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Understanding the dynamics of bubble growth and collapse during gas–liquid two-phase transient flow is crucial for minimizing pipeline wear and maintaining the normal operation of the pipeline system. This paper focuses on obtaining the bubble trajectory, movement velocity, and volume change of gas bubbles. The bubble images during the transient flow are first denoised and enhanced, then the moving target detection algorithm and watershed segmentation algorithm are used to segment the bubbles, and the contour reconstruction of the bubbles is performed using fitting to extract the relevant parameters. The movement trajectories of the bubbles are then traced by analyzing the trajectories and velocities of growth and collapse of the first bubbles during the transient flow, specifically when the flow rate of the hydraulic pipeline is 0.75 m/s. Finally, the changes in gas content during the transient flow in the pipeline under different initial flow rates are compared between the algorithm proposed here and the simulation results. [ABSTRACT FROM AUTHOR]

Security issues and privacy issues are serious problems facing today's society, especially in image security, where privacy protection plays a pivotal role. To improve the security of images, we propose an image encryption algorithm based on optical chaos and Rubik's cube matrix in this paper. First, optical chaos is generated by constructing an optical device model. Second, in the image encryption algorithm, optical chaos and Rubik's cube matrix are used to encrypt the image at the bit level for the first time, and a "U" type encryption method is designed, and different "U" type encryption schemes are selected to encrypt the image for the second time. Finally, the "four-way diffusion" algorithm is used to diffuse the encrypted image, which further improves the security of the image. The computer simulations and security analysis results both confirm that ciphertext images can resist various common attack means, such as statistical attacks, differential attacks, and brute force attacks. In this paper, the proposed algorithm of decimal conversion, "U" encryption, and "quadrangle diffusion" makes the pixel value and pixel position change greatly, and the ciphertext image loses the original features of the plaintext image, which shows that the algorithm has good security performance and is suitable for image encryptions. [ABSTRACT FROM AUTHOR]

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%). [ABSTRACT FROM AUTHOR]

Steganography is widely recognized as an effective method for protecting information via digital media. This paper presents an innovative image steganography algorithm incorporating image compression, chaotic maps, and the least significant bit. The process begins with the compression of a confidential medical image using Huffman encoding. The compressed image then undergoes shuffling, facilitated by the chaotic logistic map. The bits from the shuffled image are discreetly embedded into randomly selected pixels of the cover image, guided by the chaotic piecewise smooth map. The resulting stego image is generated. Statistical analyses are applied to both the cover and stego images for evaluation. The proposed algorithm is compared against state-of-the-art algorithms, and the results demonstrate its superiority over existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach—an efficient closed-form approximation iterative fitting algorithm based on laser absorption spectra. Through this closed-form approximation iterative fitting, key parameters such as peak value, spectral line width, and normalized signal area serve as indicators for iteration completion, improving the speed without compromising accuracy. Furthermore, it employs the spectral signal of n cycles as a window for further processing, minimizing external interference. The results show that the proposed method averages 9.75 iterations, while the Levenberg–Marquardt fitting method averages 60.17 iterations. The average iteration time for the proposed method is 588.83 ms, a substantial 81.7% reduction compared to the 3210.5 ms required by the Levenberg–Marquardt fitting. These results decisively demonstrate the efficacy of the proposed method in reducing iteration time and enhancing measurement precision. [ABSTRACT FROM AUTHOR]

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user's IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users' preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability. [ABSTRACT FROM AUTHOR]

Eddy current thermography (ECT) is a non-invasive testing method that combines electromagnetic induction and infrared thermography to identify flaws in materials that conduct electricity. However, ECT faces difficulties in accurately locating and classifying defects owing to its low signal-to-noise ratio and complex defect patterns. In this paper, we propose a new method that integrates an improved faster region-convolutional neural network (R-CNN) and an adaptive Canny algorithm to enhance the defect detection performance of ECT. An improved faster R-CNN is a deep neural network that can automatically detect and locate multiple defects in a single ECT image, whereas the adaptive Canny algorithm is an edge detection technique that can identify defect boundaries. The proposed method was tested using a dataset of ECT images with different types of defects. The results demonstrated that our method achieved better accuracy, precision, and speed than existing methods. [ABSTRACT FROM AUTHOR]

The feature graph (Q-H graph) is the best way to intuitively and quantitatively reflect all features of the ventilation network. In this paper, an optimized adaptive genetic algorithm is proposed to solve the problem that rectangular blocks are cut in the process of drawing a Q-H diagram of a three-dimensional ventilation network. The algorithm adopts binary coding based on node sorting and mixed genetic coding based on integer coding. The formulas for calculating the adaptive crossover rate and mutation rate are designed, which can effectively generate new individuals and get rid of the search for local optimal values, ensuring the global optimal solution. Matlab was used to test the optimization effect of the Q-H graph; the results show that, for a complex ventilation network, the improved adaptive genetic algorithm can make the Q-H graph significantly reduce the number of rectangular pieces, which is divided, and make the Q-H graph have better effect to draw clearly and intuitively. [ABSTRACT FROM AUTHOR]

The battery needs to provide a large amount of power in a short time under the condition of a high pulse rate. Real time and accurate State of Charge (SOC) prediction can help the battery management system understand the current status of the battery better, optimize the battery charging and discharging strategy, and improve the efficiency of the battery. In order to prolong battery life and enhance battery safety, a real-time prediction algorithm for SOC of the power battery under a high pulse rate was proposed. The second order RC equivalent circuit is used to establish the model of the battery. The equivalent circuit model of the battery is designed online using the recursive least squares algorithm, and the time-varying parameter model of the battery is established. Its output value is used as the input to the gating recurrent cell neural network, and the neural network is used to output the predicted SOC value. The SOC prediction result is used as the observation vector of the adaptive extended Kalman filter algorithm to obtain the final real-time prediction result of lithium ion power battery SOC. The experimental results show that the parameters identified by the research algorithm for lithium-ion power batteries are as follows: the fluctuation range of ohmic internal resistance is 0.05–0.40 Ω, and the fluctuation range of electrochemical polarization is 0–4.5 F. The terminal voltage values collected by the research algorithm have higher accuracy, with the error being always less than 0.03 V. Moreover, the algorithm can effectively predict the SOC of lithium-ion power batteries in real time, with a maximum average absolute error of about 2%. [ABSTRACT FROM AUTHOR]

The establishment of a resistor network model has become a sharp edge to solve complex scientific problems. In this paper, we introduce Chebyshev polynomials to express the potential formula of the hammock resistor network and improve the general solution of the hammock resistor network. Moreover, through the change in different parameters, special potential formulas are proposed and displayed in 3D dynamic view. A fast algorithm of the calculating potential is given by using the matrix equation model, discrete cosine transform-II, and the fast matrix-vector multiplication. Finally, we show the advantages of our improved potential formula and fast algorithm by the calculation efficiency of the three methods. The modified potential formula and the presented fast algorithm provide a new tool for the field of science and engineering. [ABSTRACT FROM AUTHOR]

An improved version of the mayfly algorithm called the golden annealing crossover-mutation mayfly algorithm (GSASMA) is proposed to address the low convergence efficiency and insufficient search capability of existing mayfly algorithms. First, the speed of individual mayflies is optimized using a simulated annealing algorithm to improve the update rate. The position of individuals is improved using the golden sine algorithm. Second, the impact of using different crossover and mutation methods in the algorithm is compared, and the optimal strategy is selected from the algorithm. To evaluate the performance of the algorithm, simulation experiments were carried out for 10 different test functions, and the results were compared with those of existing algorithms. The simulation results show that the algorithm developed in this paper converges faster and the solutions obtained are closer to the global optimum. Finally, GSASMA was used to optimize a support vector machine (SVM) that was used to identify the P300 signal for five subjects. The experimental results show that the SVM optimized by the algorithm proposed in this paper has higher recognition accuracy than an extreme learning machine. [ABSTRACT FROM AUTHOR]

To solve the problems of noise coverage defect and low contrast between the defect and the background of ZrO2 ceramic bearing balls, a surface defect extraction algorithm based on shearlet transform image enhancement for ZrO2 ceramic bearing balls is proposed. According to the shape characteristics of ceramic bearing balls, the surface defect image acquisition platform is built to collect and analyze surface defect images. Gaussian filtering weakens the scatter-particle noise in the image, and the threshold corrects the coefficient generated by the shearlet transform. After shearlet transform, the relatively low-frequency and high-frequency parts appear. The low-frequency part reflects the edge information of defects, and the high-frequency part reflects the edge and texture information of defects. Thus, the integrity of the defect is ensured, and an enhanced surface defect image is obtained. The gray histogram of the enhanced image is observed. The optimal threshold is selected by the histogram threshold segmentation method, and the process of defects being completely extracted from the background is realized. Experimental results showed that the extraction rates of pits, scratches, and cracks in ZrO2 ceramic bearing balls' surface images are 95.00%, 92.50%, and 92.50%, respectively. [ABSTRACT FROM AUTHOR]

For maneuvering target tracking, the interactive multiple model (IMM) algorithm and its variants have shown good performance, among which the variable structure IMM (VSIMM) algorithm is the most widely studied one. The approximation degree of the algorithm and the matching degree of the model subset will affect the performance of the VSIMM. In addition, considering that smoothing can provide better estimation of the target state, a new adaptive VSIMM filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. First, an accurate model of the VSIMM algorithm is established, in which the IMM is run in parallel with and independently from the different model subsets, and the state estimation of the model subset with the highest probability is selected as the final estimation result. Then, an adaptive VSIMM (AVSIMM) algorithm was designed based on the VSIMM algorithm. The adaptation is reflected in the construction of a new model subset from the original model subsets, which improves the matching degree between the model subset and the actual maneuvering model of the target. Finally, by smoothing the filtering data of the AVSIMM algorithm, the AVSIMMFS algorithm is obtained. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Simulation results show that the tracking performance of the AVSIMMFS algorithm is better than that of other methods. [ABSTRACT FROM AUTHOR]

This document is an erratum for a publication titled "Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm" in the journal AIP Advances. The erratum acknowledges several errors in the original publication, including missing names of components in a figure, a mistake in the placement of another figure, and a correction in a table. The authors assure readers that these corrections do not significantly affect the overall findings and conclusions of the paper. [Extracted from the article]

A study proposed an improved war strategy algorithm called the chaos genetic manifestation war strategy algorithm (CGM-WSO) to extract parameters of solar cell model. The CGM-WSO algorithm incorporates three improvement measures. First, it introduces the logistic–tent chaos map to enable random perturbation during initialization. Second, it utilizes the crossover and mutation operators of the genetic algorithm to enhance the algorithm's ability to escape local optima. Finally, the Lambert W function is employed to obtain explicit analytic expressions of single and double diode models. The performance of the CGM-WSO algorithm was validated using several cell's models, and the results demonstrated significantly improved accuracy, reliability, and convergence speed compared to other algorithms. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel implementation of Shor's algorithm specifically designed for the Graphics Processing Unit (GPU) acceleration framework. Our focus lies on achieving efficient execution of the modular multiplication circuit through GPU simulation. To seamlessly integrate our design into the PyQPanda library framework, we made necessary modifications, making a deliberate trade-off by sacrificing a small number of quantum resources to leverage the advantages of GPU acceleration. Subsequently, we conducted simulations and rigorously validated the functionality of our circuit using the PyQPanda library, resulting in a significant speedup compared to a central processing unit-only mode. [ABSTRACT FROM AUTHOR]

A DC model for silicon carbide (SiC) metal–oxide–semiconductor field effect transistors (MOSFETs) is proposed in this paper using a hybrid modeling method based on the artificial neural network and artificial bee colony (ABC) algorithm. A multi-layer perceptron neural network using the Levenberg–Marquardt (LM) method is applied to model SiC MOSFETs based on the data provided by the datasheet. The search strategy of artificial bees is improved based on the standard ABC, which enhances the search ability of the standard ABC. In view of the sensitivity of the LM method to the initial value, the improved ABC algorithm is adopted to help the neural network find initial weights and biases, which improves the accuracy of the modeling results. Comparing the modeling results with the I–V curves in the datasheet, the accuracy of the DC model is verified under different temperatures. In addition, the small signal parameters gm and gd that are not exposed in the training process also fit well with the datasheet, which fully demonstrates the feasibility of this hybrid modeling method. [ABSTRACT FROM AUTHOR]

Given the lack of a standardized evaluation system for the infrared jamming effectiveness of false targets, this paper first uses a co-saliency detection model to extract the main parts of the true and false targets. Then the perceptual similarity algorithm is improved by combining the operational requirements of false targets in the infrared band. Finally, a background-independent evaluation model for infrared jamming effectiveness of false targets is constructed. The experimental results show that the model can quantitatively reflect the infrared jamming effectiveness of a single false target and distinguish the infrared jamming effectiveness of different types of false targets. In addition, the model has stronger robustness than traditional evaluation models. [ABSTRACT FROM AUTHOR]

In this paper, a method based on Bayesian inference is proposed to conjointly estimate the following two fields of thermophysical parameters. The first is a thermal characteristic time directly linked to the thermal diffusivity and the thickness, whereas the second is the Biot number, which is directly linked to the heat loss and thermal conductivity. This method is robust to noise and leads to very good estimations of the parameters with an algorithm that is very fast and less time consuming than a classical minimization method. At the end of the study, a setup and a methodology are also presented to estimate the average value of the thermal conductivity of an unknown material. [ABSTRACT FROM AUTHOR]

At present, microseismic technology is a widely used method for monitoring the rock burst phenomenon during the construction of deep-buried tunnels. The rock fracture in the tunnel will generate seismic waves. The seismic wave has strong randomness and low energy and is usually mixed with environmental noise, which is called the microseismic signal. The microseismic signal received by the geophone will contain other noises. So, there is an urgent need for an algorithm that can quickly decompose the mixed signal. To solve this problem, this paper proposes a blind source wavelet algorithm to extract the rock fracture signal. First, the signal is preprocessed, and the mixed signal matrix is established. Second, the blind source decomposition algorithm is used to process the signal, and the effective signal is reconstructed. Finally, the wavelet algorithm is used to further remove the noise to enhance the microseismic signal. The proposed method is compared with the wavelet decomposition method and the empirical mode decomposition (EMD) method through the laboratory simulation data and the actual signal of Baihetan Hydropower Station. It is concluded that the proposed algorithm can effectively decompose and remove the noise in the mixed signal, and the decomposition accuracy is better than the wavelet decomposition method and the EMD method. The proposed algorithm has certain practical significance for the identification of microseismic signals of rock fracture and for rock burst early warning. [ABSTRACT FROM AUTHOR]

Of growing concern for the security of many nations are numerous incidents of lost or stolen radioactive sources or materials. The detection of and search for these abnormal radioactive sources plays an important role in monitoring nuclear safety and disposal of nuclear waste. In this paper, a method for autonomously searching for radioactive sources in a flat open rectangular-shaped field through mobile platforms was proposed. In this method, by using the possibility particle filter, the search for radioactive sources was realized according to a series of radiation information measured by the mobile platform carrying a Geiger–Müller counter. According to the inverse square law and the radiation counting governed by Poisson distribution, a radioactive source localization model was constructed. Then, a mobile platform controlled by an information entropy strategy constantly moved within the search area and detected radiation at specific points. The possibility filter algorithm, implemented via the sequential Monte Carlo method, is used to update posterior probability distributions of the source parameters. The performance of the proposed search algorithm, including a comparison with a standard particle filter algorithm, is studied by simulations. The simulation experiment proves that the possibility particle filter algorithm has good robustness. The successful application of the experimental dataset collected in the simulations verifies the measurement model and theoretical consideration. [ABSTRACT FROM AUTHOR]

In order to improve the stability of the second-order magnetic gradient tensor data under magnetic interference, a stability optimization algorithm based on the improved central difference method is proposed in this paper, and a new measuring device is designed according to the new algorithm. In the simulation, the root mean square error (RMSE) of the old and new methods under different noise conditions is studied, and the results show that the proposed method is more stable. In the experiment, the measurement was carried out in a site with complex magnetic interference, and the positioning results were analyzed through the RMSE. The RMSE of the positioning results obtained by the traditional method and the proposed method was (3.3782, 1.3482, 0.3337) and (0.3988, 0.0070, 0.0510), respectively. The simulation and the experiment showed the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

To ensure the speed, recall, and precision of the algorithm to solve the algorithm's measurement failure due to the problem of high underdetermination and the deviation of some outgoing lines from accessible nodes, the ideal resistance method is proposed to diagnose the corrosion of the grounding grid. The information extraction algorithm, a two-way heuristic network resistance diagnosis method, is added. A non-linear multi-objective optimization model of the deviation between the measured potential and the real potential is established. This paper puts forward that the grounding network's fault diagnosis is analogous to a neural network's training process, which makes full use of the excellent robustness and rapidity of a neural network training method. Simultaneously, the algorithm adds the retraining method combined with the field excavation in training and proves its feasibility through the Grobner basis codimension theory in algebraic geometry. Through result verification, it is found that the new algorithm can avoid the inversion failure caused by the algorithm failure due to the lack of accessible nodes and the abnormal part of input data at the same time as a fast solution. [ABSTRACT FROM AUTHOR]

As a non-destructive testing technology, γ-photon imaging technology has a good application prospect in the industrial field. However, due to the γ-photon scattering problem caused by high-density metal and the rapid industrial detection requirement, γ-photon imaging technology has not been effectively applied in industrial detection. Thus, this paper proposed a compensatory single scatter simulation (C-SSS) and combined it with the 3D ordered subset expectation maximization (3D-OSEM) image reconstruction algorithm for optimization to solve the above problems. To verify the effectiveness of the C-SSS algorithm, this paper compared C-SSS with the single scatter simulation by two types of phantoms and the inner cavity detection of industrial components. The comparison results proved that C-SSS can effectively improve the measurement accuracy and reduce the scanning time. In addition, after the algorithm code was optimized and accelerated by the graphic processing unit, the execution time of C-SSS was ∼4 min and a single iteration of 3D-OSEM took ∼3 min, basically satisfying the requirements of rapid industrial detection. Simulation and experiment results show the potential of the proposed algorithm in industrial detection. [ABSTRACT FROM AUTHOR]

This paper studies the sparsity prior to compressed video reconstruction algorithms. An effective non-convex 3DTPV regularization (0 < p < 1) is proposed for sparsity promotion. Based on the augmented Lagrangian reconstruction algorithm, this paper analyzes and compares three non-convex proximity operators for the ℓp-norm function, and numerous simulation results confirmed that the 3DTPV regularization can gain higher video reconstruction quality than the existing convex regularization and is more competitive than the existing video reconstruction algorithms. [ABSTRACT FROM AUTHOR]

Aerodynamic parameters play a decisive role in the ballistic characteristics of the projectile. How to accurately obtain the aerodynamic parameters of the projectile is an important task in the development process of the projectile. In order to further improve the identification accuracy of the projectile drag coefficient, this paper generates huge ballistic data through numerical simulation and uses the extreme learning method to identify the ballistic drag coefficient under three kinds of noise conditions. The method avoids the iterative updating process of weights and thresholds by randomly generating the input weights and threshold values of hidden layer neurons and overcomes the problem of long identification time of the traditional back propagation (BP) neural network algorithm. Based on the least squares principle, the Moore–Penrose generalized inverse matrix of the hidden layer output matrix was solved to determine the optimal output weight of the network, and then, the projectile drag coefficient was accurately identified. Comparing the extreme learning method with the traditional BP neural network method, the results show that the proposed method has higher identification accuracy and faster convergence speed and can effectively identify the projectile drag coefficient, which can meet the practical needs of engineering. [ABSTRACT FROM AUTHOR]

Contact imaging based on MeV energy laser accelerated protons is studied in this paper. First, we show that both external structures and the internal organs of ants can be distinguished with micrometer spatial resolution by proton radiography. Then, we systematically study several specific influence factors and their coupled effects on the spatial resolution of proton radiography, i.e., the accumulated shot number, the proton irradiation dose, the different types of detectors, and the incident proton energy, using two specially designed resolution calibration targets. Under our experimental conditions with MeV protons, the best resolution obtained with the radiochromic film is 20 μm, and that with the solid-state nuclear-track detector (CR39) is 10 μm. In the end, we propose an image deblurring algorithm that uses deconvolution to eliminate the blurring caused by the nonzero spatial extent of the source and multiple Coulomb scattering. This work studies the comprehensive factors of laser accelerated proton radiography with resolution calibration targets and presents incremental additions to previous work on proton radiography. [ABSTRACT FROM AUTHOR]

This study contemplates the Finite Element Method (FEM), a well-known numerical method, to find numerical approximations of the Convection–Diffusion–Reaction (CDR) equation. We concentrate on analyzing the convergence and stability of the nonlinear parabolic partial equations. The method is generally applied without truncating the nonlinear terms and avoiding restrictive assumptions. Regular and irregular geometrical shapes are the key objective of this research paper. This study also focuses on the accuracy and acceptance of the FEM method by utilizing dissipation error, dispersion error, and total error analysis. The results are portrayed both graphically and in a tabular form, which virtually ensures the method's validity and the algorithm's efficiency to sustain the accuracy, simplicity, and applicability for solving nonlinear CDR equations. The proposed technique may also be applied for solving any nonlinear reaction–diffusion equations. [ABSTRACT FROM AUTHOR]

In marine magnetic detection, in order to improve the accuracy of the measurement data of the field, to consider the impact of the storm interference environment, and to design a weakly driven USV tracking control system, it is necessary to drive the USV in a complex marine environment to determine the accuracy and stability of tracking. First, a 3-degrees of freedom mathematical model is built and decoupled by virtue of coordinate transformation to rely only on the absolute geographic velocity. Second, the variable forward-looking distance integral line-of-sight method is used to design the guidance law and stabilize the course tracking error. Moreover, sliding mode control is adopted to design the heading and velocity virtual control law and stabilize the heading angle and longitudinal velocity errors. Third, an adaptive observer is designed by introducing tracking errors to estimate the interference. The algorithm effectively improves the accuracy and reduces the oscillation during convergence. In the end, theoretical analysis and simulation experiments are carried out to confirm whether the proposed method is effective and advanced. [ABSTRACT FROM AUTHOR]

Indoor mobile robots are widely used in modern industry. Traditional motion control methods for robots suffer from discontinuous path curvature, low planning efficiency, and insufficient verification of theoretical algorithms. Therefore, a motion control system for an intelligent indoor robot was designed. By optimizing the radar map detecting and positioning, path planning, and chassis motion control, the performance of the system has been improved. First, a map of the warehouse environment is established, and the number of resampling particles interval is set for the Gmapping building process to improve the efficiency of map construction. Second, an improved A* algorithm is proposed, which converts the path solution with obstacles between two points into the path solution without obstacles between multiple points based on the Rapidly expanding Random Trees and Jump Point Search algorithms and further improves the pathfinding speed and efficiency of the A* algorithm by screening the necessary expansion nodes. The Dynamic Window Approach (DWA) algorithm based on the dynamic window is used to smooth the path, and the target velocity is reasonably assigned according to the kinematic model of the robot to ensure the smooth motion of the chassis. By establishing raster map models of different sizes, the traditional and improved A* pathfinding algorithms are compared and validated. The results illustrate that the improved pathfinding algorithm reduces the computing time by 67% and increases the pathfinding speed by 47% compared with the A* algorithm. Compared with the traditional method, the speed and effect are greatly improved, and the motion control system can meet the requirements of autonomous operation of mobile robots in indoor storage. [ABSTRACT FROM AUTHOR]

Quantitative measurement of small forces and small displacement using holographic optical tweezers (HOTs) is finding increasing applications due to the features of non-contact and high accuracy manipulation. Although hologram optimization algorithms have been widely reported, the holographic optical trapping performance relying on the algorithms has not been studied systematically. In this paper, we investigated the force measuring the performance of various types of HOTs generated with six different hologram algorithms (GSW, GAA, GS, SR, S, and RM). To do this, we built up a HOT instrument and compared the light fields' intensity distribution, trap stiffness, efficiency, and calculation time of multi-point trap arrays generated by six hologram algorithms with this setup. Our work will provide a better understanding of the performance of different hologram algorithms in HOTs. [ABSTRACT FROM AUTHOR]

This paper proposes an effective image encryption algorithm based on the transformational prospective synchronization of a fractional-order hyperchaotic system. Compared with other chaos-based algorithms, fractional orders and synchronization precision are added as secret keys. It is shown that fractional-order, in comparison with its integer counterpart, has bigger complexity and larger key-space. Numerical simulation test results and security analyses demonstrate good performance of the proposed algorithm by encrypting the color image, gray medical image, and binary image. Furthermore, it is found that the synchronization precision accounted for in the decryption process has a significant effect on the decryption resolution. [ABSTRACT FROM AUTHOR]

As a complex system with multiple variables, nonlinearity, and strong coupling, the BLDCM (Brushless Direct Current Motor) has many problems, such as bad parameter tuning, poor adaptability, low control accuracy, and weak anti-interference ability by using the double closed loop traditional PI (Proportional Integral) control algorithm. In order to obtain good control performance, a fuzzy parameter adaptive PI algorithm based on speed loop was designed by combining fuzzy control with traditional PI control. This paper analyzes the mathematical model and operating characteristics of the BLDCM and designs a fuzzy system that takes the deviation e and deviation change rate ec of the reference speed and feedback speed as input and takes the corresponding PI adjustment parameters as output. The step response of the BLDCM at different reference speeds is analyzed. The variable speed response with the initial speed of 4000 r/min under different control algorithms and the changes in the three-phase current, back electromotive force, and electromagnetic torque in this state are compared. The results show that the designed fuzzy parameter adaptive PI algorithm based on the speed loop can make the motor have a faster response time, a smaller overshoot, and a steady-state error when the motor achieves the stable operation. The proposed algorithm also has better control effect, robustness, and stable operation under variable speed conditions. [ABSTRACT FROM AUTHOR]

We present a new method, the solving fractional order dynamical systems using reservoir computing (RC-FODS) algorithm, for solving fractional order nonlinear dynamical systems using deep learning. The method is shown to have advantages over traditional methods, such as less calculation time and higher accuracy. This study also compares the RC-FODS algorithm with the traditional recurrent neural network and echo state network algorithms and finds that it has a higher accuracy and shorter computation time. The accuracy of the method is validated using the largest Lyapunov exponent, and the study also analyzes the advantages and disadvantages of different deep learning models. Our study concludes that the RC-FODS algorithm is a promising method for solving fractional order nonlinear dynamical systems with a high accuracy and low error rate. [ABSTRACT FROM AUTHOR]

The subdivision scheme is used to illustrate smooth curves and surfaces. It is an algorithmic technique which takes a coarse polygon as an input and produces a refined polygon as an output. In this paper, a 10-point interpolating subdivision scheme is used to develop a numerical algorithm for the solution of fourth order nonlinear singularly perturbed boundary value problems (NSPBVPs). The studies of convergence and approximation order of the numerical algorithm are also presented. The solution of NSPBVPs is presented to see the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Stochastic Brownian dynamics is an extremely powerful way to simulate the polymer dynamics in solutions and melts. Mathematically, these models are described by stochastic differential equations. The most challenging problems are the Monte Carlo algorithm, which simulates the motion of a large number of model particles and hence requires an enormous amount of computer time. It is therefore necessary to develop an efficient numerical method in operational emergency response applications. In this paper, we give an improved multilevel Monte Carlo (improved-MLMC) method based on equilibrium control variables at each level to calculate the propagation of polymers. The improved-MLMC method can be shown to result in asymptotically optimal random errors and reduce total cost when compared to the standard Monte Carlo and MLMC methods. Finally, the effect of the Wi number (dimensionless parameter) on the total cost of the presented MLMC method is also discussed in detail. [ABSTRACT FROM AUTHOR]

Doppler radar-based fast heart rate (HR) extraction has great application potential in stress and emotion recognition, anxiety treatments, etc. However, fast extraction of HR is still a great challenge for the vital sign detection applying the traditional discrete Fourier transform (DFT) method based on a continuous-wave (CW) Doppler radar sensor. When applying traditional DFT to the baseband signal analysis, the spectrum resolution will become insufficient if the time window is less than 10 s. In this paper, an interpolated DFT algorithm based on a poly-item cosine window is introduced to achieve fast extraction of HR with only 3 s data length. In addition, the leakage and grid effect phenomenon is presented to show the defects caused by the traditional DFT method. To verify the effectiveness of the proposed method, simulations are performed and experiments are executed using a 10-GHz CW Doppler radar sensor platform. Compared with the traditional DFT method, the interpolated DFT method reduces the average HR error from 8.48% to 1.87% based on the Hanning window and from 8.48% to 1.45% based on the rectangular window. [ABSTRACT FROM AUTHOR]

The improved particle filter (PF) based on the geometric center and likelihood estimation is proposed in order to solve the problem of particle dilution and degradation. In the resampling stage, the geometric center is used to resample the particles. The particles are filtered according to the distance between the particles and the geometric center, and then the particles are resampled. The resampled particles are composed of newborn particles and non-resampled particles. The former can help alleviate the degradation problem, while the latter can keep the diversity of the particle set. In order to ensure effectiveness of the PF, the positioning error threshold of the particle filter is introduced. In the phase of particle weighting calculation, in view of the problem of low accuracy and divergence of PF state estimation caused by non-stationary and non-Gaussian noise, it adopts non-Gaussian noise parameter estimation based on likelihood to approximately estimate the measurement noise instead of the Gaussian density function. The proposed model is applied to particle weight calculation to avoid particle degradation caused by Gaussian density function approximation. The simulation results show that, after the improved algorithm, the root-mean-square error is reduced to 0.085, the variance is reduced to 0.014, and the running time is shortened by 14.8% compared with the polynomial resampling algorithm, which can effectively alleviate particle degradation and dilution in the traditional PF algorithm, and the positioning accuracy is also improved. [ABSTRACT FROM AUTHOR]

The paper describes the improvements and corrections to the method of estimation of correlation dimension of d for high-dimensional signals (HDS). The following problems are described: (a) presentation of the quick version of the algorithm that reduces computation time, (b) improvement of the precision of the elementary Takens-Ellner formula estimating d, (c) the problem of strongly non-gaussian signals and its possible normalisation. Appendix discusses the use of author's Matlab function estimating d which is available for public use. [ABSTRACT FROM AUTHOR]

The development of the computer network makes image files transportation via network become more and more convenient. This paper is concerned with the image encryption algorithm design based on the chaotic S-box mechanism. This paper proposes an Image Encryption algorithm involving both chaotic dynamic S-boxes and DNA sequence operation(IESDNA). The contribution of this paper is three folded: Firstly, we design an external 256-bit key to get large key space; secondly, we design a chaotic system to scramble image pixels; thirdly, DNA sequence operations are adopted to diffuse the scrambled image pixels. Experimental results show that our proposed image encryption algorithm can meet multiple cryptographic criteria and obtain good image encryption effect. [ABSTRACT FROM AUTHOR]

Memristor-based computer architectures are becoming more attractive as a possible choice of hardware for the implementation of neural networks. However, at present, memristor technologies are susceptible to a variety of failure modes, a serious concern in any application where regular access to the hardware may not be expected or even possible. In this study, we investigate whether certain training algorithms may be more resilient to particular hardware failure modes and, therefore, more suitable for use in those applications. We implement two training algorithms—a local update scheme and a genetic algorithm—in a simulated memristor crossbar and compare their ability to train for a simple image classification task as an increasing number of memristors fail to adjust their conductance. We demonstrate that there is a clear distinction between the two algorithms in several measures of the rate of failure to train. [ABSTRACT FROM AUTHOR]

Automated generation of reasonable atomic-level interface models, for example, at a grain boundary, is generally computationally intensive partly because of the three degrees of freedom in a rigid-body translation (RBT) of one side of the interface against the other. We propose an algorithm to obtain reasonable interface models using as few first-principles calculations as possible. The valence charge densities of two surface slabs constituting the interface are calculated using first-principles calculations. The surface charge densities are filtered with an exponential function using a parameter λ to obtain the reaction front. Models where the overlap of filtered charge densities between the two slabs takes a local maximum are adopted as initial models with desirable RBTs, which are then relaxed using first-principles calculations to obtain a reasonable interface model. The proposed algorithm successfully generated reasonable initial models for three out of three orientations in 75% of homointerfaces of body-centered cubic, face-centered cubic, and hexagonal close-packed non-magnetic elementary metals. For the Al {001} Σ5 twist grain boundary, the present algorithm also reproduced γ-surface features of RBTs showing correct displacement shift complete lattice periodicity. Further modifications and improvements to this method are expected to accelerate automated interface model generation from a previously unexplored approach. [ABSTRACT FROM AUTHOR]

The quantum Monte Carlo algorithm can provide significant speedup compared to its classical counterpart. So far, most reported works have utilized Grover's state preparation algorithm. However, this algorithm relies on costly controlled Y rotations to apply the correct amplitudes onto the superposition states. Recently, a comparison-based state preparation method was proposed to reduce computational complexity by avoiding rotation operations. One critical aspect of this method is the generation of the comparison threshold associated with the amplitude of the quantum superposition states. The direct computation of the comparison threshold is often very costly. An alternative is to estimate the threshold with a Taylor approximation. However, Taylor approximations do not work well with heavy-tailed distribution functions such as the Cauchy distribution, which is widely used in applications such as financial modeling. Therefore, a new state preparation method needs to be developed. In this study, an efficient comparison-based state preparation method is proposed for the heavy-tailed Cauchy distribution. Instead of a single Taylor approximation for the entire function domain, this study uses quantum piecewise arithmetic to increase accuracy and reduce computational cost. The proposed piecewise function is in the simplest form to estimate the comparison threshold associated with the amplitudes. Numerical analysis shows that the number of required subdomains increases linearly as the maximum tolerated approximation error decreases exponentially. 197 subdomains are required to keep the error below 1 8192 of the maximum amplitude. Quantum parallelism ensures that the computational complexity of estimating the amplitudes is independent from the number of subdomains. [ABSTRACT FROM AUTHOR]

The blur around the contact points significantly decreases the evaluated static contact angle for superhydrophobic surface which is clearly presented in the paper. To improve the accuracy in the evaluated static contact angle for superhydrophobic surface, an accurate static contact angle algorithm, namely ADSA-PH (axisymmetric drop shape analysis-profile and height), is proposed. It discards the extracted drop edge points close to the contact points and makes use of the residual points and the drop height to determine the static contact angle. The contact angle errors caused by the blur close to the contact points are significantly reduced. The classical ADSA-P algorithm, the modified selected-plane method and the proposed algorithm are used to evaluate static contact angle. The results validate the proposed algorithm. The accuracy in the evaluated contact angle increases with increasing image resolution. To reduce the error caused by a limitation of image resolution, the minimum allowable image resolutions are presented. [ABSTRACT FROM AUTHOR]

An improved peak-selection algorithm is proposed for mesh deformation. With the use of the newly derived block-based recurrence Cholesky (BRC) decomposition scheme, the computational complexity for solving the linear algebraic system in the data reducing procedure is reduced from O ( N c 4 / N p ) to O ( N c 3 ) , where Nc denotes the total number of support nodes and Np denotes the number of support nodes added at a time. Because the BRC decomposition scheme introduces block matrices, it involves more multiplications between matrices rather than between vectors. Due to the fact that the computation of matrix multiplication is more efficient with the use of the linear algebraic library, the efficiency for solving the linear algebraic system can be further increased. Two deformation problems are applied to validate the algorithm. The results show that it significantly increases the efficiency for solving the linear algebraic system, allowing the time consumption of this process to be reduced to only one sixth. Moreover, the efficiency will increase with the mesh scale. The results also show that it allows the efficiency of the data reducing procedure to improve by two times. Furthermore, it is found that only 1.094 s in total is required to solve the linear algebraic system with serial computing by constructing a set of as many as 2999 support nodes in a large-scale mesh deformation problem. It is indicated that the bottleneck of mesh deformation caused by inefficient parallel computing for solving the linear algebraic system can thus be removed. This makes the algorithm favorable for large-scale engineering applications. [ABSTRACT FROM AUTHOR]

A pairwise nuclear fusion algorithm for particle-in-cell simulations for arbitrarily weighted macro-particles at relativistic energies is proposed. When comparing with a recent study by Higginson et al. [J. Comput. Phys. 388, 439 (2019)], our method is also fitted with the widely used Coulomb scattering algorithm by Nanbu and Yonemura [J. Comput. Phys. 145, 639 (1998)] and Sentoku and Kemp [J. Comput. Phys. 227, 6846 (2008)] and can be implemented into a particle-in-cell simulation code with marginal efforts. The algorithm is benchmarked in situations with like-particles, unlike-particles, thermonuclear plasmas, and beam–target fusion. It is shown that only 50–100 macro-particles per cell are needed for the repeatability of fusion yields around 1%. [ABSTRACT FROM AUTHOR]

The acoustic-based detection is regarded as an effective way to detect the internal defects of arc magnets. Variational mode decomposition (VMD) has a significant potential to provide a favorable acoustic signal analysis for such detection. However, the performance of VMD heavily depends on the proper parameter setting. The existing optimization methods for determining the optimal VMD parameter setting still expose shortcomings, including slow convergences, excessive iterations, and local optimum traps. Therefore, a parameter-optimized VMD method using the salp swarm algorithm (SSA) is proposed. In this method, the relationship between the VMD parameters and their decomposition performance is quantified as a fitness function, the minimum value of which indicates the optimal parameter setting. SSA is used to search for such a minimum value from the parameter space. With the optimized parameters, each signal can be decomposed accurately into a series of modes representing signal components. The center frequencies are extracted from the selected modes as feature data, and their identification is performed by random forest. The experimental results demonstrated that the detection accuracy is above 98%. The proposed method has superior performance in the VMD parameter optimization as well as the acoustic-based internal defect detection of arc magnets. [ABSTRACT FROM AUTHOR]

Thermal ionization is the main characteristic of long air gap breakdown. Using the thermal characteristics of the discharge channel can not only analyze the formation and development mechanism of the leader discharge but also better reflect the insulation recovery process of the discharge channel. The background oriented schlieren (BOS) technique is a method generally used for diagnosing flow fields. It can also be used to extract the gradient profile of the refractive index field in long-gap discharge channels, which can then be inverted to yield the temperature field. Based on the BOS, a 1-m rod–plate gap discharge observation system was built and the discharge test was carried out. By optimizing the refractive index reconstruction algorithm, a more accurate temperature field distribution of the discharge channel was obtained. By analyzing the evolution process of the discharge channel, the shock wave radius variation law and insulation recovery time of the channel were obtained, which had a guiding significance for the reclosing time setting. [ABSTRACT FROM AUTHOR]

As an efficient technique, microwave heating is applied for various thermal processes in recent years. Information of temperature field distribution is important to security and stability in the process of microwave heating. In this paper, we investigate a three-dimensional temperature field distribution reconstruction algorithm using ultrasonic thermometry. Four three-dimensional temperature field distribution models with different complex levels are used in the simulation experiments. The error analysis between the temperature field distribution models and the reconstructed ones indicates that the proposed reconstruction algorithm possesses high reconstruction precision. [ABSTRACT FROM AUTHOR]

In order to improve the Heart Sound recognition rate and reduce the recognition time, in this paper, we introduces a new method for Heart Sound pattern recognition by using Heart Sound Texture Map. Based on the Heart Sound model, we give the Heart Sound time-frequency diagram and the Heart Sound Texture Map definition, we study the structure of the Heart Sound Window Function principle and realization method, and then discusses how to use the Heart Sound Window Function and the Short-time Fourier Transform to obtain two-dimensional Heart Sound time-frequency diagram, propose corner correlation recognition algorithm based on the Heart Sound Texture Map according to the characteristics of Heart Sound. The simulation results show that the Heart Sound Window Function compared with the traditional window function makes the first (S1) and the second (S2) Heart Sound texture clearer. And the corner correlation recognition algorithm based on the Heart Sound Texture Map can significantly improve the recognition rate and reduce the expense, which is an effective Heart Sound recognition method. [ABSTRACT FROM AUTHOR]