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This paper aims to solve the typical thermal airflow sensor's high power consumption and integration difficulties, based on the FS5 thermal element and constant temperature measurement method, a flow sensor is developed with high measurement accuracy, low power consumption, small size, low cost and easy system integration.

A small wind tunnel was used to test and assess the sensor's measurement range, reaction time, stability, repeatability, measurement accuracy and multi-temperature calibration was performed in the temperature range of −10°C to 30°C. The effect of ambient temperature on the sensor's measurement data is investigated, and the coefficient correction method of power function was investigated to implement the sensor's software temperature compensation function.

The results show that the sensor is stable and repeatable, the output voltage has a power function relationship with the airflow rate, the flow rate measurement range is 0–18 m/s, the response time is less than 3 s, the measurement accuracy at high flow rates is within 0.4 m/s and the temperature-corrected airflow rate measurement error is less than 5%. Setting the temperature calibration interval to 2°C and 5°C has the same temperature compensation effect, reducing the sensor's calibration effort significantly.

This paper demonstrates that a thermostatic method is used to construct a thermal wind speed sensor that delivers accurate measurements in the wind speed measuring range of 0–18 m/s under test conditions. In addition, the sensor's performance is evaluated, and calibration tests for a wide range of temperatures are done. Finally, based on the power function correction method, a temperature compensation algorithm is proposed.

Many studies have proposed variant fuzzy time series models for uncertain and vague data. The purpose of this paper is to adapt a fuzzy time series combined with genetic algorithm (GA) to forecast tourist arrivals in Taiwan.

Different cases are studied to understand the effect of variation of fuzzy time series order, number of intervals and population size on the fitness function which decreases with increase in fuzzy time series order and number of fuzzy intervals, but do not have marginal effect due to change in population size.

Results based on an example of forecasting Taiwan’s tourism demand was used to verify the efficacy of proposed model and confirmed its superiority to existing models providing solutions for different orders of fuzzy time series, number of intervals and population size with a smaller forecasting error as measured by root mean square error.

This study provides a viable forecasting methodology, adapting a fuzzy time series combined with an evolutionary GA. The proposed hybridized framework of fuzzy time series and GA, where GA is used to calibrate fuzzy interval length, is flexible and replicable to many industrial situations.

Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users.

MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored.

According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems.

This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

The purpose of this paper is to measure overconfidence amongst finance professionals in domain relevant knowledge, and test for the impact of different debiasing methods.

The approach used was survey field experiments with varying debiasing attempts.

The subjects were overconfident in terms of probability calibration, better‐than‐average beliefs, and unfounded confidence. Debiasing attempts yielded mixed results. Explicit written warnings reduced better‐than‐average‐type of overconfidence. There was a further strong effect from attending lectures on investor psychology covering relevant examples. In contrast, there was only limited success in reducing miscalibration in probability assessments.

Different types of overconfidence are distinct and respond differentially to debiasing. Future research on debiasing professional judgment should concentrate on testing in‐depth/personally engaging methods.

It is important for bankers to acknowledge the dangers of overconfidence. Correct confidence interval calibration is needed in order to have a sense of the risks involved in different asset allocation policies and trading strategies. Bankers should also be able to help their clients avoid overconfidence.

Debiasing overconfidence in the finance industry likely carries public benefits. The results imply that this task is not easy, but not impossible either. The authors think further investment in this endeavor is justified.

Documenting an important judgment bias among finance professionals and estimating the effects of debiasing.

The purpose of this paper is to produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy.

A maintenance cost estimation model is utilised within the research and development of this decision support system (DSS). An empirical-based methodology is pursued and validated through case study analysis.

A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case study, a 28 per cent reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period.

The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge.

The paper presents an adaptive DSS to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique.

The cross‐correlation flow measurement technique, applied for measuring the coolant flow rate in a nuclear reactor, was calibrated with the use of numerical simulations of turbulent flow. The three‐dimensional domain was collapsed into two dimensions. With a two‐dimensional calculation of steady‐state flow with transient thermal characteristics the response of thermocouples to a temperature variation was calculated. By cross‐correlating the calculated thermocouple responses, the link between total flow rate and measured transit times was made. The reliability of the calibration was estimated at ±4.6%. In addition, a measured velocity profile effect was successfully predicted.

According to the problems of visual perception, we propose a model for the processing of vision in adverse situations of illumination, scale, etc. In this paper, a model for image segmentation and labelling obtained in real conditions with different scales is proposed.

The model is based on the texture identification of the scene's objects by means of comparison with a database that stores series of each texture perceived with successive optic parameter values. As a basis for the model, self‐organising maps have been used in several phases of the labelling process.

The model has been conceived to systematically deal with the different causes that make vision difficult and allows it to be applied in a wide range of real situations. The results show high success rates in the labelling of scenes captured in different scale conditions, using very simple describers, such as different histograms of textures.

Our interest is directed towards systematising the proposal and experimenting on the influence of the other variables of the vision. We will also tackle the implantation of the classifier module so that the different causes can be dealt with by the reconfiguration of the same hardware (using reconfigurable hardware).

This research approaches a very advanced angle of the vision problems: visual perception under adverse conditions. In order to deal with this problem, a model formulated with a general purpose is proposed. Our objective is to present an approach to conceive universal architectures (in the sense of being valid with independence of the implied magnitudes).

This study aims to accurately evaluate the influence of various error intervals on the performance of the wiper.

The wiper structural system is decomposed into classical four-link planar for kinematics analysis, and it was modeled respectively by using interval method, universal grey number theory and enumeration approach depending on the nature of uncertainty.

The universal grey number theory is a viable methodology for the accurate analysis of uncertain structural system.

(1) The model of uncertain wiper structural system is established. (2) Universal grey number theory and new parameters are adopted to analyze the presence of uncertain wiper structural system. (3) Comparative analysis of response quantities is obtained by interval method, universal grey number theory and enumeration method.

The purpose of this paper is to present a vision-based method for the kinematic calibration of a six-degrees-of-freedom parallel robot named Hexa using only one Universal Serial Bus (USB) camera and a chess pattern installed on the robot's mobile platform. Such an approach avoids using any internal sensors or complex three-dimensional measurement systems to obtain the pose (position/orientation) of the robot's end-effector or the joint coordinates.

The setup of the proposed method is very simple; only one USB camera connected to a laptop computer is needed and no contact with the robot is necessary during the calibration procedure. For camera modeling, a pinhole model is used; it is then modified by considering some distortion coefficients. Intrinsic and extrinsic parameters and the distortion coefficients are found by an offline minimization algorithm. The chess pattern makes image corner detection very straightforward; this detection leads to finding the camera and then the kinematic parameters. To carry out the calibration procedure, several trajectories are run (the results of two of them are presented here) and sufficient specifications of the poses (positions/orientations) are calculated to find the kinematic parameters of the robot. Experimental results obtained when applying the calibration procedure on a Hexa parallel robot show that vision-based kinematic calibration yields enhanced and efficient positioning accuracy. After successful calibration and addition of an appropriate control scheme, the robot has been considered as a color-painting prototype robot to serve in relevant industries.

Experimental results obtained when applying the calibration procedure on a Hexa parallel robot show that vision-based kinematic calibration yields enhanced and efficient positioning accuracy.

The enhanced results show the advantages of this method in comparison with the previous calibration methods.

Emerging market multinationals often face a variety of legitimacy challenges as they engage in cross-border acquisitions in developed countries, which requires an assortment of legitimacy strategies best aligned with the legitimacy challenges they face. This study advocates for a configurational perspective that examines how different configurations of legitimacy challenges, organizational characteristics, and legitimacy strategies influence the likelihood of deal completion in cross-border acquisitions by emerging market multinational enterprises (EMNEs).

Based on 328 cross-border acquisition cases by Chinese firms, this study adopts the fuzzy-set qualitative comparative analysis to examine the combined effects of institutional distance, political affinity, equity sought, architecture design, sensitive·industry and state-owned and enterprise (SOE) on cross-border acquisition completion.

This study identifies six pathways with different configurations for deal completion, suggesting that a deal's overall legitimacy falls at the intersection of the country-level institution and the firm-level characters and strategy evaluations.

This study investigates how nested legitimacy influences cross-border acquisition completion by offering a holistic and configurational understanding of the deal completion of cross-border acquisitions by EMNEs and yields useful insights for future research on cross-border acquisition completion and legitimacy.