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The purpose of this paper is to study how to improve the performance of RFID robot system by anti-collision algorithms. For radio frequency identification (RFID) robots operating in mobile scenes, effective anti-collision algorithm not only reduces missed reading but also enhances the speed of RFID robots movement.

An effective anti-collision algorithm is proposed to accelerate tag identification in RFID robots systems in this paper. The tag collisions in the current time slot are detected by a new method, and then further resolve each small tag collision to improve system throughput, rather than the total tags number estimation. After the reader detected the collision, three different collision resolution methods were described and studied, and the situation of missing tag caused by reader moving is also discussed.

The proposed algorithm achieves theoretical system throughput of about 0.48, 0.50 and 0.61 and simulates to show that the proposed algorithm performance is significantly improved compared with the existing ALOHA-based algorithm.

The proposed RFID anti-collision algorithm is beneficial to improve the moving speed and identification reliability of the RFID robots in complex environments.

In manufacturing environments, mobile radio frequency identification (RFID) robots need to quickly identify and collect various types of passive tag and active tag sensor data. The purpose of this paper is to design a robot system compatible with ultra high frequency (UHF) band passive and active RFID applications and to propose a new anti-collision protocol to improve identification efficiency for active tag data collection.

A new UHF RFID robot system based on a cloud platform is designed and verified. For the active RFID system, a grouping reservation–based anti-collision algorithm is proposed in which an inventory round is divided into reservation period and polling period. The reservation period is divided into multiple sub-slots. Grouped tags complete sub-slot by randomly transmitting a short reservation frame. Then, in the polling period, the reader accesses each tag by polling. When tags’ reply collision occurs, the reader tries to re-query collided tags once, and the pre-reply tags avoid collisions through random back-off and channel activity detection.

The proposed algorithm achieves a maximum theoretical system throughput of about 0.94, and very few tag data frame transmissions overhead. The capture effect and channel activity detection in physical layer can effectively improve system throughput and reduce tag data transmission.

In this paper, the authors design and verify the UHF band passive and active hybrid RFID robot architecture based on cloud collaboration. And, the proposed anti-collision algorithm would improve active tag data collection speed and reduce tag transmission overhead in complex manufacturing environments.

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

The purpose of this paper is to design a glass handling robot and conduct a finite element analysis and structural optimization to solve the automation handling problem of large-scale glass production line and aiming at the phenomenon that the vibration of robot manipulator may result in breakage of glass products, especially the fragile chemical or medical glassware. Making modal analysis for the robot is to determine its natural frequencies and vibration modes and lay a foundation for the transient analysis to study the vibration shock response of the robot during its start-up and emergency stop operation.

First, a 3D model of the robot is established according to the requirements of the production field and a finite element model is built on the basis of the 3D model. Then the modal and transient analyses of the robot are carried out according to the fact that the maximum vibration impact of the robot usually appears at the start and emergency stop.

The structure of the robot is improved according to the results of finite element analysis. The dynamic analysis results show that the improved robot’s ability to resist deformation under the impact of vibration shock is enhanced, and the robot can operate smoothly and meet the requirements of design in industrial environments.

The research results avoided the damage caused by the vibration and improved the service life of the robot, providing a foundation for the structural design and mass production of the glass handling robot.

An accurate and fast dynamic analysis innovative approach for vehicle parts is provided for engineering practice.

This paper presents an innovative dynamic analysis approach for vehicle parts based on parallel optimization algorithm with CUDA.

This project is supported by the National Science Foundation of China (No. 51805076, No. U1708255 and No. 51775093), the Fundamental Research Funds for the Central Universities (No. N170503011) and the Natural Science Foundation of Liaoning Province, China (No. 20180551058).

This paper presents an innovative approach for vehicle parts using parallel optimization algorithm based on CUDA, which can improve the computing accuracy and speed effectively.

This study aims to deal with the problem of trajectory tracking control for the quadrotor under external environmental disturbance and variable payloads.

In the field of unmanned aerial vehicle (UAV) control, external environmental disturbance and internal variable payloads as two major interference factors lead to control performance degradation or even instability, thus a trajectory tracking controller which innovatively combines sliding mode control technology and model-free control technique is proposed. The proposed controller is constructed with a learning rate-based sliding mode controller and an ultra-local model. Based on the proposed controller, the nonlinear system model of variable load quadrotor is locally estimated and the system’s uncertainties and disturbances can be compensated.

The simulation and actual test results demonstrate the satisfactory control performance and the robustness of the proposed controller compared with the PID and Backstepping controller under external environmental disturbance and variable payloads. Moreover, the proposed controller solves the trajectory tracking control problem not only when payloads change at the center of gravity but also when the position of load variation deviates from the center of gravity.

In both military and civilian domains, the quadrotor may encounter such situations that the payloads change, such as transporting goods, aerial refueling and so on. As a large internal interference factor, variable load tends to lead to unstable control. The research results provide theoretical guidance and technical support for trajectory tracking control of quadrotor under variable payloads.

The proposed controller combines learning rate-based sliding mode controller and model-free control technique to achieve a more efficient and accurate trajectory control of the quadrotor when considering system uncertainties and the load variation that happens in the unknown location.

The purpose of this paper is to study the control strategy of transition mode of the stopped-rotor (SR) aircraft under the condition of redundant control and complex aerodynamic characteristics.

This paper first proposes a transition strategy for the conversion between helicopter mode and fixed-wing mode. Then, aiming at the redundancy of the two control systems in the transition process, a control model is proposed, which greatly simplifies the control in conversion mode. Then, to facilitate the design of the control system, the Takagi-Sugeno model of the SR aircraft in transition mode is established. Finally, an explicit model tracking and tuning parameter stability augmentation control system is designed, so that the SR aircraft has a good stability during the transition process. Then, the outer loop control system of transition flight is designed.

The simulation results show that the control strategy proposed in this paper can realize the mode conversion well. It lays a solid foundation for the subsequent engineering flight test for the SR aircraft.

The work done in this paper provides ideas and methods for the flight control system design of SR aircraft in transition mode. The method of designing control model to solve the coordination of redundant control system is also applicable for other multimode aircraft, which provides a simple and convenient method for the multimode aircraft control.

This paper aims to review the main literature on the relationship between the natural environment and management in hospitality and tourism firms and uses strategic lenses to propose a general framework of previous works and a map for future research.

A detailed collection of the most relevant literature on organizations and the natural environment in general is used, along with a specific compilation of the analysis in hospitality and tourism firms. The analytical comparison between the general studies and tourism literature provides opportunities for the discussion of research gaps.

The growing volume of research on environmental management in the hospitality and tourism firms suggests increasing interest in the topic in the past decade. However, our analysis uses a strategic framework to identify multiple relevant topics that are due for exploration. The generation of more robust theoretical and empirical contributions should also be prioritized in the future.

The findings provide insight into the growing importance of environmental issues in multiple areas of hospitality and tourism firms, including corporate strategy (new green business and implications of the environmental issues on the attractiveness of traditional tourism activities), competitive business strategy (differentiation and reduction of costs through environmental management), functional strategy (eco-labels, certifications and environmental management systems), green marketing, responsible supply chain and training.

While previous literature has emphasized the macro analysis of environmental challenges in the industry, this paper is one of the first to provide an analytical review of the literature on the natural environment and management of hospitality and tourism firms and will be particularly useful to better understand the organizational challenges.

The research identifies literature on Home-Based Businesses (HBBs) from 2000 to August 2023, focuses on their economic roles, challenges for entrepreneurs and success strategies, reflecting societal and technological changes. This guides future studies and highlights knowledge gaps.

A systematic literature review of published, peer-reviewed research between the years 2000 and 2023 is performed to examine how research on HBBs has changed over time, areas needing more study and how research has been done.

A total of 58 articles were analyzed and categorized into five distinct themes. Key insights into the evolution, significance and multifaceted aspects of HBBs are presented, revealing the impact and role of these businesses in a modern economic context.

The synthesis of existing literature enhances our understanding of recent studies on HBBs, focusing on challenges, and identifies promising directions for future research.

This study aims to understand the components of the street food experiences of the local-guided tour in the meal-sharing economy based on the online reviews of tourists who experienced a meal-sharing activity with a local guide in Bangkok.

Based on the qualitative approach, this study involved a content analysis of 384 narratives on Withlocals.

The study identified five components that embrace the street food experience: a local guide’s attributes, perceived food authenticity, local culture, perceived hygiene or cleanliness. Results also revealed that the Thai street foods are unique and authentic and can reach this experience level through a local guide.

Although the importance of international travellers' street food experiences and the popularity of the meal-sharing economy platforms are rapidly growing, there is no study which had combined both of these phenomena together to date. It is the first attempt to reveal the components of street food experiences in a meal-sharing platform.