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There is a great desire to integrate multiple energy systems by forming low-carbon energy hubs. Among various energy platforms, the integration of power and gas networks via Power-to-Gas (PtG) technologies is introduced as a promising solution to enhance the flexibility of power grids and facilitate seamless adoption for high penetration levels of renewable energy resources. In addition, the production of green hydrogen from waste/inexpensive clean electricity has recently been identified as a potential means for large-scale energy storage, deployment of hydrogen mobility, and decarbonization of several processes that utilize hydrogen. As integrated hydrogen-based energy hubs (H 2 Hubs) attract more attention for widespread deployment, the security of such new platforms against various cyberattacks is also becoming a critical topic for investigation. This paper proposes a new data estimation technique, developed and integrated with H 2 Hubs scheduling models, for real-time optimal adaptive collection and organization of the system operating parameters for cybersecurity assessment. A new formulation is presented for the adaptive formation of a newly proposed concept, referred to as the system feasibility area , via real-time acquisition of the system operating information. The created feasibility areas are utilized for real-time post-occurrence detection of the bad scheduling data that is invisible to the existing cybersecurity layers. Numerical studies are conducted to demonstrate the efficacy and feasibility of the proposed model. The results demonstrate that the historical data can be adaptively collected in real time and successfully utilized to create feasibility areas to categorize the system operating points. • An optimal scheduling model is proposed for hydrogen-based energy hubs (H 2 Hubs). • A new data estimation method is developed and integrated with the scheduling model. • A new formulation is given for the adaptive creation of the feasibility area. • Simulation case studies are presented and discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a partially periodic oscillation model, which is motivated by time series modelling for COVID-19 daily confirmed cases, in particular, to represent more accurately the dynamic features of the 7-day periodicity. In order to express the phenomenon of the partial 7-day cycle in the COVID-19 data, some partial periodic part is added to a heterogeneous autoregression model. Estimation algorithm based on the least squares errors and regression analysis is provided and parameter estimation consistency is given along with its proof. A Monte-Carlo simulation study is carried out to investigate the finite-sample performance. The proposed model is applied to the COVID-19 daily confirmed cases of the most affected eight countries which posses the partially periodic oscillation. Model criteria such as RMSE, MAE, HMAE, AIC and BIC are compared with other existing models. Efficiency of the model, relative to the benchmark, is evaluated to reveal its better accuracy performance. Out-of-sample forecasting analysis is conducted as well. The novelty is that this work is a challenging trial to identify the partially periodic oscillation of COVID-19 data, without smoothing, as well as the proposed model outperforms the existing time series models in the empirical analysis of the worldwide COVID-19. • A partially periodic oscillation model combined with heterogeneous autoregression is proposed. • The model is applied to the COVID-19 daily confirmed cases in the top eight countries with partially periodic oscillation. • A simple and robust estimation method is developed. • Model criteria measures such as RMSE, MAE, HMAE, AIC and BIC are evaluated and compared with existing models. • Efficiency of the proposed model, relative to benchmark models, is reported as 126%-163% improvement in some countries. [ABSTRACT FROM AUTHOR]

Ground-Based Synthetic Aperture Radar (GB-SAR) enables to monitor a scene in a distance up to a few km and with an accuracy of around one mm. It is mainly used for landslide monitoring but also used to monitor man-made structures such as bridges and dams. Due to the particular distance-dependent imaging concept and related geometric distortion effects, the interpretation of the SAR images can be difficult, especially in the case of complex man-made structures. The geocoding of the SAR data enables to project the 2D SAR image on a 3D digital elevation model (DEM) or a 3D terrain model and highly improves the interpretation of the observed deformation. It also allows to fuse SAR-based observations with other data sources. The purpose of this paper is to present a new algorithm to geocode GB-SAR data in complex scenarios. The method introduces two new components, a Bayesian statistical approach and a ray-tracing algorithm. The first one uses an a priori function on the received intensity to improve the geocoding in case of strong layover, while the latter considers areas in the radar shadow to be excluded from geocoding. Moreover, ray-tracing provides the starting point for the analysis of signals with multiple reflections which are also discussed in this paper. Our algorithm is applied to and evaluated with two case studies. The first one demonstrates the improved geocoding of GB-SAR data on the Linach dam in the Black Forest in Germany, which is characterized by a rather complex architecture. The second case study shows the application of the method at the Enguri dam in Georgia, which is characterized by its large dimensions and a dam height over 270 m. [ABSTRACT FROM AUTHOR]

Abstract: Two modifications have been proposed for the Nurse-Saul maturity function to get better estimates of compressive strength of concrete cured at different temperatures in this paper. The modifications account for the effect of w/c ratio on the temperature dependence of strength development and the effect of curing temperature on the long-term strength. This discussion focuses on strength development behaviour of concretes cured at different temperatures. Aiming to understand the strength development of cement based materials relevant experimental studies on mortar and concrete specimens in the literature are evaluated. [Copyright &y& Elsevier]

Interbull's multiple across-country evaluaftion provides national breeding organizations with breeding values for internationally used bulls, which integrate performance data obtained in different breeding populations, environments, and production systems. However, breeding value-based selection decisions on domestic individuals born to foreign sires can only benefit from Interbull breeding values if they are integrated such that their information can contribute to the breeding values of all related domestic animals. For that purpose, several methods have been proposed which either model Interbull breeding values as prior information in a Bayesian approach, as additional pseudo data points, or as correlated traits, where these methods also differ in their software and parameterization requirements. Further, the complexity of integration also depends on the traits and genetic evaluation models. Especially random regression models require attention because of the dimensionality discrepancy between the number of Interbull breeding values and the number of modeled genetic effects. This paper presents the results from integrating 16,063 Interbull breeding values into the domestic single-step random regression test-day model for milk, fat, and protein yield for Australian Red dairy cattle breeds. Interbull breeding values were modeled as pseudo data points with data point-specific residual variances derived within animal across traits, ignoring relationships between integrated animals. Results suggest that the integration was successful with regard to alignment of Interbull breeding values with their domestic equivalent as well as with regard to the individual and population-wide increase in reliabilities. Depending on the relationship structure between integration candidates, further work is required to account for those relationships in a computationally feasible manner. Other traits with separate parity effects nationally could use a similar approach, even if not modeled with a test-day model. [ABSTRACT FROM AUTHOR]

This paper addresses current needs of the Multi-Unit Probabilistic Safety Assessment (MUPSA) methodology, which consists of a series of eight essential steps for conducting Probabilistic Safety Assessment (PSA) in the context of a multi-unit Nuclear Power Plant (NPP), co-located on a single site. The focus of this study revolves around one of the steps of the MUPSA methodology, which includes the examination of the importance factors and uncertainty analysis. Emphasizing the role of this step within MUPSA, the paper highlights the hypothesis and estimations made, along with the determination of system importance factors and failure frequency. To illustrate the practical application of these concepts, the paper presents three illustrative examples. The first example involves a 3-terminal reliability network, while the second example features an illustrative calculation applied to a system designed for potential use in a real NPP, compared to a Monte-Carlo simulation. Moreover, the second example involves the evaluation of a medium voltage distribution system (MVDS) designed to meet the auxiliary supply requirements of a nuclear power plant. This analysis is performed using a binary decision diagram (BDD), and the results derived from the BDD evaluation are then verified with those obtained through a Monte Carlo simulation for a comprehensive comparative analysis. Finally, an uncertainty analysis is achieved using a BDD reliability model, using the results of fault trees developed by National Company SN Nuclearelectrica S.A. (SNN), Department of Nuclear Safety, in the context of the Coordinated Research Project (CRP- I31031). • A meaningful method for probabilistic estimation of uncertainty is proposed. • Plug-in estimator allows for calculating system unavailability and estimating corresponding variance. • Presented method is applicable in the uncertainty analysis for importance factors computation in Level 1 multi-unit PSA. • Adaptation of state merging scheme on large-scale systems can be further studied. [ABSTRACT FROM AUTHOR]

• For the first time in Lithuania, food losses in primary production are estimated. • Data come from a survey and Farm Accountancy Data Network. • Food loss rates range from less than 1% for milk to more than 20% for beets. • Environmental factors and quality issues are critical for crop products. • Diseases are the most important for animals. Food loss and waste occur along the whole food supply chain and need to be addressed by dedicated strategies. Among all the stages of the food supply chain, food loss in primary production has been the least studied to date. In order to address this gap, this paper focuses on on-farm food loss, using Lithuania as a case study. The research comprises eleven crop products and five livestock products prevailing in Lithuanian agricultural production. A survey-based method is utilized to collect primary data from farmers. The final data set consists of 408 completely answered questionnaires. The survey-based data are merged with those from the Farm Accountancy Data Network in order to devise an effective framework for estimating self-reported on-farm food loss. The results show that there exists substantial variation in on-farm food loss rates ranging from less than 1% for milk to slightly more than 20% for beets. Environmental factors and those related to consumer and buyer requirements are the main reasons for losses of crop products, while diseases appear as the main causes of death for animals. The food loss and waste reduction measures are derived based on the results of the survey. [ABSTRACT FROM AUTHOR]

Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, ▪ penalties are inputs used by the optimizer to find the optimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort. • Achieving low network return temperature by optimizing energy flows at district heating substations. • Maintaining the primary and secondary sides mass flows by introducing the new factor. • Finding optimal network temperatures and flow rates by estimating energy demands of the buildings. • Improved network techno-economical and environmental performance and thermal comfort of consumer. [ABSTRACT FROM AUTHOR]

This paper aims to assess and tackle the cyber–physical security threats stemming from vulnerabilities of battery-based electric bus (BEB) fleets to cyberattacks. An optimization model is adopted for scheduling the battery swapping in BEB fleets and is utilized for cybersecurity assessments. New methods are proposed for detection of false data injection attacks (FDIAs) against the information of the BEB scheduling model. A supervised machine learning-based method is used for identification of potential cyberattacks against the system parameters that govern the operation of the BEB transit fleet. The proposed cybersecurity model is integrated with the BEB scheduling model to form a cyberattack-resilient scheduling framework for the BEB transit fleet. Two categories of data are considered for the simulation of attack scenarios and assessment of the proposed cybersecurity framework: Type 1 – Signals that are received from sources local to the battery swapping station (BSS), and Type 2 – Signals that are received from sources external to the BSS which can be intercepted and tampered by the attacker. The simulation results are presented to evaluate potential disruptions in the BEB transit operation due to FDIAs against the system's operating parameters. It is demonstrated that a soft cyberattack mildly deviating the fleet operating parameters, e. g. , the bus arrival time by up to 10 min or battery SoC by 5%, does not cause a considerable impact on the scheduling parameters or the financial metrics. A moderate or aggressive attack, however, affecting the fleet operating parameters considerably, can adversely impact the scheduling information and the fleet financial metrics. • A model is proposed to verify battery electric bus (BEB) fleet operating parameters. • A machine learning-based model is developed for false data injection attack detection. • The proposed cybersecurity algorithm is integrated with the BEB scheduling model. • Simulation case studies are presented to evaluate the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state-of-charge (SOC) and state-of-energy (SOE) of lithium-ion batteries (LIBs) is fundamental for the battery management system. This paper proposes a method based on the combination of domain adaptation (DA) and unscented Kalman filter (UKF) (DA-UKF) to achieve joint estimation of SOC and SOE at distinct temperatures. A data-driven network consisting of source domain (SD) and target domain (TD) parts is adopted. A gated recurrent unit network and linear layer are used to extract features of the SD and TD datasets, while maximum mean difference and adversarial DA are adopted to align the features. The linear layer outputs SOC and SOE joint estimation results, and the UKF smooths the outputs to obtain accurate and stable joint estimation. Experimental results show that, regardless of whether performing in supervised or unsupervised mode, the DA-UKF can achieve highly robust and accurate joint estimation of SOC and SOE at various temperatures. Compared with other advanced methods, the root mean square error and the mean absolute error of the DA-UKF, at different temperatures, reduce, on average, between 49.760% and 84.150%, and 53.579% and 84.787%, respectively. Moreover, the DA-UKF does not require complex adjustments to the hyperparameters of the network. • New method for joint estimating SOC and SOE of lithium-ion battery (LIB). • Data-driven method domain adaptation (DA) and unscented Kalman filter combined. • DA used to achieve cross-temperature state estimation. • MMD and adversarial DA used to perform domain feature alignment. • New scheme is compared with existing alternatives for estimating SOC and SOE. [ABSTRACT FROM AUTHOR]

Mangroves, tidal marshes, and seagrasses have been recognized as blue carbon coastal ecosystems as they accommodate large carbon stocks, enable long-term carbon storage, have the capacity to control greenhouse gas emissions and possess the potential to reduce the rise in atmospheric CO 2 levels. In the tropical and subtropical intertidal zones, mangroves show significant productivity and rapid deposition rates. Mangroves play an important role in capturing, transforming, and storing CO 2 in the atmosphere into coastal sediments for a long time, displacing organic carbon from the coastal zone to the offshores and the ocean. Mangroves also help in preventing storms, hurricanes, coastal erosion, and tidal waves. Recently, mangroves have been threatened by natural and anthropogenic activities such as urbanization, pollution, aquaculture and tourism. They are expected to hamper their essential services, such as coastal defense, breeding grounds for aquatic organisms' seafood supply, etc. Despite the importance of mangrove forests in carbon sequestration and the numerous goods and services they offer humans, wood and non-wood forest products, food, fisheries, medicines, eco-tourism, and recreation. These mangrove ecosystems are deteriorating at alarming rates, necessitating immediate intervention. Thus, the protection, restoration and conservation of this ecosystem are gaining considerable attention from researchers across the globe. This paper provides information on different mangrove adaptations, their mechanisms, roles in the ecosystem, carbon estimation, influencing factors, threats, and conservation strategies for carbon sequestration in this invaluable coastal habitat. • Carbon sequestration is the most crucial ecosystem function among the ecosystem services offered by coastal ecosystems. • Mangroves play an important role in capturing, transforming, and storing carbon. • Blue carbon ecosystems play a critical role in regulating our climate. • Various factors that impact carbon sequestration and conservation strategies were discussed. • Economic incentives via ecotourism in mangrove ecosystems were discussed. [ABSTRACT FROM AUTHOR]

The cumulative incidence function (CIF) plays an important role in the comparison of competing risks in a competing risks model. Its value at time t is the probability of failure by time t from a particular type of risk in the presence of other risks. In this paper we consider the estimation of two CIFs, F 1 and F 2 , corresponding to two competing risks when the ratio R (t) ≡ F 1 (t) / F 2 (t) is nondecreasing in t > 0. First, we derive their nonparametric maximum likelihood estimators (NPMLE) of these CIFs in the continuous case under this order constraint and show that they are inconsistent. We then develop projection-type estimators that are uniformly strongly consistent and study the weak convergence of the resulting processes. Through simulations, we compare the finite sample performance of the NPMLEs and our estimators and show that our estimators outperform them in general in terms of mean square error at all the scenarios that we consider. We also develop a test for the presence of this order constraint and extend all these results to the censored case. To illustrate the applicability of the theory we develop, we provide a real life example. • Uniformly consistent estimation of two cumulative incidence functions in a competing risks model when the ratio is monotonic. • Weak convergence of the estimators of two cumulative incidence functions in a competing risks model when the ratio is monotonic. • Test of equality of two cumulative incidence functions against the alternative that their ratio is monotonic in a competing risks model. [ABSTRACT FROM AUTHOR]

To properly manage market risk industrial companies use tools based on value-at-risk, which requires proper modeling of future risk factors dynamics. One of the major challenges faced by this technique applied to modeling currency exchange rates is the choice of an appropriate model for simulation of the future paths. In order to fit the data, it should reflect its properties including heavier than Gaussian tail distributions, stabilizing volatility and mean reversion in long term horizon. In this paper, we propose to apply the Chan–Karolyi–Longstaff–Sanders (CKLS) model for the description of currency exchange rates data. This model was introduced for describing the evolution of the short interest rate and could be considered as the natural extension of the classical Ornstein–Uhlenbeck process. The standard version of CKLS process was based on the Brownian motion (BM). However, it can be easily extended to any class of distributions. Since the financial data of interest exhibit non-Gaussian behavior, we modified the CKLS model to use skewed generalized Student's t-distribution (SGT). In this paper, we apply the generalized method of moments to estimate the parameters of the CKLS/SGT process. The accuracy and behavior of the estimators are analyzed using Monte Carlo simulations. Further, we propose a new validation and selection technique. We apply the CKLS/SGT and GBM models to two currency exchange rates and compare their performance. • The CKLS model with general class of SGT distribution is considered. • The innovative estimation procedure for CKLS model parameters is proposed. • The Monte Carlo approach is used in order to check the effectiveness of the proposed estimation procedure. • The new model validation factor is proposed. • The proposed model is applied to two real data sets describing the currency exchange rates, the main risk factors in the KGHM company. [ABSTRACT FROM AUTHOR]

Abstract This paper is concerned with estimation and stability of control systems over communication links subject to limited capacity, power constraint, fading, noisy feedback, and different transmission rate rather than system sampling rate. A key issue addressed in this paper is that in the presence of noisy feedback associated with channel, which models transmission of finite number of bits over such links as is the case in most practical scenarios, the well-known eigenvalues rate condition is still a tight bound for stability. Based on an information theoretic analysis, necessary conditions are derived for stability of discrete-time linear control systems via the distant controller in the mean square sense. By construction of a specific coding scheme and by the design of a proper controller, the tight sufficient conditions for stability of control systems are also derived for linear discrete-time control systems over both Additive White Gaussian Noise (AWGN) and fading channels. This implies that the proposed coding scheme is efficient. The results are presented with the mismatch assumption between channel symbol and plant sample rates. As one key result of this paper, it is shown that if the channel symbol rate is less than the system sample rate, the control system is still stabilizable via increasing the transmission power. [ABSTRACT FROM AUTHOR]

• Developing countries lack sound methodologies to estimate solid waste generation. • Mismatch of observation and analysis level generates inference fallacies. • A local and two foreign studies on waste generation in Panama were examined in detail. • Seven different inference fallacies were found, five of which a novelty of this study. • Foreign entities perpetrated most, thus revealing that endogenous knowledge is key. The absence of sound sampling procedures and statistical analyses to estimate solid waste generation in many developing countries has resulted in incomplete historical records of waste quantity and composition. Data is often arbitrarily aggregated or disaggregated as a function of waste generators to obtain results at the desired spatial level of analysis. Inference fallacies arising from the generalization or individualization of results are almost never considered. In this paper, Panama, one of the fastest-growing developing countries, was used as a case-study to review the main methodological approaches to estimate solid waste generation per capita per day, and at different hierarchical levels (from households to the country). The solid waste generation intensity indicator is used by the Panamanian waste management authority to run the waste management system. It was also the main parameter employed by local and foreign companies to estimate solid waste generation in Panama between 2001 and 2008. The methodological approaches used by these companies were mathematically formalized and classified as per the expressions suggested by Subramanian et al. (2009). Seven inference fallacies (ecological, individualistic, stage, floating population, linear forecasting, average population and mixed spatial levels) were identified and allocated to the studies. Foreign companies committed three of the seven inference fallacies, while one was committed by the local entity. Endogenous knowledge played an important role in these studies to avoid spatial levels mismatch and multilevel measurements appear to produce more reliable information than studies obtained via other means. [ABSTRACT FROM AUTHOR]

• Accident modification functions for traffic law enforcement are compared. • All functions show that more enforcement improves safety. • Differences-in-differences estimates capture both increases and decreases in enforcement. • A decrease in enforcement worsens road safety. Traffic law enforcement is a road safety measure whose effects on accidents or injuries is best described by means of a function rather than a point estimate. An informative function should comprise both increases and decreases in enforcement. Currently available accident modification functions cannot serve this need. A fruitful approach to developing accident modification functions covering both increases and decreases in enforcement is differences-in-differences estimates based on multivariate accident prediction models. The paper explains how to develop such estimates and illustrates them. The interpretation of the results of empirical studies can be informed by a game-theoretic model of the effects of enforcement, previously published in Accident Analysis and Prevention (Bjørnskau and Elvik 1992, 507–520). [ABSTRACT FROM AUTHOR]

Following the recent advent of Process Analytical Technologies, dataset production has undergone significant leverage. In this new abundance of data, isolating meaningful, informative content is critical for process dynamic modeling. This paper proposes a data-driven algorithm based on low-rank matrix approximation, the so-called successive projection algorithm, to retrieve a minimal set of macroscopic reactions, the corresponding stoichiometry, and a consistent kinetic model structure from the measurements of the trajectories of the species concentrations during cultures in a bioreactor. The proposed method is successfully validated in simulation, considering a case study related to monoclonal antibody (MAb) production with hybridoma cell cultures. • A data-driven tool for macroscopic modeling of bioprocesses is proposed. • The approach is based on a successive projection algorithm. • The algorithm retrieves the number of macroscopic reactions and computes stoichiometry. • The data-driven method infers a consistent kinetic structure for bioprocess modeling. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance analysis of cyber–physical systems under the stealthy deception attack. Two types of attack models, strictly stealthy attack and ϵ -stealthy attack, are considered. The strictly stealthy deception attack model is firstly formulated based on the difference equations of the normal and compromised systems. We then present a necessary and sufficient condition for the existence of the strictly stealthy deception attack. The design method for the attacker to achieve the strictly stealthy deception attack is also provided. Considering that the strictly stealthy attack strategy is associated with the undetectable points of the difference equations, we propose a computationally efficient algorithm to find such points. It is shown that these points form a subspace which can be obtained in finite recursion steps. The corresponding defense strategy is further designed by avoiding the initial state falling into the subspace. For ϵ -stealthy attack, a necessary condition is provided for its existence. Furthermore, due to the fact that the difference system matrix does not have an unstable eigenvalue, it is proved that the ϵ -stealthy attack does not exist. Simulation examples are presented to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Highlights • Current studies are based on different methodological approaches and data sources. • A robust food waste accounting methodology is needed for food waste reduction. • Food waste ranges: 194 and 389 kg/p/y globally and 158 and 290 kg/p/y in EU. • Waste management and food security objectives lead to different accounting needs. • Distinction in edible and inedible is crucial for food waste prevention and food security. Abstract Contributing to environmental pollution and resources depletion, food waste represents a considerable inefficiency of the global food system. Within the United Nations Sustainable Development Goal 12.3, countries committed to halve per-capita food waste generated at retail and consumer levels and to decrease food waste along the food supply chain by 2030. Reliable and detailed information on food waste is of utmost importance for the actors of the food supply chain, organizations and governments willing to implement and monitor effective reduction strategies. The present paper is a review of existing studies on food waste generation at the global and European scales and aims primarily at describing and comparing the approaches adopted, and secondarily at analysing their potential in supporting food waste related European interventions and policies. Ten studies were selected among relevant scientific papers and grey literature and their underlying quantification methodologies were systematically analysed. Methodological elements discussed in the paper include type of waste streams captured by estimations, distinction between edible and inedible food waste along the agro-food supply chain, reported units of measure, overall inefficiencies of the food system, and uncertainty of data. Current estimations of food loss and waste generation range between 194–389 kg per person per year at the global scale, and between 158–298 kg per person per year at the European scale. However, further efforts are needed to improve their level of detail and reliability and to foster their support to food loss and waste-related strategies. [ABSTRACT FROM AUTHOR]

This paper is a review of literature with an analysis on a selection of scientific studies for detection of non-technical losses. Non-technical losses occurring in the electric grid at level of transmission or of distribution have negative impact on economies, affecting utilities, paying consumers and states. The paper is concerned with the lines of research pursued, the main techniques used and the limitations on current solutions. Also, a typology for the categorization of solutions for detection of non-technical losses is proposed and the sources and possible attack/vulnerability points are identified. The selected literature covers a wide range of solutions associated with non-technical losses. Of the 103 selected studies, 6 are theoretical, 25 propose hardware solutions and 72 propose non-hardware solutions. Data based classification models and data from consumption with high resolution are respectively required in about 47% and 35% of the reported solutions. Available solutions cover a wide range of cases, with the main limitation found being the lack of an unified solution, which enables the detection of all kinds of non-technical losses. [ABSTRACT FROM AUTHOR]

• Features and challenges of radar networks reviewed from the perspective of signal processing. • Different applications discussed. • Extensive literature covered with state-of-the-art methods categorized. • In each category, existing methods discussed and compared. • Important practical issues highlighted. Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic. [ABSTRACT FROM AUTHOR]

This paper proposes a computational data-driven adaptive optimal control strategy for a class of linear stochastic systems with unmeasurable state. First, a data-driven optimal observer is designed to obtain the optimal state estimation policy. On this basis, an off-policy data-driven ADP algorithm is further proposed, yielding the stochastic optimal control in the absence of system model. An application example of the learning mechanism of central nervous system in arm movement control is given to illustrate the effectiveness and practicality of the strategy. [ABSTRACT FROM AUTHOR]

• These methods accurately and efficiently implement fusion of N-D non-Gaussian PDFs • Improving efficiency in belief fusion can be done with little cost to accuracy • The proposed technique is dramatically faster than grid-based fusion • Formulation of multi-Gaussian shapes allows for useful modeling of certain problems • The proposed decision-making method makes full use of probabilistic information This paper describes the mathematical formulation of belief fusion of multi-Gaussian probability distribution functions (PDFs) in N -D, as well as the construction of some useful non-Gaussian structures. An emphasis of this work is the development of concise algorithms for constructing and efficiently fusing these non-Gaussian structures in N -space. In order to address decision-making using multi-Gaussian PDFs, an efficient probabilistic decision-making scheme is introduced and validated here. We investigate the trade-off between precision and efficiency in comparison with spatially discretizing fusion methods, concluding that the proposed techniques offer an improvement in both accuracy and efficiency for many contexts requiring non-Gaussian representation of belief. The proposed framework can be implemented in a diverse range of scenarios, potentially with real-time capability, and often without substantial sacrifice in accuracy. [ABSTRACT FROM AUTHOR]

The Advanced Composition Explorer (ACE) spacecraft has measured 235 solar-based interplanetary (IP) shock waves between the years of 1998–2014. These were composed of 203 fast forward (FF), 6 slow forward (SF), 21 fast reverse (FR) and 5 slow reverse (SR) type shocks. These data can be obtained from the Interplanetary Shock Database of Harvard-Smithsonian Centre for Astrophysics. The Solar Section of American Association of Variable Star Observers (AAVSO) is an organization that counts the number of the sunspots. The effects of interplanetary shock waves on some physical parameters can be computed using a hydrodynamical model. There should be some correlations between these effects and the sunspot variations. The major objective of this paper is twofold. The first one is to search these correlations with sunspots given in the database of AAVSO. As expected, high correlations between physical parameters and sunspots have been obtained and these are presented in tables below. The second objective is to make an estimation of these parameters for the 22nd solar cycle and the years between 2015 and 2018 using an artificial neural network. Predictions have been made for these years where no shock data is present using artificial intelligence. The correlations were observed to increase further when these prediction results were included. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology to estimate global tilted irradiation in 5-min steps using only global irradiation on the horizontal plane. The methodology is based on a combination of the two well-known conventional Perrin Brichambaut and Liu and Jordan models. Tilted irradiation is of great importance for the design and short-term performance assessment of fixed-tilt flat-plate collectors and photovoltaic (PV) systems. Intermediate key parameters of the state of the sky, referred to here as cloudiness factors, are determined and introduced to transform isotropic models into an anisotropic model. The results of the proposed model for all sky conditions with regard to normalized root mean square error (nRMSE), relative percentage error (RPE), normalized mean absolute error (nMAE) and coefficient of correlation (R2) range from 4.7 to 6.41%, 5.5–5.9%, 3.07–4.73% and 0.97 to 0.99, respectively, which are very accurate results, especially for such a short time step. A comparison with the best conventional models and even artificial neural network (ANN) models described in the literature has confirmed that the developed model has smaller errors. • A model to estimate solar global radiation on tilted plane for 5 min is proposed. • The proposed model only uses global solar radiation as input and simplified equations. • The nRMSE range from 4.7 to 6.41% for three sites with different climatic conditions. • The prediction errors are lower than those obtained with ANN and classical methods. • The proposed model can be used for all sky conditions. [ABSTRACT FROM AUTHOR]

• Bayesian inference based state estimation of voltage dips in power system. • Network uncertainties have been incorporated. • A iterative sampling process is used to accelerate the Monte Carlo integration. • The required measurement set-up is quantified. The magnitudes of voltage dips on non-monitored buses can be estimated by applying Bayes's theorem with measured voltage dips on a limited number of monitored buses. Bayes's theorem is used to obtain the grid conditions, including the network impedance, load, distributed generation vector, and short-circuit location, from which the voltage magnitudes along the feeder can be calculated. In order to implement this approach effectively, it is necessary to understand which parameters among the grid conditions are more significant for the estimation performance and how the uncertainties associated with these parameters affect the estimation results. Moreover, since the measurement structure provides observations to calculate the posterior distribution from the prior distribution of the grid conditions. The measurement parameters, quantities, and accuracy levels are varied in simulations to obtain an optimum measurement structure balancing between the cost and the measurement performance. This paper presented the results of the sensitivity studies which have been done on the IEEE 13-Bus and 123-Bus test systems. The results shows the performance of the voltage dip state estimation approach is less sensitive to the uncertainty level of the load while more sensitive to those of the DG, the short-circuit power, the zero sequence impedance of the cable and the short-circuit resistance. The voltage measurement accuracy is more important than that of current and phase angles. The proposed method shows much stronger robustness compared to the conventional one and the estimation performance can be improved through several measures, e.g. adding more measurement points. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of traditional sliding mode control design subject to the mismatched disturbance. Existing methods fail to provide an efficient and simple solution. To counteract the unmatched disturbance in the system, a new sliding mode control strategy based on a non-smooth integral sliding manifold is proposed. The proposed sliding manifold has two important features. First, it acts like a reduced order disturbance observer since it estimates the mismatched disturbances by itself without any requirement of an additional observer design. Secondly, the system always starts from the sliding surface, hence, there is no reaching phase dynamics. Thus, the system trajectory is insensitive towards the disturbance from the initial point. The proposed method is tested on a coupled tank system to demonstrate its effectiveness. Both the simulation and experimental results are provided. • A non-smooth integral sliding manifold is proposed with modified sliding mode control design. • Act as reduced order disturbance observer to estimate mismatched disturbances without any additional observer design. • System trajectory is insensitive towards the disturbance as there is no reaching phase dynamics. • Proposed work is tested on a coupled tank system to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Accurate and reliable sensor information is crucial for intelligent vehicles to ensure safety and reliability. It is significant to devise effective solutions to diagnose possible sensor faults. In this paper, a sensor fault detection, isolation, and estimation approach is developed for intelligent vehicle-integrated motion systems. The Luenberger observer is designed and embedded into the fault diagnosis framework to real-time estimate the system states. Interval observers are constructed for vehicle subsystems to estimate the interval bounds of the corresponding observation errors. The estimation errors enter into the corresponding intervals generated by the adaptive laws when the sensor fault is free. In the event of a sensor fault, it can be detected efficiently and the faulty sensor can be isolated. Moreover, occurred sensor fault can be estimated by modifying the interval observer structure. The simulation results in the standard J-Turn test scenario are provided to evaluate and verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a method to monitor battery state of health (SOH) by estimating the number of cyclable Li-ions, a health-relevant electrochemical variable. SOH monitoring is critical to battery management in balancing the trade-off between maximizing system performance and minimizing battery degradation. The decrease of cyclable Li-ions indicates the effect on the SOH of degradation mechanisms that consume cyclable Li-ions. The unavailability of the number of cyclable Li-ions through non-invasive measurements makes its estimation necessary for in-situ SOH monitoring. In this paper, the extended Kalman filter (EKF) is used to estimate the number of cyclable Li-ions as an unknown battery parameter. The single particle model (SPM), a simplified battery electrochemical model, is used as the model in the EKF to achieve a computational complexity suitable for on-line estimation. Simulations are performed under typical electric vehicle current trajectories using an example parameter set for a hybrid-electric-vehicle battery. In the simulations, the battery is represented by the Doyle–Fuller–Newman (DFN) model, an electrochemical model with higher fidelity than the SPM. To comply with the practice, instead of using the same parameters as the DFN model in the SPM, parameterization of the SPM is performed before estimation of the number of cyclable Li-ions. The simulations show high estimation accuracy of the number of cyclable Li-ions using the EKF, even with the structural and parametric differences between the DFN model and the SPM, under both the ideal conditions and various non-ideal conditions (i.e., SOC estimation error, additional modeling error, and measurement noise). [ABSTRACT FROM AUTHOR]

Solar radiation data is critical to the design and operation of solar energy utilization systems, so a large number of models have been proposed and developed to estimate solar radiation in the past ten years. However, the performances of such models are controversial in different studies, and there is a lack of systematic comparison among them. In addition, few studies pay attention to the time scales and practicability of the models. This paper focuses on solving these questions through a critical literature review and the authors believe it can benefit researchers to perform further investigations about solar radiation. This paper reviews and compares the models from the points of view of time scale and estimation type for the first time. Furthermore, a large amount of data about the evaluation metrics (root mean square error and mean absolute percentage error) from different studies is summarized to clarify the performances of proposed models. The questions arising from the processing of source data are also carefully examined. This paper has presented a novel method to compare the estimation models and has provided a detailed analysis on available models. The results indicate that the sunshine duration fraction models and artificial neural networks have similar performances when used to estimate monthly average daily global radiation and daily global radiation, while more work is needed to study the estimation method on smaller time intervals and the mechanisms of atmospheric attenuation for solar radiation. [ABSTRACT FROM AUTHOR]

Due to the increasing availability of georeferenced microdata in several fields of research, surveys can benefit greatly from the use of the most recent spatial sampling methods. These methods facilitate selection of spatially balanced samples, which lead to particularly efficient estimates, by incorporating the distances between the exact locations of statistical units into the design. Unfortunately, since locations of units are rarely exact in practice due to imperfections in the geocoding processes, the implementation of spatial sampling designs is actually often limited. This paper aims at demonstrating that spatial sampling designs can be effectively carried out even when spatial information is not completely accurate. By means of Monte Carlo simulation studies, this paper proves that even when the geocoding of population is not exact, spatial sampling methods still facilitate more spatially balanced samples and more efficient estimates. [ABSTRACT FROM AUTHOR]

This paper develops an algorithm for estimating photovoltaic (PV) module temperature and effective irradiation level. The power output of a PV system depends directly on both of these states. Estimating the temperature and irradiation allows for improved state-based control methods while eliminating the need of additional sensors. Thermal models and irradiation estimators have been developed in the literature, but none incorporate feedback for estimation. This paper outlines an Extended Kalman Filter for temperature and irradiation estimation. These estimates are, in turn, used within a novel state-based controller that tracks the maximum power point of the PV system. Simulation results indicate this state-based controller provides up to an 8.5% increase in energy produced per day as compared to an impedance matching controller. A sensitivity analysis is provided to examine the impact state estimate errors have on the ability to find the optimal operating point of the PV system. [ABSTRACT FROM AUTHOR]

• We propose a two-dimensional (2D) dual particle filter that allows for real-time estimation and identification of batch processes. • We fully exploit the dynamics of a batch process both across the time and batch directions to deliver high-quality state and parameter estimates. • The proposed method is illustrated by its application in two simulation examples. This paper deals with the problem of real-time estimation and identification in batch processes represented by stochastic nonlinear state-space models (SSMs). To address this, the two problems – estimation and identification – are formulated under a common Bayesian filtering framework. In contrast to a continuous process, a batch process is typically operated for a relatively shorter duration, which limits the number of measurements available in a campaign to effectively solve the filtering problem. To overcome the limitations of existing solutions, we propose a two-dimensional dual particle filtering algorithm to solve the nonlinear filtering problem. The proposed algorithm performs filtering both along the time and the batch directions. This allows to combine measurements from multiple short campaigns for improved estimation and identification results. The efficacy of the proposed method is demonstrated on two simulation examples. [ABSTRACT FROM AUTHOR]

One way of improving situational awareness without increasing the risk of detection is to use passive sensor systems. If this capability is provided by several aircraft in a cluster, which can incorporate small basic sensor platforms, advantages can be gained such as longer baselines and an increased number of sensors in the cluster. In this paper, a methodology is presented that links results from signal processing to a Design Space Exploration, DSE, regarding sensor clusters when designing clusters that can operate both independently and in cooperation with other systems. When using Time-Difference of Arrival, the accuracy of the estimated location of a signal source depends on errors in timing and positioning of the sensors, errors in estimating signal arrival times and number of sensors and their spatial distribution. The Cramér-Rao Lower Bound is used to investigate the accuracy of signal source estimates for five different clusters and two levels of timing and positioning accuracy. The results show that the direction of arrival estimates are more accurate than those for the range. Although more sensors generally increased the accuracy, their spatial distribution and baseline related to the distance to the signal source also influence the quality of the results. The DSE process is supported by the collected presentation of the data regarding the measurement accuracy of the different sensor configurations, incorporating both cluster configuration as well as the positioning and timing. Having readily accessible data, the decision makers can focus on choosing the sensor system that meets the operational needs. [ABSTRACT FROM AUTHOR]

• Both the combination and temperature-based ET 0 estimating equations were compared for four main climatic regions across China. • The most accurate method of ET 0 estimating was recommended for each climate region. • The Penman-Monteith Forecast and Temperature Penman-Monteith equations usually provided the most accurate ET 0 estimation. • The Thornthwaite and McCloud would not be recommended for ET 0 estimating in China. Accurate estimation of daily reference evapotranspiration (ET 0) are vital for water resource management and irrigation decision-making. Based on the public weather forecasts, numerous models have been successfully used for daily ET 0 estimating, while too many models available for selection, which causes confusion regarding model selection for specific climate regions. In this paper, the estimating performances of six ET 0 equations using public weather forecast for a lead time of 1–7 days were compared for four main climatic region across China, and then, the most accurate equation was recommended for each climate region. Meanwhile, the applicability of every equation was assessed in relation to four climates, including subtropical monsoon climate (Cwa), temperate continental climate (Dfc), temperate monsoon climate (Dwa) and mountain plateau climate (HG). The Penman-Monteith Forecast (PMF) equation, which consisting of an adaptation of FAO56-PM equation using temperature and weather type forecast as inputs, provided the best ET 0 estimation performance in Cwa and HG climates; and the Temperature Penman-Monteith (PMT) equation using only the temperature data, obtained the most accurate average results for the Dwa and Dfc climates. The best and the second best estimation performance for each climate usually provided by PMF and PMT equations, since they both have the same advantage of following the conceptual approach of the FAO56-PM equation; further the third and the fourth choice would be the Hargreaves-Samani (HS) and the Blaney-Criddle (BC), respectively, while the Thornwaite (TH) and the McCloud (MC) yielded high errors and may not be applicable for ET 0 estimation for most climatic regions. As a whole, the PMF and PMT equations were better than the other equations and thus these two equations were recommended for daily ET 0 estimation for the near-future at all climate regions across China. [ABSTRACT FROM AUTHOR]

Abstract Existing sliding mode observer (SMO) schemes for fault reconstruction in descriptor systems require stringent conditions, or do not consider disturbances which can corrupt the reconstruction. In this paper, we present a two-observer scheme that overcomes these limitations by treating certain states as unknown inputs, thereby formulating a reduced-order infinitely observable system. A SMO is implemented onto this system to reconstruct certain faults, and its switching feedback term is fed into another SMO to reconstruct the remaining faults. Linear matrix inequalities (LMIs) are used to design observer gains in order to minimise the L 2 gain of the disturbances on the fault reconstruction. The existence conditions of the scheme are investigated and are found to be less restrictive than those from other schemes in the literature, and thus the scheme is applicable to a wider class of systems compared to existing schemes. Finally, a simulation example demonstrates the efficacy of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, the potential of adaptive neuro fuzzy inference system (ANFIS) was appraised for investigation of thermal performances of a minichannel heat sink for cooling of electronics using nanofluid coolant instead of pure water. The process, which simulates the thermal performances with ANFIS network, was constructed. The developed ANFIS network was with three neurons in the input layer, and one neuron (thermal performances) in the output layer. Since there are three thermal performances investigated, three ANFIS networks are created. The inputs were flow rate, as well as the Reynolds number. The third input represents the nanofluid concentration. The Al2O3–H2O nanofluid including the volume fraction was used as a coolant. Obtained experimental results showed the higher improvement of the thermal performances using nanofluid instead of pure distilled water. ANFIS results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach for heat sink base temperature prediction. • Thermal performances of a minichannel heat sink investigation. • Cooling of electronics using nanofluid coolant instead of pure water. • Improvement in predictive accuracy and capability by the ANFIS approach. • The effectiveness of the proposed strategies is verified based on the simulation results. [ABSTRACT FROM AUTHOR]

Highlights • A PSO-NF has been applied for re-constructing a set of practical PV characteristics. • The PSO-NF can predict the MPPT of individual PV modules in real time. • The proposed method exhibits good transient performance and fast convergence. Abstract In this paper, a neuro-fuzzy system tuned by particle swarm optimization algorithm has been applied for representing the photovoltaic characteristics. The resulting model has optimum compactness and interpretability and can online estimate and predict the maximum power point of individual photovoltaic modules. Experimental data has confirmed its improved accuracy. The particle swarm tuned neuro-fuzzy model has been applied to a practical photovoltaic power generation system for maximum power point control. The simulated results showed an average error of 0.25% with respect to the maximum extractable power of the panel used under static conditions; this percentage remains universal for a range of dynamic weather conditions at sampling rate of 1 sample/12 min. The errors obtained, on average, are reduced to one fourth in comparison of the genetic algorithm based model proposed in a previous research. [ABSTRACT FROM AUTHOR]

Abstract CMOS active pixel sensors (APS) suffer from serious dark-current degradation when they are exposed in radiation environment. Considering the general situation that there are multiple kinds of energetic particles in radiation environment, a dark-current estimation method for pixel sensors due to mixed radiation particles is proposed in this paper. Based on the radiation effects induced by particles of all kinds, the dark-current amplitudes distribution among the pixels can be predicted through probabilistic analysis. Validation is implemented upon the radiation dark-image data of CMOS APS devices, which shows that the predicted dark-current distribution matches very well with the experimental data, and the difference is no more than 15%. Highlights • Dark current degradation of CMOS APS in radiation environment. • Dark current estimation methods for CMOS APS due to mixed radiation particles. • Probabilistic analysis on dark current amplitude distribution under protons and gamma rays. [ABSTRACT FROM AUTHOR]

Abstract The precise population estimation, short-term or real-time, is crucial to social and civil management, such as public resource distribution, education budgets, health care, and public safety. In this paper, we reviewed the methods for estimation of real-time population. For real-time population estimation, especially for a certain wastewater treatment plant catchment, many water quality indicators and biomarkers were selected as potential markers and their stability, consumption coefficient, and uncertainty were assessed. The conventional water quality indicators, such as wastewater discharge volume, chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen, and total phosphorus, were used to calculate the serving population within a WWTP catchment. These parameters are all affected by the behavior, living habits and health conditions of people in different regions. Among them, wastewater discharge volume and ammonia might be more suitable for population estimation than the other parameters which may be influenced by additional industrial discharge. Moreover, acesulfame could be used to estimate the general population, while caffeine, tobacco, and carbamazepine could be applied as biomarkers for a specific population. Furthermore, the per capita loading differ should be considered and measured independently. To reduce the uncertainty of population, comprehensive model with multi-parameters should be developed and applicability should be checked. Graphical abstract Unlabelled Image Highlights • Summarizes some previous methods used in population estimation • Using water quality indicators as biomarkers can monitor the real-time population • Special biomarkers can be used to monitor the health status of the population. • Biomarkers and comprehensive models are essential for the population estimation. [ABSTRACT FROM AUTHOR]

Abstract The orbital safety of operational spacecraft, regardless of the mission, relies on timely and actionable observations to maintain so-called "custody" of all trackable Resident Space Objects (RSOs), including space debris, that might pose a hazard to safe, secure, and sustainable operations. For operations in and around the Geosynchronous Earth Orbit (GEO) regime, electro-optical (EO) observations are the most prevalent observation type available for tracking and determining RSO orbits. The quality (both accuracy and precision) of the data affects the inferable kinematic, physical, and other characteristics of RSOs and, in particular, measurement biases will result in inaccurate orbital trajectories and subsequent predictions. Physically meaningful conjunction assessments rely on not only accurate orbit state prediction, but also the "realistic" covariances associated with said predictions. In this paper we demonstrate an automated near real-time (NRT) assessment of measurement biases with an appropriately implemented Unscented Schmidt Kalman Filter (USKF) [14]. Hypothesized biases that are deterministic but statistically unobservable in the measurement data and cannot be estimated are accounted for as so-called "consider" parameters. The method presented herein is assessed and quantified using both simulated and actual measurement data. This method will enable the exploitation and mining of so-called "non-traditional" sensor data to maximize Space Situational Awareness (SSA) in a robust and timely fashion toward improvement of orbital safety. The ultimate goal is to provide decision-making evidence required solve problems preventing the space domain from being safe, secure, and sustainable. Highlights • Addresses need to supplement surveillance networks with "non-traditional" data. • Goal of work is to characterize data integrity in near real-time. • Collaboration funded by European Office of Aerospace Research and Development. • Dynamic "multi-state" filter estimates sensor noise and bias characteristics. • Combined state estimation uses consider covariance to determine error sensitivity. [ABSTRACT FROM AUTHOR]

Abstract This paper deals with the one-way classification analysis on the basis of conditional proportional reversed hazard rate models when treatment effects are random. Characteristics of the response variable including reversed hazard function, mean, variance and intraclass correlation are derived in explicit expressions. Estimations of the proposed model parameters are also obtained. Best predictions for score effects are given. The proposed model is an alternative approach for one-way classifications in which generalized linear models are applied for analysing continuous responses. For illustrative purposes, a real data set is analysed using the obtained results. [ABSTRACT FROM AUTHOR]

Abstract Unmanned aircraft systems (UAS) are susceptible to malicious attacks originated by intelligent adversaries, and the actuators constitute one of the critical attack surfaces. In this paper, the problem of detecting and mitigating attacks on the actuators of a small UAS is addressed. Three possible solutions of differing complexity and effectiveness are proposed to address the problem. The first method involves an active detection strategy, whereby carefully designed excitation signals are superimposed on the control commands to increase the detectability of the attack. In the second method, an unknown input observer is designed, which in addition to detecting the attack also estimates the magnitude of the attack. The third method entails designing an actuator system that makes use of variable frequency pulse-width modulated signals to improve the resilience of the actuator against malicious attacks. The effectiveness of the proposed methods is demonstrated using flight experiments and realistic MATLAB simulations that incorporate exogenous disturbances, such as steady winds, atmospheric turbulence, and measurement noise. Highlights • Actuators constitute one of the critical attack surfaces for small unmanned aircraft. • A software approach and a resilient hardware approach are proposed. • The software approach involves developing algorithms for attack detection. • The hardware approach focuses on designing actuators that are resilient to attacks. • Realistic simulations and flight experiments show the effectiveness of the methods. [ABSTRACT FROM AUTHOR]

Highlights • A novel three-stage time estimation method based on data mining is proposed. • A strategy with the expert's opinion considered is designed when using K-Means clustering algorithm. • An adapted DEA model is presented to evaluate the efficiency of the time arrangement. • Estimation models are trained using the genetic algorithm-backpropagation neural network. Abstract Rational and precise time estimation in a manufacturing plan is critical to the success of a shipbuilding project. However, due to the large number of various ship blocks, existing means are somehow inadequate to make the expected estimation. This paper proposes a novel three-stage method to discover and reuse the knowledge about how the duration and the slack time is essential while manufacturing a specific ship block. An efficient arrangement of the duration and the slack time means that the activity is more likely to be finished within the allocated duration, or if not, the extra consumed time does not exceed the given slack time which is at its lowest level. With such knowledge, planners can rapidly estimate the time allocation of all the manufacturing activities in the planning stage, which raises the possibility of successful execution within the limited budget. Different from previous studies, this research utilizes the execution data to find efficiency frontiers of the planned time arrangement (the duration and the slack time). For the sake of the evaluation validity, ship blocks are primarily clustered according to their features using the K-Means algorithm. In the second stage, an adapted data envelopment analysis (DEA) model is presented to evaluate the planned time arrangement. By processing the results, efficient time arrangements for manufacturing all the blocks can be obtained, hence, forming a data basis to boost the time estimation accuracy. In the last stage, genetic algorithm-backpropagation neural network (GA-BPNN) models are trained to capture the knowledge for further reuse by planners. Verified through experiments, this research almost outperforms several peer methods in terms of precision. [ABSTRACT FROM AUTHOR]

Highlights • Uncertainty of the Unified Method for TEMs characterization is carried out. • The uncertainty is Evaluated by means of a Monte Carlo Simulation. • A sensitivity analysis, gives a comprehensive insight into the most critical issues. • Experimental results show that two proposed stimuli provide compatible estimates. Abstract In this paper the uncertainty evaluation of the recently proposed Unified Method for Thermo-Electric Module characterization is carried out. The measurement model is detailed and individual uncertainty contributions are highlighted, with close reference to the instrumentation and measurement setup. The uncertainty evaluation is performed by means of a Monte Carlo Simulation. The same algorithm is used to perform a sensitivity analysis, giving a comprehensive insight into the most critical issues of the proposed method and assessing the performance of the two adopted electrical stimuli. The experimental results thereby obtained are discussed and improvements to the measurement setup and technique are finally proposed. [ABSTRACT FROM AUTHOR]

Abstract Nowadays, one of the most active research fields in space engineering is autonomous relative navigation around uncooperative objects. A common approach used to tackle this problem is through vision-based pose determination techniques. This paper investigates the possibility of using non-linear filtering techniques to improve the attitude estimation performance of vision-based methods. Furthermore, a simulation study is presented to compare the proposed nonlinear techniques with the multiplicative extended Kalman filter for attitude estimation. First-order and second-order nonlinear filters are adapted, implemented and tested for relative attitude estimation. Finally, the consequences of uncertainty in the knowledge of the target inertia matrix are investigated. [ABSTRACT FROM AUTHOR]