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Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

Interpolation of parametric curves is one of the most effective methods in high-performance computer numerical control (CNC) machining. Its precision highly depends on chord errors from feedrate scheduling and contour errors from real-time machining. In order to improve machining precision, a novel feedrate optimization method is proposed in this paper. The osculating circle (OC) method can effectively estimate the chord errors, but its precision degrades if the trajectory curvature changes dramatically. Therefore, an iterative algorithm based on OC method is developed to reevaluate the chord errors, which can upgrade the estimation precision and further improve the feedrate constraints. To reduce the contour error, feature zones on the trajectory are firstly selected according to the kinestates and a specified zone length. In each feature zone, the projection of tracking error onto the zone chord-line is calculated as a new vector, based on which an indirect contour error compensation method is proposed through refining the feedrate profile. To ensure that the contour error can be reduced, the compensation performance is pre-estimated before implementation. Finally, simulation results in different scenarios are presented to validate the high precision of the proposed method.

To add greater weight to new information, discrete grey model with the weighted accumulation (WDGM(1,1)) is put forward. This paper proved the stability of WDGM(1,1) disturbance boundary and the influence of the analysis parameter λ on the reduction error. The prediction ability of the WDGM(1,1) is verified by five cases. The results show that WDGM(1,1) not only satisfies the new information priority to a certain extent, but also has better stability. Moreover, the parameter λ in WDGM(1,1) can effectively reduce the reduction error, so that it has better prediction accuracy in practical applications. Therefore, the proposal of WDGM(1,1) is not only very theoretical, but also has good practical significance.

Despite upper extremity function playing a crucial role in maintaining one’s independence in activities of daily living, upper extremity impairments remain one of the most prevalent post-stroke deficits. To enhance the upper extremity motor recovery and performance among stroke survivors, two training paradigms in the fields of robotics therapy involving modifying haptic feedback were proposed: the error-augmentation (EA) and error-reduction (ER) paradigms. There is a lack of consensus, however, as to which of the two paradigms yields superior training effects. This systematic review aimed to determine (i) whether EA is more effective than conventional repetitive practice; (ii) whether ER is more effective than conventional repetitive practice and; (iii) whether EA is more effective than ER in improving post-stroke upper extremity motor recovery and performance. The study search and selection process as well as the ratings of methodological quality of the articles were conducted by two authors separately, and the results were then compared and discussed among the two reviewers. Findings were analyzed and synthesized using the level of evidence. By August 1st 2017, 269 articles were found after searching 6 databases, and 13 were selected based on criteria such as sample size, type of participants recruited, type of interventions used, etc. Results suggest, with a moderate level of evidence, that EA is overall more effective than conventional repetitive practice (motor recovery and performance) and ER (motor performance only), while ER appears to be no more effective than conventional repetitive practice. However, intervention effects as measured using clinical outcomes were under most instance not ‘clinically meaningful’ and effect sizes were modest. While stronger evidence is required to further support the efficacy of error modification therapies, the influence of factors related to the delivery of the intervention (such as intensity, duration) and personal factors (such as stroke severity and time of stroke onset) deserves further investigations as well.

From an economic perspective, rice is not only a principal staple food for nearly half of the world’s population but also a significant commodity in many countries. Consequently, the accuracy of demand uncertainty concerning rice imports, which can be useful information to support critical decision-making on trading and food security management, is very challenging. The proposed model of simple exponential smoothing, support vector regression, and generalized simulated annealing is proposed and developed to predict annual rice imports based on twenty datasets across importer countries. The proposed model takes advantage of both suitable parameter selection and noise reduction in systematic error reduction with smoothing series to achieve more accuracy and precision. The empirical results revealed that the proposed model can improve accuracy based on five accuracy measures and is significantly different from other models at 0.05 significance levels. Moreover, the proposed model can provide consistency and reliability for forecasting rice imports in advance. Consequently, the proposed model can be a promising tool to support decision-making for policymakers.

Free space optical (FSO) communication, which has the potential to meet the demand for high-data-rate communications between satellites and ground stations, requires accurate alignment between the transmitter and receiver to establish a line-of-sight channel link. In this paper, we propose a machine learning (ML)-based approach to reduce beam pointing errors in FSO satellite-to-ground communications subjected to satellite vibration and weak atmospheric turbulence. ML models are utilized to find the optimal gain, which plays a crucial role in reducing pointing error displacement in a closed-loop FSO system. In designing the FSO environment, we employ several system model parameters, including control and system matrix components of the transmitter and receiver, noise parameters for the optical channel, irradiance, and the scintillation index of the signal. To predict the gain matrix of the closed-loop system, ML methods, such as tree-based algorithms, and a 1D convolutional neural network (Conv1D) are applied. Experimental results show that the Conv1D model outperforms other ML methods in gain value prediction, helping to maintain the beam position centered on the receiver aperture, minimizing beam pointing errors. When constructing a closed-loop system based on the Conv1D model, the error variance of the pointing error displacement was obtained as 0.012 and 0.015 in clear weather and light fog conditions, respectively. In addition, this research analyzes the impact of input features in a closed-loop FSO system, and compares the pointing error performance of the closed-loop setup to the conventional open-loop setup under weak turbulence.

Measuring the dimensions of personalized devices can provide relevant information for the production of future such devices used in various medical specialties. Difficulties with standardizing such measurement and obtaining high accuracy, alongside cost-intensive measuring methodologies, has dampened interest in this practice. This study presents a methodology for automatized measurements of personalized medical appliances of variable shape, in this case an orthodontic appliance known as Tübingen Palatal Plate (TPP). Parameters such as length, width and angle could help to standardize and improve its future use. A semi-automatic and custom-made program, based on Rhinoceros 7 and Grasshopper, was developed to measure the device (via an extraoral scanner digital file). The program has a user interface that allows the import of the desired part, where the user is able to select the necessary landmarks. From there, the program is able to process the digital file, calculate the necessary dimensions automatically and directly export all measurements into a document for further processing. In this way, a solution for reducing the time for measuring multiple dimensions and parts while reducing human error can be achieved.

Human reliability analysis (HRA) is a critical component in ensuring the safety and efficiency of railway engineering. As railway systems grow more complex, the methodologies used to assess and improve human reliability must also advance. This review provides a comprehensive analysis of the evolution of HRA, from the first-generation techniques to the third-generation approaches currently in use. Through a broad survey of the literature, comparative analysis, and detailed case studies, this review traces the development of HRA methods, showing the evolution from traditional techniques to modern hybrid approaches. The review also emphasizes the significance of hybrid Human Error Assessment and Reduction Technique (HEART) methods, which integrate multiple HRA approaches to provide a more comprehensive and accurate assessment of human reliability. The hybrid technique offers a more accurate estimation than standard methods, as evidenced by the determined Pearson coefficient of 0.9990 between the simulation findings and the HEP values of HEART-related methodologies. It also explores the integration of human factors into railway safety systems, underscoring the importance of considering human-machine interactions and the cognitive and behavioural aspects of railway operations. Key findings indicate that while traditional HRA methods laid the groundwork, there is a growing need for continuous innovation to address the increasing complexity of railway systems. This includes the development of hybrid models that combine insights from various HRA techniques and the incorporation of advanced human-machine interaction paradigms to further minimize human error rates. The objective of this review is to offer recommendations for future research in the field of HRA for railway engineering. It advocates for the development of advanced hybrid models with the use of cutting-edge technology like machine learning and artificial intelligence. By combining historical insights with modern technological advancements, the goal is to create more robust and reliable HRA methods that can better support the safety and efficiency of railway operations.

Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations. However, the uncertainty of observations affects the evaluation. This work introduces a novel method to quantify the uncertainties in the observational datasets and how these uncertainties affect the evaluation of RCM simulations. Besides precipitation and temperature, our method uses geographic variables (e.g. elevation, variability of elevation, effect of station), which are considered as uncertainty sources. To assess these uncertainties, a complex analysis based on various statistical tools, e.g. correlation analysis and permutation test, was carried out. Furthermore, we used a special metric, the reduction of error (RE) to identify where the RCM shows improvement compared to the lateral boundary conditions (LBCs). We focused on the Carpathian region, because of its unique orographic and climatic conditions. The method is applied to two observational datasets (CarpatClim and E-OBS) and to RegCM simulations for 2010, the wettest year in this region since 1901.The results show that CarpatClim is wetter than E-OBS, while temperature is similar over the lowland; however, E-OBS is significantly warmer than CarpatClim over the mountains. By the RE metric, RegCM has improvement against the LBCs over mountains for temperature and areas with dense station network for precipitation. Nevertheless, there are significant differences in the results depending on which observational dataset was used concerning precipitation. The evaluation method can be applied to other datasets, different time periods and areas. It is also suitable to find dataset errors, which is also exemplified in this paper.

The wireless capsule endoscope (WCE) is a pill-sized device taking images, which are transmitting to an on-body receiver, while traveling through the digestive system. Since image data is transmitted through the human body, which is a harsh medium for electromagnetic wave propagation, noise may at times heavily corrupt the reconstructed image frames. A common way to combat noise is to use error-correcting codes. In addition one may also utilize inter- and intra frame correlation to reduce the impact of noise at the receiver side, placing no extra demand on the WCE. However, it is then of great importance that the chosen post processing methods do not alter the content of the image as this can lead to miss-detection by gastroenterologists. In this paper we will investigate the possibility for additional noise suppression and error concealment at the receiver side in a high intensity error regime. Due to the high correlation generally inherent in WCE video, satisfactory results are obtained, as concluded from both subjective tests with gastroenterologists as well as the structural similarity (SSIM) metric. More surprisingly, the subjective tests indicate that the inpainted frames in many cases can be used for clinical assessment. These results indicate that one can apply error reduction through post processing together with error-correcting codes to obtain a more noise-robust system without any further demand on the WCE.