Search results

The purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system (EHSS). Specifically, the adaptive neuro-fuzzy inference system (ANFIS) is selected to improve the control performance for EHSS.

Two types of input–output data were chosen to train the ANFIS models. The inputs are the desired and actual forces, and the output is the current. The first type is to set a sinusoidal signal for the current to produce the actual driving force, and the desired force is chosen as same as the actual force. The other type is to give a sinusoidal signal for the desired force. Under the action of the PI controller, the actual force tracks the desired force, and the current is the output of the PI controller.

The models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller. The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot, rise time and mean absolute error and show adaptivity to different tracking conditions, including sinusoidal signal tracking and sudden change signal tracking.

This paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.

The purpose of this paper is to present a direct force control which uses two closed-loop controller for one-degree-of-freedom human-machine system to synchronize the human position and machine position, and minimize the human-machine force. In addition, the friction is compensated to promote the performance of the human-machine system.

The dynamic of the human-machine system is mathematically modeled. The control strategy is designed using two closed-loop controllers, including a PID controller and a PI controller. The frictions, which exist in the rotary joint and the hydraulic wall, are compensated separately using the Friedland’s observer and Dahl’s observer.

When human-machine system moves at low velocity, there exists a significant amount of static friction that hinders the system movements. The simulation results show that the system gives a better performance in human-machine position synchronization and human-machine force minimization when the friction is compensated.

The acquired results are based on simulation not experiment.

This paper is the first to apply the electrohydraulic servo systems to both actuate the human-machine system, and use the direct force control strategy consisting of two closed-loop controllers. It is also the first to compensate the friction both in the robot joint and hydraulic wall.

The purpose of this paper is to study the screen surface parameters of the double deck vibrating screen, in sections, to determine the influence of each part of the screen surface on the screening efficiency of the vibrating screen. Finally, the best screening parameters were calculated to obtain the best screening performance.

In this paper, the discrete element method is used to simulate the process of two-layer subsection screening. Response surface test was used to analyze the influence of various factors and their interactions on screening results. Finally, based on the binomial regression model of screening efficiency, the optimal combination of vibration parameters is calculated.

In the screening process of vibrating screen, due to the different screening environments in each area of the screen surface, the single-layer linear vibrating screen with equal screen surface parameters cannot obtain the best screening performance. Among the single factors, the effect of vibration frequency is the most significant.

To address the issue of single layer linear vibrating screens with equal screen surface parameters being unable to maintain optimal screening performance when handling large amounts of materials. This article proposes a double layer vibrating screen with different screen surface grids and screen surface angles to address the problem of low screening performance of traditional single layer linear vibrating screens.

The purpose of this paper is to analyze the effect and mechanism of platform incentives on users’ knowledge collaboration performance (KCP) and the configuration leading to high KCP in online knowledge communities (OKCs) in the post-COVID-19 pandemic era from a cross-culture perspective.

A survey method and a standard questionnaire were applied. The data was analyzed using multiple regression and fuzzy set qualitative comparative analysis.

The results indicate that, for both kinds of users, self-enhancement and communication positively affect the KCP. User engagement significantly mediates the relationship between communication and KCP and knowledge absorptive capacity moderates the relationship between user engagement and KCP. In contrast, material incentive positively affects the KCP of Chinese users, while hurting the cross-cultural sample. And the promotion of KCP for cross-cultural samples does not require a higher engagement and knowledge absorptive capacity, while paying more attention to short-term interests, such as communication and self-enhancement.

The study only divides users into Chinese and cross-cultural foreign users, without a distinction between foreign users in different countries. In addition, the research is based on cross-sectional data and failed to try to explore the long-term effects of these incentives from the time dimension.

This study explores the incentive mechanism and configuration of OKC platforms to achieve high KCP for different users from a cross-cultural perspective. It provides new ideas and solutions for precise incentives for users of OKC platforms.

Urban MICE competitiveness research consists of two clusters, one that is public-statistics-based and another that is questionnaire-based. Supply-side research on urban MICE competitiveness is rare. Based on the findings of Chen (2014) and other scholars, the purpose of this paper is to design counterpart statistical indicators to empirically analyze CMCA member cities.

After calculating the standardized Z value of the original statistical data for 17 CMCA member cities, the authors conducted confirmatory factor analysis for the first-level principal components, based on which hierarchical clustering was performed; then, regression analysis was conducted with the MICE profit factor as the dependent variable and the cost factor, tight support factor and facilitating factor as the independent variables to support publishing articles.

The confirmatory factor analysis showed that the urban MICE competitiveness indicators from the supply-side perspective include the profit factor, cost factor, tight support factor and facilitating factor.

On the basis of research findings from the demand perspective and the literature review, the authors constructed an urban MICE competitiveness indicator system from the perspective of the supply side and conducted principal component analysis. However, because of the inaccessibility of panel data, the current data were only sufficient to conduct the research. If panel data could be acquired, further research could be conducted to perfect the current indicator system for urban MICE competitiveness.

The findings suggest that tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.

The tight support factor exerts a significant positive influence on urban MICE competitiveness from the supply-side perspective, while the cost factor exerts a significant negative influence. The findings suggest that the tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.

The research bridge the empirical statistics and the questionnaire-based perception study on urban MICE tourism image, and advance to construct an empirical statistics based indicator system for urban MICE tourism image.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.

The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.

The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.

This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.

Currently on additive manufacturing, extensive research is directed toward mitigating the main challenges associated with multi-material in fused filament fabrication which has a weak bonding strength between dissimilar materials. Low interfacial bonding strength leads to defects, anisotropy and temperature gradient in materials which negatively impact the mechanical performance of the multi-material prints. The purpose of this study was to assess the performance of different interface geometry designs in terms of the mechanical properties of the specimens.

Tensile test specimens were printed using: mono-material without a boundary interface, mono-material with the interface geometries (Face-to-face; U-shape; T-shape; Dovetail; Encapsulation; Mechanical interlocking; and Overlap) and multi-material with the interface geometries. The materials chosen with high and low compatibility were Tough polylactic acid (PLA) and TPU.

The main results of this study indicate that the interface geometries with the mechanical constriction between materials provide better structural integrity to the specimens. Moreover, in the case of the mono-material parts, the most effective interface design was the mechanical interlocking for both Tough PLA and TPU. On the other hand, in the case of multi-material specimens, the encapsulation showed the highest ultimate tensile strength, whereas the overlap and T-shape presented more robust bonding.

This study examines the mechanical performance, particularly tensile strength, strain at break, Young’s modulus and yield strength of different interface designs which were not studied in the previous studies.

This study aims to explore the influence of science fiction on innovators and present a comprehensive model using the theory of planned behavior and social support theory to discuss the impact of science fiction on the intention of becoming an innovation worker.

Partial least squares structural equation modeling (PLS-SEM) was adopted in this study and responses were obtained from 244 Chinese innovators.

The results revealed the adequacy of the proposed model and the above-mentioned constructs in explaining innovation intention. Science fiction perception was found to influence the intention of becoming an innovation worker directly. Subjective norm, perceived behavioral control, and attitude directly influence the intention of becoming an innovation worker. Additionally, attitude is a mediator between science fiction perception and the intention of becoming an innovation worker. Moreover, social support network moderates the relationship between attitude and intention.

These results shed light on the mechanism by which science fiction influence innovators as well as provide critical managerial implications for policymakers and practitioners.

In this paper, with the goal of reducing the fuel consumption of UAV, the engine performance optimization is studied and on the basis of aircraft/engine integrated control, the minimum fuel consumption optimization method of engine given thrust is proposed. In the case of keeping the given thrust of the engine unchanged, the main fuel flow of the engine without being connected to the afterburner is optimally controlled so as to minimize the fuel consumption.

In this study, the reference model real-time optimization control method is adopted. The engine reference model uses a nonlinear real-time mathematical model of a certain engine component method. The quasi-Newton method is adopted in the optimization algorithm. According to the optimization variable nozzle area, the turbine drop-pressure ratio corresponding to the optimized nozzle area is calculated, which is superimposed with the difference of the drop-pressure ratio of the conventional control plan and output to the conventional nozzle controller of the engine. The nozzle area is controlled by the conventional nozzle controller.

The engine real-time minimum fuel consumption optimization control method studied in this study can significantly reduce the engine fuel consumption rate under a given thrust. At the work point, this is a low-altitude large Mach work point, which is relatively close to the edge of the flight envelope. Before turning on the optimization controller, the fuel consumption is 0.8124 kg/s. After turning on the optimization controller, you can see that the fuel supply has decreased by about 4%. At this time, the speed of the high-pressure rotor is about 94% and the temperature after the turbine can remain stable all the time.

The optimal control method of minimum fuel consumption for the given thrust of UAV is proposed in this paper and the optimal control is carried out for the nozzle area of the engine. At the same time, a method is proposed to indirectly control the nozzle area by changing the turbine pressure ratio. The relevant UAV and its power plant designers and developers may consider the results of this study to reach a feasible solution to reduce the fuel consumption of UAV.

Fuel consumption optimization can save fuel consumption during aircraft cruising, increase the economy of commercial aircraft and improve the combat radius of military aircraft. With the increasingly wide application of UAVs in military and civilian fields, the demand for energy-saving and emission reduction will promote the UAV industry to improve the awareness of environmental protection and reduce the cost of UAV use and operation.