Maximum Uncertainty Research Articles

Surrogate-assisted MOEA/D for expensive constrained multi-objective optimization

In this paper, an adaptive surrogate-assisted MOEA/D framework (ASA-MOEA/D) is proposed for solving computationally expensive constrained multi-objective optimization problems, in which three specific search strategies are adaptively implemented based on the optimization states of subproblems to achieve targeted searches for different subproblems. To maintain feasibility, the RBF-based local search models are constructed by comprehensively considering the orthogonal distance difference and constraint satisfaction information for guiding infeasible solutions of the infeasible subproblems into feasible regions. To maintain convergence, the RBF surrogates of the aggregated objective and constraints are employed to construct local search models for locating better feasible solutions. To maintain diversity, the subregions of unexplored subproblems are effectively explored by utilizing the valuable information of their neighboring elite solutions. Moreover, the solution with the maximum overall uncertainty of RBF surrogates is selected for progressively increasing the prediction accuracies of surrogates. Therefore, ASA-MOEA/D strikes an adaptive balance among diversity, feasibility and convergence with the assistance of RBF surrogates as the optimization progresses. Empirical studies on three classical test suites demonstrate that ASA-MOEA/D with tchebycheff approach achieves highly competitive performance over other four state-of-the-art algorithms.

Predicting the Excitation Dynamics in Lanthanide Nanoparticles

AbstractWith their dipole‐forbidden 4f transitions, lanthanides doped in nanoparticles promise high excited state lifetimes and quantum yields that are required for applications such as composite lasers or nanoscale quantum memories. Quenching at the nanoparticle surface, however, severely reduces the lifetime and quantum yield and requires resource‐consuming experimental optimization that could not be replaced by simulations due to the limitations of existing approaches until now. Here, a versatile approach is presented that fully accounts for spatiotemporal dynamics and reliably predicts the lifetimes and quantum yields of lanthanide nanoparticles. LiYF4:Pr3+nanoparticles are synthesized as a model system, and the lifetimes of a concentration series (≈10 nm, 0.7−1.47 at%) are used to match the model parameters to the experimental conditions. Employing these parameters, the lifetimes and quantum yields of a size series (≈5 at%, 12−21 nm) are predicted with a maximum uncertainty of 12.6%. To demonstrate the potential of the model, a neutral shell is added around the core particles in the model which extends the lifetime by up to 44%. Furthermore, spatiotemporal analysis of single nanoparticles points toward a new type of energy trapping in lanthanide nanoparticles. Consequently, the numerical optimization brings applications such as efficient nanoparticle lasers or quantum memories within reach.

Assessing ecosystem vulnerability under severe uncertainty of global climate change

Assessing the vulnerability and adaptive capacity of species, communities, and ecosystems is essential for successful conservation. Climate change, however, induces extreme uncertainty in various pathways of assessments, which hampers robust decision-making for conservation. Here, we developed a framework that allows us to quantify the level of acceptable uncertainty as a metric of ecosystem robustness, considering the uncertainty due to climate change. Under the framework, utilizing a key concept from info-gap decision theory, vulnerability is measured as the inverse of maximum acceptable uncertainty to fulfill the minimum required goal for conservation. We applied the framework to 42 natural forest ecosystems and assessed their acceptable uncertainties in terms of maintenance of species richness and forest functional type. Based on best-guess estimate of future temperature in various GCM models and RCP scenarios, and assuming that tree species survival is primarily determined by mean annual temperature, we performed simulations with increasing deviation from the best-guess temperature. Our simulations indicated that the acceptable uncertainty varied greatly among the forest plots, presumably reflecting the distribution of ecological traits and niches among species within the communities. Our framework provides acceptable uncertainty as an operational metric of ecosystem robustness under uncertainty, while incorporating both system properties and socioeconomic conditions. We argue that our framework can enhance social consensus building and decision-making in the face of the extreme uncertainty induced by global climate change.

Alternative Approach Leading to Reduction in Measurement Instrument Uncertainty of EMI Measurement

Abstract Even in the field of electromagnetic compatibility, low measurement uncertainty means high measurement quality. Although there are standardized procedures for obtaining the uncertainty of such a measurement, which facilitate uncertainty estimation, modern approaches show further reduction possibilities. The paper presents an alternative approach to reducing measurement instrument uncertainty in the case of electromagnetic interference measurement based on many years of our experience and a large number of measurements in this field. In the paper, two different methods of uncertainty reduction are described. The first method is based on a detailed analysis of the sources of uncertainty and the subsequent division of the analyzed frequency band into more subranges. Another method uses the choice of the antenna factor, which also contains information about the test site where the measurement is carried out. In this way, despite a lengthy analysis, it is relatively easy to achieve a measurement instrument uncertainty that is below the maximum measurement uncertainty given by the CISPR standard.

Uncertainty quantification of once-through steam generator for nuclear steam supply system using latin hypercube sampling method

To study the influence of parameter uncertainty in small pressurized water reactor (SPWR) once-through steam generator (OTSG), the nonlinear mathematical model of the SPWR is firstly established. Including the reactor core model, the OTSG model and the pressurizer model. Secondly, a control strategy that both the reactor core coolant average temperature and the secondary-side outlet pressure of the OTSG are constant is adopted. Then, the uncertainty quantification method is established based on Latin hypercube sampling and statistical method. On this basis, the quantitative platform for parameter uncertainty of the OTSG is developed. Finally, taking the uncertainty in primary-side flowrate of the OTSG as an example, the platform application work is carried out under the variable load in SPWR and step disturbance of secondary-side flowrate of the OTSG. The results show that the maximum uncertainty in the critical output parameters is acceptable for SPWR.

Uncertainty Analysis of Spherical Joint Three-Dimensional Rotation Angle Measurement

A precision spherical joint is a type of spherical motion pair that can realize three degrees of rotation freedom. In this paper, a specific method is used to assess the uncertainty of our measurement system. The measurement system uses the codes and eddy current sensor to measure the angle. Different codes are engraved on the surface of the spherical joint. Eddy current sensors are embedded in the spherical socket to detect and recognize the spherical code. The uncertainty of the measurement system hardware and an artificial neural network are studied. Based on the Monte Carlo method, the uncertainty components are evaluated and combined, and the comprehensive uncertainty of the measurement system is obtained. The evaluation results of system measurement uncertainty are verified from specific angles. In the three-axis measurement uncertainty, the minimum is about 2′, and the maximum uncertainty is about 1°. The proposed method can be used to evaluate the uncertainty of other multiple-input multiple-output measurement systems.

Uncertainty estimation in geostationary ocean color imager (GOCI) bio-optical products for detecting diurnal variability in coastal water

ABSTRACT Geostationary Ocean Colour Imager (GOCI) provides ocean colour products for monitoring ecosystem dynamics and assessing spatiotemporal changes. However, producing reliable satellite-based product estimates remains challenging in optically complex coastal waters, and bio-optics algorithms must be assessed. To date, assessments of satellite products have been analysed based on clear, natural waters. In this study, we assume stable waters that express little to no diurnal variability due to biological or physical processes in coastal water. Then, a suitable bio-optical algorithm with the lowest uncertainty is selected. The Tassan (MS) algorithm proposed in 2016 provided the appropriate representations of the temporal variation in chlorophyll in the results. The uncertainty of chlorophyll a estimated at the Mooring site located at the Yangtze River mouth and at the Ieodo site located off the coast of the Tsushima Strait were less than 14.5%, and the maximum uncertainty at the Socheongcho site, which was close to the Korean Peninsula, was 28.7%.

Machine learning identifies straightforward early warning rules for human Puumala hantavirus outbreaks

Human Puumala virus (PUUV) infections in Germany fluctuate multi-annually, following fluctuations of the bank vole population size. We applied a transformation to the annual incidence values and established a heuristic method to develop a straightforward robust model for the binary human infection risk at the district level. The classification model was powered by a machine-learning algorithm and achieved 85% sensitivity and 71% precision, despite using only three weather parameters from the previous years as inputs, namely the soil temperature in April of two years before and in September of the previous year, and the sunshine duration in September of two years before. Moreover, we introduced the PUUV Outbreak Index that quantifies the spatial synchrony of local PUUV-outbreaks, and applied it to the seven reported outbreaks in the period 2006–2021. Finally, we used the classification model to estimate the PUUV Outbreak Index, achieving 20% maximum uncertainty.

Uncertainty Propagation in UAM Time-Dependent Neutronics PWR Studies

A subset of pressurized water reactor (PWR) studies from Exercise II-2 of the Uncertainty Analysis in Modeling for Light Water Reactors (UAM-LWR) benchmark study has been performed to quantify the importance of both nuclear data uncertainties and manufacturing uncertainties in an assembly depletion calculation and a mini-core rod movement transient. The depletion study of the 15 × 15 PWR assembly using the SAMPLER and TRITON modules of the SCALE code system revealed a maximum uncertainty in keff of 0.49% for fresh fuel, decreasing to 0.38% at the midpoint of the fuel cycle and rising back to 0.48% at the end of the fuel cycle. Uncertainties and correlations of various homogenized cross sections and other group constant data, such as keff, have been determined, and the effect of randomly applied manufacturing uncertainties was found to be largely negligible relative to nuclear data uncertainties for bulk lattice parameters. However, for local parameters, such as the pin power factors, assembly discontinuity factors, and diffusion coefficients, the effects from manufacturing uncertainties were appreciable and sometimes dominant. Nuclear data uncertainties were found to be the dominant contributors to uncertainty in the isotopic composition of the overall assembly, with the exception of very early in the fuel cycle, where manufacturing uncertainties such as perturbations to the fuel density and pin radius made nonnegligible contributions to total uncertainty. The contribution of manufacturing uncertainties to isotopic uncertainties was nonnegligible at a pin-by-pin level, but still smaller than the contributions from nuclear data uncertainty. Studies of the PWR mini-core rod movement transient using homogenized data from the SCALE models in the PARCS diffusion code showed little difference between the tested modeling approaches and demonstrated that nuclear data uncertainties dominated the manufacturing uncertainties in the global figures of merit considered, such as the equilibrium core boron concentration, the maximum core power factor, and the maximum reactivity insertion. For local effects, such as maximum pin power during the transient, the randomly applied manufacturing uncertainties were dominant. It was found in general that for global system properties, nuclear data uncertainties made significantly larger contributions to total uncertainty, whereas for local parameters the impact of manufacturing uncertainties was at least nonnegligible, and for some parameters, dominant.

Current performance of C-reactive protein determination and derivation of quality specifications for its measurement uncertainty.

From External Quality Assessment data, current harmonization of CRP measuring systems appears to be satisfactory, the inter-assay CV being well below 10%. The inter-method variability is even better (close to 3%) when the widely used measuring systems are compared at CRP concentrations employed as cut-off for detecting sub-clinical infection (i.e.,10.0mg/L) and measurement variability estimated, according to ISO 20914:2019 Technical Specification, from the intermediate within-lab reproducibility of 6-month consecutive measurement data. According to the state-of-the-art model (which is better suited for CRP), the maximum allowable measurement uncertainty (MAU) for CRP measurement on clinical samples with 10.0mg/L concentrations is 3.76% (desirable quality). As measurement uncertainty (MU) of the only available reference material (ERM-DA474/IFCC) is ∼3%, to fulfil desirable MAU on clinical samples, IVD manufacturers should work to keep the contribution of remaining MU sources (commercial calibrator and intermediate within-lab reproducibility) lower than 2.3%.

Multi-fidelity hydrodynamic analysis of an autonomous surface vehicle at surveying speed in deep water subject to variable payload

Autonomous surface vehicles (ASV) allow the investigation of coastal areas, ports, and harbors as well as harsh and dangerous environments such as the arctic regions. In the past decade, several studies addressed the shape optimization, control and stability, and seakeeping of ASVs. Nevertheless, the hydrodynamic analysis of ASV performance subject to variable operational parameters lacks a thorough investigation. In this context, this paper presents a multi-fidelity (MF) hydrodynamic analysis of a catamaran ASV, namely the shallow water autonomous multipurpose platform (SWAMP), at surveying speed in calm water and subject to variable payload and location of the center of mass, accounting for the variety of equipment that the vehicle can carry. The analysis is conducted in deep water, which is the condition mostly encountered by the ASV during surveys of coastal and harbor areas. The objective of the analysis is the investigation of the effects of the payload and its arrangement on the variations of the resistance, the vehicle attitude, and the wave generated in the region between the catamaran hulls. These are assessed using an unsteady Reynolds–averaged Navier–Stokes equation (URANSE) code and a linear potential flow (PF) solver. The latter also includes variable grid refinement and coupling between hydrodynamic loads and rigid body equations of motion. Furthermore, the limitation of a PF analysis in the current context is investigated. Finally, a multi-fidelity Gaussian process (MF-GP) model is obtained combining high- (URANSE) and low-fidelity (PF) solutions following an additive correction approach of the latter. The MF-GP model is iteratively refined using an active learning approach based on the maximum prediction uncertainty and the computational cost of the numerical simulations. Numerical results show that the MF-GP is effective in producing response surfaces of the SWAMP performance with a limited computational cost. It is highlighted how the SWAMP performance is significantly affected not only by the payload, but also by the location of the center of mass. The latter can be therefore properly calibrated to minimize the resistance and allow for longer-range operations.

Does Corporate Governance Influence the Performance of Romanian Companies in A Period of Maximum Uncertainty? Empirical Analysis on The Case of Companies Listed on The Bucharest Stock Exchange

This article aims to highlight the impact that a company's management structure has on the profitability of an enterprise during the period of uncertainty that the emergence of the new COVID-19 virus has created. In order to achieve the objective, a series of panel regressions is carried out, following which an analysis over a period of 3 years (more precisely the years 2019, 2020 and 2021) will be outlined. In Romania, this virus appeared at the beginning of 2020, more precisely on 26.02.2020.

Accurate Classification of Chunmee Tea Grade Using NIR Spectroscopy and Fuzzy Maximum Uncertainty Linear Discriminant Analysis.

The grade of tea is closely related to tea quality, so the identification of tea grade is an important task. In order to improve the identification capability of the tea grade system, a fuzzy maximum uncertainty linear discriminant analysis (FMLDA) methodology was proposed based on maximum uncertainty linear discriminant analysis (MLDA). Based on FMLDA, a tea grade recognition system was established for the grade recognition of Chunmee tea. The process of this system is as follows: firstly, the near-infrared (NIR) spectra of Chunmee tea were collected using a Fourier transform NIR spectrometer. Next, the spectra were preprocessed using standard normal variables (SNV). Then, direct linear discriminant analysis (DLDA), maximum uncertainty linear discriminant analysis (MLDA), and FMLDA were used for feature extraction of the spectra, respectively. Finally, the k-nearest neighbor (KNN) classifier was applied to classify the spectra. The k in KNN and the fuzzy coefficient, m, were discussed in the experiment. The experimental results showed that when k = 1 and m = 2.7 or 2.8, the accuracy of the FMLDA could reach 98.15%, which was better than the other two feature extraction methods. Therefore, FMLDA combined with NIR technology is an effective method in the identification of tea grade.

Dynamic Behavior and Stability Analysis of Automatic Voltage Regulator with Parameter Uncertainty

This research article describes a novel optimization technique called simulink design optimization (SDO) to compute the optimal PID coefficients for an automatic voltage regulator (AVR). The time-domain performance of the proposed controller was analyzed using MATLAB/Simulation, and its performance was compared with that of water cycle algorithm, genetic algorithm, and local unimodal sampling algorithm-based PID controllers. The robustness of the proposed controller was verified by applying the disturbances to the generator field voltage and the amplifier parameter uncertainty. The studies presented in literature were discussed the AVR loop stability using the Bode plot which will not give the minimum stability margins. This study proposes a novel stability analysis called disk-based stability analysis to authenticate the stability of the AVR loop which is obtained by the classical analysis. This stability was compared with the proposed stability analysis. The MATLAB results reveal that the SDO-PID controller regulates the terminal voltage of the generator precisely, is more robust to parameter uncertainty, and is more stable than the other controllers. The maximum allowable parameter uncertainty of the amplifier model was identified as 102% of its nominal parameters. The stability margins are recognized as DGM = 10.40 dB and DPM = 56.50° for the AVR stability.

The resolved chemical composition of the starburst dwarf galaxy CGCG007-025: direct method versus photoionization model fitting

ABSTRACTThis work focuses on the gas chemical composition of CGCG007-025. This compact dwarf is undergoing a galaxy wide star-forming burst, whose spatial behaviour has been observed by VLT/MUSE. We present a new line measurement library to treat almost 7800 voxels. The direct method chemical analysis is limited to 484 voxels with good detection of the $[\mathrm{S}\, {\small III}]6312\, \mathring{\rm A}$ temperature diagnostic line. The recombination fluxes are corrected for stellar absorption via a population synthesis. Additionally, we discuss a new algorithm to fit photoionization models via neural networks. The eight ionic abundances analysed show a spatial normal distribution with a $\sigma \sim 0.1\, {\rm dex}$, where only half this value can be explained by the uncertainty in the measurements. The oxygen abundance distribution is 12 + log(O/H) = 7.88 ± 0.11. The ${\it T}_{e}[\mathrm{S}\, {\small III}]$ and ne[S ii] are also normally distributed. However, in the central and brightest region, the ne[S ii] is almost thrice the mean galaxy value. This is also reflected in the extinction measurements. The ionization parameter has a distribution of ${\rm log}(U) = -2.52^{0.17}_{0.19}$. The parameter spatial behaviour agrees with the S2+/S+ map. Finally, the discrepancies between the direct method and the photoionization model fitting are discussed. In the latter technique, we find that mixing lines with uneven uncertainty magnitudes can impact the accuracy of the results. In these fittings, we recommend overestimating the minimum flux uncertainty one order below the maximum line flux uncertainty. This provides a better match with the direct method.

A performance approximation assisted expensive many-objective evolutionary algorithm

Surrogate-assisted multi-objective evolutionary algorithms have been paid much attention to solve expensive multi-objective problems in recent years. However, with the number of objectives increasing, an improper solution may be picked for expensive objective evaluation due to the accumulation error of approximated values on objective functions. Furthermore, the time to construct surrogate models for all objectives will significantly increase. Thus, in this paper, Gaussian process (GP) models are proposed for performance indicators instead of for objective functions. Furthermore, solutions are selected from either of two ways to be evaluated using the expensive objective function. When there are non-dominated solutions found so far that are approximated, they will be exactly evaluated using the objective function. Otherwise, the solution with the maximum approximation uncertainty among the current population will be evaluated using the real objective functions. The efficiency of the presented approach is validated on the DTLZ test suite with 3, 6, 10, 15, and 20 objectives, MaF benchmark problems with 3, 6, 10, 15, and 20 objectives, and a real-world optimization problem called filter design. The experimental results show that the method proposed in this paper is competitive compared to recently proposed peer algorithms for expensive many-objective problems.

Capacitive platform for real-time wireless monitoring of liquid wicking in a paper strip.

Understanding the phenomenon of liquid wicking in porous media is crucial for various applications, including the transportation of fluids in soils, the absorption of liquids in textiles and paper, and the development of new and efficient microfluidic paper-based analytical devices (μPADs). Hence, accurate and real-time monitoring of the liquid wicking process is essential to enable precise flow transport and control in microfluidic devices, thus enhancing their performance and usefulness. However, most existing flow monitoring strategies require external instrumentation, are generally bulky and unsuitable for portable systems. In this work, we present a portable, compact, and cost-effective electronic platform for real-time and wireless flow monitoring of liquid wicking in paper strips. The developed microcontroller-based system enables flow and flow rate monitoring based on the capacitance measurement of a pair of electrodes patterned beneath the paper strip along the liquid path, with an accuracy of 4 fF and a full-scale range of 8 pF. Additionally to the wired transmission of the monitored data to a computer via USB, the liquid wicking process can be followed in real-time via Bluetooth using a custom-developed smartphone application. The performance of the capacitive monitoring platform was evaluated for different aqueous solutions (purified water and 1 M NaCl solution), various paper strip geometries, and several custom-made chemical valves for flow retention (chitosan-, wax-, and sucrose-based barriers). The experimental validation delivered a full-scale relative error of 0.25%, resulting in an absolute capacitance error of ±10 fF. In terms of reproducibility, the maximum uncertainty was below 10 nl s-1 for flow rate determination in this study. Furthermore, the experimental data was compared and validated with numerical analysis through electrical and flow dynamics simulations in porous media, providing crucial information on the wicking process, its physical parameters, and liquid flow dynamics.

A proposed method for addressing large unphysical uncertainties in mubar

Mubar uncertainties in Section MF 34 MT 2 of ENDF/B-VIII.0 data are sometimes too large. Physical limitations of the bounds of mubar limit the maximum uncertainty to be < 1.0 and usually ≪ 1.0. Two ad hoc methods are proposed to artificially constrain the randomly sampled values of mubar to allowable values. The application of NJOY’s mubar covariance matrix to the “sandwich rule” is also discussed and a consistent way to apply sensitivity coefficients is described. Mubar uncertainties for the Jezebel keffwere found to be 156 pcm – a value comparable to other evaluated cross section uncertainties.

Variational Bayes to Accelerate the Lagrange Multipliers towards the Dual Optimization of Reliability and Cost in Renewable Energy Systems

Renewable energy sources are constantly increasing in the modern power systems. Due to their intermittent and uncertain potential, increased spinning reserve requirements are needed to conserve the reliability. On the other hand, each action towards efficiency improvement and cost reduction contradicts the participation of variable resources in the energy mix, requiring more accurate tools for optimal unit commitment. By increasing the renewable contribution, not only does the overall system inertia decrease with the decreasing conventional generation, but more generators that are expensive are also introduced. This work provides a radically different approach towards a tractable optimization task based on the framework of Lagrange relaxation and variational Bayes. Following a dual formulation of reliability and cost, the Lagrange multipliers are accelerated via a machine learning mechanism, namely, variational Bayesian inference. The novelty in the proposed approach stems from the employed acquisition function and the effect of the Gaussian process. The obtained results show great improvements compared with the Lagrange relaxation alternative, which can reach over USD 1 M in production cost credits at the least number of function evaluations. The proposed hybrid method promises global solutions relying on a proper acquisition function that is able to move towards regions with minimum objective value and maximum uncertainty.

Stationary and mobile storages-based renewable off-grid system planning considering storage degradation cost based on information-gap decision theory optimization

This paper presents the planning of a hybrid renewable system with wind turbines and bio-waste energy units along with stationary (i.e., batteries) and mobile (i.e., electric vehicles) energy storage. This model minimizes the cost of construction, maintenance and storage degradation. In this model, the energy supply priority is given to renewable sources. Then the mentioned storage devices are used to cover the gap between the load profile and the renewable generation power. The proposed scheme addresses the uncertainties of loads, renewable power and energy consumption of mobile storage devices. Therefore, robust optimization based on information-gap decision theory (IGDT) is utilized to obtain a robust solution against the prediction error of the aforementioned uncertainties. Also, the hybrid optimization algorithm of honey bee mating and artificial bee colony is derived, showing the optimal solution with low dispersion in the final response. The proposed design is applied to the data of the city of Espoo, Finland. The obtained numerical results show the capability of the proposed design in deriving robust economic planning for the proposed hybrid system. So, the used hybrid solution algorithm is able to obtain the best solution in a lower computation time compared to non-hybrid solvers, and it has a low standard deviation around 0.94 % in the final response. The presence of the aforementioned renewable sources leads to attaining an environment-friendly hybrid system. The optimal performance of the storage devices leads to the robustness of the optimal solution against the maximum error of 18.19 % in the prediction of uncertainties. This occurs considering the smart charging management of mobile storage, but the maximum uncertainty level of 11.12 % holds to non-smart management of these storage devices in the proposed scheme. The energy management of mobile storage devices based on smart (non-smart) charging strategy also reduces (increases) the planning cost of the off-grid system by 7.62 % (39.68 %) compared to their absence.