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In this paper, we study fundamental trade-offs in privacy-preserving biometric identification systems with noisy enrollment. The proposed identification systems include helper data, secret keys, and private keys. Helper data are stored in a public database and used for identification. Secret keys are either stored in a secure database or provided to the user, and can be used in a next step, e.g. for authentication. Private keys are provided by users, and are also used for identification. In this paper, we impose a noisy enrollment channel and an arbitrarily small privacy and secrecy leakage rate. We characterize the optimal trade-off among the identification, secret key, private key, and helper data rates. Depending on how secret keys are produced, we study two cases of the proposed privacy-preserving identification systems, where the secret keys are generated and chosen respectively. By introducing private keys, it is shown that the identification system achieves close to zero privacy leakage rate in both generated and chosen secret key settings. The results also show that the identification rate and the secret key rate can be enlarged by increasing the private key rate. This work provides a framework for analyzing privacy-preserving identification systems and an insight on the design of optimal systems. QC 20210710

In model predictive control ( MPC ), the control input is computed by solving a constrained finite-time optimal control ( CFTOC ) problem at each sample in the control loop. The main computational effort when solving the CFTOC problem using an active-set ( AS ) method is often spent on computing the search directions, which in MPC corresponds to solving unconstrained finite-time optimal control ( UFTOC ) problems. This is commonly performed using Riccati recursions or generic sparsity exploiting algorithms. In this paper, the focus is efficient search direction computations for AS type methods. The system of equations to be solved at each AS iteration is changed only by a low-rank modification of the previous one, and exploiting this structured change is important for the performance of AS -type solvers. In this paper, theory for how to exploit these low-rank changes by modifying the Riccati factorization between AS iterations in a structured way is presented. A numerical evaluation of the proposed algorithm shows that the computation time can be significantly reduced by modifying, instead of re-computing, the Riccati factorization. This speedup can be important for AS -type solvers used for linear, nonlinear, and hybrid MPC .

This paper presents a passivity-based analysis and damping method for the low-frequency dynamics of grid-forming (GFM) control. A closed-form analytical solution to the positive realness of control input admittance is developed, which reveals the inherent non-passive property of GFM control. This feature gives rise to the risk of instability, especially in connection with a nearby non-passive GFM inverter, which may induce undesired control interactions. It is further revealed that the passivity index is determined by a low-frequency resonance around 10 Hz in the dq-frame, and this resonance can be better dampened, therefore with enhanced passivity index, by using a virtual reactance than using a virtual resistance. Finally, the theoretical findings are verified by the experimental results.

Automatic image detection is one of the most im- portant areas in computing due to its potential application in numerous real-world scenarios. One important tool to deal with that is called overlap indices. They were introduced as a procedure to provide the maximum lack of knowledge when comparing two fuzzy objects. They have been successfully applied in the following fields: image processing, fuzzy rule-based systems, decision making and computational brain interfaces. This notion of overlap indices is also necessary for applications in which type-2 fuzzy sets are required. In this paper we introduce the notion of type-(2, k) overlap index (k 0, 1, 2) in the setting of type-2 fuzzy sets. We describe both the reasons that have led to this notion and the relationships that naturally arise among the algebraic underlying structures. Finally, we illustrate how type- (2, k) overlap indices can be employed in the setting of fuzzy rule-based systems when the involved objects are type-2 fuzzy sets. This manuscript has been partially supported by Junta de Andalucía by Projects A-FQM-170-UGR20 (Program FEDER Andalucía 2014-2020) and FQM-365 (Andalusian CICYE), and also by Projects PID2020-119478GBI00 and PID2019-108392GB-I00 (AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación), by CNPq (301618/2019-4), FAPERGS (19/2551- 0001660) and grant VEGA 1/0267/21.

Copy prediction is a renowned category of prediction techniques in video coding where the current block is predicted by copying the samples from a similar block that is present somewhere in the already decoded stream of samples. Motion-compensated prediction, intra block copy, template matching prediction etc. are examples. While the displacement information of the similar block is transmitted to the decoder in the bit-stream in the first two approaches, it is derived at the decoder in the last one by repeating the same search algorithm which was carried out at the encoder. Region-based template matching is a recently developed prediction algorithm that is an advanced form of standard template matching. In this method, the reference area is partitioned into multiple regions and the region to be searched for the similar block(s) is conveyed to the decoder in the bit-stream. Further, its final prediction signal is a linear combination of already decoded similar blocks from the given region. It was demonstrated in previous publications that region-based template matching is capable of achieving coding efficiency improvements for intra as well as inter-picture coding with considerably less decoder complexity than conventional template matching. In this paper, a theoretical justification for region-based template matching prediction subject to experimental data is presented. Additionally, the test results of the aforementioned method on the latest H.266/Versatile Video Coding (VVC) test model (version VTM-14.0) yield an average Bjøntegaard-Delta (BD) bit-rate savings of -0.75% using all intra (AI) configuration with 130% encoder run-time and 104% decoder run-time for a particular parameter selection.

In contrast to their seemingly simple and shared structure of independence and stationarity, L\'evy processes exhibit a wide variety of behaviors, from the self-similar Wiener process to piecewise-constant compound Poisson processes. Inspired by the recent paper of Ghourchian, Amini, and Gohari (2018), we characterize their compressibility by studying the entropy of their double discretization (both in time and amplitude) in the regime of vanishing discretization steps. For a L\'evy process with absolutely continuous marginals, this reduces to understanding the asymptotics of the differential entropy of its marginals at small times, for which we obtain a new local central limit theorem. We generalize known results for stable processes to the non-stable case, with a special focus on L\'evy processes that are locally self-similar, and conceptualize a new compressibility hierarchy of L\'evy processes, captured by their Blumenthal-Getoor index., Comment: 34 pages, 1 figure

The privatization of distribution systems has resulted in the development of multiple-microgrid (multiple-MG) systems where each microgrid independently operates its local resources. Moreover, the high integration of independent distributed energy sources could lead to operational issues such as grid congestion in future distribution systems. Therefore, this paper provides a transactive-based energy management framework to operate multiple-MG distribution systems; while, alleviating grid congestion in a decentralized manner. In this respect, alternating direction method of multipliers (ADMM) is considered to develop an operational framework that copes with distributed nature of multiple-MG systems. In this context, a novel procedure in the context of ADMM is proposed to distributedly determine transactive coordinator signals which address energy prices as well as power losses and grid congestions. Furthermore, each MG takes into account stochastic programming and the conditional value-at-risk index to handle the uncertainty of its operational scheduling. At last, the proposed framework is applied on IEEE 37-bus and 123-bus test grids to investigate its efficacy in distributed energy management of multiple-MG systems.

Obstructive sleep apnea is a common sleep disorder with a high prevalence and often accompanied by significant snoring activity. To diagnose this condition, polysomnography is the standard method, where a neck microphone could be added to record tracheal sounds. These can then be used to study the characteristics of breathing, snoring or apnea. In addition cardiac sounds, also present in the acquired data, could be exploited to extract heart rate. The paper presents new algorithms for estimating heart rate from tracheal sounds, especially in very loud snoring environment. The advantage is that it is possible to reduce the number of diagnostic devices, especially for compact home applications. Three algorithms are proposed, based on optimal filtering and cross-correlation. They are tested firstly on one patient presenting significant pathology of apnea syndrome, with a recording of 509 min. Secondly, an extension to a database of 16 patients is proposed (16 hours of recording). When compared to a reference ECG signal, the final results obtained from tracheal sounds reach an accuracy of 81% to 98% and an RMS error from 1.3 to 4.2 bpm, according to the level of snoring and to the considered algorithm.

Network management, whether for malfunction analysis, failure prediction, performance monitoring and improvement, generally involves large amounts of data from different sources. To effectively integrate and manage these sources, automatically finding semantic matches among their schemas or ontologies is crucial. Existing approaches on database matching mainly fall into two categories. One focuses on the schema-level matching based on schema properties such as field names, data types, constraints and schema structures. Network management databases contain massive tables (e.g., network products, incidents, security alert and logs) from different departments and groups with nonuniform field names and schema characteristics. It is not reliable to match them by those schema properties. The other category is based on the instance-level matching using general string similarity techniques, which are not applicable for the matching of large network management databases. In this paper, we develop a matching technique for large NEtwork MAnagement databases (NEMA) deploying instance-level matching for effective data integration and connection. We design matching metrics and scores for both numerical and non-numerical fields and propose algorithms for matching these fields. The effectiveness and efficiency of NEMA are evaluated by conducting experiments based on ground truth field pairs in large network management databases. Our measurement on large databases with 1,458 fields, each of which contains over 10 million records, reveals that the accuracies of NEMA are up to 95%. It achieves 2%-10% higher accuracy and 5x-14x speedup over baseline methods., Comment: 14 pages, 13 Figures, 7 tables

Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability.

The advent of elastic Content Delivery Networks (CDNs) enable Content Providers (CPs) to lease cache capacity on demand and at different cloud and edge locations in order to enhance the quality of their services. This paper addresses key challenges in this context, namely how to invest an available budget in cache space in order to match spatio-temporal fluctuations of demand, wireless environment and storage prices. Specifically, we jointly consider dynamic cache rental, content placement, and request-cache association in wireless scenarios in order to provide just-in-time CDN services. The goal is to maximize the an aggregate utility metric for the CP that captures both service benefits due to caching and fairness in servicing different end users. We leverage the Lyapunov driftminus-benefit technique and Jensen’s inequality to transform our infinite horizon problem into hour-by-hour subproblems which can be solved without knowledge of future file popularity and transmission rates. For the case of non-overlapping small cells, we provide an optimal subproblem solution. However, in the general overlapping case, the subproblem becomes a mixed integer non-linear program (MINLP). In this case, we employ a randomized cache lease method to derive a scalable solution. We show that the proposed algorithm guarantees the theoretical performance bound by exploiting the submodularity property of the objective function and pick-and-compare property of the randomized cache lease method. Finally, via real dataset driven simulations, we find that the proposed algorithm achieves 154% utility compared to similar static cache storage-based algorithms in a representative urban topology.

Made available in DSpace on 2020-12-12T02:25:29Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01 One of the impacts of harmonic resonances in wind parks is to overload wind park components. The process of identifying a problematic component may be time-consuming due to the number of scenarios and different components (capacitors, transformers, cables and generators) that must be analyzed. In this context, this paper develops a first-screening approach to narrow down the number of scenarios that must be studied in detail. It first identifies that the capacitor bank is the most critical component. Then, it uses the operating limits of the capacitor bank and the harmonic distortions measured at the wind park entrance to obtain a chart that maps the problematic operating conditions of the park. Detailed investigations are needed only if the wind park condition is in specific regions of the chart. The proposed chart is attractive for practical applications as it is obtained with a simple equation, without the need to run any computer simulation, and can be consulted with few information from the wind park that is readily available to engineers. Electrical Engineering School, Universidad de Costa Rica, 27915 San Jose, San Jose Costa Rica 11501-2060 (e-mail: andres.arguello.guillen@gmail.com) Department of Systems and Energy, University of Campinas, Campinas, Sao Paulo Brazil 13083-852 (e-mail: torquato@ieee.org) State University of Campinas, 28132 Campinas Brazil 13083-872 (e-mail: walmir@unicamp.br) Engenharia Eletrica, UNESP, Ilha Solteira Brazil 15385-000 (e-mail: padilha@dee.feis.unesp.br)

Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals. Defining signals on graphs relaxes such complexities by representing the evolution of signals over a space using relevant graph kernels such as the heat diffusion kernel. However, this kernel alone does not fully capture the actual dynamics of the data as it only relies on the graph structure. The gap can be filled by combining the graph kernel representation with data-driven models that utilize historical data. This paper proposes a traffic propagation model that merges multiple heat diffusion kernels into a data-driven prediction model to forecast traffic signals. We optimize the model parameters using Bayesian inference to minimize the prediction errors and, consequently, determine the mixing ratio of the two approaches. Such mixing ratio strongly depends on training data size and data anomalies, which typically correspond to the peak hours for traffic data. The proposed model demonstrates prediction accuracy comparable to that of the state-of-the-art deep neural networks with lower computational effort. It notably achieves excellent performance for long-term prediction through the inheritance of periodicity modeling in data-driven models.

Made available in DSpace on 2020-12-11T19:25:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-01 Recently, the Brazilian Electricity Regulatory Agency (ANEEL) regularized the electricity prepayment system and opened a general discussion about the technologies, resources, and implementations for the prepayment metering system. In several countries, the consumer charges the value equivalent to the kilowatt-hours that he wishes to buy for use. However, some consumers purchase credit with the expectation that the charge remains for a specific time, but they may be surprised with the credit end before the expected time. For this reason, this paper proposes automatic consumption management for prepaid meters that analyzes the consumer appliance behavior, performs the temporal analysis of consumption by appliances, defines the cost management per load with high energy consumption and sends sound signals to the consumer ends the appliance usage. The objective of this management system is to ensure that the credit will be used for the consumer expected time. Univ Estadual Campinas, Campinas, SP, Brazil Univ Fed Sao Joao del Rei, Ouro Branco, MG, Brazil Univ Estadual Paulista, Campus Sorocaba, Sao Paulo, Brazil Univ Estadual Paulista, Campus Sorocaba, Sao Paulo, Brazil

This paper proposes a theory for understanding perceptual learning processes within the general framework of laws of nature. Artificial neural networks are regarded as systems whose connections are Lagrangian variables, namely, functions depending on time. They are used to minimize the cognitive action, an appropriate functional index that measures the agent interactions with the environment. The cognitive action contains a potential and a kinetic term that nicely resemble the classic formulation of regularization in machine learning. A special choice of the functional index, which leads to the fourth-order differential equations-Cognitive Action Laws (CAL)-exhibits a structure that mirrors classic formulation of machine learning. In particular, unlike the action of mechanics, the stationarity condition corresponds with the global minimum. Moreover, it is proven that typical asymptotic learning conditions on the weights can coexist with the initialization provided that the system dynamics is driven under a policy referred to as information overloading control. Finally, the theory is experimented for the problem of feature extraction in computer vision.

International audience; This paper presents an optimization model for Home Energy Management Systems from an aggregator’s standpoint. The aggregator manages a set of resources such as PV, electrochemical batteries and thermal energy storage by means of electric water heaters. Resources are managed in order to participate in the day-ahead energy and local flexibility markets, also considering grid constraint support at the Point of Common Coupling. The resulting model is a Mixed-Integer Linear Programming problem in which the objective is to minimize day-ahead operation costs for the aggregator while complying with energy commitments in the day-ahead market and local flexibility requests. Three sources of uncertainty are considered: energy prices, PV production and load. Adjustable Robust Optimization is used to find a robust counterpart of the problem for including uncertainty. The results obtained show that using robust optimization allows strategic bidding to capture uncertainties while complying with obligations in the wholesale and local market. Data from a real-life energy community with 25 households is used to validate the proposed robust bidding methodology.

Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics.

According to current energy efficiency labels, the majority of light-emitting diode (LED) lamps on the market are considered as highly efficient devices. This makes it difficult to distinguish between lamps with different operational characteristics and performance. This paper introduces a comprehensive experimental-based labeling methodology for comparing LED lamp performance with reference to two additional important characteristics: light flicker and power factor. The new labeling methodology reveals that there is high diversity between different LED lamps with different circuit topologies but also for a given topology with different design choices. General consumers and design engineers can benefit from the simple and clear information presented by the set of comparative labels when comparing LED lamp performance.

International audience; This paper develops a geometrical construction of a shortest Dubins path in a discontinuous orientation restricted environment. The method proposed here builds the shortest path from one pose to the other while avoiding a no-go zone in terms of orientation, and being constrained to move forward. Finally, an application to autonomous sailboats is then provided to validate the feasibility of the planned shortest path in a position keeping scenario.

This paper proposes non-orthogonal sharing of available resources between latency-critical and latency-tolerant communication for fulfilling tight requirements of ultra-reliable low-latency communication (URLLC) as well as avoiding inefficient spectrum utilization of grant-based (GB) access for sporadic URLLC traffic. In the proposed system, grant-free (GF) access is adopted for URLLC to reduce transmission latency, while GB access is used for latency-tolerant communication. Due to GF access, collision emerges between the communications, and use of OFDM technology for both communications leads to wideband interference (WB-I) on URLLC. Therefore, a novel non-orthogonal multiple accessing (NOMA) scheme based on orthogonal frequency division multiplexing (OFDM) and OFDM with index modulation (OFDM-IM) is proposed in order to reduce the impact of the collision on URLLC, that requires 99.999% success probability within 1ms. OFDM-IM technology is used for latency-tolerant communication since WB-I is converted to either narrowband dominant interference (NB-DI) or narrowband interference (NB-I) by fractional subcarrier activation in OFDM-IM. In this way, URLLC is partially affected by latency-tolerant communication. It is shown that the proposed NOMA scheme significantly reduces the latency in comparison to classical NOMA scheme based on pure OFDM while guaranteeing $10^{-5}$ reliability for URLLC, via both computer-based simulations and theoretical analysis.

Different solutions have been proposed to address orientation in indoor/outdoor environments and among them, radio frequency identification (RFID)-based systems represent a strategic choice for its reduced complexity and cost. Widespread practices to validate the behavior of RFID-based systems use specific metrics related to the addressed application, while a general strategy to assess technical performances of the RFID-based system is lacking. In this paper, a novel measurement approach is proposed to perform optimal choices in developing an RFID-based orientation system and to impartially assess its technical performances. A multivariate measurement strategy is suggested to fully investigate performances of the system as a function of a set of operating quantities (user-tag distance, tag height, and intertag distance). An analysis is also proposed, which aims to assess the system performances in terms of sensitivity and specificity. Moreover, the use of a synthetic index, representing the overall behavior of the system under investigation is addressed. A case of study is presented. The system uses transparent RFID tags positioned in the environment and an embedded system including an RFID reader, as well as a dedicated module to convey audio messages to end-users. Results highlight that the device sensitivity improves in the case of a high intertag distance while the specificity decreases for short intertag distance, which is also confirmed by the synthetic index proposed. Specifically, an optimal tag height of 1 m has been estimated, while best performances are obtained in the case of intertag distance and user-tag distance belonging to ranges [1.50–2.50] m and [0.40–1.60] m, respectively.

Although the recent years have witnessed a growing interest in functional connectivity (FC) through brain sources, the FC in extreme situations has not been completely elucidated. This paper is aimed at investigating whether the expertise acquired during the deep-sea diving is reflected in FC in a group of professional divers (PDs) compared to a group of new divers (NDs), and how it could affect the concentration and stress levels. The sources of brain frequency rhythms, derived by the electroencephalography acquisition in a hyperbaric chamber, were extracted in different frequency bands and the corresponding FC was estimated in order to compare the two groups. The results highlighted a significant decrease of the alpha source in PDs during air breathing and a significant increase of the upper beta source over central areas at the beginning of post-oxygen air, as well as an increase of beta FC between fronto-temporal regions in the last minutes of oxygen breathing and in the early minutes of post-oxygen air. This provides evidence in support of the hypothesis that experience and expertise differences would modulate brain networks. These experiments provided the unique opportunity of investigating the impact of the neurophysiological activity in simulated critical scenarios in view of the investigation in real sea-water experiments.

This is a post-peer-review, pre-copyedit version of an article published in IEEE Transactions on Computers. The final authenticated version is available online at: http://dx.doi.org/10.1109/TC.2018.2853747 [Abstract] A formal, high-level representation of programs is typically needed for static and dynamic analyses performed by compilers. However, the source code of target applications is not always available in an analyzable form, e.g., to protect intellectual property. To reason on such applications it becomes necessary to build models from observations of its execution. This paper presents an algebraic approach which, taking as input the trace of memory addresses accessed by a single memory reference, synthesizes an affine loop with a single perfectly nested statement that generates the original trace. This approach is extended to support the synthesis of unions of affine loops, useful for minimally modeling traces generated by automatic transformations of polyhedral programs, such as tiling. The resulting system is capable of processing hundreds of gigabytes of trace data in minutes, minimally reconstructing 100 percent of the static control parts in PolyBench/C applications and 99.9 percent in the Pluto-tiled versions of these benchmarks. Ministerio de Economía y Competitividad; TIN2016-75845-P National Science Foundation (Estados Unidos); 1626251 National Science Foundation (Estados Unidos); 1409095 National Science Foundation (Estados Unidos); 1439057 National Science Foundation (Estados Unidos); 1213052 National Science Foundation (Estados Unidos); 1439021 National Science Foundation (Estados Unidos); 1629129

Similarity search in metric spaces finds similar objects to a given object, which has received much attention as it is able to support various data types and flexible similarity metrics. In real-life applications, metric spaces might be combined with graphs, resulting in geo-social network, citation graph, social image graph, to name but a few. In this paper, we introduce a new notion called quasi-metric graph that connects metric data using a graph, and formulate similarity search on quasi-metric graphs based on the combined similarity metric considering both the metric data similarity and graph similarity. We propose two simple efficient approaches, the best-first method and the breadth-first method, which traverse the quasi-metric graph following the best-first and the breadth-first paradigms, respectively, and utilize the triangle inequality to prune unnecessary evaluation. Extensive experiments with three real datasets demonstrate, compared with several baseline methods, the effectiveness and efficiency of our proposed methods.

In this paper, a distributed model predictive control (DMPC) is proposed for static formation of unconstrained double-integrator multiagent systems. The formation problem is formulated in the leader-follower framework, where the leaders have access to both their own and relative neighboring information, and the followers only have access to relative neighboring information. In the process of optimization, only current relative information is communicated between neighboring agents. For each agent, its predicted control vector and neighboring control vectors are regarded as decision variables for optimization, but only the own optimized current control of the agent is implemented. An analytical solution to the DMPC is obtained, and new sufficient conditions are given for achieving the static formation.

Ultra-reliable and low-latency communications (URLLC) partakes a major role in 5 G networks for mission-critical applications. Sparse vector coding (SVC) appears as a strong candidate for future URLLC networks by enabling superior performance in terms of bit error rate (BER). SVC exploits the virtual digital domain (VDD) and compressed sensing (CS) algorithms to encode and decode its information through active symbol indices. In this paper, first, a clever encoding/decoding algorithm is proposed for the SVC scheme, which allows the use of all possible activation patterns (APs) resulting in increasing spectral efficiency. Second, a novel solution is proposed to convey additional information bits by further exploiting index modulation (IM) for the codebooks of the SVC scheme. Computer simulation results reveal that our low-complexity algorithm and novel IM solution provide not only a superior BER performance but also an increase in the number of bits conveyed by IM compared to the ordinary SVC approach., Scientific and Technological Research Council of Turkey (TÜBİTAK)

International audience; Pansharpening regards the fusion of a high-spatial resolution panchromatic image with a low-spatial resolution multispectral image. One of the most debated topics about pansharpening is related to the quality assessment of fused products. Two main assessment procedures are usually exploited in the literature: the reduced resolution validation and the full-resolution (FR) validation. The former has the advantage to be accurate, but the hypothesis of invariance among scales has to be assumed. The latter overcomes this limitation but paying it with a lower accuracy. In this paper, we will focus on the FR assessment proposing an approach for estimating an overall quality index at FR by using multiscale FR measurements. The problem is recast into the sequential Bayesian framework exploiting a Kalman filter to find its solution. The proposed procedure for quality evaluation has been tested on four real data sets acquired by the Pléiades, the GeoEye-1, the WorldView-3, and the WorldView-4 sensors assessing the quality of 19 pansharpened methods. The proposed approach has demonstrated its superiority with respect to the benchmark consisting of state-of-the-art quality assessment procedures.

This paper proposes a novel cascading faults graph (CFG) based on fault chain theory to assess the transmission network vulnerability. The proposed CFG is able to effectively reveal the mechanism of fault propagation in a transmission network by transforming an electrical network with spatial information into a fault propagation graph with temporal information, thus providing a better way for transmission network vulnerability assessment. Numerical results on IEEE 39-, 118-bus systems and French grid demonstrate the effectiveness of the proposed method.

This paper aims at providing a novel techno-economic flexibility index to quantify the relative flexibility of different conventional generation technologies. The proposed measure takes into account a comprehensive set of technical flexibility characteristics of generation units including minimum stable generation level, operating range, minimum up/down times, and ramp up/down capability. The impact of various technical parameters on the flexibility restriction is determined based on the additional costs imposed to the system operator in order to satisfy that constraint. Afterward, the calculated flexibility index is incorporated into day-ahead market clearing procedure as a new objective function beside operation cost using a multiobjective decision-making approach. Simulation results show that the need of different flexibility levels changes the optimal generation dispatch and results in different operation costs. Furthermore, the impact of two emerging flexible options, i.e., bulk energy storages and a typical demand response program in the flexibility contribution of other generation technologies is also investigated. The results reveal that the integration of emerging flexible options frequently changes the flexibility share of conventional units and can provide the required flexibility level at a lower cost.

An intelligent machine learning-based method is developed in this paper for modeling and prediction of the wind turbine (WT) output power. The developed technique makes use of the advanced machine learning models for creating prediction intervals around the WT power samples. Compare to the other techniques which make a point-by-point prediction, the proposed technique can capture the uncertainty of the forecast error using generating lower and upper bounds around the samples. Moreover, a fuzzy min-max technique is employed to get into a Nash equilibrium between the prediction confidence level and the average prediction bandwidth. In order to get into reliable results, two support vector machine is deployed which compete with each other to improve the prediction performance. This game can potentially contribute to a highly accurate prediction model. Also, a new optimization method based on the flower pollination algorithm (FPA) is developed due to the high complexity and nonlinearity of the modeling, which can search for finding the most optimal structure of the model. The feasibility and appropriate performance of the model is assessed using the experimental data from Australia islands. IEEE Scopus 2-s2.0-85098765325

During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability. Science Foundation Ireland Peer-review status unspecified