Search results

Underwater shuttle is widely used in scenarios of deep sea transportation and observation. As messages are transmitted via the limited network, high transmission time-delay often leads to information congestion, worse control performance and even system crash. Moreover, due to the nonlinear issues with respect to shuttle’s heading motion, the delayed transmission also brings extra challenges. Hence, this paper aims to propose a co-designed method, for the purpose of network scheduling and motion controlling.

First, the message transmission scheduling is modeled as an optimization problem via adaptive genetic algorithm. The initial transmission time and the genetic operators are jointly encoded and adjusted to balance the payload in network. Then, the heading dynamic model is compensated for the delayed transmission, in which the parameters are unknown. Therefore, the adaptive sliding mode controller is designed to online estimate the parameters, for enhancing control precision and anti-interference ability. Finally, the method is evaluated by simulation.

The messages in network are well scheduled and the time delay is thus reduced, which increases the quality of service in network. The unknown parameters are estimated online, and the quality of control is enhanced. The control performance of the shuttle control system is thus increased.

The paper is the first to apply co-design method of message scheduling and attitude controlling for the underwater unmanned vehicle, which enhaces the control performance of the network control system.

The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise.

A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples.

A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

With the advent of the information age, this paper aims to apply risk analysis theories to study the risk prevention mechanism of information disclosure, thus supporting the green electricity supply.

This paper conducts a comprehensive evaluation and analysis of the impact of power market transactions, power market operations and effective government supervision, so as to figure out the core risk content of power market information disclosure. Moreover, AHP-entropy method is adopted to weigh different indicators of information disclosure risks for the participants in the electricity market.

The potential reasons for information disclosure risk in the electricity market include insufficient information disclosure, high cost of obtaining information, inaccurate information disclosure, untimely information disclosure and unfairness of information disclosure.

Some suggestions and implications on risk prevention mechanism of information disclosure in the electricity market are provided, so as to ensure the green electricity supply and promote the electricity market reform in China.

Climate change will have a significant impact on China’s potential agricultural production and change the distribution of the population in various regions of China, thus producing population migration. This paper aims to analyze China’s population migration in response to climate change and its socio-economic impact.

In this paper, the Potential Agriculture Production Index is introduced as an analytical tool with which to estimate the scale of the population migration induced by climate change. Also, this paper constructs a multi-regional computable general equilibrium (CGE) model and analyzes the effect of change in the population distribution pattern on regional economies, regional disparity and resident welfare.

The key finding of this paper is that, as a result of changes in potential agricultural production induced by climate change, the Circum-Bohai-Sea region, the industrialized region and the industrializing region, which are the main destination regions of the migrating population, will face a severe labor shortage. In response to population migration, the economic growth rate of the immigrating population regions has accelerated. Correspondingly, the economic growth rate of the emigrating population regions has decreased. In addition, the larger the scale of population migration is, the larger the economic impact is. Migration increases inner-regional disparity and decreases inter-regional disparity. However, overall regional disparity is only somewhat decreased.

This paper introduces a Potential Agriculture Production Index to estimate the scale of the population migration and introduce a multi-regional CGE model to analyze the correlated social-economic impacts.

The relationship between Cr/C and properties of Fe‐C‐Cr high chromium white irons was studied by calculating the valence electron structure of austenite of Fe‐C‐Cr high chromium white irons with the empirical electron theory of solids and molecules (EET) and the equilibrium phase diagram of Fe‐C‐Cr system. Results show that the C‐Cr bond is the strongest bond of all bonds in alloying austenite in Fe‐C‐Cr high chromium white irons of industrial application and, thereby, causes partial aggregation of C‐Cr atomic groups. The weight of partial aggregation of C‐Cr atomic groups would be increased greatly and more austenite would be reserved to room temperature when Cr/>6. The Fe‐CCr high chromium white irons achieve best mechanical property when Cr/C=5.5‐6.5.

To supply temporary pressure testing devices with favorable performance for emergency environments, this paper aims to present a pressure sensor with a central boss and straight-annular grooves. The structural feature is modeled and optimized by neural network-based method, and the device prototype is fabricated by 3D printing techniques.

The study initially compares mechanical properties of the proposed structure with two conventional designs using finite element analysis. The impacts from structural dimensions on sensor performance are modeled using a Backpropagation neural network and optimized through genetic algorithms. The sensing diaphragm is fabricated using stereolithography (SLA) 3D printing, while the piezoresistors and necessary interconnects are realized with screen printing techniques.

The experimental results demonstrate that the fabricated sensor exhibits a sensitivity of 2.8866 mV/kPa and a nonlinearity of 6.81% within the pressure range of 0–100 kPa. This performance is an improvement of 118% in sensitivity and a decrease of 54% in nonlinearity compared to flat diaphragm structure, highlighting the effectiveness of proposed diaphragm configuration.

This research offers a holistic methodology that encompasses the structural design, optimization and fabrication of pressure sensors. The proposed diaphragm and corresponding modelling method can provide a practical approach to enhance the measurement capabilities of pressure sensors. By leveraging SLA printing for diaphragm and screen printing for circuit, the prototype can be produced in a timely manner.

This study aims to examine the impact of climate change on the total factor productivity (TFP) of tourism in Chinese cities. Using temperature and precipitation as proxies for climate change, the research analyzes both the direct negative effects of climate change on tourism productivity and the positive spillover effects on neighboring cities. In addition, it investigates how geographic location and economic development contribute to the variation in these effects. The study also explores the mechanisms through which government intervention and industrial structure upgrading influence these impacts.

This study uses a spatial Durbin model to analyze the relationship between climate change and tourism TFP in 287 Chinese cities from 2000 to 2020. Panel data is used, with temperature and precipitation serving as proxies for climate change. The model evaluates both the direct and spillover effects of climate change on tourism productivity, while also analyzing the mechanisms through which government intervention and industrial upgrading affect these relationships. The study further considers how geographic location and economic development impact the results.

This study finds that climate change directly reduces tourism TFP, while generating positive spillover effects for neighboring cities. Cities in the eastern and more economically developed regions are more sensitive to climate change, experiencing stronger impacts compared to cities in central and western regions. The findings suggest that government intervention and industrial structure upgrading are important mechanisms through which climate change affects tourism productivity in Chinese cities.

This research fills a gap in the literature regarding how climate change affects tourism productivity in developing countries, particularly in China. By applying a spatial Durbin model and panel data analysis, the study provides empirical evidence on both the direct and spillover effects of climate change on tourism productivity. It highlights the critical role of government intervention and industrial upgrading as mechanisms shaping the impact of climate change, offering new insights for policymakers and tourism businesses to address the challenges posed by climate change and enhance productivity and competitiveness.

本研究旨在探讨气候变化对中国城市旅游全要素生产率的影响。通过温度和降水量作为气候变化的代理变量, 研究分析了气候变化对旅游全要素生产率的直接抑制效应及其对邻近城市的积极溢出效应。此外, 研究考察了地理位置与经济发展水平如何导致这些效应的异质性。通过分析政府干预和产业结构升级的机制, 本研究为气候变化影响旅游全要素生产率的机制分析提供了理论支持, 为提升发展中国家旅游竞争力提供了指导。

本研究采用空间杜宾模型分析2000年至2020年期间, 中国287个城市的气候变化与旅游全要素生产率之间的关系。研究使用面板数据, 温度和降水量作为气候变化的代理变量。模型分析了气候变化对旅游全要素生产率的直接效应与溢出效应, 并研究了政府干预与产业结构升级的机制效应。研究还考察了基于地理位置与经济发展水平的异质性影响, 提供了气候变化对城市旅游全要素生产率影响的综合分析。

气候变化直接抑制旅游全要素生产率, 同时对邻近城市产生积极的溢出效应。东部城市及高经济水平地区对气候变化更为敏感, 影响强于中西部地区。研究发现, 政府干预与产业结构升级是气候变化影响中国城市旅游全要素生产率的关键机制。

本研究填补了气候变化如何影响发展中国家, 尤其是中国, 旅游全要素生产率领域的研究空白。通过运用空间杜宾模型和面板数据分析, 提供了气候变化对旅游全要素生产率的直接效应与溢出效应的实证证据。研究强调了政府干预和产业结构升级作为气候变化影响旅游全要素生产率的主要机制。通过关注区域异质性与经济发展水平, 本研究为旅游企业与政策制定者应对气候变化挑战,提升生产力和竞争力提供了新的思路。

Este estudio examina el impacto del cambio climático en la productividad total de los factores (PTF) del turismo en las ciudades chinas. Utilizando la temperatura y las precipitaciones como indicadores del cambio climático, la investigación analiza tanto los efectos negativos directos del cambio climático sobre la productividad del turismo como los efectos indirectos positivos sobre las ciudades vecinas. Además, investiga cómo la ubicación geográfica y el desarrollo económico contribuyen a la variación de estos efectos. El estudio también explora los mecanismos a través de los cuales la intervención gubernamental y la mejora de la estructura industrial influyen en estos impactos.

Este estudio utiliza un modelo espacial de Durbin (SDM) para analizar la relación entre el cambio climático y la productividad total de los factores del turismo en 287 ciudades chinas entre 2000 y 2020. Se emplean datos de panel, en los que la temperatura y las precipitaciones sirven como variables sustitutivas del cambio climático. El modelo evalúa tanto los efectos directos como los indirectos del cambio climático sobre la productividad del turismo, al tiempo que analiza los mecanismos a través de los cuales la intervención gubernamental y la modernización industrial afectan a estas relaciones. El estudio examina además cómo influyen en los resultados la ubicación geográfica y el desarrollo económico.

El estudio concluye que el cambio climático reduce directamente la productividad total de los factores del turismo, al tiempo que genera efectos indirectos positivos para las ciudades vecinas. Las ciudades de las regiones orientales y económicamente más desarrolladas son más sensibles al cambio climático y experimentan impactos más fuertes que las ciudades de las regiones centrales y occidentales. Los resultados sugieren que la intervención gubernamental y la mejora de la estructura industrial son mecanismos importantes a través de los cuales el cambio climático afecta a la productividad del turismo en las ciudades chinas.

Esta investigación llena un vacío en la literatura sobre cómo el cambio climático afecta a la productividad del turismo en los países en desarrollo, especialmente en China. Aplicando un modelo espacial de Durbin y un análisis de datos de panel, el estudio aporta pruebas empíricas sobre los efectos directos y indirectos del cambio climático en la productividad del turismo. Destaca el papel decisivo de la intervención pública y la modernización industrial como mecanismos que determinan el impacto del cambio climático, ofreciendo nuevas perspectivas a los responsables políticos y las empresas turísticas para afrontar los retos que plantea el cambio climático y mejorar la productividad y la competitividad.

The Chinese government hopes to achieve the goal of benefiting citizens by building a National Integrated Online Government Service Platform (NIOGSP). However, citizens' low adoption of the platform makes it difficult for the government to achieve its goal. Research on the influencing factors of citizen adoption of NIOGSP can help the government fully understand the concerns and needs of its citizens and take targeted measures to increase citizen adoption.

First, this research builds a model of the citizen adoption process, including attention, retention and motivation, based on an observational learning model. Next, research variables are determined based on social cognitive theory, literature review and real-world needs. Finally, based on the questionnaire survey and structural equation model, the influencing factors of each stage of the citizen adoption process model are studied and the relationship between the three stages of the model is verified.

Results show that perceived usefulness (PU) and self-efficacy (SE) positively affect attention. SE positively affects retention, while perceived privacy (PP) negatively affects retention. PU, social influence, PP and anxiety positively affect motivation.

The conclusion of this study can provide reference for governments in various countries to establish and improve online one-stop government. In addition, this study verifies the citizen adoption process model and finds that there is no obvious causal relationship between attention and retention, but both have positive effects on motivation.

This paper aims to propose a calibration model for kinematic parameters identification of serial robot to improve its positioning accuracy, which only requires position measurement of the end-effector.

The proposed model is established based on local frame representation of the product of exponentials (local POE) formula, which integrates all kinematic errors into the twist coordinates errors; then they are identified with the tool frame’ position deviations simultaneously by an iterative least squares algorithm.

To verify the effectiveness of the proposed method, extensive simulations and calibration experiments have been conducted on a 4DOF SCARA robot and a 5DOF drilling machine, respectively. The results indicate that the proposed model outperforms the existing model in convergence, accuracy, robustness and efficiency; fewer measurements are needed to gain an acceptable identification result.

This calibration method has been applied to a variable-radius circumferential drilling machine. The machine’s positioning accuracy can be significantly improved from 11.153 initially to 0.301 mm, which is well in the tolerance (±0.5 mm) for fastener hole drilling in aircraft assembly.

An accurate and efficient kinematic calibration model has been proposed, which satisfies the completeness, continuity and minimality requirements. Due to generality, this model can be widely used for serial robot kinematic calibration with any combination of revolute and prismatic joints.

To gain accurate support for large aircraft structures by ball joints in aircraft digital assembly, this paper aims to propose a novel approach based on visual servoing such that the positioner’s ball-socket can automatically and adaptively approach the ball-head fixed on the aircraft structures.

Image moments of circular marker labeled on the ball-head are selected as visual features to control the three translational degrees of freedom (DOFs) of the positioner, where the composite Jacobian matrix is full rank. Kalman–Bucy filter is adopted for its online estimation, which makes the control scheme more flexible without system calibration. A combination of proportional control with sliding mode control is proposed to improve the system stability and compensate uncertainties of the system.

The ball-socket can accurately and smoothly reach its desired position in a finite time (50 s). Positional deviations between the spherical centers of ball-head and ball-socket in the X-Y plane can be controlled within 0.05 mm which meets the design requirement.

The proposed approach has been integrated into the pose alignment system. It has shown great potential to be widely applied in the leading support for large aircraft structures in aircraft digital assembly.

An adaptive approach for accurate support of large aircraft structures is proposed, which possesses characteristics of high precision, high efficiency and excellent stability.