Stability Analysis and Control Measures of Large-Span Open-Off Cut with Argillaceous Cemented Sandstone Layered RoofRead the full article
Mathematical Problems in Engineering is a broad-based journal publishing results of rigorous engineering research across all disciplines, carried out using mathematical tools.
Chief Editor, Professor Guangming Xie, is currently a full professor of dynamics and control with the College of Engineering, Peking University. His research interests include complex system dynamics and control and intelligent and biomimetic robots.
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The Impact of Icing on the Airfoil on the Lift-Drag Characteristics and Maneuverability Characteristics
Icing has now become an important factor endangering flight safety. This paper takes the icing data of the NACA 23012 airfoil as an example, establishes an icing influence model for real-time simulation based on icing time and aircraft angle of attack, and analyzes the influence of different icing geometry on aircraft characteristics. The two-dimensional interpolation method is used to improve the model of the aircraft’s stall area, which is mainly divided into the correction of the lift-drag coefficient linear area and the stall area and the correction of the aircraft stability derivative and the control derivative. The aerodynamic equation of the airplane after icing is established, and the modal analysis of the airplane under different icing conditions is completed through the linearization of the flight equation. The closed-loop simulation system of the altitude holding mode and roll attitude holding mode is used to calculate and analyze the flight quality changes of the aircraft after the wing surface is frozen. The analysis results show that, under icing conditions, in the range of small angles of attack, icing has no obvious influence on the aircraft mode. As the degree of icing increases, the throttle skewness and the negative deflection angle of the airplane’s level flight requirements continue to increase. The case of icing flight in altitude hold and roll hold modes shows that flying in the autopilot mode under severe icing conditions is very dangerous and is prone to cause the aircraft to stall.
Optimal Allocation Model of Virtual Power Plant Capacity considering Electric Vehicles
To push forward the development of electric vehicles while improving the economy and environment of virtual power plants (VPPs), research on the optimization of VPP capacity considering electric vehicles is carried out. In this paper, based on this, this paper first analyzes the framework of the VPP with electric vehicles and models each unit of the VPP. Secondly, the typical scenarios of wind power, photovoltaic, electric vehicle charging and discharging, and load are formed by the Monte Carlo method to reduce the output deviation of each unit. Then, taking the maximization of the net income and clean energy consumption of the VPP as the objective function, the capacity optimal allocation model of the VPP considering multiobjective is constructed, and the conditional value-at-risk (CVaR) is introduced to represent the investment uncertainty faced by the VPP. Finally, a VPP in a certain area of Shanxi Province is used to analyze a calculation example and solve it with CPLEX. The results of the calculation example show that, on the one hand, reasonable selection of the optimal scale of EV connected to the VPP is able to improve the economy and environment of the VPP. On the other hand, the introduction of CVaR is available for the improvement of the scientific nature of VPP capacity allocation decisions.
A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning
Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.
Coordinated Planning and Energy Conservation for Distribution Network with Renewable Energy: Standardized Information Model and Software
In recent years, energy conservation and environmental protection have attracted great attention by the state, and many efforts have been made from the policy and planning level. In view of the current distribution network planning requirements about energy-saving and environmental protection attributes such as loss reduction, carbon reduction, and environmental friendliness, this study proposes a set of energy-saving and environmental protection evaluation indicators for distribution network. Then, the CIM file library is constructed for typical equipment. Based on the CIM file, the digital planning technology for distribution network is designed. Besides, the feature library of energy conservation and environmental protection indicators, power flow calculation module, carbon flow calculation module, and renewable energy integration planning module are described.
The Relationship between Extent of Internationalization and Firm Performance (Taiwan 1992–2017)
The purpose of this study was to discuss the impact of the extent of internationalization on firm performance measured for firms with a high Tobin’s Q (firms with good operating performance), a median Tobin’s Q (firms with average operating performance), and a low Tobin’s Q (firms with poor operating performance). In addition to discussion on the impact of internationalization on firm performance, this study also discussed the impact of corporate proprietary assets (using assets, R&D, marketing, and management-related variables as moderating variables) and control variables (scale of company, debt-asset ratio, firm age, board structure, and proportion of pledged shares by directors) on firm performance. The research results showed that there is an S-shaped relationship between extent of internationalization and firm performance. However, further discussion found that there is an S-shaped relationship between extent of internationalization and performance for firms with a high Tobin’s Q but a slight decline in the middle of the S-shaped curve, as well as a general linear negative correlation between extent of internationalization and performance for firms with a median Tobin’s Q and an inverted U-shaped correlation between extent of internationalization and performance for firms with a low Tobin’s Q.
Risk Evaluation Method of Import and Export Goods Based on Fuzzy Reasoning and DeepFM
At present, the inspection mode of China's import ports is generally manual based on experience, or random inspection by the document review system according to a preset random inspection ratio. In order to improve the detection rate of unqualified goods and realize the best allocation of limited human and material resources of inspection and quarantine institutions, a method composed of fuzzy reasoning, deep neural network, and factorization machine (DeepFM) was proposed for the intelligent evaluation of risk sources of imported goods. Fuzzy reasoning is used to realize the fuzzy normalization of the dataset samples, the DeepFM deep neural network is finally used for training and learning to classify and evaluate the risks of goods. Results of experimental tests on a specific customs import and export dataset verify the effectiveness of the proposed research method.