Search results

Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.

The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.

Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.

A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

The Internet of Things (IoT) offers substantial potential for improving efficiency and effectiveness in various applications, notably within the domain of smart construction. Despite its growing adoption within the Architecture, Engineering, and Construction (AEC) industry, its utilization remains limited. Despite efforts made by policymakers, the shift from traditional construction practices to smart construction poses significant challenges. Consequently, this study aims to explore, compare, and prioritize the determinants that impact the acceptance of the IoT among construction practitioners.

Based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT2), Task-Technology Fit (TTF), and perceived risk. A cross-sectional survey was administered to 309 construction practitioners in China, and the collected data were analyzed using structural equation modeling (SEM) to test the proposed hypotheses.

The findings indicate that TTF, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and perceived risk exert significant influence on construction practitioners’ intention to adopt IoT. Conversely, social influence and habit exhibit no significant impact. Notably, the results unveil the moderating influence of gender on key relationships – specifically, performance expectancy, hedonic motivation, and habit – in relation to the behavioral intention to adopt IoT among construction practitioners. In general, the model explains 71% of the variance in the behavioral intention to adopt IoT, indicating that the independent constructs influenced 71% of practitioners’ intentions to use IoT.

These findings provide both theoretical support and empirical evidence, offering valuable insights for stakeholders aiming to gain a deeper understanding of the critical factors influencing practitioners’ intention to adopt IoT. This knowledge equips them to formulate programs and strategies for promoting effective IoT implementation within the AEC field.

This study contributes to the existing literature by affirming antecedents and uncovering moderators in IoT adoption. It enhances the existing theoretical frameworks by integrating UTAUT2, TTF, and perceived risk, thereby making a substantial contribution to the advancement of technology adoption research in the AEC sector.

This study aims to introduce a novel technique for nonlinear sensor time constant estimation and sensor dynamic compensation in hot-bar soldering using an extended Kalman filter (EKF) to estimate the temperature of the thermocouple.

Temperature optimal control is combined with a closed-loop proportional integral differential (PID) control method based on an EKF. Different control methods for measuring the temperature of the thermode in terms of temperature control, error and antidisturbance are studied. A soldering process in a semi-industrial environment is performed. The proposed control method was applied to the soldering of flexible printed circuits and circuit boards. An infrared camera was used to measure the top-surface temperature.

The proposed method can not only estimate the soldering temperature but also eliminate the noise of the system. The performance of this methodology was exemplary, characterized by rapid convergence and negligible error margins. Compared with the conventional control, the temperature variability of the proposed control is significantly attenuated.

An EKF was designed to estimate the temperature of the thermocouple during hot-bar soldering. Using the EKF and PID controller, the nonlinear properties of the system could be effectively overcome and the effects of disturbances and system noise could be decreased. The proposed method significantly enhanced the temperature control performance of hot-bar soldering, effectively suppressing overshoot and shortening the adjustment time, thereby achieving precise temperature control of the controlled object.

The purpose of this study is to investigate the thermal elastohydrodynamic lubrication (TEHL) analysis of a deep groove ball bearing.

The TEHL model for the groove ball is first established, into which the elastic deformation is incorporated. In doing so, the elastic deformation is solved with the fast Fourier transform (FFT). And the bearing temperature rise is solved by the point heat source integration method. Then, effects of the applied load, relative velocity and the slide-roll ratio on the TEHL of the bearing are analyzed.

There exist the large pressure peaks at two edges of the raceway along its width direction and the increment in the relative velocity between the roller and the raceway, or one in the slide-roll ratio arguments the temperature rise.

This study conducts a detailed discussion of the TEHL analysis of deep groove ball bearing and gives a beneficial reference to the design and application of this kind of bearings.

The aim of this paper is to solve the toxic and harmful problems caused by traditional volatile corrosion inhibitor (VCI) and to analyze the effect of the layered structure on the enhancement of the volatile corrosion inhibition prevention performance of amino acids.

The carbon dots-montmorillonite (DMT) hybrid material is prepared via hydrothermal process. The effect of the DMT-modified alanine as VCI for mild steel is investigated by volatile inhibition sieve test, volatile corrosion inhibition ability test, electrochemical measurement and surface analysis technology. It demonstrates that the DMT hybrid materials can improve the ability of alanine to protect mild steel against atmospheric corrosion effectively. The presence of carbon dots enlarges the interlamellar spacing of montmorillonite and allows better dispersion of alanine. The DMT-modified alanine has higher volatilization ability and an excellent corrosion inhibition of 85.3% for mild steel.

The DMT hybrid material provides a good template for the distribution of VCI, which can effectively improve the vapor-phase antirust property of VCI.

The increased volatilization rate also means increased VCI consumption and higher costs.

Provides a new way of thinking to replace the traditional toxic and harmful VCI.

For the first time, amino acids are combined with nano laminar structures, which are used to solve the problem of difficult volatilization of amino acids.

The ownership structure in Japanese firms has experienced a significant change recently, fueled primarily by regulatory changes. This has important repercussions on corporate performance and risk. This paper examines the impact of insider ownership on the default risk of Japanese firms.

We collected data from the Nikkei Corporate Governance Evaluation System (CGES) database for the period 2004–2019. Our final dataset yields 36,116 firm-year observations. We apply a firm fixed effect model for baseline regression. Endogeneity was checked by applying propensity score matching (PSM) and two-stage least squares (2SLS) techniques. Furthermore, the robustness of baseline regression results was checked using alternative estimation techniques.

Results show a significant positive influence of insider ownership on default risk. Furthermore, ROA volatility and stock price volatility appear to be the major channels through which insider ownership affects a firm’s default risk. We further document that external monitoring mechanisms, including traditional main bank ties, institutional ownership and analyst coverage, are the key risk-mitigating factors.

Our research deals with Japanese firms only. Future research may attempt to analyze the cases of emerging economies. Furthermore, future research might examine the ownership-default risk relationship for financial institutions to see if this relationship differs between financial and nonfinancial firms.

Insider ownership enhances the probability of default. Hence, policymakers may consider instituting a ceiling for insider ownership in Japanese firms. Moreover, we highlight various risk-mediating channels that would help policymakers adopt guidelines for mitigating corporate risk.

Our study is the first to investigate the effect of insider ownership on default risk in Japanese settings. Prior studies identified various determinants that affect firms’ default risk. Our study contributes to this stream of literature by examining the impact of insider ownership on default risk and extending the limited literature related to insider ownership.