Computational Efficiency Research Articles

A Sampling-Based Approach to Solve Difficult Path Planning Queries Efficiently in Narrow Environments for Autonomous Ground Vehicles

Path planning is an essential subproblem of autonomous robots’ navigation. Reaching a given goal pose or covering the available space are typical navigation missions, that require different planning approaches. We focus on such problems in this paper, where a goal pose must be reached by a wheeled autonomous ground vehicle in challenging situations, i.e. in complex environments with limited free space. Many path-planning methods are available, from which the sampling-based approaches gained the highest interest due to their computational efficiency. However, the performance of such methods degrades if the free space is limited and narrow passages have to be crossed on the way to the goal. Finding real-time planning methods to deliver high-quality paths in such situations is challenging. This paper aims to take steps toward solving this problem. On the one hand, an approach is presented to characterize free space narrowness and the difficulty of planning tasks. This can be used as a tool to compare planning queries and evaluate the performance of planning methods from the perspective of their sensitivity to environmental narrowness. On the other hand, an improved variant of our previously proposed RTR planner, an incremental sampling-based path-planning method, is introduced that exhibits good performance even in narrow and difficult planning situations. It is shown by simulations that it outperforms the popular RRT and RRT* planners in terms of running time and path quality, and that it is less sensitive to the narrowness of the environment where the planning task has to be solved.

Development of Parallel On-the-Fly Crystal Algorithm for Global Exploration of Conical Intersection Seam Space.

Conical intersection (CI) seams are configuration spaces of a molecular system where two or more (spin) adiabatic electronic states are degenerate in energy. They play essential roles in photochemistry because nonradiative decays often occur near the minima of the seam, i.e., the minimum energy CIs (MECIs). Thus, it is important to explore the CI seams and discover the MECIs. Although various approaches exist for CI seam exploration, most of them are local in nature, requiring reasonable initial guesses of geometries and nuclear gradients during the search. Global search algorithms, on the other hand, are powerful because they can fully sample the configurational space and locate important MECIs missed by local algorithms. However, global algorithms are often computationally expensive for large systems due to their poor scalability with respect to the number of degrees of freedom. To overcome this challenge, we develop the parallel on-the-fly Crystal algorithm to globally explore the CI seam space, taking advantage of its superior scaling behavior. Specifically, Crystal is coupled with on-the-fly evaluations of the excited and ground state energies using multireference electronic structure methods. Meanwhile, the algorithm is parallelized to further boost its computational efficiency. The effectiveness of this new algorithm is tested for three types of molecular photoswitches of significant importance in material and biomedical sciences: photostatin (PST), stilbene, and butadiene. A rudimentary implementation of the algorithm is applied to PST and stilbene, resulting in the discovery of all previously identified MECIs and several new ones. A refined version of the algorithm, combined with a systematic clustering technique, is applied to butadiene, resulting in the identification of an unprecedented number of energetically accessible MECIs. The results demonstrate that the parallel on-the-fly Crystal algorithm is a powerful tool for automated global CI seam exploration.

A Web Data Mining Algorithm Based on Manifold Distance for Mixed Data in Cloud Service Architecture

Due to the complex distribution of web data and frequent updates under the cloud service architecture, the existing methods for global consistency of data ignore the global consistency of distance measurement and the inability to obtain neighborhood information of data. To overcome these problems, we transform the multi-information goal and multi-user demand (constraint conditions) in web data mining into a constrained multi-objective optimization model and solve it by a constrained particle swarm multi-objective optimization algorithm. While we measure the distance between data by manifold distance. In order to make it easier for the constrained multi-objective particle swarm algorithm to solve different types of problems to find an effective solution set closer to the real Pareto front, a new manifold learning algorithm based on the constrained multi-objective particle swarm algorithm is built and used to solve this problem. Experiments results demonstrate that this can improve the service efficiency of cloud computing.

A robust numerical method based on a deep-learning operator for solving the 2D acoustic wave equation

The numerical solution of a wave equation plays a crucial role in computational geophysics problems, which forms the foundation of inverse problems and directly impacts the high-precision imaging results of earth models. However, common numerical methods often lead to significant computational and storage requirements. Due to the heavy reliance on forward-modeling methods in inversion techniques, particularly full-waveform inversion, enhancing the computational efficiency and reducing the storage demands of traditional numerical methods have become key issues in computational geophysics. We develop the deep Lax-Wendroff correction (DeepLWC) method, a deep-learning-based numerical format for solving 2D hyperbolic wave equations. DeepLWC combines the advantages of traditional numerical schemes with a deep neural network. We provide a detailed comparison of this method with the representative traditional Lax-Wendroff correction method. Our numerical results indicate that the DeepLWC significantly improves the calculation speed (by more than 10 times) and reduces the space needed for storage by more than 10,000 times compared with traditional numerical methods. In contrast to the more popular physics-informed neural network method, DeepLWC maximizes the advantages of traditional mathematical methods in solving partial differential equations and uses a new sampling approach, leading to improved accuracy and faster computations. It is particularly worth pointing out that DeepLWC introduces a novel research paradigm for numerical equation solving, which can be combined with various traditional numerical methods, enabling acceleration and reduction in the storage requirements of conventional approaches.

Exposure manipulation strategies for balancing computational efficiency and precision in seismic risk analysis

AbstractExposure models for regional seismic risk assessment often place assets at the centroids of administrative units for which data are available. At best, a top-down approach is followed, where such data are spatially disaggregated over a denser spatial grid, using proxy datasets such as the distribution of population or the density of night-time lights. The resolution of the spatial grid is either dictated by the resolution of the proxy dataset, or by constraints in computational resources. On the other hand, if a building-by-building database is available, it often needs to be aggregated and brought to a resolution that ensures acceptable calculation runtimes and memory demands. Several studies have now investigated the impact of exposure aggregation on loss estimates. Herein, unlike previous attempts, we can leverage upon an extensive building-by-building database for the Swiss territory, which we can use as ground truth. We firstly proceed to assess the aggregation-induced errors of standard risk metrics at different spatial scales. Then a new strategy for performing said aggregation is proposed, relying on a K-means clustering of site parameters and a reduction of the loss ratio uncertainty for aggregated assets. These interventions are designed with the objective of minimizing errors, while keeping the computational cost manageable.

A computational efficient approach for distributionally robust unit commitment with enhanced disjointed layered ambiguity set

AbstractTo achieve the sustainable development of the society, renewable energy dominated power systems are gradually formed. However, the uncertainty of renewable power poses challenges for power system operations, such as balancing the load and generation in day‐ahead unit commitment problems. To address this issue, an enhanced disjointed layered ambiguity set is proposed in this paper to effectively capture the uncertainty of renewable generation forecast errors. On this basis, a two‐stage distributionally robust unit commitment optimization problem is formulated, which is computational challenge. To this end, a novel reformation approach and a worst‐case support set identification method are proposed to derive the optimal solution in a computationally efficient way. Simulation results demonstrate that the proposed ambiguity set brings lower operation costs of unit commitment while maintaining similar reliability compared to existing methods. In detail, the proposed ambiguity set achieves a reduction in operating costs of approximately 10.01%. The proposed novel reformation approach and solution method are also shown to have higher computation efficiency than the existing Benders decomposition method, resulting in a reduction in computation time to less than 3.78% of the original duration.

Dealiased seismic data interpolation by dynamic matching

Interpolation is a critical step in seismic data processing. Gaps in seismic traces can lead to severe spatial aliasing phenomena in the corresponding F-K spectra. The aliasing caused by regularly spaced gaps has similar F-K spectra as those of the actual data. Existing dealiasing interpolation algorithms generally assume that seismic events are linear, and cannot handle non-stationary events. To address this shortcoming, we proposed a novel dealiased seismic data interpolation approach using dynamic matching. First, we matched two adjacent seismic traces using the local affine regional dynamic time-warping algorithm. Subsequently, we calculated the local slope between two seismic traces. Finally, we performed linear interpolation on the regularly missing seismic data using local slope information. The proposed approach was tested on both synthetic and field seismic datasets. The interpolation results showed that the proposed approach has a better anti-aliasing ability and computational efficiency than the traditional Spitz and seislet-based approaches. Additionally, this method can also be applied to interpolate irregularly sampled seismic data and for simultaneous seismic data interpolation and denoising.

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics.

In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data through the NLTK toolkit. This approach is carefully tailored to the prevailing language used in logistics communication. MLEn utilizes innovative methods, including sentiment intensity analysis, Word2Vec, and Doc2Vec, ensuring comprehensive feature extraction. This proves particularly suitable for logistics in e-commerce, capturing nuanced communication essential for efficient operations. Ethical considerations are a cornerstone in logistics communication, and MLEn plays a pivotal role in detecting and categorizing inappropriate language, aligning inherently with ethical norms. Leveraging Tf-IDF and Vader for feature enhancement, MLEn adeptly discerns and labels ethically sensitive content in logistics communication. Across diverse datasets, including Emotions, MLEn consistently achieves impressive accuracy levels ranging from 92% to 97%, establishing its superiority in the logistics context. Particularly, our proposed method, DenseNet-EHO, outperforms BERT by 8% and surpasses other techniques by a 15-25% efficiency. A comprehensive analysis, considering metrics such as precision, recall, F1-score, Ranking Loss, Jaccard Similarity, AUC-ROC, sensitivity, and time complexity, underscores DenseNet-EHO's efficiency, aligning with the practical demands within the logistics track. Our research significantly contributes to enhancing precision, diversity, and computational efficiency in aspect-based sentiment analysis within logistics. By integrating cutting-edge preprocessing, sentiment intensity analysis, and vectorization, MLEn emerges as a robust framework for multi-label datasets, consistently outperforming conventional approaches and giving outstanding precision, accuracy, and efficiency in the logistics field.

Privacy-preserving sports data fusion and prediction with smart devices in distributed environment

With the rapid advancement of sports analytics and fan engagement technologies, the volume and diversity of physique data generated by smart devices across various distributed sports platforms have grown significantly. Extracting insights and enhancing fan experiences from such data offer considerable benefits. Yet, this process unveils two primary challenges. Firstly, efficiently utilizing the vast datasets in sports analytics is daunting due to the complex nature of the sports industry. Secondly, the data collected from diverse sources and stored in distributed platforms contain sensitive information like fan preferences and athlete performance metrics, posing risks of privacy breaches. To address these challenges, we leverage an advanced Locality-Sensitive Hashing technique, known as PSDFPALSH\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{\ ext {ALSH}}$$\\end{document}, tailored for the sports domain. This paper presents a new privacy-preserving method for sports data fusion and prediction in distributed environments, utilizing enhanced Locality-Sensitive Hashing to protect sensitive information while maintaining high data utility. Through extensive experimentation, our approach demonstrates superior performance over existing methods in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and computational efficiency.

Aerodynamic design optimization of a NACA 0012 airfoil: An introductory adjoint discrete tool for educational purposes

The adjoint method is a powerful tool in high-fidelity aerodynamic shape optimization, providing an efficient means to compute derivatives of a target function with respect to various design variables. This paper delves into the discrete adjoint method. It offers a theoretical exploration of its implementation as an innovative tool for calculating partial derivatives (sensitivities) related to objective functions and design variables, specifically applied to a subsonic NACA0012 airfoil. The study conducts a qualitative evaluation using a designated test case, considering specified Mach number and Reynolds number values of 0.297 and 6,667 million, respectively. The Spalart-Allmaras turbulence model is employed to enhance computational cost efficiency. The results affirm the efficacy of the introduced tool, DAFoam, showcasing its ability to generate optimal geometries. The achieved performance optimization is evidenced by minimizing the drag coefficient value (CD) to an impressive 0.0131. While this research does not delve into the post-processing of sensitivity calculations, it acknowledges the potential for future investigations. The primary objective and novelty of this study is to provide the elementary background of the state of the art test case (NACA0012) within the subsonic regime, introducing the pioneer discrete adjoint aerodynamic optimization methodology (DAFoam) with the potential to explore its higher order capabilities in other aerodynamic related studies. Furthermore, it caters the educational needs of both graduate students and engineers in this exciting field. By presenting this cutting-edge methodology, it contributes to future advancements for the aerodynamicists in terms of optimal solutions.

SVM and RF Based Performance Enhancement in Organization: A Study on KGI, Odisha and its Organizational Growth

Organization and organizational growth are two important aspects of a society and its relevant advancement. Now along with organization and its growth, the society advancement becomes most priority for the new age. Technologies that are becoming essential for these growths must have to be inculcated in new age society for complete advancement. Artificial intelligence, machine learning and deep learning are taking industry in a different horizon for complete globalization and adequate smarter than ever before. This paper dives into the organizational growth in particular having studied different aspects of making it even better and implementing different algorithms of AI/ML technologies to provide the best result for organizational growth. The Industry 4.0 has paved the way to build a strong organizational infrastructure for its sustainability in this competitive scenario. It is continuously leveraging different technologies and techniques to strengthen the growth. The next step is to adapt to the Industry 5.0 which works only with efficient robots and quantum computations. Researchers are searching and implementing every possible algorithm to its current infrastructure so as to produce effective results and methodologies that are best suited for fetching higher productivity. Artificial intelligence and Machine learning has produced immense growth in organizational success in recent times. The detailed study of Machine learning based algorithms in organizational growth is rapidly increasing in current years. Basically resource management, staff selection, training, monitoring and data analysis in an organization can be implemented with Machine learning algorithms for its complete growth. The challenge is to choose the proper algorithm from a specific learning technique and properly implementing it to the organization’s different aspects. In this paper basically Support vector machine (SVM) and random forest (RF) algorithms are analyzed and implemented to the specific organization KGI for effective resource management. The result is compared to get best suitable organizational infrastructure that is suited for industry 4.0.

Improvement of augmented free-water flash algorithm based on HV mixing rule and simulated annealing optimization algorithm

The calculation of three-phase equilibrium in CO2-hydrocarbon-water mixtures holds significant importance within numerical simulations, particularly in applications such as CO2-enhanced oil recovery and carbon dioxide sequestration. However, due to the non-ideality of CO2 and the polarity of water, three-phase equilibrium calculations often encounter convergence challenges. The augmented free-water flash algorithm [6], specifically focusing on CO2 dissolution in the aqueous phase, addresses the convergence challenges encountered by traditional three-phase flash algorithms. Despite its accuracy diminishes under conditions of high pressure and high CO2 concentrations, limiting its capability to accurately describe CO2 dissolution in oil-gas-water multiphase systems. The GE (excess Gibbs free energy) type mixing rule can be effectively integrated with equations of state, including PR and SRK, by utilizing activity models like NRTL and UNIFAC, which can be applied to the calculation of thermodynamic parameters for both nonpolar and polar systems and high-pressure complex systems. In this study, based on the augmented free-water assumption, we have developed a new augmented free-water three-phase flash algorithm for CO2/hydrocarbon/water mixtures. This algorithm integrates the PR equation of state with the NRTL activity model, employing the HV mixing rule for CO2-water interactions and the Van der Waals rule for other non-polar components. Furthermore, to enhance computational efficiency, the algorithm incorporates the simulated annealing global optimization algorithm, replacing Newton iteration to more effectively identify the global minima of the complex objective function. The example calculations show that the improved augmented free-water flash algorithm has higher accuracy and efficiency than the traditional augmented free-water flash algorithm. The augmented free-water three-phase flash algorithm proposed in this paper offers a precise model for phase equilibrium calculations essential for numerical simulations in CO2-enhanced oil recovery and carbon dioxide sequestration.

An Incremental Interpolation Scheme With Discrete Cosine Series Expansion for Multimaterial Topology Optimization

Abstract Topology optimization is a powerful tool for structural design, while its computational cost is quite high due to the large number of design variables, especially for multilateral systems. Herein, an incremental interpolation approach with discrete cosine series expansion (DCSE) is established for multilateral topology optimization. A step function with shape coefficients (i.e., ensuring that no extra variables are required as the number of materials increases) and the use of the DCSE together reduces the number of variables (e.g., from 8400 to 120 for the optimization of the clamped–clamped beam with four materials). Remarkably, the proposed approach can effectively bypass the checkerboard problem without using any filter. The enhanced computational efficiency (e.g., a ∼89.2% reduction in computation time from 439.1 s to 47.4 s) of the proposed approach is validated via both 2D and 3D numerical cases.

Stability Evaluation of Slope Based on Global Sensitivity Analysis

The uncertainty of parameters will have a significant impact on slope stability, where sensitivity analysis is a commonly used method in uncertainty research. However, traditional sensitivity analysis method costs much computation time. When calculating the sensitivity index of one parameter, all other parameters are taken as fixed values, and the uncertainty of all parameters cannot be considered simultaneously. Therefore, the variance-based and the moment-independent global sensitivity analysis (GSA) methods are both introduced to determine the influence of geotechnical parameters on slope stability in this study. To solve the importance index of GSA, the least angle regression algorithm, the kernel density estimation, and orthogonal polynomial estimation methods are developed to obtain variance-based importance index and the moment-independent importance index, respectively. The proposed methods allow all variables to change simultaneously within their variation range and have high computational efficiency. The results are in good at with those obtained by the variance-based Monte Carlo simulation method, which is considered as the exact solution forobtaining the importance index. The influence of the correlation between the shear strength parameters (c and φ) on the importance index is also studied, which indicates that the negative correlation will have a great impact on the importance index, which in turn affects the safety assessment of slope. Three engineering cases have been studied for engineering application, and the compared results indicate that the impact of the geotechnical parameters uncertainty on the safety factor (Fs) and failure probability (pf) are different. Therefore, the approaches based on GSA which can integrate the Fs with pf will be a promising approach for slope stability evaluation.

Robust Contact Computation in Non-Rigid Variation Simulation

Abstract Geometric variation is an inevitable element of any fabrication process. To secure the geometric quality of the assembled products, variation simulation is performed to control compliance with the set geometric requirements. In non-rigid variation simulation, contact modeling is used to avoid the virtual penetration of the components in the adjacent areas, enhancing the simulation accuracy. For frictionless contact models, numerical errors and convergence issues due to the deformation behavior of the interacting surfaces are still limiting the computational efficiency of solving this optimization problem. The optimization problem associated with a contact model is often large-scale, and in practice, fast and robust methods for achieving convergence are required. Previous implementations of contact modeling for non-rigid variation simulation have been prominently based on the Iterative or Penalty Methods. In this paper, a quadratic programming approach has been introduced, based on the Lagrangian multiplier method, for robust contact modeling in non-rigid variation simulation, and the performance of the proposed approach has been compared to the previously applied Iterative and Interior Point Method. The methods have been compared on three industrial reference cases, and the convergence and time-efficiency of each method are compared. The results show that robust optimization of the quadratic program associated with the contact model is highly dependent on the reduced stiffness matrix condition. Furthermore, it has been shown that robust and efficient contact modeling in non-rigid variation simulation is achievable through the proposed quadratic programming method.

Advanced Computational Analysis of Cobalt-Based Superalloys through Crystal Plasticity.

This study introduces an advanced computational method aimed at accelerating continuum-scale processes using crystal plasticity approaches to predict mechanical responses in cobalt-based superalloys. The framework integrates two levels, namely, sub-grain and homogenized, at the meso-scale through crystal plasticity finite element (CPFE) platforms. The model is applicable across a temperature range from room temperature up to 900 °C, accommodating various dislocation mechanisms in the microstructure. The sub-grain level explicitly incorporates precipitates and employs a dislocation density-based constitutive model that is size-dependent. In contrast, the homogenized level utilizes an activation energy-based constitutive model, implicitly representing the γ' phase for efficiency in computations. This level considers the effects of composition and morphology on mechanical properties, demonstrating the potential for cobalt-based superalloys to rival nickel-based superalloys. The study aims to investigate the impacts of elements including tungsten, tantalum, titanium, and chromium through the homogenized constitutive model. The model accounts for the locking mechanism to address the cross-slip of screw dislocations at lower temperatures as well as the glide and climb mechanism to simulate diffusions at higher temperatures. The model's validity is established across diverse compositions and morphologies, as well as various temperatures, through comparison with experimental data. This advanced computational framework not only enables accurate predictions of mechanical responses in cobalt-based superalloys across a wide temperature range, but also provides valuable insights into the design and optimization of these materials for high-temperature applications.

A real-time surface defects detection model via dual-branch feature extraction and dynamic multi-scale fusion attention

Surface defect detection is a challenging task in industrial manufacturing, and the detection method based on deep learning has become the mainstream trend in the industry. However, the task of detecting defects on transparent plastic bottles poses unique challenges due to their clear, smooth surfaces and minimal texture, which contribute to specular reflections and make small, low-contrast defects difficult to discern. To tackle these challenges, we introduce the BiD-YOLO framework, an advanced version of the YOLOv5 model, tailored for real-time surface defect detection on such substrates. This framework notably enhances the detection accuracy for small, low-contrast defects and minimizes miss rates. At its core, BiD-YOLO incorporates a dual-branch feature extractor (DBFE) that utilizes both standard, dilated convolutions and a specialized small-object feature extraction branch to capture a broad spectrum of contextual information, optimizing the detection of fine details. Additionally, the dynamic multi-scale pyramid fusion attention (DyMFA) block with coordinate attention (CA) effectively addresses the loss of information in small-scale defects—a typical shortfall in conventional attention mechanisms—thereby improving detection precision across both channel and spatial dimensions. To achieve an optimal balance between accuracy and computational efficiency, we introduce two versions of the dynamic multi-scale pyramid attention block: multi-scale pyramid channel attention and dynamic channel attention. We also curated and annotated the TPSD dataset, a specialized collection of images showcasing surface defects on transparent plastic bottles under diverse real-world production conditions. Together with the NEU-DET dataset, these resources were employed to validate the BiD-YOLO model's effectiveness. Experimental results demonstrate a substantial improvement in defect detection capabilities, achieving mean average precision (mAP) values of 63.1% and 85.6%, respectively. Moreover, the two working models exhibit FPS values of 47.23FPS and 50.21FPS respectively. These findings underscore the BiD-YOLO model's pivotal role in reducing oversight in defect detection, thereby enhancing quality control within industrial manufacturing environments. The BiD-YOLO framework represents a significant advancement in the automated detection of surface defects on transparent plastic bottles, merging innovative technical features with robust practical applicability for industrial use.

Fractional gaussian noise: Spectral density and estimation methods

The fractional Brownian motion (fBm) process, governed by a fractional parameter , is a continuous‐time Gaussian process with its increment being the fractional Gaussian noise (fGn). This article first provides a computationally feasible expression for the spectral density of fGn. This expression enables us to assess the accuracy of a range of approximation methods, including the truncation method, Paxson's approximation, and the Taylor series expansion at the near‐zero frequency. Next, we conduct an extensive Monte Carlo study comparing the finite sample performance and computational cost of alternative estimation methods for under the fGn specification. These methods include two semi‐parametric methods (based on the Taylor series expansion), two versions of the Whittle method (utilising either the computationally feasible expression or Paxson's approximation of the spectral density), a time‐domain maximum likelihood (ML) method (employing a recursive approach for its likelihood calculation), and a change‐of‐frequency method. Special attention is paid to highly anti‐persistent processes with close to zero, which are of empirical relevance to financial volatility modelling. Considering the trade‐off between statistical and computational efficiency, we recommend using either the Whittle ML method based on Paxson's approximation or the time‐domain ML method. We model the log realized volatility dynamics of 40 financial assets in the US market from 2012 to 2019 with fBm. Although all estimation methods suggest rough volatility, the implied degree of roughness varies substantially with the estimation methods, highlighting the importance of understanding the finite sample performance of various estimation methods.

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic systems. Traditional displacement measurement methods cannot meet the requirements of the internal testing environment of the accumulator. Therefore, this paper proposes an accumulator piston displacement signal compensation method based on rotational factors and support vector regression. Firstly, empirical mode decomposition is utilized to denoise the signal. Then, rotational factors are used to generate a delay compensation module to compensate for the signal attenuation and time delay caused by metallic reflection and scattering within the cylinder of the radar signal. The support vector regression model is improved based on a hash table to enhance its computational efficiency and achieve radar displacement signal compensation. Finally, a simulation experiment is designed to verify the effectiveness of the proposed method.

A Kriging Surrogate Model for Ball Grid Array Electronic Packaging With Stochastic Material and Geometrical Parameters

Abstract Ball grid arrays (BGAs) offer significant advantages in the automotive industry, such as their small size and high integration density, making them a promising electronic packaging approach. However, the operating environment of automobiles is more complex compared to other applications, primarily due to vibrations generated by power engines and oscillations caused by pavement roughness. The resonant frequencies of electronic packaging structures play a crucial role in system reliability and safety. However, accurately describing the implicit relationship between system resonant frequencies and material and geometrical parameters can be challenging. A Kriging surrogate model (KSM) is proposed by the combination of the Latin Hypercube stochastic sampling with finite element computation. Four different BGA configurations are established with either the initial values in the deterministic model or the specified sampling interval ranges in the stochastic model. The results of the finite element model (FEM) for BGA electronic packaging are validated and demonstrate qualitative agreement with published literature. The impacts of material and geometrical parameters on the resonant frequencies are investigated and compared. The mean, maximum, minimum, and variance are recorded based on a large database of stochastic samples. The feasibility of KSM for the resonant frequency prediction of BGA is confirmed by its satisfactory accuracy and computational efficiency.