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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 purpose of this paper is to study the aerodynamic characteristics of Ahmed body in longitudinal and lateral platoons under crosswind by computational fluid dynamics simulation. It helps to improve the aerodynamic characteristics of vehicles by providing theoretical basis and engineering direction for the development and progress of intelligent transportation.

A two-car platoon model is used to compare with the experiment to prove the accuracy of the simulation method. The simplified Ahmed body model and the Reynolds Averaged N-S equation method are used to study the aerodynamic characteristics of vehicles at different distances under cross-winds.

When the longitudinal distance x/L = 0.25, the drag coefficients of the middle and trailing cars at β = 30° are improved by about 272% and 160% compared with β = 10°. The side force coefficients of the middle and trailing cars are increased by 50% and 62%. When the lateral distance y/W = 0.25, the side force coefficients of left and middle cars at β = 30° are reduced by 38% and 37.5% compared with β = 10°. However, the side force coefficient of the right car are increased by about 84.3%.

Most of the researches focus on the overtaking process, and there are few researches on the neat lateral platoon. The innovation of this paper is that in addition to studying the aerodynamic characteristics of longitudinal driving, the aerodynamic characteristics of neat lateral driving are also studied, and crosswind conditions are added. The authors hope to contribute to the development of intelligent transportation.

This paper primarily aims to focus on a review of convolutional neural network (CNN)-based eye control systems. The performance of CNNs in big data has led to the development of eye control systems. Therefore, a review of eye control systems based on CNNs is helpful for future research.

In this paper, first, it covers the fundamentals of the eye control system as well as the fundamentals of CNNs. Second, the standard CNN model and the target detection model are summarized. The eye control system’s CNN gaze estimation approach and model are next described and summarized. Finally, the progress of the gaze estimation of the eye control system is discussed and anticipated.

The eye control system accomplishes the control effect using gaze estimation technology, which focuses on the features and information of the eyeball, eye movement and gaze, among other things. The traditional eye control system adopts pupil monitoring, pupil positioning, Hough algorithm and other methods. This study will focus on a CNN-based eye control system. First of all, the authors present the CNN model, which is effective in image identification, target detection and tracking. Furthermore, the CNN-based eye control system is separated into three categories: semantic information, monocular/binocular and full-face. Finally, three challenges linked to the development of an eye control system based on a CNN are discussed, along with possible solutions.

This research can provide theoretical and engineering basis for the eye control system platform. In addition, it also summarizes the ideas of predecessors to support the development of future research.

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.

The purpose of this paper is to investigate the optimal average drag coefficient of the Ahmed body for mixed platoon driving under crosswind and no crosswind conditions using the response surface optimization method. This study has extraordinary implications for the planning of future intelligent transportation.

First, the single vehicle and vehicle platoon models are validated. Second, the configuration with the lowest average drag coefficient under the two conditions is obtained by response surface optimization. At the same time, the aerodynamic characteristics of the mixed platoon driving under different conditions are also analyzed.

The configuration with the lowest average drag coefficient under no crosswind conditions is 0.3 L for longitudinal spacing and 0.8 W for lateral spacing, with an average drag coefficient of 0.1931. The configuration with the lowest average drag coefficient under crosswind conditions is 10° for yaw angle, 0.25 L for longitudinal spacing, and 0.8 W for lateral spacing, with an average drag coefficient of 0.2251. Compared to the single vehicle, the average drag coefficients for the two conditions are reduced by 25.1% and 41.3%, respectively.

This paper investigates the lowest average drag coefficient for mixed platoon driving under no crosswind and crosswind conditions using a response surface optimization method. The computational fluid dynamics (CFD) results of single vehicle and vehicle platoon are compared and verified with the experimental results to ensure the reliability of this study. The research results provide theoretical reference and guidance for the planning of intelligent transportation.

Wireless sensor networks, due to their potentially wide application perspectives, may proliferate in future. Two major stumbling blocks are the dynamic variance of the network caused by both the capacity constraint of sensor nodes and uncertainties of wireless links, and secure routing in the special security sensitive environment. Therefore, adaptable and defendable routing mechanism is in urgent need for the deployment of sensor networks. This paper aims to propose a feedback‐based secure routing protocol (FBSR).

Feedback from the neighboring nodes serves as the dynamic information of the current network, with which sensor nodes make forwarding decisions in a secure and energy aware manner. Feedback message is included in the MAC layer acknowledgement frame to avoid network congestion, and it is authenticated with the proposed Keyed One Way Hash Chain (Keyed‐OWHC) to avoid feedback fabrication. FBSR's resilience to node compromise is enhanced by statistic efforts accomplished by the base station.

Both mathematical analysis and simulation results show that FBSR is not only reliable but also energy efficient.

The paper introduces a novel routing scheme for wireless sensor networks.

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.

This paper aims to address the challenges of the capacitor tower maintenance robot during bolt tightening in high-voltage substations, including difficulties in bolt positioning due to tilted angles and anti-bird cover occlusion and issues with fast and accurate docking of bolts while the base is moving.

This paper proposes a visual servoing method for the capacitor tower maintenance robot, including bolt pose estimation and visual servoing control. Bolt pose estimation includes four components: constructing a keypoint detection network to identify the approximate position, precise positioning, rapid prediction and calculation of bolt pose. In visual servoing, an improved position-based visual servoing (PBVS) is proposed, which eliminate steady-state error and enhance response speed during dynamic tracking by incorporating integral and differential components.

The bolt detection method exhibits high robustness against varying lighting conditions, partial occlusions, shooting distances and angles. The maximum positioning error at a distance of 250 mm is 2.8 mm. The convergence speed of the improved PBVS is 10% higher than that of the traditional PBVS when the base and target remain relatively stationary. When the base moves at a constant speed, the improved method eliminates steady-state error in dynamic tracking. When the base moves rapidly and intermittently, the maximum error of the improved method in the tracking process is 30% smaller than that of traditional PBVS.

This method enables real-time detection and positioning of bolts in an unstructured environment with tilt angles, variable lighting conditions and occlusion by anti-bird covers. An improved PBVS is proposed to enhance its capability in tracking dynamic targets.

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 causes of high-speed railway failures are complex, and it is difficult to quantitatively and accurately describe safety evaluation. The purpose of this paper is to construct a model to ensure the safety of high-speed railway operations.

The authors construct a high-speed railway operation safety evaluation index system from four aspects: personnel, equipment, environment and management and analyze the inter-coupling relationship of various safety factors. Based on the evaluation index system, the use of network analytic hierarchy process (ANP) and fuzzy comprehensive evaluation will be used to establish a high-speed railway operation safety evaluation model.

Through the literature investigation and field investigation, combined with high-speed railway safety key points and system composition, 4 first-level indicators and 17 second-level indicators were selected to construct a high-speed railway operation safety evaluation index system. It can be seen from the results that the personnel management system and the signal and control system have the largest weight.

On the basis of establishing an evaluation index system, comprehensively considering the internal coupling relationship between evaluation indexes and the fuzziness of high-speed railway operation safety evaluation, high-speed railway uses ANP fuzzy network analysis method to construct high-speed railway operation, and the safety evaluation model has certain advantages and practicability in the case of the relative lack of high-speed railway operation data and fault data.