High Precision Performance Research Articles

A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer

ABSTRACTReal‐time monitoring, prediction, and early warning of operating status during intelligent mining are the key to ensuring stable production. To solve the problem of lag in determining the operating status of a shearer, this study proposes a new method for predicting and warning the real‐time operating status of the shearer involving the Storm framework, based on parallel optimization of data processing and the gated recurrent unit (GRU) model based on hyperparameter optimization. First, the GRU model is optimized through hyperparameter optimization to achieve adaptive and accurate prediction and early warning of multidimensional state parameters of the shearer. Second, a virtual machine is constructed to host the Storm framework, parallel optimized real‐time processing of data is performed on the Storm framework, and real‐time data flow patterns are constructed to speed up data processing and retrieval, ensuring each tuple is fully processed through the topology structure. Finally, the optimized GRU model is embedded into the optimized Storm framework to achieve real‐time prediction and early warning of different dimensional data of the shearer. The prediction accuracy, early warning accuracy, and processing efficiency of the Storm platform are used as evaluation indicators to analyze and evaluate the model, verifying the efficiency and applicability of the proposed model. Experimental results show that the model has a prediction accuracy of 93%, an early warning accuracy of 93.05%, and consumes 10 s. It can achieve high performance, low latency, and high precision in predicting and providing early warnings for the shearer's state parameters, greatly improving the efficiency of predicting and early warning the operating status parameters of the shearer. This model realizes real‐time prediction and early warning of the shearer's operating status, providing technical support for intelligent mining in coal mines.

Ultra-low frequency active vibration isolation system with quasi-zero stiffness characteristic using self-tuning filter-based feedforward control

Purpose of studyThis study aims to resolve the compromise between low suspension stiffness and high load-bearing capability in vibration isolation and enhance the suppression performance of ultra-low frequency vibration through advanced active feedforward control methods, featuring high-performance precision isolation for large-scale ultra-precision instruments. Describe methodsAn air-magnetic hybrid parallel configuration of positive and negative stiffness is employed to achieve adjustable stiffness, endowing the vibration isolation system with quasi-zero stiffness characteristics. A novel self-tuning feedforward control strategy is proposed through a self-tuning filter to update the controller parameters online, minimizing the loss caused by model uncertainties. ResultsThe vertical and horizontal natural frequencies of the system exhibit a remarkably low resonance frequency of lower than 0.5 Hz. With the self-tuning feedforward strategy, the maximum vibration attenuation reaches 78 dB in the vertical direction and 70 dB in the horizontal direction, reducing the cumulative power at 100 Hz by 61.4 % and 47.8 %, respectively. The above results showcase the proposed approach with excellent performance in isolating low-frequency vibrations. Conclusions/DiscussionThe ultra-low frequency active vibration isolation system designed in this paper achieves exceptionally low suspension stiffness. The implemented self-tuning feedforward controller isolates large precision machinery from broadband floor vibrations and significantly enhances the vibration isolation performance of the system.

Research on temperature compensation of MEMS pressure sensors based on optimized multi-kernel relevance vector machine

Abstract The high-temperature coefficient in MEMS pressure sensors significantly affects sensor accuracy. Software compensation effectively reduces temperature drift. However, compensation methods such as neural networks tend to overfit sparse and noisy datasets used for calibration, leading to reduced measurement accuracy. To address the above-mentioned crucial problems, this paper proposes a multi-kernel relevance vector machine (MKRVM) based on a snow ablation optimizer (SAO) to establish the temperature-voltage-pressure relationship of pressure sensors. Optimized weight distribution from this method mitigates noise impact on the model in sparse data scenarios. Experimental results show that within the range of -15°C to 60°C, the proposed temperature compensation method achieves a mean square error as low as 0.058, effectively reducing temperature influence on pressure sensor outputs and ensuring high precision performance of the sensor.

A New Approximation for the Perimeter of an Ellipse

We consider the problem of approximating the perimeter of an ellipse, for which there is no known finite formula, in the context of high-precision performance. Ellipses are broadly used in many fields, like astronomy, manufacturing, medical imaging, and geophysics. They are applied on large and nanoscales, and while numerical integration can be used to obtain precision measurements, having a finite formula can be used for modeling. We propose an iterative symbolic regression approach, utilizing the pioneering work of Ramanujan’s second approximation introduced in 1914 and a known Padé approximation, leading to good results for both low and high eccentricities. Our proposed model is also compared with a very comprehensive historical collection of different approximations collated by Stanislav Sýkora. Compared with the best-known approximations in this centuries-old mathematical problem, our proposed model performs at both extremities while remaining consistent in mid-range eccentricities, whereas existing models excel only at one extremity.

Numerical Studied for Solving Fuzzy Integro-Differential Equations via Caputo Fractional Derivative

In this paper, we extended some numerical methods to solve fuzzy integro differential equations. The considered problem involves the fractional Caputo derivatives under some conditions on the ordered. As we combine Euler's method with composite Simpsons have been used to determine the solutions of these equations. We extend these numerical techniques to find the best solutions. Extended difference Euler technique is used for this. The results show that the extended Euler method is more accurate in terms of absolute error. Illustrative examples are given to demonstrate the high precision and good performance of the new class.

Point-Based Fusion for Multimodal 3D Detection in Autonomous Driving

In the broader field of mechanical technology, and specifically within the domain of self-driving vehicles, cameras and LIDAR are crucial sensor modalities that provide complementary information, offering significant potential for sensor fusion. However, directly merging multi-sensor data through point projection can lead to information loss due to quantization, and managing the differences between data formats from multiple sensors remains a challenge. To address these issues, we propose an new fusion method that leverages continuous convolution, point-pooling, and a learned MLP to achieve superior detection performance. Our approach integrates the segmentation mask with raw LIDAR points instead of using projected points, thereby avoiding quantization loss. We conduct neighbor searches on the points and retrieve corresponding semantic features from images to concatenate image and LIDAR data. Subsequently, we apply continuous convolution, point-pooling, and a learned MLP to obtain the fused output. The pooling and aggregation operations, as extensions of convolution, are specifically designed to handle the disparities in data formats. Our detection network is divided into two stages: in the first stage, preliminary proposals and segmentation features are generated; in the second stage, the fusion result with the segmentation mask is refined to produce the final prediction. Our method aims to achieve precise object detection in 3D environments by enhancing LIDAR point data with semantic features from images, allowing for the flexibility to alternate segmentation sub-algorithms as needed. Extensive experiments on the KITTI dataset demonstrate the effectiveness of our approach, which achieves high precision and robust performance in 3D object detection tasks.

Kinetics of monolayer MoS2-encapsulated nanobubbles on hexagonal boron nitride substrates

Understanding the kinetics of nanobubbles encapsulated by ultrathin two-dimensional (2D) layered van der Waals crystal membranes on atomically flat substrates is important to the applications of 2D materials and the pursuit of 2D nanobubble technologies. Here, we investigate the controlled motion of monolayer molybdenum disulfide (MoS2)-encapsulated nanobubbles on flat hexagonal boron nitride substrates using atomic force microscopy (AFM). Our study reveals a distinct transition from standstill bubble deformations to stable, stepwise bubble translations on flat substrates. The membrane tension-dominated 2D nanobubble behaves like an elastic soft body in its collision interaction with the AFM tip. This delicate motion-control technique enables neighboring 2D nanobubbles to move closer and eventually coalesce into larger nanobubbles. These findings pave the way for high-precision manipulation of nanobubbles and facilitate the exploration of their emerging applications.

Broadband metamaterial linear polarization converter designed by a hybrid neural network with data augmentation

Deep learning techniques provide a new approach to the design and optimization of electromagnetic metamaterials. This study used a convolutional neural network and long short-term memory (CNN–LSTM) hybrid network to design and optimize a broadband metamaterial reflective linear polarization converter. The data augmentation method was also employed in few-shot learning to reduce optimization costs and improve model prediction performance. With the inverse prediction, a linear polarization converter that perfectly covers the Ku-band was obtained and fabricated with flexible printed circuit technology. Both simulation and experimental results indicate that this network can accurately predict the structural parameters. The polarization converter not only achieves remarkable broadband polarization conversion efficiency spanning the 2.2–18 GHz range but also maintains precise cross-polarization control across the entire Ku-band. The mean polarization conversion ratio in the Ku-band was calculated to be an impressive 99.69%. Finally, the mechanism of polarization conversion and the influence of each structural parameter on its performance further verify the optimality of the inverse design model. The use of CNN–LSTM deep learning methods significantly simplified the design process of electromagnetic metamaterials, reducing design costs while ensuring high design precision and excellent performance.

Fuzzy Logic and Neural network-based self-tuned PID controller of Quadcopter

Quadcopters have been established as a viable tool for the current era of technology. Quadcopters are also known as drones or unmanned aerial vehicles. The quadcopter can be used for aerial surveys for deliveries, filming, and missions such as detecting and mapping unreachable areas, hills, forests metropolitan areas, rocks, and obstructions that can be a danger or risk to navigation for commercial and security projects. It conducts its survey mission without human operator intervention. After the mission is accomplished, data can be fetched by GPS or the Quadcopter returns to a pre-programmed location where the data can be downloaded and processed. It can lead to an improvement in the position tracking performance due to an intrinsic change in the object's weight, buoyancy, and aerodynamic forces. In addition, quadcopters must navigate the extremely dynamic airborne environment that is represented by urban, rural, metropolitan, or natural environments. In light of this, efficient dock tracking is required to attain the requisite high precision performance, resulting in less time consumption, when the system's dynamic characteristics are time-dependent or the operating conditions and vision guidance alter. The automatic identification of objects by classifying images and self-tuning the gains of a PID (Proportional + Integral + Derivative) controller has been proposed in this paper. The object recognition is based on machine learning guided vision and self-tuning PID controller based on fuzzy logic and neural network.

Enhanced deep leaning model for detection and grading of lumbar disc herniation from MRI.

Lumbar disc herniation is one of the most prevalent orthopedic issues in clinical practice. The lumbar spine is a crucial joint for movement and weight-bearing, so back pain can significantly impact the everyday lives of patients and is prone to recurring. The pathogenesis of lumbar disc herniation is complex and diverse, making it difficult to identify and assess after it has occurred. Magnetic resonance imaging (MRI) is the most effective method for detecting injuries, requiring continuous examination by medical experts to determine the extent of the injury. However, the continuous examination process is time-consuming and susceptible to errors. This study proposes an enhanced model, BE-YOLOv5, for hierarchical detection of lumbar disc herniation from MRI images. To tailor the training of the model to the job requirements, a specialized dataset was created. The data was cleaned and improved before the final calibration. A final training set of 2083 data points and a test set of 100 data points were obtained. The YOLOv5 model was enhanced by integrating the attention mechanism module, ECAnet, with a 3 × 3 convolutional kernel size, substituting its feature extraction network with a BiFPN, and implementing structural system pruning. The model achieved an 89.7% mean average precision (mAP) and 48.7 frames per second (FPS) on the test set. In comparison to Faster R-CNN, original YOLOv5, and the latest YOLOv8, this model performs better in terms of both accuracy and speed for the detection and grading of lumbar disc herniation from MRI, validating the effectiveness of multiple enhancement methods. The proposed model is expected to be used for diagnosing lumbar disc herniation from MRI images and to demonstrate efficient and high-precision performance.

Macro-micro synchronization strategy based on fuzzy time-delay compensation for high-precision laser on-the-fly processing

Real-time control systems of macro-micro manipulators commonly confront time-delay challenges to achieve high precision and synchronization performance, because macro and micro subsystems always have significant difference mainly in mechanical inertia. In this paper, a self-designed PC-based real-time controller utilized in laser on-the-fly processing is introduced as a study case of macro-micro systems. A dynamic time-delay compensation method for this case is proposed based on efficient fuzzy-model prediction and special data compression/extension algorithm. This method takes account of the practical time-delay fluctuation and has capability of adjusting the compensator adaptively rather than fixing the compensator as a constant, compared with the conventional static compensation. Experimental results show that, after applying the dynamic compensation, the process error caused by time-delay is eliminated from 1.059 mm to 0.001 mm, while the quality of synchronization appears even better than the static compensation with the error of 0.014 mm. Besides, the proposed method is computationally optimized, and the extra load of real-time kernel merely fluctuates within 3 % of the total computation performance capacity.

High-precision BaTiO3 piezoelectric ceramics via vat photopolymerization 3D printing

BaTiO3 ceramics with high precision and superior performance are fabricated by vat photopolymerization 3D printing technology. The curing behaviors and photosensitive mechanisms are systematically investigated. Through the optimization of printing parameters, BaTiO3 ceramics with a high relative density of 95 % and few defects are obtained. Notably, the sintered BaTiO3 ceramics exhibit impressive properties, including a high piezoelectric coefficient d33 of 201 pC/N and strong ferroelectric characteristics with a maximum polarization Pmax of 31.4 μC/cm2 and residual polarization Pr of 18.5 μC/cm2. More importantly, a series of complex BaTiO3 structures including the body-centered cubic lattice and triply periodic minimal surface (TPMS) structures are created perfectly with the feature size as fine as 75 μm. Compared with other reported BaTiO3 structures printed by vat photopolymerization and material extrusion technologies, this work realizes a much higher printing resolution. The excellent performance and high precision of these BaTiO3 ceramics make them great potential for practical applications in piezoelectric components.

The effects of electrolyte composition and deposition voltage on the copper-nickel alloy micropillars fabricated by Jet ECD

With the rapid development of micro electronic component technology, higher requirements are put forward for high performance and high precision manufacturing process. In this paper, copper-nickel alloy micropillars were manufactured by jet electrochemical deposition (Jet ECD) technology with high deposition rate, aiming to provide an effective material and process solution for the manufacture of micro-thermocouples and other components. By optimizing the process parameters such as electrolyte composition, pH value and deposition voltage, the homogeneous deposition of copper-nickel alloy micropillar was realized. The cross-section morphology and elementary composition of the deposition micropillars were characterized in detail by using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The results show that the adjusted formula and process parameters can stably manufacture copper-nickel alloy micropillars with a nickel-copper ratio (close to 0.8), and compact, void free microstructure.

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level.

Dynamic liquid level monitoring and measurement in oil wells is essential in ensuring the safe and efficient operation of oil extraction machinery and formulating rational extraction policies that enhance the productivity of oilfields. This paper presents an intelligent infrasound-based measurement method for oil wells' dynamic liquid levels; it is designed to address the challenges of conventional measurement methods, including high costs, low precision, low robustness and inadequate real-time performance. Firstly, a novel noise reduction algorithm is introduced to effectively mitigate both periodic and stochastic noise, thereby significantly improving the accuracy of dynamic liquid level detection. Additionally, leveraging the PyQT framework, a software platform for real-time dynamic liquid level monitoring is engineered, capable of generating liquid level profiles, computing the sound velocity and liquid depth and visualizing the monitoring data. To bolster the data storage and analytical capabilities, the system incorporates an around-the-clock unattended monitoring approach, utilizing Internet of Things (IoT) technology to facilitate the transmission of the collected dynamic liquid level data and computed results to the oilfield's central data repository via LoRa and 4G communication modules. Field trials on dynamic liquid level monitoring and measurement in oil wells demonstrate a measurement range of 600 m to 3000 m, with consistent and reliable results, fulfilling the requirements for oil well dynamic liquid level monitoring and measurement. This innovative system offers a new perspective and methodology for the computation and surveillance of dynamic liquid level depths.

Enhanced trapping properties of coupled plasmonic tweezers via plasmon-exciton interaction.

Excited plasmonic nanoantennas enable the manipulation of photons coupled with quantum emitters or the trapping of particles as plasmonic tweezers, leveraging the strong evanescent gradient fields at the nanoscale. However, the ohmic loss of metals presents a significant challenge for the stable and high-precision manipulation of nanoparticles without causing damage. In this study, we investigated the enhanced trapping properties induced by plasmon-exciton interaction for coupled plasmonic tweezers. Through the coupling between plasmons and excitons, dynamic particle trapping is achievable under low excitation power conditions of 0.45 mW, with the trapping stiffness increasing by nearly 20 times. Furthermore, the trapping stiffness can be fine-tuned by modulating the quantity of excitons to regulate the coupling strength. Coupled plasmonic tweezers offer an effective strategy to mitigate the influence of ohmic loss on trapping performance, by manipulating particles with minimal laser power. These findings provide insights into enhancing trapping performance through plasmon-exciton coupling, with potential applications in biomedicine and quantum information science.

Nonlinear time-reversal interferometry with arbitrary quadratic collective-spin interaction

Abstract Atomic nonlinear interferometry has wide applications in quantum metrology and quantum information science. Here we propose a nonlinear time-reversal interferometry scheme with high robustness and metrological gain based on the spin squeezing generated by arbitrary quadratic collective-spin interaction, which could be described by the Lipkin–Meshkov–Glick (LMG) model. We optimize the squeezing process, encoding process, and anti-squeezing process, finding that the two particular cases of the LMG model, one-axis twisting and two-axis twisting outperform in robustness and precision, respectively. Moreover, we propose a Floquet driving method to realize equivalent time reverse in the atomic system, which leads to high performance in precision, robustness, and operability. Our study sets a benchmark for achieving high precision and high robustness in atomic nonlinear interferometry.

Improving radial lens distortion correction with multi-task learning

With computer vision and machine learning, sports image analysis can enhance viewer engagement and contribute to the overall understanding of sports events. However, beneath the apparent simplicity of sports images lies a complex challenge of radial distortion, which can impede their accurate interpretation. The need for high precision and real-time performance is paramount. We present a new regression-based method for radial distortion correction that improves the accuracy of distortion model coefficient prediction by introducing a secondary learning task that compares the distortion level of two random training samples. The secondary task requires no additional annotation, and because it is also related to radial distortion, it encourages the common feature extractor component to learn more general and robust features while preventing overfitting and improving the efficiency of training data. We have evaluated our proposed method using two public datasets and compared it against five other methods. Our method surpassed them all, both in the accuracy of the radial distortion correction and speed as well. You can find the source code and trained models at https://vgg.fiit.stuba.sk/improving-radial.

Precise Fabrication and Manipulation of Individual Polymer Nanofibers.

Polymer nanofibers hold promise in a wide range of applications owing to their diverse properties, flexibility, and cost effectiveness. In this study, we introduce a polymer nanofiber drawing process in a scanning electron microscope and focused ion beam (SEM/FIB) instrument with in situ observation. We employed a nanometer-sharp tungsten needle and prepolymer microcapsules to enable nanofiber drawing in a vacuum environment. This method produces individual polymer nanofibers with diameters as small as ∼500 nm and lengths extending to millimeters, yielding nanofibers with an aspect ratio of 2000:1. The attachment to the tungsten manipulator ensures accurate transfer of the polymer nanofiber to diverse substrate types as well as fabrication of assembled structures. Our findings provide valuable insights into ultrafine polymer fiber drawing, paving the way for high-precision manipulation and assembly of polymer nanofibers.

Pulse current electroforming of Ni-PTFE nanocomposite mold insert with long-lifetime and anti-adhesive properties

Polymeric micro features tend to deform and break under the demolding force during injection molding process, which becomes a bottleneck problem limiting its precision manufacturing. To reduce the polymer-mold interfacial adhesion, nickel-polytetrafluoroethylene (Ni-PTFE) nanocomposite molds with low surface adhesion and high wear resistance were manufactured via pulse current electroforming. The electrodeposited Ni-PTFE nanocomposite showed a good hydrophobic property and low friction coefficient (between 0.1 and 0.3) than pure nickel (0.78). Compared with direct current, pulse current electrodeposition was beneficial to reduce the crystallite size and internal stress, thus improving the wear resistance and reducing the cracking defects. The hardness of pulse current electrodeposited Ni-PTFE reached 442.3 HV, which was 51.7 % higher than that of pure nickel. Compared to nickel mold, Ni-PTFE nanocomposite mold significantly improved the dimensional accuracy of molded microfluidic chips. This study systematically investigates the Ni-PTFE nanocomposite electroforming process under both direct and pulse currents, which provides a new approach for the manufacturing of high-performance precision injection mold insert.

A method combining active control with passive regulation to enhance the vibration suppression capability of linear motor-driven aerostatic stage

The Linear Motor-Driven Aerostatic Stage (LMDAS) has been extensively applied in high-precision measuring instruments and precision equipment requiring exceptional velocity and acceleration performance. This is attributable to its low friction, high precision, and high dynamic performance. Nevertheless, the LMDAS is vulnerable to external disturbances due to weak damping of the aerostatic guideway's gas film. This weakness directly affects the positioning accuracy and dynamic characteristics of the system and results in its flutter. This paper proposed a method for improving the overall damping of the system and its ability to suppress vibrations by enhancing the damping of the driving direction via combining active control and passive regulation. The active control algorithm based on the active disturbance rejection controller and the passive regulation technology based on the linear motor were presented, respectively. Both numerical and experimental studies demonstrated that this approach was superior to the traditional PID controller, commonly employed in LMDAS, regarding steady-state performance, dynamic response, and vibration suppression. In particular, this method demonstrated a significant improvement in reducing the maximum vibration acceleration of the LMDAS in all three directions, achieving a 50 % reduction compared to the conventional PID controller.