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In this paper, we study the uniqueness questions of finite order transcendental entire functions and their difference operators sharing a set consisting of two distinct entire functions of finite smaller order. Our results in this paper improve the corresponding results from Liu (2009) and Li (2012).

The concept of frame multiresolution analysis (FMRA) on local fields of positive characteristic was given by Shah in his paper, Frame Multiresolution Analysis on Local Fields published by Journal of Operators. The author has studied the concept of minimum-energy wavelet frames on these prime characteristic fields. We continued the studies based on frame multiresolution analysis and minimum-energy wavelet frames on local fields of positive characteristic. In this paper, we introduce the notion of the construction of minimum-energy wavelet frames based on FMRA on local fields of positive characteristic. We provide a constructive algorithm for the existence of the minimum-energy wavelet frame on the local field of positive characteristic. An explicit construction of the frames and bases is given. In the end, we exhibit an example to illustrate our algorithm.

The agglomeration health output effect of the medical service industry in the era of big data is an important part of the agglomeration innovation of medical resources. This paper used the regression model of data mining to set up the fixed effect model and system GMM model to study the relationship between the agglomeration of medical service industry and resident’s health level, based on the panel data of 31 provinces of China from 2003 to 2017. The results show that the health outcome of the medical industrial agglomeration is positive and different in provinces. The influence of medical service cluster on residents’ health level in the eastern region fails the significance test, while the medical service cluster in the central and western regions can significantly improve residents’ health level. And, this effect is also related to the characteristics of medical resources, economic development, demographic characteristics, and other heterogeneous factors. On this basis, the paper puts forward policy suggestions to promote the market structure of the medical industry from the aspects of strengthening synergies and policy guidance.

This paper is devoted to a study of the concept of edge-group choosability of graphs. We say that G is edge- k -group choosable if its line graph is k -group choosable. In this paper, we study an edge-group choosability version of Vizing conjecture for planar graphs without 5-cycles and for planar graphs without noninduced 5-cycles (2010 Mathematics Subject Classification: 05C15, 05C20).

The optimal control of automatic transmission plays an important role in the shifting smoothness and fuel economy of heavy-duty mining trucks. In this paper, a dynamic model of the powertrain system is built to study the clutch pressure control during the shifting process. A linear-quadratic optimal regulator is used to achieve the optimum control pressure of clutches, where shifting jerk and clutch friction loss are chosen to a form quadratic performance index function. Besides, a detailed solution of the linear-quadratic problem with the disturbance matrix in the state equations is provided. This paper also carries out a software simulation and verification of the normal condition (no load without slope) and the extreme condition (full load with maximum slope). Compared with the preset reference trajectory control, the simulation results show that the proposed optimal clutch pressure control can effectively reduce jerk and friction loss during the shifting process and has good robustness to different operating conditions.

This paper is a response to two papers. We improve the lengthy proof for the first paper by an elegant verification. For the second paper, we point out the three-sequence approach will result in different convergent rates such that when the other two sequences are converged, the ordering quantity sequence may still not converge to the optimal solution. We construct a novel iterative method to simplify the previous approach proposed by the three-sequence approach for the optimal solution. By the same numerical examples of three published papers, we demonstrate that we can control our findings to converge more accurately than previous results. Moreover, we show that there are three distinct features of our proposed approach. (i) It converges to the desired solution within the preassigned threshold value. (ii) We estimate the convergent ratio. (iii) We find the dominant factors for our proposed convergent sequence.

At present, real-time online measurement of fluid density is of great significance to improve the automation level of petrochemical and food industries. The tuning fork density sensor is widely used because of its characteristics of real-time online measurement, high measurement accuracy, simple structure, and convenient use. The traditional tuning fork density sensor in the market has the disadvantage of low resolution and being susceptible to liquid viscosity, which makes the sensor’s measurement accuracy low and not suitable for the measurement of high-viscosity liquid density. The measurement resolution and antiviscosity interference capability of the tuning fork density sensor are two major indexes to measure the measurement performance of the sensor, among the antiviscosity interference capability refers to the degree to which the measurement results of the sensor are affected by viscosity properties. However, the structural design of the tuning fork density sensor results in the conflict between the measurement resolution and the antiviscosity interference capability of the sensor, and the improvement of one performance is bound to affect the performance of the other. Aiming at the problem of how to balance the measuring performance of the tuning fork sensor, a density calculation model based on viscosity compensation is proposed in this paper. By studying the working principle and structure design of the tuning fork, the vibration characteristics of tuning fork in liquid with different viscosities and densities are modelled and simulated. From the results of simulation analysis, the better set of dimensions with balanced measuring performance is selected. Not only does the structure of the tuning fork have the characteristics of high resonance frequency, but also the measured results are less affected by the viscosity of the liquid. To solve the problem that density measurement is still affected by high-viscosity liquid after tuning fork dimension optimization, in this paper, the partial least square model is used to fit the experimental data of the frequency-density characteristics; then, the density calculation model based on the viscosity compensation is obtained by combining the frequency-viscosity characteristic experiment. Finally, through the performance test experiment comparing with the traditional tuning fork density sensor, the measurement resolution of the improved tuning fork density sensor is as high as 0.0001 g/cm3; within the viscosity range of 180 MPa·s, the accuracy reached ±0.001 g/cm3, and within 480 MPa·s, the measurement accuracy reached ±0.002 g/cm3. When the liquid viscosity reaches more than 10 MPa·s, the improved tuning fork density sensor has better overall measurement performance than the traditional tuning fork density sensor, and both of its measurement resolution and antiviscosity interference capability have been greatly improved.

For signals reconstruction based on compressive sensing, to reconstruct signals of higher accuracy with lower compression rates, it is required that there is a smaller mutual coherence between the measurement matrix and the sparsifying matrix. Mutual coherence between the measurement matrix and sparsifying matrix can be expressed indirectly by the property of the Gram matrix. On the basis of the Gram matrix, a new optimization algorithm of acquiring a measurement matrix has been proposed in this paper. Firstly, a new mathematical model is designed and a new method of initializing measurement matrix is adopted to optimize the measurement matrix. Then, the loss function of the new algorithm model is solved by the gradient projection-based method of Gram matrix approximating an identity matrix. Finally, the optimized measurement matrix is generated by minimizing mutual coherence between measurement matrix and sparsifying matrix. Compared with the conventional measurement matrices and the traditional optimization methods, the proposed new algorithm effectively improves the performance of optimized measurement matrices in reconstructing one-dimensional sparse signals and two-dimensional image signals that are not sparse. The superior performance of the proposed method in this paper has been fully tested and verified by a large number of experiments.

Quantitative analysis of the flow field is an effective method to study hydrodynamics. As a flow field measurement technology, the Background Oriented Schlieren (BOS) is widely used. However, it is difficult to measure the complex transparent flow field (flow field with large refractive index gradient) using the BOS experiment. In order to overcome this difficulty and improve the accuracy of the BOS experiment, this paper presents a hybrid adaptive wavelet-based optical flow algorithm for the BOS. The current algorithm is a combination of the traditional optical flow algorithm and the wavelet-based optical flow algorithm. By adding the initial value constraints, the adaptive scale constraints, and the adaptive regularization constraints, the algorithm can effectively overcome the above-mentioned difficulty and also improve its accuracy. To further illustrate the feasibility of the proposed method, this paper uses the simulation data, the data of the DNS datasets, and the data of the BOS experiment to verify the performance of the algorithm. The experiment of comparing the proposed algorithm with the existing ones is carried out on the DNS datasets and the data of the BOS experiment. Finally, the proposed method is verified by a practical BOS experiment. The results show that the proposed algorithm can effectively improve the measurement accuracy of displacements.

EFA (extended factored approach) algorithm is the main method of space-time adaptive processing technology (STAP) for airborne phased array radar, but it is faced with many problems, such as large number of samples and large amount of calculation. Therefore, this paper uses a method of spatial data dimensionality reduction processing based on cyclic iterative calculation to optimize its STAP. The final experimental results show that, after spatial data dimensionality reduction processing optimization, the STAP performance of EFA algorithm is further expanded in the range of sample number adaptation; especially in the case of small sample number, the optimized STAP performance has been basically close to the ideal compared with other optimization schemes; tap performance also proves that the optimization scheme in this paper has better convergence speed and STAP performance.

Facing more and more severe global warming problems, renewable energy, as an alternative to traditional fossil fuels, is attracting more and more attentions due to its capability of reducing carbon emission. This paper considers two-tier HetNets with orthogonal frequency division multiple access (OFDMA), where the macro base station (MBS) is powered by power grid and small base stations (SBSs) have hybrid energy supplies. Through smart grid, SBSs can share their renewable energy with each other. We consider the problem of cross-layer interference caused by spectrum reuse, the burst of user data, and the randomness of renewable energy arrivals. Through energy cooperation, this paper investigates maximizing the time-average energy efficiency of SBSs. Based on user data queue and SBS energy queue, the optimal problem is decoupled into two subproblems by Lyapunov optimization: resource allocation subproblem and energy scheduling and energy cooperation subproblem. By solving two subproblems, the online solution to the optimization problem is obtained. Through theoretical analysis and simulations, both user data queues and energy queues have an upper bound, the network is stable, and the proposed algorithm performs better than the basic algorithm without energy cooperation.

This paper proposes a novel predictive strategy based on a model predictive control (MPC) for the interior permanent magnet synchronous motors (IPMSMs) driven by a three-level simplified neutral-point clamped inverter (3L-SNPC) for electric vehicle applications (EVAs). Based on the prediction of the future behavior of the controlled variables, a predefined multiobjective cost function incorporates the control objectives which are evaluated for every sampling period to generate the optimal switching state applied directly to the inverter without the modulation stage. The control objectives in this paper are tracking current capacity, neutral-point voltage balancing, common-mode voltage control, and switching frequency reduction. The principal concepts of the novel scheme are summarized as follows: first, the delay compensation based on the long horizon of prediction is adopted by a multilevel power converter structure. Second, based on the modified Lyapunov candidate function, both stability and recursive feasibility are ensured of the proposed predictive scheme. Third, the practicability of the real-time implementation is improved by the proposed “static voltage vector” (SVV) and “single state variation” (SSV) principles. Finally, the proposed concepts are implemented in the novel predictive control formulation as additional constraints without compromising the complexity and the good performances of the predictive controller. Therefore, only the switching states that guarantee the stability and the reduction of calculation burden criteria are considered in the evaluation of cost function. The proposed predictive scheme based on the “SVV” principle has demonstrated superior performance in simulation compared with the proposed scheme with the “SSV” principle. The computational burden and switching frequency rates are reduced by 35% and 56.22%, respectively.

The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages.

This paper proposes a new class of difference methods with intrinsic parallelism for solving the Burgers–Fisher equation. A new class of parallel difference schemes of pure alternating segment explicit-implicit (PASE-I) and pure alternating segment implicit-explicit (PASI-E) are constructed by taking simple classical explicit and implicit schemes, combined with the alternating segment technique. The existence, uniqueness, linear absolute stability, and convergence for the solutions of PASE-I and PASI-E schemes are well illustrated. Both theoretical analysis and numerical experiments show that PASE-I and PASI-E schemes are linearly absolute stable, with 2-order time accuracy and 2-order spatial accuracy. Compared with the implicit scheme and the Crank–Nicolson (C-N) scheme, the computational efficiency of the PASE-I (PASI-E) scheme is greatly improved. The PASE-I and PASI-E schemes have obvious parallel computing properties, which show that the difference methods with intrinsic parallelism in this paper are feasible to solve the Burgers–Fisher equation.

Target detection and segmentation algorithms have long been one of the main research directions in the field of computer vision, especially in the study of sea surface image understanding, these two tasks often need to consider the collaborative work at the same time, which is very high for the computing processor performance requirements. This article aims to study the deep learning sea target detection and segmentation algorithm. This paper uses wavelet transform-based filtering method for speckle noise suppression, deep learning-based method for land masking, and the target detection part uses an improved CFAR cascade algorithm. Finally, the best separable features are selected to eliminate false alarms. In order to further illustrate the feasibility of the scheme, this paper uses measured data and simulation data to verify the scheme and discusses the effect of different signal-to-noise ratio, sea target type, and attitude on the algorithm performance. The research data show that the deep learning sea target detection and segmentation algorithm has good detection performance and is generally applicable to ship target detection of different types and different attitudes. The results show that the deep learning sea target detection and segmentation algorithm fully takes into account the irregular shape and texture of the interfering target detected in the optical remote sensing image so that the accuracy rate is 32.7% higher and the efficiency is increased by about 1.3 times. The deep learning sea target detection is compared with segmentation algorithm, and it has strong target characterization ability and can be applied to ship targets of different scales.

In this paper, the field monitoring method is used to study the variation of rock mass pressure during the construction of a tunnel in phyllite stratum, and three functions are used to fit and analyze the variation of rock mass pressure with deformation, excavation time, and space. The results show the following (1) When the deformation increases significantly, the rock mass pressure decreases firstly and then increases. This is caused by the insufficient bearing capacity of the rock mass in the arch foot of the supporting structure after the excavation of the upper bench, which leads to a settlement of supporting structure and surrounding rock. (2) Compared with other kinds of fitting functions, the logistic function can better characterize the variation of the pressure of surrounding rock with deformation, excavation time, and distance from the face. This paper provides a reliable reference for the design and construction of the tunnel in phyllite stratum. The logistic function can be used to present and predict the change of rock mass pressure with deformation, excavation time, and space in similar rock mass conditions.

With the development of the economic and technology, decision-making problems are more and more complex and uncertain. Experts have difficulty in expressing evaluation information because of different research background and insufficient cognition of knowledge structure. Attribute weight information has been often incomplete in decision-making problems. Considering that nested probabilistic-numerical linguistic term sets (NPNLTSs) are flexible to express qualitative and quantitative information, in this paper, we firstly establish an optimization model based on distance measures to obtain the attribute weight. Combined with a classical decision-making method, an optimization-based TOPSIS method with NPNLTSs is proposed to deal with complex decision-making problems. After that, a case study about the river health assessment is given to show the effectiveness and practicability of the proposed method. Finally, some comparative analysis and discussion are provided from three aspects, including the impact for the results without weight optimization, the impact for the results under other uncertain environments, and the impact for the results using other decision-making methods. As a result, the proposed optimization-based TOPSIS method is effective and reliable. The optimization-based TOPSIS method proposed in this paper provides a new way to deal with uncertain and practical problems, which makes a technically sound contribution to the decision-making field.

The interpolating boundary element-free method (IBEFM) is a direct solution method of the meshless boundary integral equation method, which has high efficiency and accuracy. The IBEFM is developed based on the interpolating moving least-squares (IMLS) method and the boundary integral equation method. Since the shape function of the IMLS method satisfies the interpolation characteristics, the IBEFM can directly and accurately impose the essential boundary conditions, which overcomes the shortcomings of the original boundary element-free method in enforcing the essential boundary approximately. This paper will study the error estimations of the IBEFM for two-dimensional potential problems and the relationship between the errors and the influence radius and the condition number of the coefficient matrix. Two numerical examples are presented to verify the correctness of the theoretical results in this paper.

In recent years, more and more scientists have been interested in research on driving two-wheel bicycles. The problems in two-wheel bicycle control problem are self-balancing, uncertain models, and the impact of noise. In the paper, to solve the self-balancing problem, we use the flywheel method according to the inverted pendulum principle. To overcome the effects of the uncertain model, the impact of noise, we designed the vehicle balance controller according to the robust control algorithm. However, robust controllers often have a high order, which affects the quality during real control. To simplify the robust controller, we propose the use of a model order reduction algorithm. The simulation and experimental results have proved the correctness of the solutions given in the paper.

This paper examines the threshold effect of resource misallocation between specialization and technological innovation performance in the high-tech industry in China. Contrary to the existing literature, this paper applies the static panel threshold model to evaluate the threshold effect of industrial specialization on the innovation performance, including the single threshold effect and double threshold effect. The empirical results shed new light on the topic and signified that high-tech industry specialization has a nonlinear effect on innovation performance and the resource misallocation threshold. This paper unveils a novel, double threshold effect of resource misallocation between industrial specialization and innovation performance. Moreover, the twenty-eight regions in China are divided into three types according to a resource misallocation, which unfold the current situation of the resource misallocation in pharmaceutical manufacturing—providing directional guidance for structural reforms of the supply side of pharmaceutical manufacturing in various regions.

This paper analyzes the “resource curse” and “financial threshold effect” that may exist in China and then uses the data from 30 provinces from 2004 to 2018 as research samples. We used linear regression and nondynamic panel threshold models to analyze the financial threshold effects of the “resource curse” hypothesis and the “resource curse” phenomenon. At the same time, we divided the level of financial development to verify the robustness of the research conclusions in this paper. The study found the following: (1) There is a certain correlation between the abundance of resources and economic growth. Whether this can be seen as a “curse” or a “blessing” of resources is significantly related to the degree of financial development. (2) Whether financial development can alleviate the “resource curse” depends on the degree of financial development. In the extremely scarce stage of financial resources, the resource endowment effect is obvious, and the level of economic development in resource-based regions will be higher than in other regions; when the level of financial development is low (financial resources are not scarce and have not reached a reasonable level), the phenomenon of the “resource curse” appears; when the level of financial development is highly developed, economic development benefits more from financial development, and the effects of resource endowment decline. Only when financial development is at a reasonable level can resource endowments effectively raise the level of economic development.

In this paper, by using the partial order method, the existence and uniqueness of a solution for systems of a class of abstract operator equations in Banach spaces are discussed. The result obtained in this paper improves and unifies many recent results. Two applications to the system of nonlinear differential equations and the systems of nonlinear differential equations in Banach spaces are given, and the unique solution and interactive sequences which converge the unique solution and the error estimation are obtained.

In this paper, the concept of a fuzzy soft point is redefined, and the definition of a fuzzy soft net in a fuzzy soft topological space is given. On this basis, the convergence of a fuzzy soft net is defined by using the Q-neighborhood theory, and the continuity of fuzzy soft mappings is characterized by the net. The obtained results demonstrate that the concepts proposed in this paper are very suitable and will provide powerful research tools for further research in this field.

Human action recognition is an important part for computers to understand the behavior of people in pictures or videos. In a single image, there is no context information for recognition, so its accuracy still needs to be greatly improved. In this paper, a single-image human action recognition method based on improved ResNet and skeletal keypoints is proposed, and the accuracy is improved by several methods. We improved the backbone network ResNet-50 and CPN to a certain extent and constructed a multitask network to suit the human action recognition task, which not only improves the accuracy but also balances the total number of parameters and solves the problem of large network and slow operation. In this paper, the improvement methods of ResNet-50, CPN, and whole network are tested, respectively. The results show that the single-image human action recognition based on improved ResNet and skeletal keypoints can accurately identify human action in the case of different human movements, different background light, and occlusion. Compared with the original network and the main human action recognition algorithms, the accuracy of our method has its certain advantages.

Recently, a unified integral operator has been introduced by Farid, 2020, which produces several kinds of known fractional and conformable integral operators defined in recent decades (Kwun, 2019, Remarks 6 and 7). The aim of this paper is to establish bounds of this unified integral operator by means of α,m-convex functions. The resulting inequalities provide the bounds of all associated fractional and conformable integral operators in a compact form. Also, the results of this paper hold for different kinds of convex functions connected with α,m-convex functions.

Linearized model of the system is often used in control design. It is generally believed that we can obtain the linearized model as long as the Taylor expansion method is used for the nonlinear model. This paper points out that the Taylor expansion method is only applicable to the linearization of the original nonlinear function. If the Taylor expansion is used for the derived nonlinear equation, wrong results are often obtained. Taking the linearization model of the maglev system as an example, it is shown that the linearization should be carried out with the process of equation derivation. The model is verified by nonlinear system simulation in Simulink. The method in this paper is helpful to write the linearized equation of the control system correctly.

This paper considers a multinational green supply chain (MGSC), composed of a manufacturer and a foreign retailer affected by import tariff. The main theme of this paper is to explore supply chain decisions and coordination contract. Four decision models were investigated: (1) the centralized decision supply chain model (model CS), (2) the decentralized decision supply chain model (model DS), (3) the R&D cost sharing contract model (model RD) with the retailer sharing a portion of green R&D cost, and (4) the quantity discount-cost sharing contract model (model QD), which combined a quantity discount contract with a cost sharing contract. The equilibrium decisions of these models were derived. The R&D cost sharing contract was found to improve greenness and performance of the supply chain but cannot reach the optimal performance compared to the centralized decision model. While the quantity discount-cost sharing contract can achieve a perfect coordination of MGSC. Furthermore, import tariff has an adverse effect on the performance of MGSC. The increase in tariff shrinks the threshold of the highest wholesale price and quantity discount coefficient offered by the manufacturer, which will hamper the cooperation of supply chain. However, the enhancement of consumers’ green preference is conducive to encourage the retailer to participate in cooperation and smoothen the adverse effects of tariff fluctuation. In the same model, the adverse effect of tariff on product greenness and the retail price does not change with the increase in its value. However, the adverse effect on the green performance price ratio and the profits from all parties in the supply chain are slow.

An adaptive high-order difference solution about a 2D nonlinear degenerate singular reaction-diffusion equation with a convection term is initially proposed in the paper. After the first and the second central difference operator approximating the first-order and the second-order spatial derivative, respectively, the higher-order spatial derivatives are discretized by applying the Taylor series rule and the temporal derivative is discretized by using the Crank–Nicolson (CN) difference scheme. An alternating direction implicit (ADI) scheme with a nonuniform grid is built in this way. Meanwhile, accuracy analysis declares the second order in time and the fourth order in space under certain conditions. Sequentially, the high-order scheme is performed on an adaptive mesh to demonstrate quenching behaviors of the singular parabolic equation and analyse the influence of combustion chamber size on quenching. The paper displays rationally that the proposed scheme is practicable for solving the 2D quenching-type problem.

The development of power energy structures and information communication technology has promoted the renewal of smart grid information-physical structures. At the same time, the changes in the smart grid energy structure and the vulnerability of the information network threaten the stability of the power system and uses multiagent control theory to improve the transient stability of the power grid which has strong practicability. In this paper, an optimized distributed control scheme is proposed for application to the smart grid model so that the grid system can flexibly adapt to the external operating conditions and recover to stable operating conditions after being disturbed. In this paper, an intelligent power grid information-physical network simulation system is established. According to the information exchange within the multiagent system, groups of coherent generators in the disturbed power grid in different regions are identified and controlled. Distributed control is applied to maintain the exponential frequency synchronization and phase angle aggregation of the synchronous generators to achieve transient stability. Finally, the effectiveness and rapidity of the proposed distributed optimal control scheme are verified by simulation analysis of the IEEE 39 node model.

Beam pumping unit is widely applied in the oilfield area. However, the falling process of the pumping unit horsehead causes large-amplitude variations of the motor torque and greatly reduces the efficiency of the system. This paper proposes a late-model hydraulic-electromotor hybrid system which can realize energy recovery and assist electromotor work. The hybrid system can improve the matching characteristics between the electromotor and the pumping unit. A mathematical model is built and verified by experiment studies. According to the experimental and simulation results, it can be concluded that energy recovery function of the designed hydraulic-electromotor hybrid system is demonstrated to be effective, and the electromotor can keep the output power stable. As the efficiency of the hybrid power system affects the energy-saving effect, this paper establishes the efficiency model of the system and studies the key parameters affecting the efficiency of the system through experiments and simulation. The key parameters include the minimum displacement ratio of the pump and hydraulic motor, working pressure, and transmission ratio. This paper proposes a parameter optimization design method of the hydraulic-electromotor hybrid system. Under the optimal parameter combination, the hydraulic-electromotor hybrid system efficiency can reach 92%, and the electric power transmission efficiency of the system can be raised to 75%.

In recent years, Chinese economic development has slowed down and competition in the financial industry has become increasingly fierce. The purpose of this paper is to study the efficiency characteristics of China’s banking industry in the new environment and provide suggestions for banks to improve efficiency. This paper uses a data envelopment analysis (DEA) SBM-undesirable model and window analysis to measure the technical efficiency of 13 nationwide commercial banks in China during the period from 2008 to 2017. Furthermore, the convergence characteristics of bank technical efficiency are examined. The empirical results show that state-owned banks were more efficient than joint stock banks before 2012. After 2012, state-owned banks were less efficient than joint stock banks. Finally, this paper explores the influential factors of technical efficiency. Noninterest income ratio, net interest margin, growth rate of total investment in fixed assets, and consumer price index have a significant positive impact on bank efficiency. The cost-to-income ratio has a significant negative impact on bank efficiency. Further research using the threshold model shows that noninterest income ratio has a threshold effect on bank efficiency.

This paper presents an abstract approach of analysing population growth in the field of algebraic topology using the tools of homology theory. For a topological space X and any point vn∈X, where vn is the n-dimensional surface, the group η=X,vn is called population of the space X. The increasing sequence from vin∈X to vjn∈X for i

This paper is focused on a kind of distributed optimal control design for a class of switched nonlinear systems with the state time delay which have a prescribed switching sequence. Firstly, we design a bounded controller to make the system stable for each mode of the nominal system. Then, a distributed optimal controller which can satisfy input constraint is designed based on the bounded stabilization controller. A sufficient condition to guarantee ultimate boundedness of the system is given based on appropriate assumption. The significance of this paper is that distributed optimal control method is applied to switched nonlinear systems with the state time delay. Finally, a simulation example is given to verify the effectiveness of the proposed method.

Sea surface temperature (SST) forecasting is the task of predicting future values of a given sequence using historical SST data, which is beneficial for observing and studying hydroclimatic variability. Most previous studies ignore the spatial information in SST prediction and the forecasting models have limitations to process the large-scale SST data. A novel model of SST prediction integrated Deep Gated Recurrent Unit and Convolutional Neural Network (DGCnetwork) is proposed in this paper. The DGCnetwork has a compact structure and focuses on learning deep long-term dependencies in SST time series. Temporal information and spatial information are all included in our procedure. Differential Evolution algorithm is applied in order to configure DGCnetwork’s optimum architecture. Optimum Interpolation Sea Surface Temperature (OISST) data is selected to conduct experiments in this paper, which has good temporal homogeneity and feature resolution. The experiments demonstrate that the DGCnetwork significantly obtains excellent forecasting result, predicting SST by different lengths flexibly and accurately. On the East China Sea dataset and the Yellow Sea dataset, the accuracy of the prediction results is above 98% on the whole and all mean absolute error (MAE) values are lower than 0.33°C. Compared with the other models, root mean square error (RMSE), root mean square percentage error (RMSPE), and mean absolute percentage Error (MAPE) of the proposed approach reduce at least 0.1154, 0.2594, and 0.3938. The experiments of SST time series show that the DGCnetwork model maintains good prediction results, better performance, and stronger stability, which has reached the most advanced level internationally.

In this paper, the fixed-point theorem for monotone contraction mappings in the setting of a uniformly convex smooth Banach space is studied. This paper provides a version of the Banach fixed-point theorem in a complete metric space.

This paper proposes a U-model-based fault-tolerant controller design method in order to ensure the unmanned aerial vehicle (UAV) flight performance when subject to the actuator failures. Depending on the decoupled quadrotor model, this paper presents a sliding mode control method based on U-model in detail and realizes fault-tolerant control for the quadrotor UAV with the stability theory and simulation experiment verifications. The results show that the new controller designed by using the U-model method can simplify the controller design process which has good fault-tolerant characteristics when actuator faults occur compared with the traditional method.

In this paper, a mathematical model based on graph theory is proposed to calculate the heat distribution of LED lights’ convective cooled heat sink. First, the heat and mass transfer process of a single fin under moisture environment is analyzed. Then, the heat transfer process is characterized by a digraph, defining fins and joints of a heat sink as edges and vertices in graph theory. Finally, the whole heat transfer process is described by two criteria achieved based on graph theory. Therefore, the temperature-heat calculation equations of the whole heat sink are deduced. The accuracy of this model is verified by testing the junction temperature of different LED chips mounted on the same heat sink under moisture environment, and the relative errors between the calculated value and the experimental data are all within 5%, and it is also concluded from the model that heat sinks with an identical heat digraph but different types have close cooling performance and are verified by two typical heat sinks, cylindrical heat sink and rectangular plate-fin heat sink, under the same conditions. The mathematical model based on group theory developed in this paper combined with computer technology is convenient for the performance analysis among a large number of heat sink fin arrangement schemes.

Exoskeleton robot is a typical application to assist the motion of lower limbs. To make the lower extremity exoskeleton more flexible, it is necessary to identify various motion intentions of the lower limbs of the human body. Although more sEMG sensors can be used to identify more lower limb motion intention, with the increase in the number of sensors, more and more data need to be processed. In the process of human motion, the collected sEMG signal is easy to be interfered with noise. To improve the practicality of the lower extremity exoskeleton robot, this paper proposed a wavelet packet transform- (WPT-) based sliding window difference average filtering feature extract algorithm and the unscented Kalman neural network (UKFNN) recognition algorithm. We established an sEMG energy feature model, using a sliding window difference average filtering method to suppress noise interference and extracted stable feature values and using UKF filtering to optimize the neural network weights to improve the adaptability and accuracy of the recognition model. In this paper, we collected the sEMG signals of three muscles to identify six lower limb motion intentions. The average accuracy of 94.83% is proposed in this paper. Experiments show that the algorithm improves the accuracy and anti-interference of motion intention recognition of lower limb sEMG signals. The algorithm is superior to the backpropagation neural network (BPNN) recognition algorithm in the lower limb motion intention recognition and proves the effectiveness, novelty, and reliability of the method in this paper.

The dam reliability study is essential for dam operation safety, regarding the complexity in dam failure causes. The assessment of the dam reliability is now mainly probabilistic or nonprobabilistic. The probabilistic method is usually applied to the cases with sufficient knowledge on dam parameters, while the nonprobabilistic method is suitable for the cases with insufficient knowledge on dam parameters. Since a dam can contain multiple parameters, information abundancy can vary among those parameters, and neither the probabilistic method nor the nonprobabilistic method alone is enough for dam reliability assessment. In this paper, the probabilistic method and nonprobabilistic method are modified based on the adjusted first-order second-moment method and the interval analysis method to suit the dam reliability assessment. Based on characterization on these two methods and the research of the fusion method, the secondary performance function of the dam is constructed, and the construction method of the risk assessment model for dam is proposed. Combined with a case study, this paper contributes to the safe operation of the dam.

In this paper, the wheel loader with electrohydraulic proportional control technology is used as the carrier. For the first time, the dual-angle sensor is used for intelligent operation, which allows the wheel loader working device to be precisely controlled. First, the theoretical analysis of the electrohydraulic proportional control technology on the wheel loader studied in this paper is carried out. Next, according to the feedback of the boom and bucket angle sensor signals, the electrohydraulic proportional control technology is used to initially realize the boom memory and bucket automatic levelling function of the wheel loader working device. Finally, the data acquisition equipment is connected to provide experimental verification, although the test results did not achieve precise control of the working device. After analysis, the detected problems were solved by constructing a neural network algorithm model, which successfully realizes the intelligent and precise operation of the wheel loader, reducing unnecessary energy loss.

This paper investigates the C-logit stochastic user equilibrium (SUE) problem on a bimodal transportation network with road and rail travel modes. The C-logit model captures the overlapping effect among the different paths via commonality factors; sequentially, it has ability to obtain a more realistic traffic flow distribution pattern. In this paper, when we redefine the link travel cost functions and employ a binary Logit model for the mode split, the bimodal C-logit SUE model can be simplified into an unconstrained nonlinear mathematical programming formulation. Such model is verified to satisfy the bimodal C-logit SUE conditions at its stationary point and can be solved by existing algorithms. So, the simplified model can be convenient to be used on the general bimodal transportation network.

This paper addresses a single-machine scheduling problem with periodic preventive maintenance activities that are predeterministic so that the machine is not available all the time, and jobs have to be processed between two consecutive maintenance periods. We propose a mixed integer programming (MIP) model and two heuristics to minimize the makespan. With more constraints in our model, the model is more efficient than the recent model of Perez-Gonzalez and Framinan , and our model could solve problems with up to fifty jobs. Two heuristic algorithms, namely, H (MW) and H (LB∗), are also proposed, in which two bin-packing policies of the minimum waste and minimum lower bound are used, respectively. Furthermore, we also proposed an improvement procedure. The results showed that the heuristic H (MW) outperformed other heuristics of the paper, indicating that the bin-packing policy of the minimum waste is more effective than well-known ones such as full batch and best fit. Additionally, all the heuristic algorithms addressed in this paper combined with the improvement procedure could achieve a similar and high quality of solutions with a very tiny increase in computational expense.

The integration of information technology and manufacturing technology (IOITMT) is a strategic idea proposed by China. As the smallest unit of integration, manufacturing enterprises can be seen as the root of IOITMT. However, the current situation of IOITMT in China shows that intelligent manufacturing still has a long way to go and that the implementation effect of IOITMT requires urgent improvement. The dynamic evolution of IOITMT is accompanied by the integration and coevolution of internal technologies and also affects the performance of enterprises. Taking technical efficiency as a measure of IOITMT, the purpose of the current study is to develop an internal dynamic evolutionary mechanism framework for IOITMT of a single cycle in manufacturing enterprises. The framework is based on the theory of self-organization and convergence and uses convergence theory to discuss the evolution mode of technical efficiency in the three stages of IOITMT under the guidance of order parameters. This paper finds that the technical efficiency of IOITMT is influenced by the ratio of convergence parameters r1/r2, and adjusting the convergence intensity can effectively improve the performance of enterprises. Hence, this paper provides important theoretical guidance for the advancement of IOITMT of Chinese manufacturing enterprises.

This paper considers some multiscale radial basis function collocation methods for solving the two-dimensional Monge–Ampère equation with Dirichlet boundary. We discuss and study the performance of the three kinds of multiscale methods. The first method is the cascadic meshfree method, which was proposed by Liu and He (2013). The second method is the stationary multilevel method, which was proposed by Floater and Iske (1996), and is used to solve the fully nonlinear partial differential equation in the paper for the first time. The third is the hierarchical radial basis function method, which is constructed by employing successive refinement scattered data sets and scaled compactly supported radial basis functions with varying support radii. Compared with the first two methods, the hierarchical radial basis function method can not only solve the present problem on a single level with higher accuracy and lower computational cost but also produce highly sparse nonlinear discrete system. These observations are obtained by taking the direct approach of numerical experimentation.

In order to accurately divide the traffic control subarea and improve the regional coordination control effect, this paper studies the key technologies of traffic control subarea division from the influence of the discrete characteristics of the fleet on the coordinated control. Correlation analysis method and regression analysis method are used to construct the correlation model of adjacent intersections. The principle and process of transportation subarea division are designed by using analytic hierarchy process, and a new traffic subarea division model is proposed. Taking Gulou Street in Miyun District of Beijing as an example, combined with the green wave coordination optimization method, the new model was applied and simulated in SimTraffic. The simulation results show that the average delay of the intersection is reduced by 21.4%, the total number of stops is reduced by 9.62%, and the travel time is shortened by 8.75%. The new intersection degree model and the coordinated control scheme obtained by the traffic subarea division model make the highest traffic efficiency. That is, to verify the scientific research method of this paper, the research results are reliable and the practical application value is high.

The nonlinear time-delay dynamic absorber has a good control effect on the main system under harmonic excitation, but the control effect on the main system under complicated excitation is not obvious. In the previous study, they do not have an effective method to obtain the time-delay control parameters under different external excitations. Because of the above problems, this paper proposed an innovative method to obtain the control parameters of time-delay dynamic absorbers. For the vibration system including the nonlinear time-delay dynamic absorber, the transient time-integration method was used to solve the nonlinear delay dynamic differential equation of the system under the specific excitation. Then, the quantitative relationship was established between the time-domain responses of the vibration system, the external excitation, and time-delay control parameters. The time-delay control parameters would be obtained when the time-domain response of the main system is minimized under specific excitation using an algorithm that continuously compares the objective function that is related to the time-domain response of the main system. This paper took the two-degree-of-freedom vibration system with a nonlinear time-delay dynamic absorber as the target system to simulate the time-domain response of the main system. The simulation results show that the time-delay control parameters calculated by the new method can obtain significant vibration reduction effects for both harmonic excitation and multiharmonic excitation. The suppression performance of the nonlinear time-delay dynamic absorber is significantly improved.

The differences in development and situation of karst caves lead to two kinds of karst caves, and the karst cave may be on the pile side or at the pile bottom, which has a different influence on the bearing capacity of pile foundation. The paper presents a numerical analysis of the influence of karst caves at pile side on the bearing capacity of super-long pile foundation in karst areas. According to the size of pile foundation of a real bridge project, this paper modelized karst caves and investigated the karst cave from the effect of length, height, and thickness of roof on horizontal and vertical bearing capacity of pile foundation. The main conclusions can be drawn as: when the horizontal displacement at the top of pile foundation is greater than 0.05 m, the horizontal load is correlated positively with the length of karst cave; when the vertical displacement is greater than 0.07 m, the vertical load is correlated negatively with the thickness of the roof of karst cave. However, the height of karst cave has little effect on the bearing capacity; also the existence of karst cave has little influence on the dynamic response of pile foundation. The results of this study can be important with reference to the design and construction of pile foundations in karst areas.

Elastic waves cause energy loss during the transmission of coal measures. These losses include propagation loss, dielectric absorption loss, scattering loss, and frequency migration loss. The absorption loss is mainly caused by the inelastic absorption. The scattering loss is caused by the uneven heat absorption in the formation. The frequency shift loss is caused by the piezoelectric effect of coal-bearing formations and the intermodulation of different frequency signals. After considering the influence factors of the coal seam structure, this paper presents a model of low-frequency elastic waves loss coefficient. The paper proposed the loss coefficient of the elastic wave in the coal measure strata by considering two main attenuation mechanisms: intrinsic absorption and scattering. This paper theoretically studied the effects of the model parameters such as density, porosity, particle size, and wave frequency on the loss of wave energy using COMSOL simulation. Besides, the comparison of MATLAB simulation results shows that the simulation results produced by the model proposed in this paper are similar to the models embedded in COMSOL. This work can be applied to coal, oil, and gas exploration and is also helpful to study the mechanisms of wave attention on the low-frequency band.

Based on the equivalent mass-spring model and considering the coupling effect between creep soil and prestressed anchors, the dynamic calculation model of prestressed anchors with frame structure is established. The soil mass is expressed in the form of concentrated mass. The action of the frame structure on the soil is treated as a parallel coupling of a linear spring and a linear damper, and the free section of the anchor is treated as a linear spring. Considering the creep characteristics, the soil is regarded as a Generalized Kelvin body and the anchoring section of the anchor is regarded as an equivalent spring body, which are coupled in parallel. Considering the effect of slope height, the dynamic calculation model is solved and the seismic response is analyzed. Finally, an engineering example is used to verify the calculation method in this paper, and the results are compared with the shaking table test and numerical simulation. It shows that the calculation model proposed in this paper is safe and reasonable for the seismic design and analysis of the slope supported by prestressed anchors with frame structure.

The lens for monitoring the rubber conveyor belt is easy to adhere to a large number of water droplets, which seriously affects the image quality and then affects the effect of fault monitoring. In this paper, a new method for detecting and removing water droplets on rubber conveyor belts based on the attentive generative adversarial network is proposed to solve this problem. First, the water droplet image of the rubber conveyor belt is input into the generative network composed of a cyclic visual attentive network and an autoencoder with skip connections, and an image of removing water droplets and an attention map for detecting the position of the water droplet are generated. Then, the generated image of removing water droplets is evaluated by the attentive discriminant network to assess the local consistency of the water droplet recovery area. In order to better learn the water droplet regions and the surrounding structures during the training, the image morphology is added to the precise water droplet regions. A dewatered rubber conveyor belt image is generated by increasing the number of circular visual attention network layers and the number of skip connection layers of the autoencoder. Finally, a large number of comparative experiments prove the effectiveness of the water droplet image removal algorithm proposed in this paper, which outperforms of Convolutional Neural Network (CNN), Discriminative Sparse Coding (DSC), Layer Prior (LP), and Attention Generative Adversarial Network (ATTGAN).