Influence Of Uncertainty Factors Research Articles

Barrier function–based adaptive STSMC for steering feel feedback of SBW vehicle with uncertainty

As an essential component of human–computer interaction in steer-by-wire (SBW) vehicles, the steering feel feedback system provides drivers with accurate steering feel by precisely controlling the output torque of the steering feel motor. Meanwhile, the existence of uncertainties makes the accurate control of steering feel feedback torque become an acknowledged challenging issue. This article proposes an adaptive super-torque sliding mode control strategy based on barrier functions for the steering feel feedback system of SBW vehicles with highly complex dynamics in the presence of uncertainties. First, to achieve intelligent modeling under the influence of uncertainty factors, a dynamic system with unknown uncertainty based on adaptive fuzzy logic system (FLS) is proposed. In the framework of this model, the uncertainty factors introduced by unknown external disturbance, parameter uncertainty, and model couplings are considered as a lumped uncertainty function. Furthermore, the membership functions of the FLS are dynamically adjusted based on nearest neighbor clustering, aiming to enhance the modeling accuracy beyond traditional fuzzy modeling. To further offset the system uncertainty and FLS approximation error, an adaptive super-twisting sliding mode control (STSMC) method based on barrier function is proposed. This algorithm retains the advantages of traditional sliding mode control (SMC) methods in dealing with systems with uncertainty and eliminates the need for a priori knowledge of the upper bound of uncertainties by introducing the adaptive law based on barrier function, avoiding the use of complex identification algorithms. Moreover, it eliminates the gain of overestimation and significantly reduces chattering phenomenon in traditional sliding mode control. Finally, based on Lyapunov stability theory, this article proves the proposed system can achieve convergence within a finite time. Compared with STSMC, traditional SMC and proportional–integral–derivative (PID) control, the results verify the effectiveness of the proposed control method. The proposed strategy provides a new solution to reduce the steering feel feedback torque error under the influence of uncertain factors.

Natural resources and sustainable development: Evidence from the dynamic correlation between crude oil and gold market

Amidst the uncertainty in markets spurred by globalization and technological advancements, the volatility of the crude oil and gold markets, which are emblematic of natural resource markets (NRMs), profoundly impacts global economic sustainability. This study innovatively quantifies and contrasts the impact of macroeconomic uncertainty factors on the volatility and dynamic correlation of these pivotal NRMs by extending the GJR-GARCH-MIDAS-X and DCC-MIDAS-RC-X models grounded in global economic policy uncertainty (GEPU), trade policy uncertainty (TPU), monetary policy uncertainty (MPU), and geopolitical risk (GPR). Concurrently, employing principal component analysis (PCA) blends numerous uncertainty factors, heightening the models’ explanatory capacity and predictive precision. Empirical findings suggest that all uncertainty factors exert significant positive effects on both crude oil and gold volatility, albeit with heterogeneity. Furthermore, the correlation between crude oil and gold fluctuates over time under the influence of uncertainty factors. Additionally, this study revealed asymmetric effects on realized volatility between the two NRMs. This research not only provides novel theoretical insights and empirical evidence concerning the intricate dynamic interplay between the two NRMs, but also has significant ramifications for policymakers, market regulators, and investors in crafting effective strategies and decisions amidst market uncertainty.

Thermal error prediction and reliability analysis of the main shaft bearing at wind turbines

During the operation of the gearless wind turbine, the phenomenon of heat generation in the main shaft bearing is inevitable and further affects the assembly preload. It is crucial to determine the effect of the thermal error on bearing assembly preload. In this paper, a reliability analysis method for main shaft bearing is proposed. Firstly, a finite element model for the thermal analysis of wind turbines is established based on heat transfer theory, and the thermal error of the preload is calculated. Subsequently, a reliability analysis of the main shaft bearing is conducted through Quasi-Monte Carlo simulation (QMCS) considering the influence of uncertainty factors. To further improve the computational efficiency, a surrogate model based on stochastic configuration network (SCN) is established to analyze the reliability and sensitivity of the bearing. Finally, the numerical example shows that the proposed model has high accuracy and applicability.

Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 2: Influence of uncertainty factors

Abstract. This study focuses on the uncertainties that influence numerical simulation results of meteorological fields (horizontal resolution: 75, 15, and 3 km; vertical resolution: 48 and 62 levels; near-surface (N-S) scheme: MM5 and Eta schemes; initial and boundary conditions: Final (FNL) and European Center for Medium-Range Weather Forecasting (ECMWF) reanalysis data; underlying surface update: model default and latest updates; model version: version 3.6.1 and 3.9.1). By further evaluating and analyzing the uncertainty factors, it is expected to provide relevance for those scholars devoted to factor analysis in order to make the results closer to the observed values. In this study, a total of 12 experiments are set up to analyze the effects of the uncertainties mentioned above, and the following conclusions are drawn: (1) horizontal resolution has a greater effect than vertical resolution; (2) the simulated effects of temperature and wind speed in the N-S scheme are smaller than those in the planetary boundary layer (PBL) scheme; (3) the initial and boundary conditions of different products have the most remarkable effect on relative humidity, while the simulation results of ECMWF data are the best; (4) the updates with urban and water bodies as the underlying surface have a more significant contribution to the meteorological fields, especially on temperature; and (5) for the PBL parameterization schemes, the update of the model version has less impact on the simulation results because each update has small changes and no major changes overall. In general, the configuration of uncertainties needs to be considered comprehensively according to what you need in order to obtain the best simulation results.

Influence of uncertainty factors on the dynamic characteristics of magnetorheological damper

The magnetorheological damper (MRD) is a kind of energy dissipation device, which converts vibration mechanical energy into self-heat energy during operation. The dynamic performance of MRD is affected by uncertain factors such as temperature and external magnetic field. In this paper, basing on the temperature rise theory and external magnetic field interference theory of MRD, it analyses the influence of temperature effect and external magnetic field on the dynamic performance of MRD. Under sinusoidal excitation, the internal temperature field and magnetic field distribution of MRD are simulated by COMSOL, and it calculates the changes of temperature and magnetic field in MRD by the conditions of input current of 0.4A, 0.8A and 1.2A. The temperature rise and external magnetic field test are carried out. Under the same input current, the temperature rise of the outer wall of the cylinder is measured. The results show that the input current increases, the temperature rise of MRD increases, and the temperature of MRD increases, the output damping force decreases obviously. When the input current is 0.4A, the temperature of MRD rises to 40℃, the maximum output damping force decreases by 11.2%, and the current is 1.2A, the maximum output damping force decreases by 18.6%. When external magnetic field is applied, the output damping force of MRD increases. The magnetic induction intensity of the external magnetic field increases from 10mT to 30mT, when the incoming current is 0.4A, the maximum output damping force increases by 5.9%, and the current is 1.2A, the maximum output damping force increases by 5.1%. Therefore, applying external magnetic field to compensate the decreasing value of output damping force caused by temperature rise effect has a certain effect. The results show that the variation trend of theoretical analysis, simulation calculation and experimental verification is consistent.

Optimum Route and Transport Mode Selection of Multimodal Transport with Time Window under Uncertain Conditions

Aiming at the problem of multimodal transport path planning under uncertain environments, this paper establishes a multi-objective fuzzy nonlinear programming model considering mixed-time window constraints by taking cost, time, and carbon emission as optimization objectives. To solve the model, the model is de-fuzzified by the fuzzy expectation value method and fuzzy chance-constrained planning method. Combining the game theory method with the weighted sum method, a cooperative game theory-based multi-objective optimization method is proposed. Finally, the effectiveness of the algorithm is verified in a real intermodal network. The experimental results show that the proposed method can effectively improve the performance of the weighted sum method and obtain the optimal multimodal transport path that satisfies the time window requirement, and the path optimization results are better than MOPSO and NSGA-II, effectively reducing transportation costs and carbon emissions. Meanwhile, the influence of uncertainty factors on the multimodal transport route planning results is analyzed. The results show that the uncertain factors will significantly increase the transportation cost and carbon emissions and affect the choice of route and transportation mode. Considering uncertainty factors can increase the reliability of route planning results and provide a more robust and effective solution for multimodal transportation.

Optimal Design of Biomass-centered Integrated Energy System under Uncertainty

To achieve the goal of carbon peak and carbon neutrality, China is actively promoting the production and consumption of clean and sustainable energy such as biomass. The optimization of integrated energy system (IES) configuration including biomass has gradually become a research hotspot. In this paper, aiming at the configuration optimization problem of biomass-centered IES under uncertainty, considering the uncertainty of user load and energy price, the maximum annual profit is selected as the objective function, and the configuration planning model of IES under uncertainty is established. Then, the artificial bee colony (ABC) algorithm is used to solve the optimization model, and the influence of uncertainty factors on the configuration optimization of IES is explored. The results of the example show that with the two-stage stochastic programming model used to deal with the uncertainty factors in the IES, the equipment configuration capacity and operating parameters in the system are changed compared with the results of the deterministic conditions. It can overcome the problem of over-optimism in the deterministic condition and reduce the system operation risk due to the random variation of uncertainty factors.

Multidisciplinary Reliability Design Considering Hybrid Uncertainty Incorporating Deep Learning

Multidisciplinary reliability design optimization is considered an effective method for solving complex product design optimization problems under the influence of uncertainty factors; however, the high computational cost seriously affects its application in practice. As an important part of multidisciplinary reliability design optimization, multidisciplinary reliability analysis plays a direct leading role in its computational efficiency. At present, multidisciplinary reliability analysis under mixed uncertainty is still nested or sequential execution mode, which leads to the problem of poor disciplinary autonomy and inefficiency in the reliability analysis of complex products. To this end, a multidisciplinary reliability assessment method integrating deep neural networks and probabilistic computational models under mixed uncertainty is proposed for the problem of multidisciplinary reliability analysis under mixed uncertainty. The method considers the stochastic-interval-fuzzy uncertainty, decouples the nested multidisciplinary probability analysis, multidisciplinary likelihood analysis, and multidisciplinary interval analysis, uses deep neural networks to extract subdisciplinary high-dimensional features, and fuses them with probabilistic computational models. Moreover, the whole system is divided into several independent subsystems, then the collected reliability data are classified, and the fault data are attributed to each subsystem. Meanwhile, the environmental conditions of the system are considered, and the corresponding environmental factors are added as input neurons along with each subsystem. In this paper, the effectiveness of the proposed method is verified on numerical calculations and real inverter power failure data.

Probabilistic Safety Factor Calculation of the Lateral Overturning Stability of a Single-Column Pier Curved Bridge under Asymmetric Eccentric Load

At present, the calculation methods for the lateral overturning stability safety factor of a single-column pier curved bridge under asymmetric eccentric load in the highway bridge code adopt deterministic calculation models. This paper presents a transverse overturning stability safety factor study of single-column pier curved bridges under asymmetric eccentric load based on inverse reliability theory. This method, which both considers the influence of uncertainty factors among the structural parameters and satisfies the prescribed reliability level, was used to obtain safety factors through a target reliability index. Transverse overturning stability factors of single-column pier curved bridges under asymmetric eccentric load were estimated using the proposed method. A new method was proposed to calculate the design parameter of the safety factor of single-pier instability, and then an application was carried out. The results show that parameter uncertainty has a major effect on transverse overturning stability safety factors of single-column pier curved bridges under asymmetric eccentric load. Ignoring parameter uncertainty will produce an overestimation of the transverse overturning stability safety factors of single-column pier bridges. The vehicle load must be strictly controlled to avoid transverse overturning due to overloading. Compared to the deterministic model, the proposed method has the advantages of calculating the safety factors considering the randomness of parameters and establishing the relationship of the safety factor with the target reliability index.

Reliability-Based Topology Optimization of Thermo-Elastic Structures with Stress Constraint

Traditional topology optimization of thermo-elastic structures is based on deterministic conditions, without considering the influence of uncertainty factors. To address the impact uncertainty on structural strength, a reliability-based topology optimization of thermo-elastic structure with stress constraint is proposed. The probabilistic uncertainty quantities are associated with the structural material property, mechanical loads and the thermal stress coefficient with the topology optimization formulation considering volume minimization and stress constraint. The relaxation stress method combined with normalized p-norm function is adopted to condense whole element stresses into the global stress measurement that approximates the maximum stress. The adjoint variable method is utilized to derive the sensitivity of the stress constraint and the optimization problem is solved by the method of moving asymptote (MMA). Finally, several numerical examples are presented to demonstrate the effectiveness and validity of the proposed approach. Compared with the deterministic design, the reliability design has distinct topological configurations and the optimized structures maintain a higher reliability level.

The Impact of Uncertainty Factors on the Decision-Making Process of Logistics Management

The article is a contribution to the discussion on the possibilities of effective logistic decisions under the conditions of uncertainty. Variable and unpredictable factors, which create the conditions of uncertainty, not only directly affect logistic processes (positive or negative impact), but can also be a determinant of making decisions. Logistics management, because it is referred to in the context of decision-making, is currently defined by the quality of management decisions taken, including factors which often constitute only partially quantifiable sets. The main goal of the article is to show the strength of the dependence between the occurrence of uncertainty factors and the type of decision. On the basis of decision-making theory, the types of decisions were defined, and then a set of factors that are most important for a given type of decision was selected. The results of the analysis allowed the strength of the influence of uncertainty factors on making logistics decisions to be determined. On this basis, a catalog of key decisions was selected, including decision types, and also the effects of decisions taken under uncertainty were determined. The study and the results of the analysis should be treated rather as a voice in the ongoing discussion. Due to the unpredictability of some uncertainty factors, the research field in the discussed problem remains open.

Analysis of Banking System Functioning and Influence of Uncertainty Factors on Strategic Management of Bank Finances

The article analyzes the banking system of Ukraine in times of crisis and highlights the current state of its functioning. It is noted that the banking crisis played an important role, as a result of which there were a number of problems, namely at the level of management of individual banks, the reason for vulnerability to crisis phenomena was, first of all, the inefficiency of strategic management, which resulted in a high-risk profile of most of them in conditions of insufficient capital coverage of these risks. It is determined that the effectiveness of strategic management of bank finances of Ukraine depends to a large extent on factors at micro (the bank is affected by the external set of economic relations in the field of finance, represented by the subjects, the interaction with them creates uncertainty), macro (political, social, etc.) and mega-levels (such as the unstable state of the global economy, changes in the trends of monetary regulation, etc.) that have high degree of uncertainty. The dynamics of the main indicators of the banking system is studied and analyzed, in particular the number of banks included in the State Register of Banks, banks with a banking license, banks with foreign capital and banks with 100% foreign capital; asset structure; volume of toxic loans (the presence of large amounts on the balance sheets of banks' toxic assets, as well as the possible deterioration of loan servicing by borrowers due to rising macroeconomic, social and political instability negatively affects the quality of strategic financial management, as it significantly increases uncertainty in management decisions), and financial results of the banking system of Ukraine. It is determined that the operational environment of banks' functioning has a number of uncertainties that further influence the bank's finances and the effectiveness of their management.

Structural-functional model of anti-crisis financial management of the enterprise

The article discloses the procedure for the formation of the structural-functional model of anti-crisis financial management at the enterprise. For the purpose of a clearer presentation, the set of works on anti-crisis financial management of the enterprise from the perspective of the process approach has been analyzed. It was determined that the modeling of the anti-crisis financial management process should be carried out using modern analytical technologies, which will allow at the system level to form the necessary solutions taking into account the production features of the enterprise and achieve their effective implementation. It is established that in order to achieve greater efficiency of the management process, it is advisable to develop a structural-functional model of anti-crisis financial management. This allows you to determine the sequence of relevant actions and visualize all participants in the management process, the stages of the simulated process, their relationship, methodological support and results, both intermediate and general. The proposed methodological approach allows us to synthesize, within the framework of a single methodology, the process of developing a set of reasonable anti-crisis measures at the enterprise; provides regulation of management activity parameters at each stage of model development; significantly reduces the influence of uncertainty factors on the accuracy of the choice of anti-crisis measures, creates the prerequisites for improving the formation of an anti-crisis financial management program for the enterprise and further effective activities. Using the proposed methodology will allow us to consistently establish the procedure for conducting anti-crisis financial management, visually present the direct participants in this process, conduct its critical analysis and determine optimization paths, that is, the ability to create an improved model to increase the effectiveness of management decisions.

Identification and evaluation of the impact of uncertainty factors on the activities of agricultural enterprises

The article considers t the problem of determination of the degree of influence of uncertainty factors on the performance of an agricultural enterprise. Uncertainty and risk are an integral part of business life, and in agriculture they are all the more significant, since agricultural companies are greatly dependent on the influence of natural factors and phenomena. Consequently, the assessment of the directions and the strength of the impact of risk factors is of great importance for the plans and projects for the company's activities. After carrying out a schematic factor analysis of the financial results of the agricultural enterprise, the author selected parameters such as crop yields and average selling price, for analyzing the degree of exposure to uncertain factors. Since both the selected parameters are affected by a large number of both certain factors and factors uncontrollable for the company's management, the author finds it unacceptable to apply factor analysis to them. In the first place, the author justifies this by the difficulty in determining the weights of each influencing factor. Instead, the author suggests grouping all the factors on the basis of their certainty and definition of the influence degree for each group. For this purpose, the variability of each parameter in different series was analyzed: for several years in one enterprise and for one year in a number of similar enterprises. It was found that fluctuations in crop yields by enterprises were higher than by years. This forced the author to bring out the conclusion that there is a significantly greater influence of certain factors on this indicator. On the other hand, when analyzing prices, the opposite situation emerged: here the influence of uncertain factors is much stronger. As a result, the proposal was made for the projecting method, such as economic and mathematical modeling, to use the data from both the firm being the subject of modeling, and from the similar enterprises available from the regional Department of Agriculture. In this case, the accuracy of the forecast will be much higher, besides, it can be developed in 3 or 5 probability scenarios.

Modelling supply chain of rice, uncertainty factors and strategic supply management

Rice is the major staple food of India where the impact of technoenvironmental uncertainty factors like logistical problems, on time delivery, opportunistic behaviour, product specification, average lateness, on time governance mechanism, reduction in cost, degree of inconsistency, increase in knowledge flow between firms and climate change on agricultural ecosystems have been a matter of concern for food security in the present situation. In this research, influence of uncertainty factors on strategic supply management relevant to rice-based supply chain of Odisha has been reported with a objective to fill the gap by providing a framework addressing how to leverage uncertainty factors to improve supply chain performance. A model has been developed to analyse and access qualitative as well as quantitative measures and the impact of uncertainties on strategic supply management is tested. The present work has efficiently demonstrated the different techno-environmental uncertainties and its significance in strategic decision-making.

Modelling supply chain of rice, uncertainty factors and strategic supply management

Rice is the major staple food of India where the impact of technoenvironmental uncertainty factors like logistical problems, on time delivery, opportunistic behaviour, product specification, average lateness, on time governance mechanism, reduction in cost, degree of inconsistency, increase in knowledge flow between firms and climate change on agricultural ecosystems have been a matter of concern for food security in the present situation. In this research, influence of uncertainty factors on strategic supply management relevant to rice-based supply chain of Odisha has been reported with a objective to fill the gap by providing a framework addressing how to leverage uncertainty factors to improve supply chain performance. A model has been developed to analyse and access qualitative as well as quantitative measures and the impact of uncertainties on strategic supply management is tested. The present work has efficiently demonstrated the different techno-environmental uncertainties and its significance in strategic decision-making.

Multi-objective robust optimization method for the modified epoxy resin sheet molding compounds of the impeller

Abstract A kind of modified epoxy resin sheet molding compounds of the impeller has been designed. Through the test, the non-metal impeller has a better environmental aging performance, but must do the waterproof processing design. In order to improve the stability of the impeller vibration design, the influence of uncertainty factors is considered, and a multi-objective robust optimization method is proposed to reduce the weight of the impeller. Firstly, based on the fluid-structure interaction, the analysis model of the impeller vibration is constructed. Secondly, the optimal approximate model of the impeller is constructed by using the Latin hypercube and radial basis function, and the fitting and optimization accuracy of the approximate model is improved by increasing the sample points. Finally, the micro multi-objective genetic algorithm is applied to the robust optimization of approximate model, and the Monte Carlo simulation and Sobol sampling techniques are used for reliability analysis. By comparing the results of the deterministic, different sigma levels and different materials, the multi-objective optimization of the SMC molding impeller can meet the requirements of engineering stability and lightweight. And the effectiveness of the proposed multi-objective robust optimization method is verified by the error analysis. After the SMC molding and the robust optimization of the impeller, the optimized rate reached 42.5%, which greatly improved the economic benefit, and greatly reduce the vibration of the ventilation system.

Estimation of Voltage Sag Loss Based on Blind Number Theory

Serious power quality issues and huge economic loss can be caused by voltage sag. It’s helpful for grid corporation to estimate voltage sag loss. Voltage sag and influence of sensitive equipment are analyzed in the paper. An estimation method of voltage sag loss based on blind number theory is proposed, which takes the sag magnitude and duration as its main characteristic parameters. First euclid distance and relative close degree between the sag magnitude and duration of voltage sag samples and threshold values is calculated based on TOPSIS. According to relative similarity degree，probable value, credibility and mean value of voltage sag loss are then calculated and influence of uncertainty factor can be considered. Example analysis shows that loss estimation method is a feasible and applicable for most sensitive equipments.

Multi-objective optimization model for multi-project scheduling on critical chain

In this paper, a multi-project scheduling in critical chain problem is addressed. This problem considers the influence of uncertainty factors and different objectives to achieve completion rate on time of the whole projects. This paper introduces a multi-objective optimization model for multi-project scheduling on critical chain, which takes into consideration multi-objective, such as overall duration, financing costs and whole robustness. The proposed model can be used to generate alternative schedules based on the relative magnitude and importance of different objectives. To respond to this need, a cloud genetic algorithm is proposed. This algorithm using randomness and stability of Normal Cloud Model, cloud genetic algorithm was designed to generate priority of multi-project scheduling activities and obtain plan of multi-project scheduling on critical chain. The performance comparison shows that the cloud genetic algorithm significantly outperforms the previous multi-objective algorithm.

Security Pricing Model Based on Dynamical Security Region under Power Market Environments

Under power market environments, generator companies and power users are economic decision-makers and the ISO is the security decision-maker. The non-unique solution of security decisions may result in unfairness. At the same time, more and more power systems are operating near their security limits, thus the influence of uncertainty factors to security can not be ignored. To solve the above problems, the presented security pricing model in this paper suggests that security prices are calculated and released, which inducts and excites the market-participants separate decision-making, thus system security is maintained. Influences of the uncertainties of power loads and fault parameters to system security are taken into account. Furthermore, the system security and economy are unified in the model.