Spline Interpolation Algorithm Research Articles

Research on Intelligent Prefabricated Reinforced Concrete Staircase Lifting Point Setting Method Considering Multidimensional Spatial Constraint Characteristics

Prefabricated reinforced concrete staircases (PC staircases) are prefabricated components that are widely used in prefabricated buildings and are used in large quantities. During the production and construction of a PC staircase, the lifting point setting directly affects the construction safety, construction efficiency, and construction quality. In this paper, we analyze the quality problems and safety risks in the design, production, and construction of PC staircases under the constraints of multidimensional spatial characteristics, clarify the key technical difficulties of prefabricated staircase lifting under the multidimensional spatial and temporal constraints, and analyze the factors that should be considered in the setting of lifting points. In this paper, a prefabricated staircase lifting point setting database is established and a thin-plate spline interpolation algorithm is introduced to expand it. Based on the support vector machine algorithm, the process of optimization is carried out for the kernel function scale parameter and penalty factor, and it is concluded that for every increase of two in the number of cross-validation folds, the percentage reduction in minimum RMSE is 9.4%, 17.8%, and 4.2%, respectively, the percentage increase in the optimization time is 39.7%, 61.8%, and 27.3%, respectively, and a PC staircase lifting point setup method based on the small-sample database is proposed. The number of lifting points and lifting point locations of the PC staircase satisfying the multidimensional spatial feature constraints can be obtained by inputting the five design parameters of the PC staircase, namely, the number of treads, the height of the treads, the width of the treads, the width of the staircase, and the weight of the staircase, into the lifting point setup method proposed in this paper. The reliability of the precast reinforced concrete staircase lifting point setting method proposed in this paper when considering the multidimensional spatial constraint characteristics is verified by the precast staircases in deep shafts for assembly construction at the Chongqing metro station.

Experimental Modeling, Analysis, and Solution of Ethanol Coupling for C4 Olefin Production Based on Cubic Spline Interpolation Algorithm

Experimental Modeling, Analysis, and Solution of Ethanol Coupling for C4 Olefin Production Based on Cubic Spline Interpolation Algorithm

Virtual reconstruction and geometric morphometric analysis of the Kocabaş hominin fossil from Turkey: Implications for taxonomy and evolutionary significance

The Kocabaş specimen comes from a travertine quarry near the homonymous village in the Denizli basin (Turkey). The specimen comprises three main fragments: portions of the right and left parietal and left and right parts of the frontal bone. The fossil was assumed to belong to the Homo erectus s.l. hypodigm by some authors, whereas others see similarities with Middle Pleistocene fossils (Broken Hill 1/Kabwe, Bodo, or Ceprano). Here, we present the first attempt to make a complete reconstruction of the missing medial portion of the frontal bone and a comprehensive geometric morphometric analysis of this bone. We restored the calotte by aligning and mirroring the three preserved fragments. Afterward, we restored the missing portion by applying the thin-plate spline interpolation algorithm of target fossils onto the reconstructed Kocabaş specimen. For the geometric morphometric analyses, we collected 80 landmarks on the frontal bone (11 osteometric points, 14 bilateral curve semilandmarks, and 41 surface semilandmarks). The comparative sample includes 21 fossils from different chronological periods and geographical areas and 30 adult modern humans from different populations. Shape analyses highlighted the presence in Kocabaş of features usually related to Middle Pleistocene Homo, such as a developed supraorbital torus associated with a relatively short frontal squama and reduced post-toral sulcus. Cluster analysis and linear discriminant analysis classification procedure suggest Kocabaş being part of the same taxonomic unit of Eurasian and African Middle Pleistocene Homo. In light of our results, we consider that attributing the Kocabaş hominin to H. erectus s.l. may be unwarranted. Results of our analyses are compatible with different evolutionary scenarios, but a more precise chronological framework is needed for a thorough discussion of the evolutionary significance of this specimen. Future work should clarify its geological age, given uncertainties regarding its stratigraphic provenance.

Clipped seismic record recovery analysis based on the cubic spline interpolation algorithm

Clipped seismic record recovery analysis based on the cubic spline interpolation algorithm

Microwave catalytic pyrolysis of heavy oil: A lump kinetic study approach

Managing heavy oil (HO) has become a vital issue in terms of environmental considerations. Microwave pyrolysis of HO is one of the efficient methods of upgrading and recycling this complex material. In this work, microwave catalytic pyrolysis (MCP) of HO was explored by investigating the effect of heating time (2–10 minutes) and temperature (460–730ºC) on the product yield and quality. The most critical point of this study was utilizing a unique design of SiC foam@HZSM-5 as a microwave absorber and catalyst simultaneously. This special design could eliminate the main problem of previous studies: the unwanted temperature gradient inside the reactor, which was misleading the prediction of kinetic parameters by recording the wrong temperature. The yield of each product’s category from the MCP process was reconstructed through a biharmonic spline interpolation (BSI) algorithm to visualize the simultaneous effect of the heating time and temperature on the yield. The optimum temperature for the MCP process was about 600ºC and a heating time of 6 minutes, which yielded the most valuable hydrocarbons in liquid and gas products. The produced liquid oil and non-condensable gases were analyzed through GC-MS and the collected results were categorized based on the properties of the detected compounds. An eight-lump kinetic model was then proposed based on that categorization. The calculated pre-exponential factors (k0) were higher than the expected values, while the apparent activation energies (Ea) were considerably lower than the reported data in the literature, which was due to the non-thermal and thermal effects of microwave irradiation on the reaction mechanism. The validation of the proposed model indicated the capability of this model to predict reliable data under these conditions.

Roller-integrated compaction monitoring and assessment of high and low liquid limit silt subgrade using Green spline interpolation algorithm

Roller-integrated compaction monitoring and assessment of high and low liquid limit silt subgrade using Green spline interpolation algorithm

Objective Evaluation of Pulse Width Using an Array Pulse Diagram.

Pulse width, which can reflect qi, blood excess, and deficiency, has been used for diagnosing diseases and determining the prognosis in traditional Chinese medicine (TCM). This study aimed to devise an objective method to measure the pulse width based on an array pulse diagram for objective diagnosis. The channel 6, the region wherein the pulse wave signal is the strongest, is located in the middle of the pulse sensor array and at the guan position of cunkou during data collection. Therefore, the main wave (h1) time of the pulse wave was collected from the channel 6 through calculation. The left h1 time was collected from the remaining 11 channels. The amplitudes at these time points were extracted as the h1 amplitudes for each channel. However, the pulse width could not be calculated accurately at 12 points. Consequently, a bioharmonic spline interpolation algorithm was used to interpolate the h1 amplitude data obtained from the horizontal and vertical points, yielding 651 (31 × 21) h1 amplitude data. The 651 data points were converted into a heat map to intuitively calculate the pulse width. The pulse width was calculated by multiplying the number of grids on the vertical axis with the unit length of the grid. The pulse width was determined by TCM doctors to verify the pulse width measurement accuracy. Meanwhile, a color Doppler ultrasound examination of the volunteers' radial arteries was performed and the intravascular meridian widths of the radial artery compared with the calculated pulse widths to determine the reliability. The pulse width determined using the maximal h1 amplitude method was comparable with the radial artery intravascular meridian widths measured using color Doppler ultrasound. The h1 amplitude was higher in the high blood pressure group and the pulse width was greater. The pulse width determined using the maximal h1 amplitude was objective and accurate. Comparison between the pulse widths of the normal and high blood pressure groups verified the reliability of the method.

Three-dimensional Visualization of Overhead Transmission Lines with UAV-LiDAR Point Cloud Data

Three-dimensional reconstruction and visualization of overhead transmission lines is an important foundation for grid operation and maintenance management. This paper introduces a 3D visualization method of transmission lines based on the unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) technology, which includes four steps: (I) Using UAV-LiDAR technology to obtain 3D point cloud data of power lines; (II) Using the least-squares method with loss minimization constraints to fit the point cloud data of the power lines, to obtain the accurate fitting function of the power lines; (III) Using the spline interpolation algorithm to draw the 3D scene in the 3D vectors, and combine with building information modeling (BIM) model to construct the transmission line visualization scene. The method of this paper can improve the authenticity and reliability of transmission line simulation and provide technical support for the digital platform of power grid management.

Computer-aided feature recognition of CFRP plates based on real-time strain fields reflected from FBG measured signals

Condition monitoring of critical and expensive carbon fiber reinforced polymer (CFRP) composite infrastructures is extremely essential task for uninterrupted operation and the durability of structures. Therefore, accurate and robust monitoring techniques and correlated damage identification algorithms need to be developed to characterize the health status of the CFRP structures. However, the effectiveness of existing methods is highly dependent on the structural properties of the CFRP plates. To identify the random damages caused by static or dynamic forces, the concept to establish the real-time strain fields of the structures based on measured signals is proposed. A moving surface spline interpolation algorithm based on Green's function has been developed to map the real-time strain fields of the CFRP plate using strain measured by surface-attached FBG sensors. A few static and impact loadings have been applied to a sample CFRP plate and the strain-field prediction by the proposed algorithm has been evaluated by a finite element model. Time and frequency domain analysis has also been conducted to recognize the dynamic response of the CFRP plates. Results indicate that the proposed algorithm can establish real-time strain field predictions with high precision, which can be used to recognize the static and dynamic responses of CFRP plate. Most importantly, the method is independent on the structural properties of the plate and the sensor layout, which is particularly significant for constructing the smart health monitoring system of CFRP.

Microwave target location method based on thediamond NV color center.

We propose a method for microwave target source localization based on the diamond nitrogen vacancy color center. We use coherent population oscillation effect and modulation and demodulation techniques to achieve the detection of microwave intensity of microwave target sources, with a minimum detection intensity of 0.59µW. Positioning of the microwave source was achieved within 50×100c m 2 distance from the system 1m away using the cubic spline interpolation algorithm and minimum mean squared error. The maximum positioning error was 3.5cm. This method provides a new, to the best of our knowledge, idea for the passive localization of microwave targets.

The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea

The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operation) and uses the cubic spline interpolation algorithm to optimize the ship trajectory and construct high-dimensional features. It proposes a new coding method for fishing boat trajectory sequences. This method uses the Geohash algorithm to divide the East China Sea into grids and generate corresponding numbers. Then, the ship trajectory is mapped to the grid, the fishing boat trajectory points are associated with the divided grid, and the ship trajectory ID is extracted from the corresponding grid. The extracted complete trajectory sequence passes through the CBOW (continuous bag of words) model, and the correlation of trajectory points is fully learned. Finally, the fishing boat trajectory is converted from coordinate sequence to trajectory vector, and the processed trajectory sequence is trained by the LightGBM algorithm. In order to obtain the optimal classification effect, the optimal superparameter combination is selected. We put forward a LightGBM algorithm based on the Bayesian optimization algorithm, and obtained the classification results of three kinds of fishing boats. The final result was evaluated using the F1_score. Experimental results show that the F1_score trained with the proposed trajectory vectorization method is the highest, with a training accuracy of 0.925. Compared to XgBoost and CatBoost, the F1_score increased by 1.8% and 1.2%, respectively. The results show that this algorithm demonstrates strong applicability and effectiveness in fishery area evaluations and is significant for strengthening fishery resource management.

Stress Calculation Method Designed of Pipeline in Landslide Section Based on SBAS-Insar and Finite Element Method

The permanent ground displacement of geological disasters is the main reason inducing large additional axial stress of pipelines. This paper presents a surface displacement measurement method above the pipeline based on SBAS-InSAR remote sensing technology, which realizes millimeter-level high-precision monitoring of surface displacement of pipeline in kilometer range. Based on the spatial coordinate data of the pipeline center line and the geometric feature data of the pipeline, combined with the cubic spline interpolation algorithm, a reconstruction method of the pipeline three-dimensional space routing is proposed. Considering the real routing of the pipeline and the process load conditions such as temperature and pressure, the finite element model of pipeline stress analysis is established by using the space pipe element and the nonlinear pipe soil element. Taking the remote sensing landslide displacement as the accurate boundary condition, the calculation method of pipeline stress in landslide section is proposed. The practice of a high-risk landslide section in Liangxiang-Xishatun of Shaanxi-Beijing Pipeline from 2021 to 2022 shows that the maximum relative error between the pipeline stress results obtained by this method and the distributed strain gauge monitoring results does not exceed 16%, which can effectively realize the spatial reconstruction and safety evaluation of the pipeline stress state in the landslide section, and provide a technical basis for the establishment of digital twins of pipeline safety state in future geological disaster sections.

RAPP: A Radio Tomography Localization Method Characterized by Performance Parameterization in Rapid-Moving RFID System

Exploring and developing the perception system is an important means to promote the development of intelligent transportation. In this paper, we propose RAPP, a radio tomography-based method enabling performance parameterization to achieve parameter adjustment of localization accuracy without dense deployment of passive UHF RFID devices. The reader antenna takes a rapid-moving system as the carrier and generates virtual antenna elements to inventory received signal strength (RSS) of the pre-deployed tags. To eliminate the impact of the lack of tag backward signals during the mobile process and supplement the link distribution for each segment of the monitored platform, the Cubic Spline Interpolation algorithm (CSIA) is proposed to homogenize the spacing of the virtual antenna elements by preprocessing the measurements. Then the optimized link information is adopted to perform radio tomographic imaging to recognize the locations of targets. Theoretical analysis on ill-condition is derived to analyze how the number of virtual interpolation elements affects the localization accuracy. Further derivation about the relationship between the Bayesian Cramér-Rao Bound (CRB) and the link density provides a solution to influence the localization performance through parameter adjustment. Extensive simulation and experiment show that RAPP outperforms on multiple targets localization and behaves robustly in a multipath environment.

A novel time-of-flight auto-diagnosis model for ultrasonic signal using a sliding window technique based on empirical mode decomposition

Ultrasonic signal analysis is an essential technique used in various fields, including non-destructive testing (NDT), structural health monitoring, and medical diagnosis. Accurate auto-diagnosis of Time-of-Flight (ToF) for ultrasonic signal is crucial in defects locating, ultrasound imaging, etc. However, requisite signal extraction for ToF estimation was interfered with noise in time and frequency domain, and there is a tendency to be interrupted by spikes in ultrasound signal envelope for wave crest auto-location. In this study, a novel auto-diagnosis model for ultrasonic signals is proposed based on the empirical mode decomposition (EMD) technique and sliding window approach. The model utilizes EMD method to extract intrinsic mode functions (IMFs) and obtain time-frequency representations of the signal. Then the ultrasound signal envelope is extracted by Hilbert transform. The sliding window technique is employed to monitor the time-varying characteristics of the ultrasonic signal and capture the changes in ToF. Spline interpolation algorithm was embedded in the sliding window technique for reducing ToF error and computer burden. The proposed model can automatically estimate ToF for the ultrasonic signal without professional assistance. The performance of the proposed model is evaluated using steel plates with a thickness of 10mm and pipelines with a wall thickness of 7mm. The results demonstrate high accuracy in detecting signal abnormalities, with maximum errors of 0.25% and 1.25% for steel plates and pipelines under constant pressure, respectively. The proposed approach holds promising potential for fully automated applications in various fields in the future, such as structural health monitoring and medical diagnosis.

An Improved Method to Enhance SNR of Non-Stationary Signal Based on Nonuniform Windows Adaptive Modulation for Measurement

It is very important for non-stationary signal processing, signal measurement, and signal enhancement to improve signal-to-noise ratio (SNR) and suppress noise. For complex non-stationary signals measurement, there is still a lack of effective methods to improve the signal-to-noise ratio without damaging original signal. Therefore, it is vital to develop signal processing method to improve SNR of non-stationary signal in many actual industrial measurement. This paper proposes a new method to enhance the SNR for non-stationary signal processing. First, a self-adapting mathematic model is presented to describe the short-time impulse with board spectrum, judge the non-stationary part of the signal, and determine the length of time window based on itself. Then, for the non-stationary signals in this short time window, the improved self-adaptive modulation is used to remove the non-stationary components and retain the stationary components. Third, the stationary component of the short-time window and the normal part of the original signal are connected via cubic spline interpolation algorithm. Finally, this paper analyzes the range of SNR improvement and measurement accuracy of the new method. Based on the results of experiment, the proposed method can effectively improve the signal-to-noise ratio of non-stationary signals and suppress noise, which is of great significance for measurement.

Analysis on the temporal and spatial characteristics of the shallow soil temperature of the Qinghai-Tibet Plateau

Shallow soil refers to the soil layer within the 50 cm depth. Shallow soil temperature (ST) directly or indirectly affects many processes in the soil, such as seed germination, plant growth, and water evaporation. Therefore, the study of shallow ST is of great significance in understanding the surface energy, water cycle, ecology and climate change. This work collected observational data from 141 meteorological stations on the Qinghai-Tibet Plateau from 1981 to 2020 and ERA5 reanalysis data, used the “Moving Surface Spline Interpolation Algorithm Based on Green’s Function” and “Fuzzy C-means algorithm”, and analyzed the temporal and spatial change characteristics of ST at different levels. The results showed that 1) the temperature increase of 0–20 cm (the surface layer of the shallow soil) was roughly the same. The average annual ST was 9.15–9.57°, and the interdecadal variabilities were 0.49–0.53 K/10a. The average annual ST of 40 cm (the bottom layer) was 8.69°, and the interdecadal variability reached 0.98 K/10a. 2) Considering the 7 regions, the warming trend was obvious, and there were certain regional differences. The average annual ST in different regions ranged from 5.2 (northeastern Plateau) to 17.1 °C (western Sichuan Plateau), with a difference of nearly 12 K. The standard deviation ranged from 0.40 (western Sichuan Plateau) to 0.61 K (Qiangtang Plateau), with a difference of 0.21 K. 3) The errors of the obtained grid data were basically less than 3%, which were much smaller than the errors obtained from the ERA5 reanalysis data. This work is significant for understanding the characteristics of ST evolution and land‒atmosphere interactions on the Qinghai-Tibet Plateau and provides important data support for improving the underlying surface boundary conditions of models.

UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased

In recent decades, RRT* algorithm has attracted much attention because of its asymptotic optimization. However, the RRT* algorithm still suffers from slow convergence rate and large randomness of search range. To overcome the shortcomings of this algorithm, this paper proposes UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased. Firstly, goal-biased strategy is used to guide the generation of random sampling points, and two mutually alternating random search trees are established by the bi-directional RRT* algorithm to perform the search, thus increasing the convergence rate of the algorithm. Secondly, the number of iterations is greatly reduced by incorporating an modified artificial potential field method into the bi-directional growth tree. In the process of smoothing the paths, a cubic spline interpolation algorithm is applied to optimize the paths to obtain the best trajectory. The combination of the two algorithms improves the direction of new node generation and reduces the path cost. Finally, the algorithm of this paper is compared with Informed-RRT*, Bi-RRT* and improved P-RRT* algorithms, and it enhances the search performance of the growing tree.

Sub-pixel high precision dimensional measurement method for aero-engine hollow turbine blade based on industrial computed tomography image

Aero-engine hollow turbine blades are work under prolonged high temperature, requiring high dimensional accuracy. Blade profile and wall thickness are important parameters to ensure the comprehensive performance of blades, which need to be measured accurately during manufacturing process. In this study, a high accuracy industrial computed tomography (ICT) measuring method was developed based on standard cylindrical pin and ring workpieces of different sizes. Combining ICT with cubic spline interpolation, a sub-pixel accuracy was achieved in measuring the dimension of component. Compared with the traditional and whole-pixel level image measurement method, the cubic spline interpolation algorithm has the advantages of high accuracy in image edge detection and thus high accuracy of dimensional measurement. Further, the technique was employed to measure the profile and wall thickness of a typical aerospace engine turbine blade, and an accuracy higher than 0.015 mm was obtained.

The role of image interpolation in pansharpening

AbstractPansharpening is an efficient way of producing images of higher spectral and spatial fidelity. Since pansharpening aims to generate a spectrally enhanced image with the same spatial detail content of the source panchromatic (PAN) image, the source multispectral (MS) image is upsampled to the size of the source PAN image prior to pansharpening. Several image interpolation algorithms have been proposed for this purpose, which may lead the analysts to a confusion as to which of these algorithms should be used for the best pansharpening performance. Hence, this study aimed to investigate the role of widely used image interpolation algorithms in the quality of the pansharpened images. For this purpose, the nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, interpolation with a polynomial kernel of 23 coefficients (INTERP23) and cubic spline interpolation algorithms were tested through several pansharpening techniques on three test sites with different characteristics. Investigations revealed that upsampling the source MS images with the INTERP23 algorithm resulted in the pansharpened images with the optimum spectral and spatial quality.

A Novel Classification Method with Cubic Spline Interpolation

Classification is the last, and usually the most time-consuming step in recognition. Most recently proposed classification algorithms have adopted machine learning (ML) as the main classification approach, regardless of time consumption. This study proposes a statistical feature classification cubic spline interpolation (FC-CSI) algorithm to classify emotions in speech using a curve fitting technique. FC-CSI is utilized in a speech emotion recognition system (SERS). The idea is to sketch the cubic spline interpolation (CSI) for each audio file in a dataset and the mean cubic spline interpolations (MCSIs) representing each emotion in the dataset. CSI interpolation is generated by connecting the features extracted from each file in the feature extraction phase. The MCSI is generated by connecting the mean features of 70% of the files of each emotion in the dataset. Points on the CSI are considered the new generated features. To classify each audio file according to emotion, the Euclidian distance (ED) is found between each CSI and all MCSIs of all emotions in the dataset. Each audio file is classified according to the nearest MCSI to the CSI representing it. The three datasets used in this work are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Berlin (Emo-DB), and Surrey Audio-Visual Expressed Emotion (SAVEE). The proposed work shows fast classification and high accuracy of results. The classification accuracy, i.e., the proportion of samples assigned to the correct class, using FC-CSI without feature selection (FS), was 69.08%, 92.52%, and 89.1% with RAVDESS, Emo-DB, and SAVEE, respectively. The results of the proposed method were compared to those of a designed neural network called SER-NN. Comparisons were made with and without FS. FC-CSI outperformed SER-NN on Emo-DB and SAVEE, and underperformed on RAVDESS, without using an FS algorithm. It was noticed from experiments that FC-CSI operated faster than the same system utilizing SER-NN.