engineers to develop water-repellent surfaces for various applications. Those natural and artificial water-repellent surfaces are typically opaque, containing micro- and nano-roughness, and their wetting properties are determined by the details at the actual liquid-solid interface. However, a generally applicable way to directly observe moving contact lines on opaque water-repellent surfaces is missing. Here, we show that the advancing and receding contact lines and corresponding contact area on micro- and nano-rough water-repellent surfaces can be readily and reproducibly quantified using a transparent droplet probe. Combined with a conventional optical microscope, we quantify the progression of the apparent contact area and apparent contact line irregularity in different types of superhydrophobic silicon nanograss surfaces. Contact angles near 180° can be determined with an uncertainty as low as 0.2°, that a conventional contact angle goniometer cannot distinguish. We also identify the pinning/depinning sequences of a pillared model surface with excellent repeatability and quantify the progression of the apparent contact interface and contact angle of natural plant leaves with irregular surface topography.

Partial shading (PS) conditions create challenging issues for the maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems such as getting stuck in the local maximum power points (LMPP), slow tracking time and fluctuations in the generated power during tracking the global maximum power point (GMPP). To address these issues, this paper brings contributions by proposing an improved PSO-based MPPT technique tailored for PS conditions. First, to highlight the weakness of the traditional PSO-MPPT technique, its stability and steady state behavior in the PS conditions is analyzed in depth. Second, the required criteria to achieve a stable response are obtained. Finally, a novel technique to estimate the convex area of the power versus voltage (P-V) curve is presented where the GMPP is located using two voltage boundaries. The performance of the proposed MPPT technique is experimentally validated. The results highlight the capabilities of the proposed technique in finding the GMPP with a rapid convergence and small fluctuations. The proposed MPPT is applicable to most PV inverters under any PS conditions.

Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This study focuses on the identification of aerodynamic coefficients through airdrop data based on a known six-degree-of-freedom (6-DOF) model. Since the precise modelling of the parafoil system is a prerequisite for design of guidance, navigation and control systems, the accurate calculation of aerodynamic coefficients is vital. This paper first presents a rigid 6-DOF nonlinear model and simplifies it by linearizing the equations around the equilibrium point, which facilitates the acquisition of aerodynamic parameters next. Then, the recursive weighted least square method is applied to identify roll and yaw coefficients from airdrop test data. At last, simulations in the wind environment are implemented to analyse the dynamics of gliding and turning. The airdrop experiment also verifies the effectiveness of the dynamic model and the accuracy of identification.

Lisää artikkeli, kun julkaistu. The use of multiple transmitters (Tx) has been an attractive solution for enabling free-positioning wireless power transfer (WPT) in a large area. One of the key challenges in such multi-Tx WPT systems is the cross-coupling between Tx coils. This letter proposes a compact self-decoupled Tx coil solution for multi-Tx WPT systems. The measured cross-coupling between Tx coils has been reduced by 95% compared to conventional coil designs. Additionally, an integration method for a compensation inductor with the main Tx coil is proposed. Therefore, the coil design proposed in this letter facilitates creation of fully modular and scalable Tx coils for multi-Tx WPT systems. The proposed designs are experimentally verified in a laboratory prototype.

An islanded microgrid (MG) including low-inertia converter-based distributed generations (DGs) is subjected to instability. The virtual inertia concept was proposed to alleviate the stability issues by imitating the synchronous generators behavior. This paper spotlights on the optimization of virtual synchronous generator (VSG) parameters and virtual impedances (VI) in islanded MGs using particle swarm optimization (PSO). A small-signal model for MG is developed at first. The permissible ranges of virtual inertia (J) and virtual damping (D) based on MG small-signal stability are scrutinized afterwards. Moreover, VI are considered to lower the reactive power mismatch between converters. Finally, considering the permitted intervals for these parameters, an optimization method and objective function are defined to calculate VSG parameters and VI in the islanded MG. The proposed optimization method enhances the small-signal stability of the MG, decreases the current overshoot and minimizes reactive power mismatches. Simulation results drawn by the “VSG + VI” control include three scenarios. The effectiveness of the proposed “VSG + VI” control method in comparison with “droop” control“, droop + VI”“, non-optimal VSG + VI”, and “VSG ” is verified through simulation studies.

Lisätään OA-artikkeli, kun julkaistu We address the problem of autonomous tracking and state estimation for marine vessels, autonomous vehicles, and other dynamic signals under a (structured) sparsity assumption. The aim is to improve the tracking and estimation accuracy with respect to the classical Bayesian filters and smoothers. We formulate the estimation problem as a dynamic generalized group Lasso problem and develop a class of smoothing-and-splitting methods to solve it. The Levenberg-Marquardt iterated extended Kalman smoother-based multiblock alternating direction method of multipliers (LM-IEKS-mADMMs) algorithms are based on the alternating direction method of multipliers (ADMMs) framework. This leads to minimization subproblems with an inherent structure to which three new augmented recursive smoothers are applied. Our methods can deal with large-scale problems without preprocessing for dimensionality reduction. Moreover, the methods allow one to solve nonsmooth nonconvex optimization problems. We then prove that under mild conditions, the proposed methods converge to a stationary point of the optimization problem. By simulated and real-data experiments, including multisensor range measurement problems, marine vessel tracking, autonomous vehicle tracking, and audio signal restoration, we show the practical effectiveness of the proposed methods.

Tallennetaan OA-artikkeli, kun julkaistu This paper considers distributed estimation methods to enable the formation of Unmanned-Aerial-Vehicles (UAVs) that track a moving target. The UAVs (or agents) are equipped with communication devices to receive a beacon signal from the target and share information with neighboring UAVs. The shared information includes the time-of-arrival (TOA) of the beacon signal from the target and estimates on the target's location. Every UAV processes the received information from the neighbors using a single-time-scale distributed estimation protocol. This differs from multi-time-scale protocols that require (i) many consensus iterations on a-priori estimates, (ii) fast communication among agents (in general, much faster than the sampling rate of the target dynamics), and thus, more-costly communication equipment and processing units. Further, our approach outperforms most single-time-scale methods in terms of observability assumption as these methods assume that the target is observable via the measurement data receivedfrom neighboring UAVs (referred to as local-observability). This requires more communications among the sensors. In contrast, our approach is only based on global-observability assumption, and thus, requires less networking (only strong-connectivity) and communication traffic along with less computational load by data-processing once at the same time-scale of sampling target dynamics. We consider modified time-difference-of-arrival (TDOA) measurements with a constant output matrix for the linearized model. UAVs make a pre-specified formation, and by estimating the target's location via these measurements, move along with the target.

Publisher Copyright: © 2022, The Author(s). Nanocellulose has emerged as a promising green dispersant for carbon nanotubes (CNTs), and there is an increasing trend in developing nanocellulose/CNT hybrid materials for electrochemical detection of various small molecules. However, there have been very few comprehensive studies investigating the role of nanocellulosic material properties upon the electroanalytical performance of the resultant hybrid electrodes. In this work, we demonstrate the influence of both nanocellulose functionalization and geometry, utilizing sulfated cellulose nanocrystals, sulfated cellulose nanofibers, and TEMPO-oxidized cellulose nanofibers. Transmission electron microscopy tomography enables direct visualization of the effect of nanocellulosic materials on the hybrid architectures. High resolution X-ray absorption spectroscopy verifies that the chemical nature of CNTs in the different hybrids is unmodified. Electroanalytical performances of the different nanocellulose/CNT hybrid electrodes are critically evaluated using physiologically relevant biomolecules with different charge such as, dopamine (cationic), paracetamol (neutral), and uric acid (anionic). The hybrid electrode containing fibrillar nanocellulose geometry with a high degree of sulfate group functionalization provides the highest electroanalytical sensitivity and strongest enrichment towards all studied analytes. These results clearly demonstrate for the first time, the extent of tailorability upon the electroanalytical response of nanocellulose/CNT hybrid electrodes towards different biomolecules, offered simply by the choice of nanocellulosic materials.

Lisäää oa-julkaisu, kun saatavilla. Recently, due to the restructuring of power systems and the high penetration level of local renewables, distribution systems have encountered with the complexity of power management. Therefore, the modern systems would be operated in a multi-agent structure which facilitates the power management as well as privacy protections of independent entities. In this structure, the distribution system is assumed to compose of several agents who independently schedule their local resources in order to maximize their own profits. Consequently, this paper provides an efficient peer-to-peer (P2P) active power management framework in a multi-agent distribution system while considering network constraints (i.e., line loadings and losses). In this context, in the proposed P2P scheme, the distribution system operator (DSO) model the network constraints in the form of line-usage costs within the transactive signals. Respectively, the developed transactive control signals enable the DSO to model the power loss as well as alleviate the congestion in the grid. Therefore, the agents automatically consider the network constraints in their power transactions management procedure without any direct interferences of the DSO in their resource scheduling. Finally, the proposed model is implemented on the modified-IEEE-37-bus-test system in order to investigate its effectiveness in the energy management of multi-agent systems.

Funding Information: This study was supported by the Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, Espoo, Finland. Publisher Copyright: © 2022 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd. Recently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility-owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost-effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day-ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self-adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K-means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE-24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near-optimal day-ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR.

Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches require precise mathematical models of the system dynamics, which may not always be available. Model-free learning of communication and control policies provides an alternative. Nevertheless, existing methods typically consider single-agent settings. This paper proposes a model-free reinforcement learning algorithm that jointly learns resource-aware communication and control policies for distributed multi-agent systems from data. We evaluate the algorithm in a high-dimensional and nonlinear simulation example and discuss promising avenues for further research., Comment: Accepted final version to appear in Proc. of the Conference on Learning for Dynamics and Control, 2023

We study the convergence of a family of numerical integration methods where the numerical integral is formulated as a finite matrix approximation to a multiplication operator. For bounded functions, the convergence has already been established using the theory of strong operator convergence. In this article, we consider unbounded functions and domains which pose several difficulties compared to the bounded case. A natural choice of method for this study is the theory of strong resolvent convergence which has previously been mostly applied to study the convergence of approximations of differential operators. The existing theory already includes convergence theorems that can be used as proofs as such for a limited class of functions and extended for wider class of functions in terms of function growth or discontinuity. The extended results apply to all self-adjoint operators, not just multiplication operators. We also show how Jensen's operator inequality can be used to analyse the convergence of an improper numerical integral of a function bounded by an operator convex function., Comment: 40 pages, 10 figures

An air–liquid interface is important in many biological and industrial applications, where the manipulation of liquids on the air–liquid interface can have a significant impact. However, current manipulation techniques on the interface are mostly limited to transportation and trapping. Here, we report a magnetic liquid shaping method that can squeeze, rotate, and shape nonmagnetic liquids on an air–ferrofluid interface with programmable deformation. We can control the aspect ratio of the ellipse and generate repeatable quasi-static shapes of a hexadecane oil droplet. We can rotate droplets and stir liquids into spiral-like structures. We can also shape phase-changing liquids and fabricate shape-programmed thin films at the air–ferrofluid interface. The proposed method may potentially open up new possibilities for film fabrication, tissue engineering, and biological experiments that can be carried out at an air–liquid interface.

Funding Information: Financial support was obtained from the Jane and Aatos Erkko Foundation and ACCC flagship “Finnish Research Flagship funded by the Academy of Finland (grant no. 337549 ). We thank Eka Dian Pusfitasari, Meri Sillanpää, Maria Andersson, Giorgia Demaria, Hangzen Lan, Nicola Zanca and Ilmari Heiskanen for their cooperation and valuable help. Publisher Copyright: © 2023 The Authors Solid phase microextraction Arrow and thermal desorption–gas chromatography–mass spectrometry allowed the collection and evaluation of volatile organic compounds (VOCs) emitted by fungal cultures from building insulation materials and in indoor air. Principal component analysis, linear discriminant analysis and supported vector machine were used for visualization and statistical assessment of differences between samples. In addition, a screening tool based on the soft independent modelling of class analogies (SIMCA) was developed for identification of fungal contamination of indoor air. Ten different fungal strains, incubated under ambient and microaerophilic conditions, were analyzed for time period ranging from 5 to 29 days after inoculation resulting in a total of 140 samples. In addition, the effect of additives on the fungal growing media was studied. The total number of compounds and concentration values were used for the evaluation of the results. Clear differences were observed for VOC profiles emitted by different fungal strains by exploiting long chain alcohols (3-octanol, 1-hexanol and 2-octen-1-ol) and sesquiterpenes (farnesene, cuprene). The analysis of glass-wool and cellulose based building insulation materials (3 samples) gave clear differences, mainly for oxygenated compounds (ethyl acetate and hexanal) and benzenoids (benzaldehyde). Moreover, the comparison of indoor air and insulation materials collected from a house with fungal indoor air problems indicated that 42% of the VOCs were found in both samples. The analysis of 52 indoor air samples demonstrated clear differences in their VOC profiles, especially for hydrocarbons, and between control (44 samples) and indoor air problem houses (8 samples). Finally, the SIMCA model enabled to recognize differences between control and fungi contaminated houses with a prediction capacity over 84%.

Funding Information: Entrepreneurship and Innovation Plan of Jiangsu Province, Grant/Award Number: JSSCBS20210641; National Natural Science Foundation of China, Grant/Award Number: 62103293; Natural Science Foundation of Jiangsu Province, Grant/Award Number: BK20210709; Suzhou Municipal Science and Technology Bureau, SYG202138 Funding information Funding Information: This research article was invested by National Natural Science Foundation of China under Grant 62103293, Natural Science Foundation of Jiangsu Province under Grant BK20210709, Entrepreneurship and Innovation Plan of Jiangsu Province under Grant JSSCBS20210641, and Suzhou Municipal Science and Technology Bureau under Grant SYG202138. Publisher Copyright: © 2022 John Wiley & Sons Ltd. The repetitive tracking task for time-varying systems (TVSs) with non-repetitive time-varying parameters at each trial, which is also called non-repetitive TVSs, is realized in this article using iterative learning control (ILC). A machine learning (ML) based nominal model update mechanism, which utilizes the linear regression technique to update the nominal model at each ILC trial using only the current trial information, is proposed for non-repetitive TVSs in order to enhance the ILC performance. Given that the ML mechanism forces the model uncertainties to remain within the ILC robust tolerance, an ILC update law is proposed to deal with non-repetitive TVSs. How to tune parameters inside ML and ILC algorithms to achieve the desired aggregate performance is also provided. The robustness and reliability of the proposed method are verified by real experiments. Real data comparison with current state-of-the-art methods demonstrates its superior control performance in terms of controlling precision. This article broadens ILC applications from time-invariant systems to non-repetitive TVSs, adopts ML regression technique to estimate non-repetitive time-varying parameters between two ILC trials and proposes a detailed parameter tuning mechanism to achieve desired performance, which are the main contributions.

Publisher Copyright: © 2022 Currently, distribution systems are continuously evolving towards modern and flexible structures while integrating promising renewable energy resources (RERs). In this regard, an optimal network constraint-based expansion planning model combined with an optimal integration framework of intermittent RERs is proposed in this work to improve the topological flexibility in modern distribution networks (DNs). Specifically, the best investment locations and times of substations, lines, and RER-based distributed generations (DGs) are jointly taken into consideration. Additionally, the uncertainty-based multiple scenarios are modeled by probability distribution functions to strengthen the robustness and reliability of DNs influenced by the stochastic of renewable energy and load behavior. The novel network constraint is combined with three levels, where the first level introduces the graph theory to guarantee the radiality topology of modern DNs. In the second level of the network constraint, graph theory and fictitious load theory are collaboratively applied to ensure that each subsystem has a reserve connection interconnected to other subsystems. The third level is modifying the conventional fictitious load theory to ensure each subsystem is linked with at least one DG. The proposed planning model is driven by the minimum present value of total cost, including investment cost of branches, DGs, and substations, cost of substations operation, the electricity purchasing cost of substations and DGs, power losses cost, and environmental penalty cost of conventional generators. Numerical results are presented to verify that a more flexible and resilient topology of the DN system is obtained, and criteria evaluation is introduced to validate its higher performance with respect to existing procedures from power supplied quality, environmental burden, and supplied flexibility three aspects.

openaire: EC/H2020/856602/EU//FINEST TWINS For platoons under the multiple-predecessor following (MPF) topology, communication delays can compromise both the internal stability and string stability. The most straightforward solution to guarantee stability is by increasing the time headway. However, time headway plays a significant role in road capacity and increasing its value is in contrast with the idea of platooning. In this study, internal stability and string stability of platoons suffering from communication delays are investigated and a lower bound for the time headway is proposed. Using this bound, platoons do not need to massively increase the time headway in order to compensate for the effects of communications delays. Finally, we evaluate the proposed lower bound on the time headway and the simulation results demonstrate its effectiveness.

Funding Information: This work was supported in part by INSF. Publisher Copyright: © 2022 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. Increasing the intermittent outputs of renewable energy sources (RESs) has forced planners to define a new concept named flexibility. In this regard, some short- and long-term solutions, such as transmission expansion planning (TEP) and energy storage systems (ESSs) have been suggested to improve the flexibility amount. A proper optimization procedure is required to choose an optimal solution to improve flexibility. Therefore, a mixed-integer linear programming (MILP) direct-optimization TEP versus ESSs co-planning model is presented in this paper to enhance power system flexibility. In doing so, a novel RES-BESS-based grid-scale system flexibility metric is proposed to investigate the improvement of flexibility amount via ESSs modules in the numerical structure. In this paper, a novel repetitive fast offline method has been proposed to quickly reach the desired amount of flexibility by defining an engineering price/benefit trade-off to finally find the best investment plan. Also, multiple uncertainties associated with wind farms and demanded loads and a practical module-type battery energy storage system (BESS) structure for each node are defined. The proposed model is applied to the modified IEEE 73-bus test system including wind farms, where the numerical results prove the model efficiency as BESS impacts on flexibility, investment plans and power system economics.

Publisher Copyright: © 2022 Acoustical Society of America. The purpose of this study is to model a circular planar loudspeaker placed near a spherical reflector to broaden its directivity pattern, which would otherwise become increasingly narrow at high frequencies. Through ray tracing, it seems intuitively feasible to thus create a virtual point source at very high frequencies, but we provide a more rigorous analysis to determine what will happen at intermediate frequencies where the wavelength is of a similar magnitude to the diameter of the disk or sphere. We show that a smoother off-axis response is obtained with a dipole pressure source, which does not obstruct the scattered sound, rather than a monopole velocity source. Hence, an electrostatic loudspeaker, for example, would be more suitable than a dynamic one. The sphere may also serve as a spherical approximation of a human head, in which case the loudspeaker would become an open headphone that is not sealed to the ear.

Lately, the integration of renewable energy sources (e.g. photovoltaic and wind generation systems) has been raised into microgrids interconnected with plug-in electric vehicles (PEV). Such intermittent generation and charging/discharging PEV profiles are challenging to ensure the secure and optimal operation of microgrids. In this paper, an optimization approach is proposed to determine the optimal locations and sizes of photovoltaic and wind generation systems in microgrids with PEV-parking lots. The developed approach addresses 1) the uncertainty of generation profiles of photovoltaic and wind generation systems and loads, 2) the DSTATCOM feature of photovoltaic and wind generation systems, and 3) microgrid constraints. The feasible PEV conditions are also considered, i.e. initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. To solve the planning model, we have developed a bi-level metaheuristic-based approach. Theupper-level and lower-level optimization tasks are to optimize the decision variables of renewable energy sources and PEV, respectively. Both energy losses through the lines and energy from the main grid are considered as sub-objectives to be minimized. Various simulations and study cases are performed to assess the effectiveness of the proposed approach. The outcomes show the efficacy of the proposed approach to simultaneously plan renewable energy sources and manage PEV to form an autonomous microgrid.

Publisher Copyright: © 2023 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd. Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand-side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance-Level ElectricityUK-DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

Recently, the regulation of photovoltaic inverters, effectively under imbalanced voltages on the grid, has been crucial for the operation of grid-connected solar systems. In this regard, determining the output current reference is an integral aspect of managing a solar inverter with an unbalanced voltage. Based on evaluations of Instantaneous Active-reactive Control (IARC), Positive Negative Sequence Control (PNSC), Balanced Positive Sequence Control (BPSC), and Average Active-reactive Control (AARC), this paper proposes a novel variable-current-t-reference calculation method for minimizing power fluctuations and current harmonics. The controller is employed to regulate both constructive and destructive sequences inside a static framework, therefore enhancing dynamic performance and facilitating the selection of suitable controls in the presence of significant network defects. This study also suggests comparing the four MPPT methodologies examined (fuzzy logic, current only, Incremental Conductance, and Perturb & Observe) to maximize energy output. The simulation results efficiently validate the suggested computation approach that is presented in the current reference.

Funding Information: This study was partially supported by FISM with a research grant (FISM2018/R/5), and financed or co‐financed with the “5 per mille” public funding. Publisher Copyright: © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. Multiple sclerosis (MS) is a neurological condition characterized by severe structural brain damage and by functional reorganization of the main brain networks that try to limit the clinical consequences of structural burden. Resting-state (RS) functional connectivity (FC) abnormalities found in this condition were shown to be variable across different MS phases, according to the severity of clinical manifestations. The article describes a system exploiting machine learning on RS FC matrices to discriminate different MS phenotypes and to identify relevant functional connections for MS stage characterization. To this end, the system exploits some mathematical properties of covariance-based RS FC representation, which can be described by a Riemannian manifold. The classification performance of the proposed framework was significantly above the chance level for all MS phenotypes. Moreover, the proposed system was successful in identifying relevant RS FC alterations contributing to an accurate phenotype classification.

J.K. and M.A.C. acknowledge funding from the Academy of Finland under the C1 Value Programme, Project No. 329483. M.A.C. also acknowledges personal funding from the Academy of Finland, Project No. 330488. H.J. acknowledges funding from the Icelandic Research Fund, Project No. 207283-053. L.B.P. acknowledges support from the EPSRC through an Early Career Fellowship (Grant No. EP/T000163/1). Computational resources for this project were obtained from the CSC—IT Center for Science and Aalto University’s Science-IT project. A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and nanostructured platinum, in particular nanoparticles. Across the range of tested properties, which include bulk elasticity, surface energetics, and nanoparticle stability, this potential shows excellent transferability and agreement with DFT, providing state-of-the-art accuracy at a low computational cost. We showcase the possibilities for modeling of Pt systems enabled by this potential with two examples: the pressure-temperature phase diagram of Pt calculated using nested sampling and a study of the spontaneous crystallization of a large Pt nanoparticle based on classical dynamics simulations over several nanoseconds.

Publisher Copyright: © 2023 The Authors. Energy Science & Engineering published by Society of Chemical Industry and John Wiley & Sons Ltd. For the past few years, the world has seen a great shift toward renewable energy resources from conventional ones. But the ever-increasing integration of distributed generation (DG) to the electrical network leads to integration limiting constraints like overvoltage, under voltage, harmonics, equipment ampacity violations, and failure of protection schemes. Therefore, an extensive investigation of the methodologies in which DGs can be incorporated into the electrical network is presented in this manuscript. This article provides an extensive review of all the hosting capacity (HC) terms, references, limiting constraints of the studied networks, geographical segregation, and their determination methodologies. Moreover, the factors defining the HCs of various networks and the architectures employed to increase them, are also explained briefly in the conducted review study.

The multiple-predecessor following (MPF) topology is used in vehicle platoons to make it robustly string stable and reduce the minimum employable time headway. It has been demonstrated that communication imperfections such as time delays coming from wireless communications can affect string stability as well as the minimum time headway required to guarantee string stability. Specifically, it was shown that the larger the time delay, the longer the minimum time headway will be. However, by utilizing on-board vehicle sensors, such as radar, lidar and cameras, the distance and speed of nearby vehicles can be measured almost instantaneously, i.e., with almost no delay. Another effective parameter on string stability and minimum time headway is the heterogeneity of the vehicles. Due to the immense complexity of the MPF topology, string stability analysis of this topology in literature has been confined to homogeneous platoons. In this paper, we consider the case of heterogeneous platoons under the MPF topology with the use of the combination of sensors and wireless communications for receiving information. Following that, we find conditions to guarantee the internal and string stability for the heterogeneous case and propose the minimum time headway required to guarantee string stability. Finally, we provide a table, in which we propose the minimum time headway for two other wireless communication scenarios as well: (i) having no communication delay and (ii) having fully-delayed information, i.e., all information, whether it comes from the ego vehicle or its predecessors, is delayed. In addition to exploring the analysis of string stability for the vehicles with more possible connections (vehicles after the $r^{th}$ vehicle, when information from $r$ immediate vehicles is used), we study the string stability conditions (with which we aim at avoiding collisions) and find the minimum time headway for the first few vehicles (vehicle $r$ and all its predecessors). Numerical results clearly show the effectiveness of the proposed lower bounds.

openaire: EC/H2020/826653/EU//NewControl The measurement of in-plane motion in microelectromechanical systems (MEMS) is a challenge for existing measurement techniques due to the small size of the moving devices and the low amplitude of motion. This paper studied the possibility of using images obtained using a scanning electron microscope (SEM) together with existing motion detection algorithms to characterize the motion of MEMS. SEM imaging has previously been used to detect motion in MEMS device. However, the differences in how SEM imaging and optical imaging capture motion, together with possible interference caused by electrical actuation, create doubts about how accurately motion could be detected in a SEM. In this work, it is shown that existing motion detection algorithms can be used to detect movement with an amplitude of 69 nm. In addition, the properties of SEM images, such as bright edges, complement these algorithms. Electrical actuation was found to cause error in the measurement, however, the error was limited to regions that were electrically connected to the actuating probes and minimal error could be detected in regions that were electrically insulated from the probes. These results show that an SEM is a powerful tool for characterizing low amplitude motion and electrical contacts in MEMS and allow for the detection of motion under 100 nm in amplitude.

The progression of breast cancer involves cancer-cell invasions of extracellular matrices. To investigate the progression, 3D cell cultures are widely used along with different types of matrices. Currently, the matrices are often characterized using parallel-plate rheometry for matrix viscoelasticity, or liquid-like viscous and stiffness-related elastic characteristics. The characterization reveals averaged information and sample-to-sample variation, yet, it neglects internal heterogeneity within matrices, experienced by cancer cells in 3D culture. Techniques using optical tweezers and magnetic microrheometry have measured heterogeneity in viscoelasticity in 3D culture. However, there is a lack of probabilistic heterogeneity quantification and cell-size-relevant, microscale-viscoelasticity measurements at breast-tumor tissue stiffness up to ≃10 kPa in Young’s modulus. Here, we have advanced methods, for the purpose, which use a magnetic microrheometer that applies forces on magnetic spheres within matrices, and detects the spheres displacements. We present probabilistic heterogeneity quantification using microscale-viscoelasticity measurements in 3D culture matrices at breast-tumor-relevant stiffness levels. Bayesian multilevel modeling was employed to distinguish heterogeneity in viscoelasticity from the effects of experimental design and measurement errors. We report about the heterogeneity of breast-tumor-relevant agarose, GrowDex, GrowDex–collagen and fibrin matrices. The degree of heterogeneity differs for stiffness, and phase angle (i.e. ratio between viscous and elastic characteristics). Concerning stiffness, agarose and GrowDex show the lowest and highest heterogeneity, respectively. Concerning phase angle, fibrin and GrowDex–collagen present the lowest and the highest heterogeneity, respectively. While this heterogeneity information involves softer matrices, probed by ≃30 μm magnetic spheres, we employ larger ≃100 μm spheres to increase magnetic forces and acquire a sufficient displacement signal-to-noise ratio in stiffer matrices. Thus, we show pointwise microscale viscoelasticity measurements within agarose matrices up to Young’s moduli of 10 kPa. These results establish methods that combine magnetic microrheometry and Bayesian multilevel modeling for enhanced heterogeneity analysis within 3D culture matrices.

Publisher Copyright: © 2023 The Authors. Energy Science & Engineering published by Society of Chemical Industry and John Wiley & Sons Ltd. As the load on distribution networks grows, system operators and planners are constantly challenged with the issue of voltage regulation or enhancing the quality of supply to customers at the load end of lengthy distribution lines. This paper presents the optimum determination of series capacitor units in a distribution system to maximize energy-saving and enhance voltage levels. Interestingly, series capacitors can enhance the capability of transmission lines, reduce line losses, enhance the performance of buses with large induction motor loads and reduce voltage flicker. At the same time, the limitations of series compensation are taken into consideration while calculating its optimum values. To achieve the planning objective and optimal load flow objective, two strategies: The Improved Grey Wolf Optimization method (I-GWO) and Tabu Search (TS), are hybridized to get the benefit of their advantages. The I-GWO has a movement strategy called dimension learning-based hunting for enhancing the balance between global and local search and maintaining diversity. The proposed (I-GWO-TS) algorithm can solve mixed-integer programming to achieve the planning and the optimal load flow objectives. The proposed method can be applied to a real Egyptian distribution system that is heavily loaded, with poor voltage regulation, and also has high-power losses. The obtained results demonstrate the capability of the proposed approach to determine optimal series capacitors’ location and sizing for maximization of energy saving. Further, the proposed method improves the network performance regarding the voltage profile and power losses, although the limitations of including series compensation were considered in the distribution system.

Publisher Copyright: IEEE With growing interest in green technologies and sustainable transport, hybrid and pure electric powertrains are attracting a lot of attention. According to demanded metrics, such as lightweight and high-power density motors, these offer clear motivation to develop advanced thermal management methods and integrate them with conventional cooling approaches. The oil spray cooling method is one of the latest cooling methodologies integrated with the conventional cooling approaches. Therefore, this article provides a comprehensive study on the oil spray cooling approach for electrical motors utilized in electric vehicle (EV) traction application drives. The objective is to provide the reader with a picture of the state of the art regarding the opportunities, challenges, and offered solutions for the cooling requirements in EV motors. In addition, this article presents the guidelines for selecting the appropriate spray nozzles and coolants as well as estimating their performance, spray characteristics, and heat transfer coefficient (HTC) during the design period. Besides, various closed-loop systems of spray cooling approach, including refrigerants as a coolant, low viscosity liquid coolant with high boiling temperature, and high viscosity lubricant oil as a coolant, are discussed with the pros and cons of each system being compared. Moreover, real-life examples of this method, applied in the latest automotive traction motor prototypes, have been described and evaluated. Finally, the performance of different schemes is addressed.

Funding Information: This study was funded by the Victorian Higher Education State Investment Fund (VHESIF) for “Electrifying Victoria’s Future Fleet: Barriers and Opportunities” and the La Trobe University Net Zero Carbon Emissions Project. Publisher Copyright: © 2023 by the authors. Electric vehicles (EVs) are advancing the transport sector towards a robust and reliable carbon-neutral future. Given this increasing uptake of EVs, electrical grids and power networks are faced with the challenges of distributed energy resources, specifically the charge and discharge requirements of the electric vehicle infrastructure (EVI). Simultaneously, the rapid digitalisation of electrical grids and EVs has led to the generation of large volumes of data on the supply, distribution and consumption of energy. Artificial intelligence (AI) algorithms can be leveraged to draw insights and decisions from these datasets. Despite several recent work in this space, a comprehensive study of the practical value of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability and charge optimisation of the EVI has not been formally investigated. The objective of this study was to design, develop and evaluate a comprehensive AI framework that addresses this gap in EVI. Results from theempirical evaluation of this AI framework on a real-world EVI case study confirm its contribution towards addressing the emerging challenges of distributed energy resources in EV adoption.

Publisher Copyright: IEEE Objective: The objective clinical evaluation of user's capabilities to handle their prosthesis is done using various tests which primarily focus on the task completion speed and do not explicitly account for the potential presence of compensatory motions. Given that the excessive body compensation is a common indicator of inadequate prosthesis control, tests which include subjective observations on the quality of performed motions have been introduced. However, these metrics are then influenced by the examiner's opinions, skills, and training making them harder to standardize across patient pools and compare across different prosthetic technologies. Here we aim to objectively quantify the severity of body compensations present in myoelectric prosthetic hand users and evaluate the extent to which traditional objective clinical scores are still able to capture them. Methods: We have instructed 9 below-elbow prosthesis users and 9 able-bodied participants to complete three established objective clinical tests: Box-and-Blocks-Test, Clothespin-Relocation-Test, and Southampton-Hand-Assessment-Procedure. During all tests, upper-body kinematics has been recorded. Results: While the analysis showed that there are some correlations between the achieved clinical scores and the individual body segment travel distances and average speeds, there were only weak correlations between the clinical scores and the observed ranges of motion. At the same time, the compensations were observed in all prosthesis users and, for the most part, they were substantial across the tests. Conclusion: The sole reliance on the currently available objective clinical assessment methods seems inadequate as the compensatory movements are prominent in prosthesis users and yet not sufficiently accounted for.

Publisher Copyright: © 2013 IEEE. The reliability of power electronic converters is a major concern in industrial applications because of using prone-to-failure elements such as high-power semiconductor devices and electronic capacitors. Hence, designing fault-tolerant inverters has been of great interest among researchers in both academia and industry over the last decade. Among the three stages of fault management, compensating the fault is the most important and challenging part. The techniques for fault compensation can be classified into three groups: hardware redundancy methods which use extra switches, legs, or modules to replace the faulty parts directly or indirectly, switching states redundancy methods which are about omitting and replacing the impossible switching states, and unbalance compensation including the techniques to compensate for the unbalances in the system caused by a fault. In this paper, an overview of fault-tolerant inverters is presented. A classification of fault-tolerant inverters is demonstrated and major cases in each of its categories are explained.

Publisher Copyright: © 2022 Elsevier Ltd In Smart Cities (SC), the efficient management of services such as health, transport, public safety, and especially the electricity ensures the welfare of citizens. In recent years, the insertion of renewable sources (RSs) (e.g., solar and wind) in the power grid (PG) of SCs has contributed to meeting the electricity needs of the various consumer units. However, the large-scale integration of these RSs can fatigue the assets, leading to their premature aging and, consequently, compromising the quality of electricity supply. To overcome these challenges, the implementation of Neighboring Energy Storage Communities (NESCs) employing demand response (DR) strategies along with efficient coordination of storage batteries (SBs) could be a promising alternative. In this sense, the present work proposes a mixed-integer linear programming (MILP) model to efficiently manage SBs and the set of household appliances, including charging electric vehicles (EVs), in an NESC provided solely by PG. The proposed model aims to minimize: the total costs related to energy consumption, the peak rebound effect on the total consumption profile, energy wastage through load factor (LF) improvement, and the deep discharges in the SBs during their daily operational cycle. Operational constraints related to the home appliances, such as average usage time, the number of times that the appliance is used daily, etc., are taking into account. The EV state-of-charge (SOC), EV charging rate limits, and initial and final SOC of the SBs, are also considered. A Monte Carlo Algorithm (MCA) is used to simulate the habitual consumption patterns of each customer. The proposed model was implemented in AMPL and solved using CPLEX. The performance of this proposed model is evaluated considering two NESCs differentiated by the number of consumer communities. A first NESC (small-scale) is analyzed considering only two consumer communities. In this NESC, two case studies (Case 1 and 2) are discussed. Next, the second NESC (large-scale) that considers 14 consumer communities is analyzed for the most complete case study (Case 2). Within each NESC, consumer communities are differentiated by the household income and the types of SBs (individual and shared) that support each community. The results corroborate the applicability of the MILP model to real case studies on a diverse scale, guaranteeing the efficient use of PG at the same time that each SB seeks the most optimized operation.

Publisher Copyright: Author Manipulating unknown objects in a cluttered environment is difficult because segmentation of the scene into objects, that is, object composition, is uncertain. Due to the uncertainty, prior work has either identified the "best" object composition and decided on manipulation actions accordingly or tried to greedily gather information about the "best" object composition. We instead, first, use different possible object compositions in planning, second, utilize object composition information provided by robot actions, third, consider the effect of competing object hypotheses on the desired task. We cast the manipulation planning problem as a partially observable Markov decision process (POMDP) that plans over possible object composition hypotheses. The POMDP chooses the action that maximizes long-term expected task-specific utility, and while doing so, considers informative actions and the effect of different object hypotheses on succeeding in the task. In simulation and physical robotic experiments, a probabilistic approach outperforms using the most likely object composition, and long term planning outperforms greedy decision making.