Influence Of Uncertain Factors Research Articles

Design and Implementation of an Intelligent Metro Project Investment Decision Support System

The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors during the construction process, the investment in subway project construction exhibits non-linear changes over time. Investment decision-making is the process through which the investment entity determines its investment activities. For typical investment entities, project investment decision-making primarily entails analyzing and evaluating proposed engineering projects based on investigation, analysis, and argumentation, ultimately deciding whether to invest. With the widespread application of information technology (IT) across various fields, decision support systems (DSS) have emerged to enhance the decision-making capabilities of enterprise management. This article designs an intelligent subway project investment DSS, leveraging data mining (DM) technology to integrate DSS with a data warehouse (DW).

Simulation and optimization of prefabricated building construction considering multiple objectives and uncertain factors

The construction of prefabricated buildings involves complex processes, which have high uncertainties and objective conflicts, hindering the improvement of construction performance. This study aims to simultaneously optimize the activity logic and multiple construction performance objectives (concerning time, cost, quality, and CO2 emissions) of prefabricated building construction under the influence of uncertain factors. The eliminate, combine, rearrange, and simplify (ECRS) technique is combined with intelligent simulation technology to develop a simulation-based optimization method for prefabricated building construction considering multiple objectives and various uncertain factors. This method is characterized by enabling activity-logic improvements, revealing mathematical relationships between uncertain factors, activity parameters and construction performance, and achieving the trade-off optimization of construction performance objectives in various uncertain environments. A typical prefabricated building project is used for demonstrating and validating the method. The results show that the proposed method reduces the construction time by 5.73% through the ECRS technique, and further achieves a 10.53% reduction in construction cost, a 10.10% improvement in construction quality, and 60.32% CO2 emission mitigation through multiobjective optimization when only resource allocation is considered. Considering the interaction between resource allocation conditions and other three uncertain factors, the construction time is mainly affected by the supply of materials and weather conditions, while the familiarity level of work affects the construction time, cost, and CO2 emissions. The proposed method and findings of this study can facilitate robust decision-making by project managers when addressing the trade-offs between cost reduction, schedule shortening, quality improvement, and CO2 reduction under various uncertain scenarios.

Research on Neural Network Terminal Sliding Mode Control of Robotic Arms Based on Novel Reaching Law and Improved Salp Swarm Algorithm

Modeling errors and external disturbances have significant impacts on the control accuracy of robotic arm trajectory tracking. To address this issue, this paper proposes a novel method, the neural network terminal sliding mode control (ALSSA-RBFTSM), which combines fast nonsingular terminal sliding mode (FNTSM) control, radial basis function (RBF) neural network, and an improved salp swarm algorithm (ALSSA). This method effectively enhances the trajectory tracking accuracy of robotic arms under the influence of uncertain factors. Firstly, the fast nonsingular terminal sliding surface is utilized to enhance the convergence speed of the system and achieve finite-time convergence. Building upon this, a novel multi-power reaching law is proposed to reduce system chattering. Secondly, the RBF neural network is utilized to estimate and compensate for modeling errors and external disturbances. Then, an improved salp swarm algorithm is proposed to optimize the parameters of the controller. Finally, the stability of the control system is demonstrated using the Lyapunov theorem. Simulation and experimental results demonstrate that the proposed ALSSA-RBFTSM algorithm exhibits superior robustness and trajectory tracking performance compared to the global fast terminal sliding mode (GFTSM) algorithm and the RBF neural network fast nonsingular terminal sliding mode (RBF-FNTSM) algorithm.

Cross-regional green certificate transaction strategies based on a double-layer game model

With the implementation of the renewable energy quota system and the development of the green certificate market, the diversification of cross-region trading subjects has brought about the problem of complicating the competition and cooperative relationship between multiple subjects. This problem makes it difficult to balance the conflict of interests of multiple participating subjects in green certificate transactions, and reduces the enthusiasm for trading. To this end, this paper proposes a multi-participant decision model of cross-regional green certificate trading behavior that takes into account the uncertainty of renewable energy generation. In the model, in order to explore the potential full set of strategies of the participating subjects, the cooperative game theory is utilized to analyze the complex interaction between multiple participating subjects in the cross-regional transaction, and the cooperative game model based on green certificates is established. As the participating subjects' information about other participants and their knowledge of the game environment are limited, the decision-making behavior is uncertain. In order to more reasonably portray the subject's game behavior in the actual decision-making scenario and to solve the defect that the traditional evolutionary game model can not reflect the influence of uncertain factors, we use the stochastic evolutionary game theory to dynamically deduce the cooperative strategies of the participating subjects under the limited rationality, and at the same time, introduce the strategy influencing factors to explore the dependence relationship between strategies. In addition, in order to cope with the impact of power generation uncertainty on cross-regional trading and to solve the defects of distributionally robust fuzzy set construction which is subjective and only for the analysis of uncertainty in a single time section, it is proposed to use Bayesian multivariate nonlinear model and mixed-effects model to construct probability distribution fuzzy sets of wind and light power generation uncertainty on the basis of distributionally robust theory. Finally, based on the arithmetic example, the cooperative strategy of multi-participants in cross-regional transactions under finite rationality is deduced, and the impact of uncertainty factors on the cooperative strategy is analyzed.

IMPROVING THE INNOVATIVE STRATEGY OF MANAGEMENT OF AGRICULTURAL ENTERPRISES IN THE CONDITIONS OF GLOBALIZATION

The purpose of the article is to study the peculiarities of the implementation of innovative strategies by Ukrainian farmers, to identify influencing factors, including the influence of global market factors on the effectiveness of the implementation of strategies, and to develop a mathematical approach to assessing the influence of uncertain factors. The methodological basis of the article is the methods and forms of scientific knowledge accepted in science, such as methods: of comparative and abstract-logical analysis, induction and deduction, abstraction, and mathematical formalization. A stratification of the factors of the negative impact of globalization, which slow down the implementation of the innovative strategy and create threats to the activity of agricultural enterprises, is carried out. It is indicated that the main directions of improvement of the innovation strategy should be assessment, consideration, and neutralization of the influence of globalization factors. It is also indicated that this leads to the heterogeneity of the implementation of the innovative management strategy of agricultural enterprises. Prospective directions for the implementation of innovative strategies in agricultural production are stratified. The stages of the implementation of the innovation strategy in the conditions of globalization and the tasks of management for the implementation of the innovation strategy in the global conditions are determined. Peculiarities of the implementation of innovative strategies in Ukraine are revealed. A mathematical model was developed to assess the influence of uncertain global factors, which will allow for reducing risks for the implementation of significant innovative strategies. It is indicated that significant innovative projects could lead to the transformation of agribusiness activities and the need for permanent implementation of innovative processes. It is indicated that thanks to the systemic innovation process, a synthesis of the resource and innovation model of enterprise development could be formed. Taking into account the specifics of innovative strategies and using the developed mathematical approach to assess the influence of uncertain global factors will allow reducing risks in the implementation of innovative strategies and improving their management.

Synchronization of reaction–diffusion neural networks with random time-varying delay via intermittent boundary control

Under boundary measurement (BM), this paper solves the synchronization problem via intermittent boundary proportional–derivative (PD) control of delayed reaction–diffusion neural networks (RDNNs), where the random time-varying delay (RTVD) belongs to two intervals and is considered by a probabilistic way to take the influence of uncertain factors. The intermittent boundary PD synchronization scheme, which considers the inevitable actuator limitations, is based on BM and only available at some specified boundary position. By using the inequality techniques and switching Lyapunov functional, the mean square exponential stability (MSES) of the synchronization error system (SES) containing the response and drive dynamics is ensured by some synchronization criteria, which are established via an intermittent boundary PD controller under BM and expressed as linear matrix inequalities. Lastly, the proposed intermittent boundary PD synchronization approach is verified by a numerical example.

Robust optimal scheduling of integrated energy systems considering multiple uncertainties

AbstractMultiple uncertainties such as renewable energy output, energy purchase prices and integrated demand responses have brought about severe challenges to the safe and economic operation of integrated energy system (IES). To meet this challenge, this study takes the combined cooling heating and power as an example of IES and proposes an optimization model considering multiple uncertainties. First, the structure of IES is given, and these mathematical models and constraints are listed according to the energy supply characteristics. Second, considering uncertain factors such as the wind and solar output, comprehensive demand response and energy purchase price, the different characterization methods are selected to model the uncertainty sources by identifying the characteristics of multiple uncertainty sources. Then, combining the stochastic scenario method and robust optimization method, a day‐ahead optimal scheduling model of IES with multiple uncertainties is established, and the whale optimization algorithm is improved to obtain the optimal solution of the model. Finally, the actual data of an IES is selected to verify the rationality and effectiveness of the model. Meanwhile, the influence of uncertain factors on scheduling is introduced to verify the perfect coordination of economy and robustness of the model at runtime.

Optimization of Vehicle Powertrain Mounting System Based on Generalized Inverse Cascade Method under Uncertainty

This paper presents a summary of the optimization design process for a multi-objective, two-level engineering problem, utilizing the generalized inverse cascade method under uncertainty. The primary objective is to enhance the vibration isolation performance of a mounting system, considering the influence of uncertain factors on its stiffness. The focus is on determining the value range of the design variables at the bottom layer, ensuring that the design goal is met with a specified confidence level. To illustrate the application of this methodology, the optimization design of a powertrain mount is used as a case study. A data-driven approach is adopted, establishing a quantitative mapping relationship between mount stiffness, force transmission rate, modal decoupling rate, and other design indicators. This is achieved through the development of a CRBM-DBN approximate model, which combines Conditional Restricted Boltzmann Machines (CRBMs) and a Deep Belief Network (DBN). Additionally, an intelligent optimization algorithm and interval search technology are employed to determine the optimal design interval for the mount stiffness. Simulation and experimental verification are conducted using selected parameter combinations. The results demonstrate notable improvements in the vibration isolation performance, modal decoupling rate, and vehicle NVH performance when compared to the original state. These findings provide valuable insights for the interval optimization design of similar multi-objective, as well as two-level engineering problems, serving as useful references for future research and applications.

Dynamic Fuzzy Boundary Output Feedback Control for Nonlinear Delayed Parabolic Partial Differential Equation Systems Under Noncollocated Boundary Measurement

For nonlinear space-varying parabolic partial differential equation systems (PPDESs) with random time-varying delay, this paper introduces a dynamic fuzzy boundary output feedback (DFBOF) control under non-collocated boundary measurement (NCBM). Initially, the nonlinear delayed PPDESs are represented by Takagi-Sugeno (T–S) fuzzy models and random time-varying delay is considered by taking the influence of uncertain factors, which belongs to two intervals in a probabilistic way. Since the system state is not fully available and NCBM makes the boundary control design very difficult, a fuzzy observer under NCBM is presented to surmount the design difficulty. Subsequently, an observer-based fuzzy boundary controller is proposed and spatial linear matrix inequality (SLMI) based sufficient conditions to ensure mean square exponential stability are obtained for closed-loop delayed PPDESs by utilizing the Lyapunov direct method and Wirtinger inequality. Then, to solve the SLMIs, the feasibility conditions of DFBOF controller design for nonlinear delayed PPDES are expressed in LMIs. Lastly, two examples are offered to demonstrate the validity of the presented dynamic fuzzy boundary control approach.

Life Cycle Environmental Impacts Assessment of Post-Combustion Carbon Capture for Natural Gas Combined Cycle Power Plant in Iraq, Considering Grassroots and Retrofit Design

In this work, the Life Cycle Assessment (LCA) methodology is used to examine the implications of CO2 capture from a natural gas combined cycle power plant with post-combustion carbon capture (NGCC-CCS) in Iraq, taking into account two different design scenarios. In the first scenario (retrofit), the carbon capture unit is considered as an end pipe technology that can be linked to an existing power plant. The second scenario considers a grassroots design, in which a new power plant equipped with a carbon capture unit needs to be constructed. The LCA is carried out based on different impact assessment (LCIA) methodologies of ReCipe 2016 Midpoint (H), TRACI 2.1, and IMPACT 2002+ to investigate whether the chosen LCIA method influences the LCA scenario analysis for decision support in process development. The results of three impact categories applied to both scenarios reveal a 28% reduction in Global Warming Potentials (GWPs) and a 14% and 17% increase in the Particulate Matter Formation Potential (PMFP) and Acidification (AP) potential in the grassroots scenario, respectively. Finally, an uncertainty analysis is performed to more accurately reflect the influence of uncertain factors on the statistical significance of the environmental impact evaluation in this research, indicating that these uncertainties may significantly affect the ultimate decision.

Structural Interval Reliability Algorithm Based on Bernstein Polynomials and燛vidence Theory

Structural reliability is an important method to measure the safety performance of structures under the influence of uncertain factors. Traditional structural reliability analysis methods often convert the limit state function to the polynomial form to measure whether the structure is invalid. The uncertain parameters mainly exist in the form of intervals. This method requires a lot of calculation and is often difficult to achieve efficiently. In order to solve this problem, this paper proposes an interval variable multivariate polynomial algorithm based on Bernstein polynomials and evidence theory to solve the structural reliability problem with cognitive uncertainty. Based on the non-probabilistic reliability index method, the extreme value of the limit state function is obtained using the properties of Bernstein polynomials, thus avoiding the need for a lot of sampling to solve the reliability analysis problem. The method is applied to numerical examples and engineering applications such as experiments, and the results show that the method has higher computational efficiency and accuracy than the traditional linear approximation method, especially for some reliability problems with higher nonlinearity. Moreover, this method can effectively improve the reliability of results and reduce the cost of calculation in practical engineering problems.

Imodel of choosing the optimal risk events management strategy: threats and opportunities

The study is devoted to the formation of an intelligent model for choosing and applying the optimal strategy for managing risk events, both threats and opportunities, of modern small IT projects with limited resources and implicit or uncertain influencing factors. The proposed model is built taking into account the decomposition of the process into three sub-processes, for which the criteria, target function, graphs of the development of events are proposed, taking into account the synergy of possible threats and opportunities. It is formed for the purpose of proactive management of risk events and is able to overcome uncertainty due to possible forecasting of future states for cases of a priori uncertainty and lack of information about possible consequences. The distribution of threats and opportunities is applied in the form of a developed unique target model with introduced weights of threats and opportunities based on expert assessments, taking into account the experience of experts from previous typical projects. Theoretical recommendations and risk management standards from the most common project management standards are taken into account. The developed intellectual model takes into account both the cost of implementing strategies and the total costs of their implementation.

Research on the Application of Uncertainty Quantification (UQ) Method in High-Voltage (HV) Cable Fault Location

In HV cable fault location technology, line parameter uncertainty has an impact on the location criterion and affects the fault location result. Therefore, it is of great significance to study the uncertainty quantification of line parameters. In this paper, an impedance-based fault location criterion was used for an uncertainty study. Three kinds of uncertainty factors, namely the sheath resistivity per unit length, the equivalent grounding resistance on both sides, and the length of the cable section, were taken as random input variables without interaction. They were subject to random uniform distribution within a 50% amplitude variation. The relevant statistical information, such as the mean value, standard deviation and probability distribution, of the normal operation and fault state were calculated using the Monte Carlo simulation (MCS) method, the polynomial chaos expansion (PCE) method, and the univariate dimension reduction method (UDRM), respectively. Thus, the influence of uncertain factors on fault location was analyzed, and the calculation results of the three uncertainty quantification methods compared. The results indicate that: (1) UQ methods are effective for simulation analysis of fault locations, and UDRM has certain application prospects for HV fault location in practice; (2) the quantification results of the MCS, PCE, and UDRM were very close, while the mean convergence rate was significantly higher for the UDRM; (3) compared with the MCS, PCE, and UDRM, the PCE and UDRM had higher accuracy, and MCS and UDRM required less running time.

Nitrogen and phosphorus concentration zoning control method of surface water in the Haihe River Basin

Various uncertain influencing factors and incomprehensible mechanisms have posed daunting challenges to the management and treatment of regional surface water environment quality. The unified measures and the “one-size-fits-all” management approach limit the treatment effectiveness. Therefore, considering natural and human activities which are major factors affecting the surface water environment quality, the present study proposed a set of zoning management and control schemes for nitrogen and phosphorus concentrations in surface water based on its natural attributes. Selecting DEM, rainfall, vegetation type, soil type, and land use, and employing “grid transformation”, “data extraction”, “attribute superposition” of GIS software and “correlation analysis”, “cluster analysis”, and “principal component analysis” of SPSS software, the Haihe River Basin was divided into the prevention zone, the control zone, and the non-control zone with different natural attribute sets by the correlation coefficient R2 and the nitrogen and phosphorus pollution therein. Based on the nitrogen and phosphorus data of 276 surface water quality monitoring sites, the multiple nonlinear stepwise regression analysis was conducted to construct the relationship between a single water quality indicator and its natural attributes in the three zones. The results are of essential practical significance to surface water environment quality zoning management and survey in the Haihe River Basin. Meanwhile, it provides innovative insights into environmental zoning management in other regions.

Trajectory tracking control strategy of the gantry welding robot under the influence of uncertain factors

In this paper, aiming at the problem of high-precision trajectory tracking of the gantry welding robot system under the influence of uncertain factors, a composite adaptive fuzzy compensation controller based on the computed torque control strategy has been proposed. The controller is composed of an adaptive fuzzy feedback control strategy and a dead zone adaptive fuzzy control strategy, which realizes high-precision trajectory tracking of the gantry welding robot, improves the solving speed of the control algorithm, and its tracking errors has been reduced. Screw theory and Lie group lie algebra are used to solve the problem of high algorithm complexity of controllers in multi-degree-of-freedom robot systems due to Newton-Euler dynamics modeling. Based on the computed torque control, the adaptive fuzzy feedback control strategy is adopted to compensate for the modeling errors and external disturbances in the gantry welding robot system, and the dead zone adaptive fuzzy control strategy is designed to compensate for the nonlinear dead zone structure with unknown parameters. The Lyapunov equation is introduced to prove the stability of the controller. Finally, the controller designed in this paper is compared with the conventional controller through simulation and experiment on the gantry welding robot platform, which verifies the effectiveness and superiority of the controller.

Dynamics of a Stochastic HIV Infection Model with Logistic Growth and CTLs Immune Response under Regime Switching

Considering the influences of uncertain factors on the reproduction of virus in vivo, a stochastic HIV model with CTLs’ immune response and logistic growth was developed to research the dynamics of HIV, where uncertain factors are white noise and telegraph noise. which are described by Brownian motion and Markovian switching, respectively. We show, firstly, the existence of global positive solutions of this model. Further, by constructing suitable stochastic Lyapunov functions with regime switching, some sufficient conditions for the existence and uniqueness of the stationary distribution and the conditions for extinction are obtained. Finally, the main results are explained by some numerical examples. Theoretical analysis and numerical simulation show that low-intensity white noise can maintain the persistence of the virus, and high intensity white noise can make the virus extinct after a period of time with multi-states.

Optimal Programming of Cyclist Power Allocation Based on Genetic Algorithm

In this paper, we have analyzed the way of bicycle road races. By establishing relevant models, the power curves of different types of athletes are obtained. Firstly, we use the least-squares method to optimize the function, and introduce two different types of passengers and get specific parameters. Then, by constructing the fatigue degree, energy consumption model and cyclic motion model, the genetic algorithm is used to solve the problem, and the optimal energy distribution of different athletes on different tracks is obtained. In addition, considering the influence of uncertain factors brought by the weather and other environments, the scheme that the driver and the directeur sportif would obtain the local optimal solution and actively change the power distribution is analyzed, and the process of the team time trial is studied. Finally, the generality and sensitivity of the model are tested, and the advantages and disadvantages of the model are evaluated.

Three-dimensional fatigue crack growth prediction method based on consistency retention

To reduce the influence of uncertain factors, a three-dimensional fatigue crack growth prediction method based on consistency maintenance is proposed. Firstly, the crack growth update under complex loads is realized based on XFEM. Subsequently, the life correction method based on the improved particle filter and the path correction method based on the improved maximum tangential stress criterion are proposed. Taking real crack data as input, the accuracy of prediction results is improved from two dimensions of life and path. Finally, five cases are used to verify the proposed framework, which proves that it can realize dynamic tracking of crack growth.

Research on Application Experience Design of Ice and Snow Sports Equipment Based on Bee Colony Model.

Sports equipment is the key to the smooth development of ice and snow sports. With the rapid development of social economy and the improvement of people's living standards, the demand for ice and snow sports equipment is increasing day by day. This article presents an improved method based on the chaos theory and the bee colony algorithm to quantify the application experience design of ice and snow sports equipment and reduce the influence of uncertain factors on the design results. First, the chaos theory can establish the dataset of application experience design and analyze the discreteness of the set. According to the bee colony algorithm, the dataset is divided into several groups, and each group obtains the best application experience design by using the design optimization strategy. Finally, the results are mixed to obtain the final experience design results. Through MATLAB simulation analysis and verification, the improved bee colony model can improve the accuracy of application experience design of ice and snow sports equipment in an uncertain environment, shorten the overall design time, and meet the requirements of application experience design of different ice and snow sports equipment. Therefore, the model proposed in this paper is suitable for the application experience design of ice and snow sports equipment.

A Risk Analysis Method of Cable Fire in Utility Tunnel Based on a Bayesian Network

Recently, the fire safety of tunnels is attracting more and more attention with the utility tunnel springing up in major cities of China. Faced with challenges from fires in utility tunnels, fire risk analysis is critical and essential to discover the weaknesses of risk. It serves as the foundation of regulatory decision-making on whether to take appropriate measures to reduce risk. This study combines a Bayesian network (BN) model and a bow-tie (BT) method to propose a fire risk analysis method to predict the probability of cable fire in utility tunnels. First, cable fire risk factors and five potential accident scenarios are analyzed by BT method; Second, to avoid the influence of uncertain factors in BT, the optimized BN model is applied to the prediction analysis of cable fire probability, which can consider the actual development of cable fire in utility tunnel. This novel approach can better reveal the causal relationship between events and determine the critical basic events by sensitivity analysis. Finally, the probability of cable fire ignition occurrence and scenarios probabilities are periodically updated using the cumulative information collected during a time interval. The results show that this approach can assist in dealing with the uncertainty problem in utility tunnel and the optimized model is more in line with the reality by comparing the results before model optimization.