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The machining quality of the sealing surface of a triple-eccentric butterfly valve is crucial to its sealing performance. Especially, the measuring of sealing surface size is the key to assessing the machining quality. Due to the special structure of the triple-eccentric butterfly valve, the sealing surface size of the triple-eccentric butterfly valve is difficult and inaccurate to measure in position, which makes it difficult to ensure the machining quality of the triple-eccentric butterfly valve sealing surface. This will affect the sealing performance of the triple-eccentric butterfly valve and result in poor bidirectional sealing, non-interchangeability, and lower machining productivity of the bodies and discs. Based on the above problems, this paper proposes a new measuring method for sealing surface size, i.e., an in-position measuring method of point-replacing surface. The reliability of the method is analyzed and proved from the level of the design principle of the triple-eccentric butterfly valve. Further, the tooling fixture and in-position measuring device of sealing surface size are designed and manufactured based on the measuring method of the point-replacing surface. Different from the existing assembling boring process, this paper designs and manufactures new boring tooling fixtures and develops an integrated process of measuring and machining. After the preliminary measuring and machining test, the reproducibility of the results was good, and parts interchangeability and bidirectional sealing could be realized. And the application of this machining process significantly shortened the machining time and increased the machining efficiency by 25%.

PurposeThe purpose of this paper is to propose a method for selecting the position and attitude trajectory of error measurement to improve the kinematic calibration efficiency of a one translational and two rotational (1T2R) parallel power head and to improve the error compensation effect by improving the properties of the error identification matrix.Design/methodology/approachFirst, a general mapping model between the endpoint synthesis error is established and each geometric error source. Second, a model for optimizing the position and attitude trajectory of error measurement based on sensitivity analysis results is proposed, providing a basis for optimizing the error measurement trajectory of the mechanism in the working space. Finally, distance error measurement information and principal component analysis (PCA) ideas are used to construct an error identification matrix. The robustness and compensation effect of the identification algorithm were verified by simulation and through experiments.FindingsThrough sensitivity analysis, it is found that the distribution of the sensitivity coefficient of each error source in the plane of the workspace can approximately represent its distribution in the workspace, and when the end of the mechanism moves in a circle with a large nutation angle, the comprehensive influence coefficient of each sensitivity is the largest. Residual analysis shows that the robustness of the identification algorithm with the idea of PCA is improved. Through experiments, it is found that the compensation effect is improved.Originality/valueA model for optimizing the position and attitude trajectory of error measurement is proposed, which can effectively improve the error measurement efficiency of the 1T2R parallel mechanism. In addition, the PCA idea is introduced. A least-squares PCA error identification algorithm that improves the robustness of the identification algorithm by improving the property of the identification matrix is proposed, and the compensation effect is improved. This method has been verified by experiments on 1T2R parallel mechanism and can be extended to other similar parallel mechanisms.

In the application domain of large-scale high-precision measurement systems, accurately calibrating the precision of point position measurements is a pivotal issue. Traditional calibration methods rely on laser interferometers and high-precision displacement stages, which are not only costly but also challenging to implement in fixed measurement systems. Addressing this challenge, this study introduces an evaluation method for the spatial point measurement accuracy in large-scale fixed high-precision measurement systems. The models for the relationship between the limit deviation and the maximum deviation of finite measurements were established, as well as the limit deviation and point position measurement accuracy. The spatial point position accuracy of the measurement field was calculated by the measurement errors of a calibration rod. The algorithm was validated using a large-scale measurement platform based on photogrammetric technology. Experimental results demonstrate that the method achieved a point position measurement accuracy calibration better than 0.1 mm within a 20 m measurement range, effectively enhancing the measurement data’s accuracy and reliability. This research optimizes the calibration process for large-scale fixed measurement systems, improves calibration efficiency, and obviates the need for complex equipment to complete the calibration process, which is of considerable importance to the development of high-precision spatial point position measurement technology.

Near infrared spectroscopy has been widely applied in the area of rapid determination for fruits’ internal qualities. Therefore, an on-line near-infrared detection system was established to predict the SSC of apples in this study. Due to random measurement positions of apples, negative influences will be exerted on the performance of prediction models. With the aim of learning more about these influences and also compensating for them, spectra were taken at different measurement positions in the present work, including six fixed positions and a random position. Besides, the relations between these positions were investigated as well. It is also found that when the concave surfaces at apple’s calyx and stem interfered with the light path in the detection system, model’s robustness and accuracy would be deteriorated. At last, average and global spectra were used to build prediction models with comparison purpose. The optimal prediction model was established by the average spectra of seven measurement positions (RMSEC 0.356%; r c 0.947; RMSEP 0.370%; r p 0.906), which was superior to results in many previous studies. Last but not least, several suggestions on compensation for these influences were made to improve the model performance in some practical applications.

In cooperative localization a group of robots exchange relative position measurements from their exteroceptive sensors and their motion information from interoceptive sensors to collectively estimate their position and heading. For the localization errors to be bounded, it is required that the system be observable, independent of the estimation technique being used. In this paper, we develop a test-bed of three ground robots, which are equipped with wheel encoders and omnidirectional cameras, to implement the bearing-only cooperative localization. The simulation and experimental results validate the observability conditions, derived in Sharma et al. (IEEE Trans Robot 28:2, 2011 ), for the complete observability of the bearing-only cooperative localization problem.

Snow pears are an important and widespread agricultural product that can relieve respiratory symptoms, constipation, and alcoholism. Lignin content (LC) has a direct and negative role on the fruit texture and taste of snow pears. Here, we studied the effect on the near‐infrared (NIR) spectroscopy determination of the LC in snow pears due to the position at which spectral measurements were obtained. NIR diffuse reflection spectra were collected from nine measurement positions on each sample by a portable NIR spectrometer. Partial least squares regression (PLSR) was used to develop spectrum compensation models of the LC for three local spectrum models, an average spectrum model, and a global spectrum model. The results indicated that the prediction accuracy of the LC was affected by the spectral measurement position. Compared with the local spectrum models and the global spectrum model, the average spectrum model had good prediction results. Next, synergy interval partial least squares, bootstrapping soft shrinkage, competitive adaptive reweighted sampling, genetic algorithm, and an improved variable stability and frequency analysis algorithm (VSFAA) method were used to select the most effective variables to build the PLSR model. The average spectrum calibration model established using the 10 effective variables selected by VSFAA reduced the influence of the variation of the spectral measurement position for LC prediction and achieved more promising results, with the correlation coefficient of calibration and prediction of 0.842 and 0.824, respectively. The root mean square error of cross‐validation and prediction were 0.736 and 0.694, respectively. The overall results showed that the average spectrum model based on the nine spectral measurement positions reduced the sensitivity to the variation of spectral measurement position for predicting the LC and combined the VSFAA variable selection algorithm to improve the accuracy and provide a robust model for prediction of LC in snow pears. Compared with the local spectrum position models and the global spectrum position model, the average spectrum position model combining the nine measurement positions (three stem‐calyx longitudes intersected three latitudes (stem, equator, calyx)) produced good prediction results and reduced the sensitivity to the variation of the spectral measurement position. The effective wavelengths (SNV‐VSFAA‐PLSR)‐average spectrum position model achieved good results, reducing the influence of the variation of the spectral measurement position for LC prediction, and the effective wavelengths selected from the average spectrum position model were helpful for offsetting the influence of the variation of spectral measurement position on the PLSR models based on the spectrum from the equatorial positions alone. Compared with the local spectrum position models and the global spectrum position model, the average spectrum position model combining the nine measurement positions(three stem‐calyx longitudes intersected three latitudes (stem, equator, calyx)) produced good prediction results and reduced the sensitivity to the variation of the spectral measurement position. The effective wavelengths (SNV‐VSFAA‐PLSR)‐average spectrum position model achieved good results, reducing the influence of the variation of the spectral measurement position for LC prediction, and the effective wavelengths selected from the average spectrum position model were helpful for offsetting the influence of the variation of spectral measure‐ment position on the PLSR models based on the spectrum from the equatorial positions alone.

This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance.

At J-PARC, the 500 μs long macro-pulses generated by the LINAC are separated into intermediate-pulses to synchronize it to the frequency of the Rapid-Cycling-Synchrotron (RCS). To secure a stable operation, the knowledge of position and length of those intermediate pulses are crucial, as the pulses need to be adjusted to the RCS frequency. The measurement for this adjustment is done by a beam position monitor (BPM), positioned directly behind the LINAC section in the low energy beam transport (LEBT) section. Since the form of the detected pulses can vary, the implementation of classical algorithms for the automatic detection and identification of pulses proved unreliable. Because of that, it was decided to develop a machine learning algorithm for the automatic pulse identification. In this paper, the background, training and results of different machine learning algorithms developed for the described problem will be introduced and discussed. Additionally, a test of the developed program during active beam operation is being planned, and will be introduced.

This paper addresses the possibility of reconstructing motion relative to the center of mass of a low Earth orbit (LEO) nanosatellite of the CubeSat 3U standard using satellite position measurements (Two-Line Element Set (TLE)). This kind of task needs to be performed in the case where it is not possible to establish radio communication with the nanosatellite after it is launched into orbit. Therefore, it is important for the nanosatellite developers to develop some understanding of what is going on with the nanosatellite in order to be able to analyze the current situation after deployment. The study was carried out on the example of the aerodynamically stabilized SamSat-218D nanosatellite developed by the professors and students of Samara National Research University. SamSat-218D was launched into a near-circular orbit with an average altitude of 486 km on April 2016 during the first launch campaign from the Vostochny cosmodrome. Knowledge of CubeSat aerodynamics allows estimating the nature of its possible motion relative to the CubeSat center of mass by ballistic coefficient changes, evaluated with the use of satellite position measurements. The analysis showed that SamSat-218D performed spatial rotation with an angular velocity of more than two degree per second and had not stabilized aerodynamically by 2 March 2022, when it entered the atmosphere and was destroyed.

With the continuous advancement and upgrading of human space exploration, the complexity and difficulty of space missions are increasing. Space manipulation, rendezvous and docking, and situation awareness of non cooperative targets have become important components of space missions. Relative position and attitude navigation system is an important part of space rendezvous and docking and space manipulation system. At present, the method of matching with cooperative target is mainly used for high-precision relative position and attitude measurement. This paper introduces a method of matching solid-state lidar with high-precision cooperative target for attitude determination. First, a design method of three-dimensional cooperative target is carried out, and then a mathematical model of target recognition is constructed. Three criteria for recognition and matching are proposed. In the third part of the article, the specific process of this method is introduced, and experimental verification is carried out. The experiment shows that the method can achieve the position measurement accuracy of 0.006m and the attitude measurement accuracy of 0.15 ° , and has important application value in various high-precision space missions.