High-precision Solution Research Articles

Secure model predictive static programming with initial value generator for online computational guidance of near-space vehicles

For online trajectory programming of near-space vehicles with limited computation resources, conventional model predictive static programming approaches have two main challenges. Firstly, an inadequate initial control guess can lead to trajectory divergence or slow convergence, resulting in mission failure. Secondly, Euler discretization is a small-step local algorithm by point-to-point recursion. To ensure high precision solution, more discretization points are required, leading to low computation efficiency and accuracy; meanwhile conventional methods cannot guarantee that the problems are solved within the constraints and feasible domains, potentially affecting the solution stability. To solve the first problem, a variable coefficient near-optimal initial value generator is developed to provide an initial control guess that approximates the optimal trajectory, preventing divergence in subsequent iterations. To address the second problem, the trust-region constrained model predictive static programming is proposed with flipped-Radau pseudospectrum. This method reduces the number of discretization points and optimizes the performance index directly, thereby enhancing efficiency; meanwhile the trust region improves accuracy and ensures the updated trajectory remains close to the reference trajectory. Finally, the combination of above approaches enhances the calculating efficiency and precision significantly for online trajectory programming. Applied to a near-space vehicle, the proposed method reduces optimization time by 25 % for rapid and high-precision solutions, and improves terminal position accuracy from 43 m to 1 m with flipped-Radau pseudospectrum.

Design of Small Permanent-Magnet Linear Motors and Drivers for Automation Applications with S-Curve Motion Trajectory Control and Solutions for End Effects and Cogging Force

This paper designs and fabricates a small-type permanent-magnet linear motor and driver for automation applications. It covers structural design, magnetic circuit analysis, control strategies, and hardware development. Magnetic circuit analysis software JMAG is used for flux density distribution, back electromotive force (back-EMF), and electromagnetic force analysis. To address the lack of a complete closed magnetic circuit path at the ends of the linear motor, which causes magnetic field asymmetry, a phenomenon known as end effects, auxiliary core structures are proposed to compensate for the magnetic field at the ends. It successfully utilizes auxiliary cores to achieve the phase voltages of each phase, which are balanced at a phase voltage error of 0.02 V. To address the cogging force caused by variations in the magnetic reluctance of the core, this paper analyzes the relationship between electromagnetic force and mover position, conducting harmonic content analysis to obtain parameters. These parameters are applied to the designed cogging force control compensation strategy. It successfully achieves q-axis current compensation of around 1.05 A based on the mover’s position, ensuring that no jerking caused by cogging force occurs during closed-loop electromagnetic force control. The S-curve motion trajectory control is proposed to replace the traditional trapezoidal acceleration and deceleration, resulting in smoother position control of the linear motor. Simulations using JMAG-RT models in MATLAB/Simulink verified these control strategies. After verification, practical test results showed a maximum position error of approximately 5.0 μm. Practical tests show that the designed small-type permanent-magnet linear motor and its driver provide efficient, stable, and high-precision solutions for automation applications.

Research on position tracking control of electro-hydraulic actuator system combining feedforward inverse model controller and exponential functional link network adaptive controller

The electro-hydraulic actuator system plays a crucial role in indoor high-frequency simulation experiments. Many components of the system exhibit unavoidable nonlinear characteristics, which reduce its dynamic position response capability. Additionally, most algorithms are unsuitable for practical high-frequency conditions due to either local instability or high computational complexity. To improve position tracking accuracy under high-frequency control, a novel combined control strategy is proposed, integrating an appropriate feedforward inverse model controller (FFIMC) with an exponential functional link network adaptive controller (EFLNAC). Initially, the system has reached a stable state under the action of the PID controller. Subsequently, the FFIMC estimates both the model and the inverse model of the PID system by applying the AIC technique developed through the DCT-VSSNLMS adaptive algorithm, which extends the system’s bandwidth. Following this, the EFLNAC, as the outermost loop, acquires the inverse model of the FFIMC system and updates internal parameters in real-time, thereby enhancing the system’s resistance to nonlinear disturbances. Finally, the combined controller is validated through simulations and experiments under various working conditions. The results show a significant increase in the PID system’s bandwidth and demonstrate that the tracking error of the signal is maintained within 0.079 mm. Therefore, the combined controller proves to be an effective solution for high-precision, high-frequency displacement tracking, offering the advantages of low computational complexity, high accuracy, and strong effectiveness.

High-Precision Monitoring during the Installation of Large Steel Structures by UAV Nap-of-the-Object Photogrammetry

Abstract. This paper proposes and validates a method based on UAV nap-of-the-object photogrammetry for refined 3D digital modeling to achieve high-precision monitoring during the installation of large steel structures, offering a comparative analysis with traditional oblique photogrammetry methods. Through field tests and data processing on the steel structure of a sports stadium in Zhejiang Province, the results demonstrate that nap-of-the-object photogrammetry can capture high-resolution images, offering significant advantages in detail representation and modeling of complex structures. Experimental data reveal that the model's root mean square error (RMSE) generated by nap-of-the-object photogrammetry is 14.56mm, markedly lower than the 51.99mm achieved by traditional methods, meeting the Level II accuracy standard specified in the "Technical Specifications for Nap-of-the-Object Photogrammetry." The study further shows that, when combined with optimized flight path planning, nap-of-the-object photogrammetry enhances both the efficiency and accuracy of data collection while improving operational safety. This technique is highly applicable and flexible, making it suitable for data acquisition of complex structures and high-rise buildings. In summary, the proposed method exhibits significant advantages in terms of accuracy, efficiency, safety, and applicability, providing a practical solution for high-precision, comprehensive digital modeling of large steel structures.

An Efficient Optimization Model and Tabu Search–Based Global Optimization Approach for the Continuous p-Dispersion Problem

Continuous p-dispersion problems with and without boundary constraints are NP-hard optimization problems with numerous real-world applications, notably in facility location and circle packing, which are widely studied in mathematics and operations research. In this work, we concentrate on general cases with a nonconvex multiply connected region that are rarely studied in the literature due to their intractability and the absence of an efficient optimization model. Using the penalty function approach, we design a unified and almost everywhere differentiable optimization model for these complex problems and propose a tabu search–based global optimization (TSGO) algorithm for solving them. Computational results over a variety of benchmark instances show that the proposed model works very well, allowing popular local optimization methods (e.g., the quasi-Newton methods and the conjugate gradient methods) to reach high-precision solutions due to the differentiability of the model. These results further demonstrate that the proposed TSGO algorithm is very efficient and significantly outperforms several popular global optimization algorithms in the literature, improving the best-known solutions for several existing instances in a short computational time. Experimental analyses are conducted to show the influence of several key ingredients of the algorithm on computational performance. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72122006, 71821001, and 72471100] Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0089 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0089 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Compact advanced pure tilt actuator for testing tilt-to-length coupling in space-based gravitational wave detection.

Tilt-to-length (TTL) coupling, caused by the jitter of test masses or satellites, is a major noise source in space-based gravitational wave detection. Calibrating and suppressing TTL coupling noise at the sub-nanometer level is essential. A key challenge in current ground-based TTL coupling testing is the residual translational movement of the tilt actuator. This paper presents the development of a compact advanced pure tilt actuator (APTA) specifically designed for testing TTL coupling. The APTA enables precise tilt motion, monitored by a four-beam interferometer measuring the displacement of attached retroreflectors. Detailed theoretical models and experimental setups are given. Experimental results demonstrate that the APTA test bed can achieve sub-nanometer-level TTL coupling calibration. Additionally, a typical test-mass interferometer tested by the APTA test bed demonstrated that the imaging system effectively suppresses TTL coupling errors. The TTL coupling coefficients were reduced from over ±30 μm/rad to within ±5 μm/rad across a range of ±200 μrad. The APTA test bed offers a compact, high-precision solution for ground-based TTL coupling tests and has the potential for broader application in related experimental setups.

YOLO-FMDI: A Lightweight YOLOv8 Focusing on a Multi-Scale Feature Diffusion Interaction Neck for Tomato Pest and Disease Detection

At the present stage, the field of detecting vegetable pests and diseases is in dire need of the integration of computer vision technologies. However, the deployment of efficient and lightweight object-detection models on edge devices in vegetable cultivation environments is a key issue. To address the limitations of current target-detection models, we propose a novel lightweight object-detection model based on YOLOv8n while maintaining high accuracy. In this paper, (1) we propose a new neck structure, Focus Multi-scale Feature Diffusion Interaction (FMDI), and inject it into the YOLOv8n architecture, which performs multi-scale fusion across hierarchical features and improves the accuracy of pest target detection. (2) We propose a new efficient Multi-core Focused Network (MFN) for extracting features of different scales and capturing local contextual information, which optimizes the processing power of feature information. (3) We incorporate the novel and efficient Universal Inverted Bottleneck (UIB) block to replace the original bottleneck block, which effectively simplifies the structure of the block and achieves the lightweight model. Finally, the performance of YOLO-FMDI is evaluated through a large number of ablation and comparison experiments. Notably, compared with the original YOLOv8n, our model reduces the parameters, GFLOPs, and model size by 18.2%, 6.1%, and 15.9%, respectively, improving the mean average precision (mAP50) by 1.2%. These findings emphasize the excellent performance of our proposed model for tomato pest and disease detection, which provides a lightweight and high-precision solution for vegetable cultivation applications.

Improving the ambiguity resolution with the consideration of unmodeled errors in GNSS medium and long baselines

Abstract Ambiguity resolution (AR) is fundamental to achieve high-precision solution in GNSS (Global Navigation Satellite System) relative positioning. Extensive research has shown that systematic errors are associated with the performance of AR. However, due to the physical complexity, some systematic errors would inevitably remain in the observation equations even after processed with some popular models and parameterization. In the medium and long baselines, these unmodeled errors are the leading cause of the slow or even incorrect fixation of ambiguity. Therefore, to improve the AR performance in the medium and long baselines, we present a procedure with the careful consideration of unmodeled errors. At first, we develop a method to estimate the unmodeled errors based on the float ambiguity bias. Then, the overall procedure and key steps to fix the float solutions corrected by the unmodeled error estimate is designed. Finally, some real-measured baselines (from 68 km to 120 km) are utilized to validate the proposed procedure. The experimental results are analyzed and discussed from the aspects of AR and positioning, respectively. For the AR performance, the time required for the first fixation have been reduced by about 41.58% to 83.51%, from 12 to 100 min. Besides, 12.72% to 48.59% and 2.96% to 36.28% improvements of the ambiguity-fixed rate and the ambiguity-correct rate can be respectively obtained in the four baselines. As for the positioning performance, the mean values and RMSEs have improved by 0.2 to 4.8 cm (1.63% to 22.43%) and 0.2 to 2.8 cm (1.47% to 10.57%), respectively.

Development and application of data-driven CHF model in system analysis code

In nuclear reactor systems, when the fuel rods reach the critical heat flux (CHF), a sharp increase in fuel temperature occurs due to a drastic reduction in heat transfer capacity, thus posing a considerable risk to the reactor’s safety. Consequently, accurate CHF prediction holds paramount importance in accurately simulating accident scenarios and enhancing the overall safety of reactor systems. To tackle the challenge of limited prediction accuracy in existing CHF models, this study initially established a comprehensive database by utilizing available experimental data and lookup tables. Subsequently, various methodologies, including the Back Propagation Neural Network (BPNN), Random Forest (RF), and Physics-Informed Machine Learning (PIML), were employed to develop multiple CHF prediction models, and their performance was thoroughly evaluated. Furthermore, the optimal CHF model was integrated into the self-developed analysis code ARSAC, which was then validated using the ORNL-THTF experiment. The results indicated that the BPNN-based model not only demonstrated exceptional prediction accuracy but also exhibited rapid calculation speeds. Notably, the average relative error between the experimental data points and the calculation results of the modified code is 3.64%, while for the original code, it is 22.84%. This study effectively leverages the strengths of data-driven approaches, providing a robust technical solution for high-precision, efficient, and adaptive numerical prediction and analysis of reactor accidents.

Numerical simulation of wave propagation by using a hybrid method with an arbitrary order accuracy in both spatial and temporal approximations

This paper introduces an innovative numerical methodology designed to achieve high precision solution of acoustic wave propagation problem in isotropic material. During the temporal discretization process, the Krylov deferred correction (KDC) technique is employed, wherein a new variable is introduced to handle the second-order time derivative in the governing equations. An improved strategy is adopted for precisely implementing boundary conditions. Following this, the arbitrary-order generalized finite difference method (GFDM) is employed to simulate the transformed boundary value problem at each time step, enabling our framework to select the Taylor series expansion order arbitrarily. Ultimately, a hybrid numerical approach for wave problems is developed to achieve arbitrary-order accuracy in both spatial and temporal approximations. The developed scheme undergoes validation through four different numerical experiments in two- or three-dimension, wherein the numerical solutions obtained are compared with either analytical solutions or results from COMSOL software.

Application of back propagation neural network in the analysis of isothermal elastohydrodynamic lubrication

Developing stable and high precision solution methods has always been the most important research topic in the field of elastohydrodynamic lubrication (EHL). The emergence of artificial neural network(ANN) provide new ideas for the research of EHL solving methods. In this article, a model based on back propagation neural network (BPNN) is proposed to obtain the film thickness and pressure distribution of EHL. The model is established to predict the EHL characteristics with the data obtained from numerical calculation, and more numerical data are used to validate the results predicted by the ANN model. The orthogonal experimental method is utilized to tune model’s parameters. In addition, the method of increasing hidden layers is used to enhance the predictive ability of model. Finally, a double layer BPNN is completed that could predict oil pressure and film thickness accurately. And the exploratory research presented in this paper will provide a simple method for predicting lubrication behavior in industrial applications.

Rapid analysis and classification of blue ballpoint pen inks using high-performance thin-layer chromatography and digital color analysis

Despite the rise of digitalization, the scrutiny of paper documents with stamps and signatures remains crucial, constituting 55–60 % of document examination court cases. Developing accessible and robust ink analysis techniques is an ongoing challenge for forensic scientists. To tackle issues related to the identification and classification of dye components in blue ballpoint pens, we have proposed an advanced approach that integrates high-performance thin-layer chromatography (HPTLC), digital color analysis (DCA), and hierarchical cluster analysis (HCA) for a simplified yet effective procedure.The method encompasses multiple development of TLC plates. Initial separation employs methanol:ethanol (4:1, v/v), followed by a second separation using methanol:propan-2-ol:formic acid (1:1:0.1, v/v/v) to achieve cationic dye separation. This TLC development enhances separation selectivity, contributing to a more precise clustering of pen inks. Employing advanced DCA as a processing method for HPTLC, the optimal descriptor for ink classification is identified as the difference between red (R) and green (G) color components. The proposed clustering algorithm achieved 8 distinct ink clusters, showing improved discriminative capability and a reduced variable space compared to well-established or state-of-the-art methods.The approach's robustness was demonstrated across different readout devices, including smartphones and scanners. Reproducibility tests yielded consistent clustering results, confirming the method's reliability. Moreover, the method proved effective in clustering pen ink extracts from paper substrates, showcasing its potential for real sample analysis.This HPTLC-DCA approach presents a simple, cost-effective, and high-precision solution for clustering blue pen inks, significantly enhancing accessibility in high-throughput forensic applications.

Physics-Informed Neural Networks for Solving High-Index Differential-Algebraic Equation Systems Based on Radau Methods

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems, and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems. Recently, the physics-informed neural networks (PINNs) have gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with an improved fully connected neural network structure, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders and time-step sizes of the Radau IIA method affect the accuracy of neural network solutions. For different time-step sizes, the experimental results indicate that utilizing a 5th-order Radau IIA method in the PINN achieves a high level of system accuracy and stability. Specifically, the absolute errors for all differential variables remain as low as 10−6, and the absolute errors for algebraic variables are maintained at 10−5. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems.

Integrated energy system evaluation and optimization based on integrated evaluation model and time-series optimization

Against the backdrop of global attention to climate change and environmental sustainability, the development timing and comprehensive cost of regional renewable energy power generation projects have become a focus of attention. By constructing effective models to evaluate them, it can help promote the healthy development of renewable energy projects. The research aims to quantitatively evaluate the development status of local renewable energy projects by constructing a comprehensive evaluation model, minimize information loss, and improve the accuracy of evaluation results. This study adopted a comprehensive evaluation model that combines Analytic Hierarchy Process (AHP) based on accelerated genetic algorithm, entropy weight method, and ideal point method. Firstly, the subjective weights of the development evaluation indicators for regional renewable energy power generation projects are calculated. Secondly, the entropy weight method is used to analyze the trend of each indicator and obtain objective weights. Finally, combined with the objective weights and the evaluation results calculated using the TOPSIS method, a comprehensive evaluation of renewable energy power generation projects in various regions is conducted. Through analysis, the core indicators of the development level of renewable energy power generation projects in various regions show specific performance, such as Hebei’s evaluation value of 0.4945 in the proportion of comprehensive energy development, and Inner Mongolia’s evaluation value of 0.4045 in the proportion of comprehensive energy installed capacity. Meanwhile, genetic optimization methods exhibit significant advantages in the calculation of optimization schemes compared to dynamic programming methods, possessing strong global search capabilities and high-precision solutions. This study provides a new research method and approach for the evaluation of regional renewable energy power generation projects, demonstrating the practical value and certain advantages of the research method.

Precision prediction of beacon center spot in atmospheric turbulent environments

Atmospheric turbulence in the laser propagation path induces phase distortion and adversely affects the acquisition of the beacon spot center for satellite–ground laser links. We introduce an optical flow method to track multiple feature points and obtain the overall motion state of the light spot to predict the inter-frame offset and offset direction of the center position of the light spot. We combined rough and specific selections to improve the feature point selection and proposed a feature point exit and supplement mechanism suitable for an atmospheric turbulence environment. The algorithm outperformed traditional algorithms in terms of stability and accuracy in experimental verification, providing a simple and high-precision solution for predicting beacon spot centers under turbulent atmospheric conditions.

Programming memristor arrays with arbitrarily high precision for analog computing.

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.

IMAGE PROCESSING AND CNN BASED MANUFACTURING DEFECT DETECTION AND CLASSIFICATION OF FAULTS IN PHOTOVOLTAIC CELLS

Renewable energy resources such as solar energy, biomass, tidal, geothermal, and hydroelectric energy are becoming increasingly important due to their potential to mitigate the negative impacts of climate change and reduce our dependence on finite and polluting fossil fuels. Solar power can provide a clean, sustainable, and reliable source of renewable energy. Important component of solar power generation is the silicon panel and its surface quality is highly related to its robustness and power generation efficiency. Cell breakages resulting from micro-cracks, degradation and shunted areas on cells are proven to cause major issues and these affect the photovoltaic module efficiency and performance. Solar cell defect identification is important because defects in solar cells significantly reduce their efficiency, which in turn affects their power output and lifespan. By identifying and classifying defects during the production of these cells, engineers and researchers can improve the quality control of solar cells, leading to more reliable and efficient solar energy systems. The proposed method in this research paper, utilizes image processing operations such as adaptive Gaussian thresholding, horizontal and vertical line extraction morphological operations, Canny edge detection, K- Means clustering and VGG16 convolutional neural network to identify the defects in solar cells and classify them as defective or non-defective during the manufacturing process itself. Once the defects are classified, the classification data is exported to Excel file and the results are visually represented as labelled images. OpenCV and Keras modules in Python are used to perform the image processing operations which contributes to cost-effective, reduced computation and high-precision solution.

High-precision Solution of Monostatic Radar Cross Section based on Compressive Sensing and QR Decomposition Techniques

In solving the monostatic electromagnetic scattering problem, the traditional improved primary characteristic basis function method (IPCBFM) often encounters difficulties in constructing the reduced matrix due to the long computation time and low accuracy. Therefore, a new method combining the compressed sensing (CS) technique with IPCBFM is proposed and applied to solve the monostatic electromagnetic scattering problem. The proposed method utilizes the characteristic basis functions (CBFs) generated by the IPCBFM to achieve a sparse transformation of the surface-induced currents. Several rows in the impedance matrix and excitation vector are selected as the observation matrix and observation vector. The QR decomposition is adopted as the recovery algorithm to realize the recovery of surface-induced currents. Numerical simulations are performed for cylinder, cube, and almond models, and the results show that the new method has higher solution accuracy, shorter computation time, and stronger solution stability than the traditional IPCBFM. It is worth mentioning that the new method reduces the recovery matrix size and the number of CBFs quantitatively, and provides a novel solution for solving monostatic RCS of complex targets.

LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach.

Drug development is a challenging and costly process, yet it plays a crucial role in improving healthcare outcomes. Drug development requires extensive research and testing to meet the demands for economic efficiency, cures, and pain relief. Drug development is a vital research area that necessitates innovation and collaboration to achieve significant breakthroughs. Computer-aided drug design provides a promising avenue for drug discovery and development by reducing costs and improving the efficiency of drug design and testing. In this study, a novel model, namely LSTM-SAGDTA, capable of accurately predicting drug-target binding affinity, was developed. We employed SeqVec for characterizing the protein and utilized the graph neural networks to capture information on drug molecules. By introducing self-attentive graph pooling, the model achieved greater accuracy and efficiency in predicting drug-target binding affinity. Moreover, LSTM-SAGDTA obtained superior accuracy over current state-of-the-art methods only by using less training time. The results of experiments suggest that this method represents a highprecision solution for the DTA predictor.

Numerical analysis of the frequency-dependent Jiles-Atherton hysteresis model using the example of Terfenol-D material

<p>The Jiles-Atherton model has been widely used in describing the hysteretic property of a magnetic material or device. However, the calculation errors are not so easily discovered. With a complex expression, the frequency-dependent Jiles-Atherton model should be solved numerically with appropriate settings. This paper proposes an effective solving method for this model and describes some necessary analysis built on the numerical results. In the numerical method proposed in this manuscript, the anhysteretic magnetization was calculated by the secant method, and the trapezoidal rule was utilized to form the implicit function, which can be calculated by the fixed-point iteration. Compared to the other common methods, the proposed one has a friendly expression and fast computation speed. The Terfenol-D material was taken as an example for the numerical analysis. The feasible region was determined and the commonly used approximation that neglects the term of the magnetic field when calculating the magnetic induction intensity was tested. At last, the required number of sampling points per period was reached to guarantee high precision from analyzing its influence on the computation precision. The proposed numerical method is helpful for high-precision solutions of the frequency-dependent Jiles-Atherton model. The results from the numerical analysis can also help users avoid some incorrect calculations when employing this hysteresis model.</p>