Mathematical Problems in Engineering

In the management and evaluation of traffic network, signal parameters are important for monitoring and evaluating the operation state and the traffic capacity of intersection. However, a wide range of real-time signal timing schemes lacks a clear and effective method. In this paper, we propose the signal parameter calculation method based on mobile navigation data. Then, the possibility of crossing intersection passing time of the stop line is studied. The time differences between passing times of different cycles are distributed periodically that several peaks appear cycle by cycle. The relationship between sampling rate and relative error is discussed. Combined with the distribution peak normality test, the appropriate distribution peak is selected through the actual case. The cycle lengths and effective red time parameters are calculated and compared with the known signal parameters. The result demonstrates the proposed method has high accuracy and provides data support for the research of the traffic management.

In recent years, increasing pollution of the ecological environment, excessive use of pesticides, and lack of effective management of agricultural product supply chains have made the problem of having a green and safe supply of fresh food increasingly prominent. The sustainability of the fresh agricultural products supply has become an inevitable focus in the development of agricultural enterprises. There are some problems in the supply chain of fresh agricultural products, such as scattered production sites and difficult logistics transportation, which makes it difficult for enterprises to choose reliable suppliers. Supplier selection is a key component of sustainable supply chain management, and the criteria for evaluating the quality of sustainable suppliers are often affected by economic, social, and environmental factors. Therefore, from the perspective of sustainability, based on triple bottom line theory and comprehensively considering the three aspects of society, environment, and economy, this paper proposes a novel evaluation index system for the selection of sustainable suppliers of fresh agricultural products. This paper innovatively integrates the intuition fuzzy analytic hierarchy process and TODIM (an acronym in Portuguese of interactive and multiple attribute decision-making), and these are applied to select sustainable suppliers. Finally, the integration method is applied to the example, and a sensitivity analysis is carried out to verify the validity of the evaluation model.

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

Daily deals are nowadays very popular. As a new form of marketing, they allow local small businesses to sell vouchers at substantial price discounts for a very limited period of time. However, it is unclear whether and to what extent a seller’s online reputation affects the outcomes of its daily deal promotions. This paper presents an analysis of 4060 daily deals scraped from Groupon. The empirical results suggest that (1) business reputation, measured by displayed average rating, is positively associated with the sales of vouchers; (2) business reputation has no significant relationship with voucher discount depth; (3) business reputation is positively associated with the increase of customer traffic following a daily deal promotion; and (4) ratings displayed on daily deal sites are more influential than ratings displayed on third-party review sites. These findings extend our understanding of daily deals and provide concrete guidance to merchants regarding how to attract more purchases and traffic through online deals as well as to platform owners by pointing out the value of reputation in moderating consumers’ and merchants’ behaviors.

To accurately predict the construction costs of foundation pit projects, a model based on the stacked denoising autoencoder (SDAE) is constructed in this work. The influencing factors of foundation pit project construction costs are identified from the four attributes of construction cost management, namely, engineering, the environment, the market, and management. Combined with Chinese national standards and the practice of foundation pit project management, a method of the quantization of the influencing factors is presented. 60 deep foundation pit projects in China are selected to obtain 13 main characteristic factors affecting these project construction cost by using the rough set. Then, considering the advantages of the SDAE in dealing with complex nonlinear problems, a prediction model of foundation pit project construction costs is created. Finally, this paper employs these 60 projects for a case analysis. The case study demonstrates that, compared with the actual construction costs, the calculation error of the proposed method is less than 3%, and the average error is only 1.54%. In addition, three error analysis tools commonly used in machine learning (the determination coefficient, root mean square error, and mean absolute error) emphasize that the calculation accuracy of the proposed method is notably higher than those of other methods (Chinese national code, the multivariate return method, the BP algorithm, the BP model optimized by the genetic algorithm, the support vector machine, and the RBF model). The relevant research results of this paper provide a useful reference for the prediction of the construction costs of foundation pit projects.

In this paper, a disturbance observer-based complementary fractional-order sliding mode control (CFOSMC) scheme is proposed for the permanent magnet synchronous motor (PMSM) drive system. First, to reconstruct the load disturbance and parameter variations, a nonlinear disturbance observer is designed. Next, a disturbance observer-based fractional-order sliding mode with a saturation function control law is designed to reduce the chattering problem in the existing fractional-order sliding mode control (FOSMC) method. Furthermore, to reduce the thickness of the boundary layer, a CFOSMC scheme is designed. By using the fractional-order Lyapunov stability theorem, the existence condition and the chattering problem are analyzed. Compared with the existing FOSMC, the obtained CFOSMC law does not contain any high-order derivatives of tracking error, which is easier to implement. Finally, the numerical simulations and experimental results are provided to show the superiority of the proposed method. To improve the performance of the permanent magnet synchronous motor (PMSM) drive system in terms of tracking rapidity, accuracy, and robustness, a complementary fractional-order sliding mode control (CFOSMC) scheme with disturbance observer is proposed in this paper.