Influence Of Uncertain Factors Research Articles

Structural design optimization based on hybrid time-variant reliability measure under non-probabilistic convex uncertainties

• The measurement of hybrid reliability under the non-probabilistic time-variant uncertainties is defined mathematically. • By virtue of the first-passage theory, a new technique of hybrid time-dependent reliability model is conducted. • The new optimization strategy based on hybrid time-dependent reliability is discussed. Structural safety assessment issue, considering the influence of uncertain factors, is widely concerned currently. However, uncertain parameters present time-variant characteristics during the entire structural design procedure. Considering materials aging, loads varying and damage accumulation, the current reliability-based design optimization (RBDO) strategy that combines the static/time-invariant assumption with the random theory will be inapplicable when tackling with the optimal design issues for lifecycle mechanical problems. In light of this, a new study on non-probabilistic time-dependent reliability assessment and design under time-variant and time-invariant convex mixed variables is investigated in this paper. The hybrid reliability measure is first given by the first-passage methodology, and the solution aspects should depend on the regulation treatment and the convex theorem. To guarantee the rationality and efficiency of the optimization task, the improved GA algorithm is involved. Two numerical examples are discussed to demonstrate the validity and usage of the presented methodology.

Novel Fuzzy PID-Type Iterative Learning Control for Quadrotor UAV.

Due to the under-actuated and strong coupling characteristics of quadrotor aircraft, traditional trajectory tracking methods have low control precision, and poor anti-interference ability. A novel fuzzy proportional-interactive-derivative (PID)-type iterative learning control (ILC) was designed for a quadrotor unmanned aerial vehicle (UAV). The control method combined PID-ILC control and fuzzy control, so it inherited the robustness to disturbances and system model uncertainties of the ILC control. A new control law based on the PID-ILC algorithm was introduced to solve the problem of chattering caused by an external disturbance in the ILC control alone. Fuzzy control was used to set the PID parameters of three learning gain matrices to restrain the influence of uncertain factors on the system and improve the control precision. The system stability with the new design was verified using Lyapunov stability theory. The Gazebo simulation showed that the proposed design method creates effective ILC controllers for quadrotor aircraft.

Integrated Optimal Dispatch of a Rural Micro-Energy-Grid with Multi-Energy Stream Based on Model Predictive Control

Due to the randomness of the intermittent distributed energy output and load demand of a micro-energy-grid, micro-sources cannot fully follow the day-ahead micro-energy-grid optimal dispatching plan. Therefore, a micro-energy-grid is difficult to operate steadily and is challenging to include in the response dispatch of a distribution network. In view of the above problems, this paper proposes an integrated optimal dispatch method for a micro-energy-grid based on model predictive control. In the day-ahead optimal dispatch, an optimal dispatch model of a micro-energy-grid is built taking the daily minimum operating cost as the objective function, and the optimal output curve of each micro-source of the next day per hour is obtained. In the real-time dispatch, rolling optimization of the day-ahead optimal dispatching plan is implemented based on model predictive control theory. The real-time state of the system is sampled, and feedback correction of the system is implemented. The influence of uncertain factors in the system is eliminated to ensure steady operation of the system. Finally, the validity and feasibility of the integrated optimal dispatching method are verified by a case simulation analysis.

Chemical Fuel Supply Chain Integration and Optimization under the Influence of Uncertain Factors

The petrochemical industry belongs to the dangerous chemical industry. The optimization of supply chain can significantly improve the overall profits of enterprises, reducing costs and risks. Based on the value-at-risk theory, this paper proposes an optimal dispatching model for chemical fuel supply chain, which is used to solve the uncertainty of market demand and capacity scale. The sample mean estimation method is used to calculate the optimal demand risk threshold and capacity risk threshold. The calculation results show that the higher the risk threshold is, the lower the supply chain cost will be. The impact of chemical fuel market demand risk threshold on the supply chain object function is greater than that of capacity risk threshold on the supply chain object function. Therefore, the supply chain optimal dispatching should take the market demand as a priority. According to calculation results, the optimal values of demand risk threshold and capacity risk threshold are set to 0.26 and 0.1, respectively. This research can provide theoretical reference for the large-scale supply chain optimal dispatching under the condition of uncertain factors.

Lyapunov exponent analysis for the evaluation of hybrid laser double-arc welding process stability.

Hybrid laser double-arc welding is a complicated process with many random and uncertain influencing factors because of the interactions of energy, forces, and fields. The wire spacing is one of the main factors affecting the coupling behavior of laser and arc plasma and the stability of welding process. However, it is difficult to establish a precise mathematical model for evaluation of welding stability. In this paper, the largest Lyapunov exponent (LLE) of characteristic currents during the welding process under different wire-wire distances were numerically evaluated by the phase space reconstruction technique. Synchronous high-speed photography was adopted to observe arc shape and droplet transfer. The experimental and calculated results indicate that hybrid laser double-pulsed arc welding is a complex process of chaos. The LLE can provide a criterion for welding stability and can agree well with the observation of electrical signal, arc shape, and droplet transfer. Research results are needed to understand the welding process and to guarantee a stable and safe joining.

Robust Design Optimization of Hydrodynamic Characteristics of Airfoil 791

In this paper, the influence of the continuous random variation of the flow velocity on hydrodynamic characteristics of airfoil 791 was discussed, and a robust design optimization method was proposed to reduce the influence of uncertain factors on the stability of hydrodynamic performance. Firstly, Bezier curve was used to parameterize the suction side of the airfoil, and its thickness was controlled by four points, which was then taken as the optimization variables. Secondly, the criterion of robustness was given, and then a robust mathematical model was established. Finally, two objective functions of robust optimization were gained on the basis of uncertainty analysis with surrogate model. Combined with multi-objective genetic algorithm, a robust optimal solution with better hydrodynamic characteristics was obtained. The results showed that compared with original case, the thickness near trailing edge altered more smoothly, the resistance and surface wave intensity were obviously decreased, and the maximum reduction of drag-lift ratio and wake unevenness were 6.89% and 25.04% respectively, which contributed to the improvement of wake quality. The change of surface pressure and resistance coefficients were also more stable under the variable inlet flow velocity conditions, and then the robustness was strengthened accordingly. In conclusion, the robust design optimization can obviously improve the hydrodynamic characteristics of the airfoil while reducing the sensitivity to uncertain factors, meanwhile, it can provide better stability during the operating process.

Regional Environmental Efficiency Based on a Modified Proportional DEA Model

With the increased attention paid to environmental and ecological issues in China, many methods have been used to evaluate the performance of ecological conservation. Scholars have evaluated environmental efficiency in the production process with undesirable outputs, and used a data envelopment analysis (DEA) model for further examination. However, previous studies do not detail the influence of uncertain factors on undesirable outputs, such as environmental capacity and risk attitude. Therefore, this study proposes and applies a modified proportional DEA model to evaluate the environmental efficiency of various textile and clothing companies located around the main stem and tributaries of the Yangtze River. The empirical results indicate that this model is more suitable to evaluate the environmental efficiency of companies in these acutely polluted regions. Based on these findings, we suggest considering environmental capacity and risk attitude in ecological conservation policies to improve environmental efficiency.

Minimum time overtaking problem of vehicle handling inverse dynamics based on interval mathematics

To analyze the influence of uncertain factors on minimum time overtaking, interval mathematics is used to describe the uncertainties, and the overtaking safety distance is calculated using interval analysis method. In addition, vehicle handling inverse dynamics is proposed in this paper. In this method, the driver-handling input can be obtained without the modeling of driver. The optimal control problem is first converted into a nonlinear programming problem based on Gauss pseudospectral method. Then sequential quadratic programming is applied to get the solution. The simulation results show that the overtaking behavior will be significantly different, if the drivers’ age groups or vehicles’ braking system parameters are different. Besides, the influence of different drivers’ estimate time is critical. The subjective judgments of the drivers are considered in this paper to realize the user-friendly design. This method may provide a reference for the research of unmanned vehicles and driving assistance system.

Air transportation delay propagation analysis with uncertainty in coloured–timed Petri nets

In the transport management of aviation networks, uncertain factors and delay propagation are two main sources of flight delays. However, the impacts of uncertainty and the degree of delays are difficult to calculate empirically in daily operations. In this paper, a novel delay propagation model is introduced utilising the mathematical modelling tool coloured–timed Petri nets. This model enables descriptions of the delay propagation progress and flight delay calculation under the influence of uncertain factors. In order to understand the influence of airline network operations with uncertainties, the mechanisms of the main uncertainties (weather factors, air traffic control (ATC) factors and other low-probability random factors) are studied and probability functions are obtained. To simulate conditions under different uncertainties, experiments were carried out on an aviation network comprising 13 airports under different settings of bad weather and ATC. The simulation results revealed the influence of different uncertain factors, delay propagation time through flight chains and delay prediction in the aviation network. Based on the predicted flight delays, flight sequence update rules were established, which showed that the updated flight sequence significantly reduced flight delays compared with the original flight sequence.

Sequential emitter identification method based on D-S evidence theory

This paper proposes a novel sequential identification method for enhancing the anti-jamming performance and for accurate recognition rate of the emitters’ individual identification in the complicated environment. The proposed method integrates the D-S evidence theory and features extraction that can get the utmost out of features of information systems and decrease the influence of uncertain factors in the signal processing. Firstly, selected features are extracted from intercepted signals. Then, the proposed self-adaptive fusing rule based on the decision vector is utilized to fuse the evidences that are transformed by features and the previous fusing information. Finally, recognition results can be obtained by judgment rules. The simulation analysis demonstrates that self-adaptive fusing rule can achieve a great balance between computational efficiency and accurate identifying rate. While comparing with other identifying methods, the proposed sequential identifying method can provide more accurate and stable recognition results, which makes the utmost care and use of existing information.

Classification and evaluation of uncertain influence factors for farm machinery service

Uncertainty extremely interferes with the execution of farm machinery operation. Treating uncertainties is especially important for machinery cooperatives providing social service since they face more uncertain influence factors (UIFs) than family farms. Under social service circumstance, uncertainties may arise from participants and environments. Classification and evaluation of UIFs were studied in this research. According to the production system, 32 UIFs are defined and classified into six categories, which include supply, demand, interactivity, nature, society and others. Uncertainty composite index (UCI) is defined to evaluate the importance of UIFs, which is the square root of the product of occurrence frequency (OF) and impact degree (ID) calculated from the well-designed questionnaire responded by farm machinery operators. UCI is divided into five ranks based on normalization distribution test to illustrate the level of importance. Results from questionnaire showed that natural UIFs have an extreme impact on farm operation, UIFs of the demand and the supply have a serious influence on farm operation, UIFs of interactivity cannot be ignored, and social UIFs have a weak impact on farm operations. This study discovered the uncertainty problems under the specific circumstance of farm machinery service, which may provide a theoretical basis and potential methods for risk management of machinery cooperatives. Keywords: uncertainty, uncertain influence factor (UIF), classification, uncertainty composite index (UCI), machinery cooperatives DOI: 10.25165/j.ijabe.20171006.3045 Citation: Wu C C, Cai Y P, Hu B B, Wang J. Classification and evaluation of uncertain influence factors for farm machinery service. Int J Agric & Biol Eng, 2017;10(6):164–174.

An approach for optimization of turning parameters considering uncertainty parameters

Regenerative chatter in turning can cause serious tool wear or breakage, along with poor surface finish on the machined workpiece. Consequently, it is critical to avoid the occurrence of chatter in turning process. In this paper, dynamic model of regenerative chatter in turning process is established to predict the limited cutting width and derive the stability lobe diagram. This study addresses the influences of uncertain factors on the turning process, and a reliability based optimization model is established to obtain optimal turning parameters. In the optimization model, the maximum material removal rate is taken as the objective function and the reliability of turning stability, surface precision, and cutting power is defined as the constraint function. The sequential optimization and reliability assessment (SORA) is utilized to solve the optimization model. Finally, a discussion of the practical application of this method is presented.

Classification and evaluation of uncertain influence factors for farm machinery service

Classification and evaluation of uncertain influence factors for farm machinery service

A probabilistic bridge safety evaluation against floods.

To further capture the influences of uncertain factors on river bridge safety evaluation, a probabilistic approach is adopted. Because this is a systematic and nonlinear problem, MPP-based reliability analyses are not suitable. A sampling approach such as a Monte Carlo simulation (MCS) or importance sampling is often adopted. To enhance the efficiency of the sampling approach, this study utilizes Bayesian least squares support vector machines to construct a response surface followed by an MCS, providing a more precise safety index. Although there are several factors impacting the flood-resistant reliability of a bridge, previous experiences and studies show that the reliability of the bridge itself plays a key role. Thus, the goal of this study is to analyze the system reliability of a selected bridge that includes five limit states. The random variables considered here include the water surface elevation, water velocity, local scour depth, soil property and wind load. Because the first three variables are deeply affected by river hydraulics, a probabilistic HEC-RAS-based simulation is performed to capture the uncertainties in those random variables. The accuracy and variation of our solutions are confirmed by a direct MCS to ensure the applicability of the proposed approach. The results of a numerical example indicate that the proposed approach can efficiently provide an accurate bridge safety evaluation and maintain satisfactory variation.

Sampled-Data Synchronization Analysis of Markovian Neural Networks With Generally Incomplete Transition Rates.

This paper investigates the problem of sampled-data synchronization for Markovian neural networks with generally incomplete transition rates. Different from traditional Markovian neural networks, each transition rate can be completely unknown or only its estimate value is known in this paper. Compared with most of existing Markovian neural networks, our model is more practical because the transition rates in Markovian processes are difficult to precisely acquire due to the limitations of equipment and the influence of uncertain factors. In addition, the time-dependent Lyapunov-Krasovskii functional is proposed to synchronize drive system and response system. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilize the upper bound of variable sampling interval and the sawtooth structure information of varying input delay. Moreover, the desired sampled-data controllers are obtained. Finally, two examples are provided to illustrate the effectiveness of the proposed method.

Addressing Uncertainty in Temporal and Spatial Scheduling for Farm Machinery Operation

Uncertainty is an important characteristic of scheduling model for scale farm machinery operation organizing. Practice shows that scheduling model without considering uncertainties is nearly useless. Uncertain influence factors arisen from natural environment, society and economy, market and supply, and customer and behavior, exist widely, emerge frequently, and affect production deeply. Uncertainties interfere with the allocation of productive factors on temporal and spatial dimensions for farm machinery operation scheduling and management. Questionnaire for farm machinery organizations was designed and finished in 2014. Both occurrence frequency and influence degree for each factor were quantified. Four influence factors including operation location change, weather mutation, parts supply delay, and operation skill defects appear in both list of high occurrence and deep influence. Then results of questionnaire and results of specific investigation were used to study temporal and spatial scheduling model and system for farm machinery management. Three case studies are introduced. The first case is about the uncertainty and countermeasure of forage harvesters scheduling and monitoring for a professional forage plantation company. The second case is about the uncertainty and counter measure of cotton-picker scheduling and monitoring for a professional cotton picking company. And the third case is about the uncertainty and countermeasure of social service management for a professional cooperative. The cases show that the research has strong pertinence to deal with uncertainties and can improve management efficiency of farm machinery operation.

Decision Support for Route Search and Optimum Finding in Transport Networks under Uncertainty

The aim of this paper is to find solution for route planning in road network for a user, and to find the equilibrium in the path optimization problem, where the roads have uncertain attributes. The concept is based on the Dempster-Shafer theory and Dijkstra's algorithm, which help to model the uncertainty and to find the best route, respectively. Based on uncertain influencing factors an interval of travel time (so called cost interval) of each road can be calculated. An algorithm has been outlined for determining the best route comparing the intervals and using decision rules depending on the user’s attitude. Priorities can be defined among the rules, and the constructed rule based mechanism for users’ demands is great contribution of this paper. The first task is discussed in more general in this paper, i.e. instead of travel time a general cost is investigated for any kind of network. At the solution of the second task, where the goal is to find equilibrium in transport network at case of uncertain situation, the result of the first task is used. Simulation tool has been used to find the equilibrium, which gives only approximate solution, but this is sufficient and appropriate solution for large networks. Furthermore this is built in a decision support system, which is another contribution of this work. At the end of the paper the implementation of the theoretical concept is presented with a test bed of a town presenting effects of different uncertain influencing factors for the roads.

Dynamical Analysis of Autonomous Underwater Glider Formation with Environmental Uncertainties

Application of multiple autonomous underwater gliders (AUGs) is a promising method for large scale, long-term ocean survey. Dynamical behaviors of AUGs are inevitably affected by uncertainties in the marine environment. This paper introduces a statistical method for uncertainty analysis in formation control of a fleet of AUGs. The AUG formation is modeled as a multibody system. Artificial potential fields are constructed between the AUGs and the goal, between the AUGs and the obstacle, and between neighboring AUGs in the formation for motion planning and coordination. Kane's method is used to describe the dynamics of the formation. Currents are addressed as the environmental uncertainties and criteria are provided for uncertainty analysis. A series of simulations is carried out for quantitative analysis on influence of uncertain factors. Results show that environmental uncertainties may greatly influence the dynamics of AUGs and should be included in the design and control of AUG formations.

Identification of Hydraulic Conductivity in Aquifer for Coupled FEM and Adaptive Genetic Algorithm

The hydraulic conductivity of a natural rock mass was difficult to determine because of the complex structure and the significant influence of uncertain factors. In this paper, hydraulic conductivity was adopted to conduct an inversion analysis according to the measurement of head materials by combining the finite element method with the adaptive genetic algorithm. The results showed that the maximum relative error of the measuring and computation groundwater levels at the measuring points was 5.3%, and the average head error was 1.41%; the effective hydraulic conductivity of intensively weathered layer, moderately weathered layer, and fresh bedrock layer in riverbed formation tended to decline gradually; the effective permeability coefficient in directionYwas the minimum in the same aquifer. Therefore, the established hydraulic conductivity inversion analysis method was effective.

Research on the Model-Building Error for EMS Maglev Vehicles

To estimate the influences of uncertain factors on the modeling error of maglev vehicles, the single electro-magnet is taken as the object in this paper. Researches on the model-building error are carried out, including factors of the uncertainty of the inductor, the elasticity of the guideway and the coupling between Modules. Also, the model-building errors are analyzed numerically from the view of L∞ norm. And the calculation results are presented, which shows severe model-building severe error with the simplified model. The results are revelatory to comprehend the phenomena of the vehicle’s vibration, and to search after new methods of levitation control for EMS maglev vehicles.