Laguillo, MiguelSegarra, PabloSanchidrian, Jose A.Beitia, Fernando...
11页
查看更多>>摘要：At present there is a lack of an instrument to measure the trajectory of up-holes in a safe and efficient way with minor disruption of underground production. To overcome this, a mechanically operated probe equipped with Inertial Measurement Units (IMU) and centralisers is integrated in an explosive charge-up unit, which allows the operator to start and stop the measurements from a safe area, while he views the records in real time in a handheld PC. The system incorporates a real-time quality indicator based in the 95% confidence standard deviational ellipse (SDE) of the toe's positions that clearly indicates whether the survey should be repeated before moving to the next blasthole to ensure measurements with limited uncertainty. In-field data shows an accuracy and repeatability between measurements below 1.3 cm/m, which ensures detecting drilling errors in production rings, often of amount to several tens of centimetres, that may compromise blast results.
查看更多>>摘要：Center extraction of stripe images is a key technique of line structured light sensors (LSLSs). As the original data for profile computation, the extraction results, which are highly affected by image quality, should be accurate and reliable. While, few works can be found for real-time uncertainty evaluation of the extraction results due to the diversity of center extraction methods and the complexity of uncertainty models. Here, a universal method is proposed to evaluate the uncertainty of center extraction results obtained from classical methods including, but not limited to, the gray gravity method (GGM), Gaussian fitting method (GFM) and Steger method. The proposed method is based on an adaptive BP neural network (ABPNN) where its weights and thresholds are adjusted automatically according to the width of each cross section profile. Experimental results show that the ABPNN can predict the uncertainty value of center extraction results accurately and efficiently.
Kim, Jong-AhnLee, Jae YongKang, Chu-ShikWoo, Jae Heun...
8页
查看更多>>摘要：This paper presents an optical measurement system, which is mainly composed of two hybrid sensor modules (HSMs), for evaluation of precision two-dimensional (2D) planar stages. The HSM is designed to integrate two different types of sensors within a single sensor module, so that it can be operated as a profile interferometer or an angle sensor by simply switching the data-processing mode. This enables us to implement efficient on-site evaluation of straightness errors of moving mirrors of a two-axis laser interferometer, and multi-axis parasitic motion errors of a 2D planar stage without reconfiguration of the evaluation setup. To estimate the performance of a prototype measurement system, an exemplary 2D stage was experimentally evaluated, and the straightness and angular motion errors were measured with expanded uncertainty (k = 2) of 40 nm and 0.14", respectively. Reliability of the measurement system was also verified by comparison with reference instruments.
查看更多>>摘要：Alternating prestress is difficult to measure. By analyzing the inverse magnetostriction effect and the magnetic hysteresis, a theoretical model was established to describe the prestress-inductance relationship. The results of the prestress monitoring experiment showed that the inductance was influenced by the irreversible magnetization, the plastic deformation, and the hysteresis effect. Keeping the stress range constant, the inductance range was unaffected by the number of load cycles. The inductance can be used to judge whether the prestress is reasonable. The parabolic fitting method was employed to establish the mapping from the inductance to the prestress. When the stress range was 10% design prestress or 20% design prestress, the prestress monitoring error, less than 12.56% or 18.77%, was close to the magnetoelastic method and the frequency-based method. Based on the proposed calculation method, the MI method could monitor the alternating prestress of steel strands.
查看更多>>摘要：Conventional adsorbents for vapors are designed for high capacity and low-pressure drop, resulting in relatively slow vapor adsorption. Current adsorption measurement methods were, therefore, developed to characterize adsorption on a time scale of minutes. However, faster vapor adsorption is relevant for processes such as FLUID COKINGTM, where fouling within the cyclones can cause a shut-down. Hydrocarbon adsorption on hot coke particles could mitigate cyclone fouling. Therefore, a new measurement method was developed to characterize the fast adsorption of vapors on hot coke particles. It uses a vertically oscillating gas-solid contacting system to provide excellent particle-gas contact, well-mixed conditions, and well-controlled isothermal conditions. Equi-librium adsorption uptake of coke is more than an order of magnitude lower than for activated carbon. However, adsorption is much faster with coke, with an adsorption time constant of about 1 min. Significant adsorption of vapors on coke particles could, therefore, take place in Fluid Cokers.
查看更多>>摘要：In this study, the influence of the geometric error of a contact probe on the accuracy of thread measurement was investigated to improve the measurement accuracy of three-dimensional (3D) screw thread measuring machines. The structure and measurement principle of the 3D thread measuring machine were studied, and the geometric error sources of the contact probe were analyzed. The principles of space coordinate transformation and the geometric theorem were used to establish a geometric model of the error sources. The experimental platform and the standard ball measurement of the three-coordinate machine were configured to verify the geometric error model. The results revealed that the measurement error was significantly reduced after compensation by the model. The proposed geometric error modeling method provides a clear theoretical basis for the accurate compensation of contact probe deviations and further improves the measurement accuracy of 3D thread measuring machines.
查看更多>>摘要：This study estimates the pose of Unmanned Aerial Vehicle (UAV) through artificial intelligence-based approaches and combines visual-inertial information in a different way than previous studies. For an effective fusion, the inertial data between both frames is normalized after denoising with the Savitzky-Golay technique and finally converted from numerical value to image. To strengthen these inertial image features with the change of motion between two frames, frames of Optical Flow (OF) are obtained and OF frames are combined with inertial images. Simultaneously, a parallel thread combines this OF frame with two consecutive raw frames. After features are extracted from inertial and camera data via Inception-v3, these features are fused and actual UAV poses are estimated via Gaussian Process Regression (GPR). Thanks to the smoothing process applied to these estimated values, a more stable pose estimation is provided. This proposed method is applied to the EuRoC dataset and our dataset produced in the Gazebo environment. The pose estimation results reveal that the proposed method has high performance compared to many previous studies.
查看更多>>摘要：The concept of measuring the multi-level deformation behavior of flexural structures via the extraction of continuous structural boundaries using a computer vision technique is proposed. The feasibility of using a salient-object-detection method to estimate the deformation and curvature profile of target structures from the image frames of a video recording structural vibrations is investigated. A framework is proposed for performing this salient-object-detection-based vibration measurement technique via the aid of a pre-trained deep neural network. A method for determining the curvature estimated from the boundary extracted from the deep neural network is then introduced. The accuracy of the proposed technique is validated via two experiments. The first experiment measures the curvature of a semi-circular plate under rigid body motions. The second experiment tracks the deformation of reinforced concrete beams under impact loads. Both experiments verify that the proposed method is feasible for accurately measuring the vibration profile of the target structure.
Salem, Ali AhmedLau, Kwan YiewAbdul-Malek, ZulkurnainTan, Chee Wei...
15页
查看更多>>摘要：The damage of the room temperature vulcanized (RTV) coating on the surface of polluted glass insulators under axial and circumferential directions has a significant influence on the insulator's leakage current and flashover voltage. To date, many investigations have been conducted on the performance of polluted glass insulators under longitudinal, ring-shaped, and fan-shaped coating damage modes. The purpose of this research was to investigate and classify the insulator conditions of RTV coated glass insulators under different coating damage modes using flashover voltage magnitude and leakage current characteristics. The harmonic component index of leakage current used to determine the health status (H) of insulators, and the crest factor (CF) of leakage current, were experimentally extracted and utilized in the classification. The support vector machine (SVM) model with various functions was designed to predict the deterioration configuration of RTV coatings on polluted glass insulators. The results indicate that the flashover voltage and leakage current of polluted glass insulators are significantly affected by different coating damage modes. Specifically, the insulator with the fan-shaped RTV coating damage experiences the most vulnerable pollution effect compared to the undamaged coating, with a 48% drop in the flashover voltage and a 125% increase in the leakage current under heavy pollution. Subsequently, the proposed SVM model demonstrates strong capabilities for predicting the flashover voltage and leakage current and classifying the pollution severity of the RTV coating damage based on the extracted indicators.
查看更多>>摘要：For the current problems of inaccurate, non-real-time and costly of the measurement of the aggregate level in concrete batching plant, this paper proposes an intelligent detection method of concrete aggregate level based on monocular imaging. This method uses a monocular camera, installed at 45-degree angle, construct a specific projection model of camera and storage bin to establish a mapping relationship between the image coordinates and actual imaging angle, and then combine peak and valley positioning information derived from YOLOv5 to find the height of aggregate level. The experimental results show that the average accuracy of the concrete batching plant aggregate level detection method based on monocular imaging is 95.3%, and the real-time monitoring speed is 111f/s, which is higher than the traditional corner detection method 21.6% and 99f/s, and higher than the YOLOv4 monocular measurement method 14.5% and 51f/s, respectively. The effects can meet the demand of aggregate level monitoring.
NSTL
Elsevier