Iteration Time Research Articles

Richardson Iterative Method for Solving Multi-Linear System with M-Tensor

In this paper, Richardson iterative method is employed to solve M-Equation. In order to guarantee the solution can be found, convergence theorems are established and confirmed numerically. The optimal α, which is a parameter of Richardson iterative method that can provide the best convergence rate, is also determined theoretically and numerically. Furthermore, a theorem establishing the range of initial vector for general splitting methods is extended from the range in past study. To further accelerate the convergence rate, Anderson accelerator and three preconditioners are incorporated into Richardson iterative method. Numerical results reveal that by including these accelerators, the convergence rates are enhanced. Finally, we show that Richardson iterative methods with optimal α perform better than the SOR type methods in past studies in terms of number of iterative steps and CPU time.

Fractal design of 3D-printing mechanical metamaterial undergoing tailorable zero Poisson’s ratio

With the development of mechanical metamaterials, structures with large and nonlinear deformations have attracted great research passions for their increasingly applications. Although it has been studied for decades, the mechanics of conflict between large deformation with non-buckling has not been well understood. In this study, a new type of mechanical metamaterial, of which the large deformation is achieved without buckling, has been proposed to achieve the zero Poisson’s ratio using fractal design. Effects of fractal iteration () and connection angle () on the Poisson’s ratio and mechanical stress-strain behaviors of 3D printing mechanical metamaterial were studied by finite element analysis and experimental tests. Finally, constitutive relationships among loading force, Poisson’s ratio, iteration time and connection angle have been explored and discussed to achieve a large and non-buckling deformation.

Node location of bi-level urban metro-based ground-underground logistics distribution

Metro-based underground logistics (M-ULS) can relieve the pressure of urban traffic and avert the road congestion through coordinating with ground logistics system. A bi-level node location model is proposed based on the ground-underground logistics distribution mode. The cost of delivery and pickup from the perspective of service companies and customers are minimized as the upper and lower objectives. Since the model is belonged to NP-hard problem, the simulated annealing algorithm (SA algorithm) whose new solution generation part was improved according to the characteristics of the model is used to solve the problem. In the case analysis part, the distribution nodes and customer points close to Nanjing Metro Line 3 were selected to verify the validity of the model and algorithm. The result shows that the improved SA algorithm outperformed in taking less iteration times and obtaining better solutions. The total transportation cost of the system can be reduced by 21.7 % with the optimized node location plan, and 63.6 % of the ground transportation distance can be replaced by the underground transportation, such results are also obtained when the cargo demand increases by 1.5 times and 2 times, which verifies the advantages of M-ULS node location in reducing truck emission and total delivery cost.

Fast Fourier ptychographic quantitative phase microscopy for in vitro label-free imaging.

Quantitative phase microscopy (QPM) is indispensable in biomedical research due to its advantages in unlabeled transparent sample thickness quantification and obtaining refractive index information. Fourier ptychographic microscopy (FPM) is among the most promising QPM methods, incorporating multi-angle illumination and iterative phase recovery for high-resolution quantitative phase imaging (QPI) of large cell populations over a wide field of-view (FOV) in a single pass. However, FPM is limited by data redundancy and sequential acquisition strategies, resulting in low imaging efficiency, which in turn limits its real-time application in in vitro label-free imaging. Here, we report a fast QPM based on Fourier ptychography (FQP-FPM), which uses an optimized annular downsampling and parallel acquisition strategy to minimize the amount of data required in the front end and reduce the iteration time of the back-end algorithm (3.3% and 4.4% of conventional FPM, respectively). Theoretical and data redundancy analyses show that FQP-FPM can realize high-throughput quantitative phase reconstruction at thrice the resolution of the coherent diffraction limit by acquiring only ten raw images, providing a precondition for in vitro label-free real-time imaging. The FQP-FPM application was validated for various in vitro label-free live-cell imaging. Cell morphology and subcellular phenomena in different periods were observed with a synthetic aperture of 0.75 NA at a 10× FOV, demonstrating its advantages and application potential for fast high-throughput QPI.

Novel and efficient implementation of multi-level fast multipole indirect BEM for industrial Helmholtz problems

Fast multipole boundary element methods (FMBEM) have been successfully applied to solve ultra-large acoustic problems. In practice, there are still a large number of industrial applications where model sizes are well below a million Degrees of freedom. Thus, it is of practical importance to further improve the solver’s efficiency for such applications. This paper presents a novel formulation of the multi-level fast multipole indirect BEM, which relies on redesigning a conventional FMBEM with considerations from several aspects. A new flexible partition algorithm is proposed to generate a flexible and efficient multi-level structure for fast multipole calculations, which also allows the explicit calculation of the far field contributions. A hybrid and efficient regularization approach for hypersingular integral is investigated and implemented, which significantly reduces the iterative solving time. The use of efficient sparse approximate inverse preconditioners is key for fast convergence. Two strategies for constructing the sparsity pattern are implemented and evaluated. Numerical results show that by bringing the aforementioned techniques all together in one package, the new flexible multi-level fast multipole indirect BEM (f-FMBEM) provides significant improvements on actual simulation time over the conventional BEM and FMBEM solvers for industrial size problems (with a non-dimensional Helmholtz number up to kA∼323) on a modern workstation. To efficiently address larger problems, a more efficient multipole translation should be considered.

Depth Estimation of Moho Discontinuity Layer at 5 BMKG Seismic Stations in East Java Using Receiver Function Method

East Java is one of the areas with a high level of seismicity due to the existence of a subduction zone in the south of Java Island. In addition, there are also active faults and active volcanos in East Java. This study aims to determine the depth of the Moho Discontinuity layer and subduction slab using receiver function method based on the iterative time domain deconvolution. This study was conducted using teleseismic earthquake data with an epicenter distance of 30°–90° from the receiver station and magnitude more than 6 (M≥6). 5 BMKG seismic stations that form a straight line are used, including those in the Bawean Arc (BWJI), Rembang Zone (BAJI), Kendeng Zone (SIJM), Modern Mountain Arc (PPJI), Southern Mountain Zone (GEJI). The depth of the Moho Discontinuity layer at BWJI Station was observed in ranges from 36–38 km, at BAJI Station it ranges from 39–40 km, at SIJM Station it ranges from 39–40 km, at PPJI Station it ranges from 46–48 km, and at GEJI Station it ranges from 33–36 km. In general, the Moho Discontinuity layer in the mountainous region is deeper due to the isostasy effect.

Identification of Moho Discontinuity Depth and Subduction Slab in Bengkulu and South Sumatra Region using Receiver Function Method: A Preliminary Result

Bengkulu and South Sumatra regions are areas with high seismic activity due to faults and subduction zones. This study aims to analyze the depth of the Moho discontinuity layer and subduction slab under 4 BMKG seismic stations that form a perpendicular line of trench in the Bengkulu and South Sumatra areas. The distribution of stations used are Enggano Station (EGSI) in the front arc islands, Manna Station (MNAI) in the front arc basin, Pagar Alam Station (PKSI) in the volcanic-arc zone, and Lahat Station (LHSI) in the proto-volcanic zone. We selected teleseismic earthquake data with a distance of 30°-90° from the stations and magnitudes above 6 (M>6). For the identification of Moho and slab depths, we used analysis of receiver functions with iterative time domain deconvolution and migration of receiver functions with the AK-135 velocity model. The Oceanic Moho layer and the subduction slab under the EGSI Station were identified at depths of 34 km and 23 km respectively, under the MNAI Station the Moho layer and the subduction slab were identified at a depth of 18 km and 87 km respectively, under the PKSI Station the Moho layer and the subduction slab were identified at a depth of 34 km and 129 km respectively, and under the LHSI Station the Moho layer and subduction slab were identified at depths of 35 km and 175 km respectively.

An automated optimization procedure for geometry parameters calibration of two-curvature conical heat source model

Welding is an important joining method in industrial manufacturing, and optimization techniques combined with advanced simulation methods in welding have greatly promoted the development of industries. Modeling the heat transfer phenomenon during the welding process can provide a basis for reliably calculating the temperature and residual stress fields of the weldments. Therefore, accurately describing the geometric parameters of the welding heat source model is crucial. An interactive computational procedure is proposed to achieve efficient automatic parameter calibration, which is combined numerical simulation with optimization algorithm. This procedure defines a novel objective function called the total fusion zone error, which measures the disparity in fusion zone shape. It calculates the fusion zone difference for each element near the weld line between the simulated value and the test value, and sums them up. The particle swarm algorithm (PSO) is then selected for the optimization procedure, with some improvement of continuously compressing the particle's search domain and replacing its un-computed fitness value by the neighboring particle's results to reduce the iterative computation time. Synthetically, the proposed procedure is implemented in ABAQUS software to automatically calculate the objective function during optimization iterations. In a case study, the two-curvature conical heat source model is deduced, its calibrated parameters conclude temperature field results with valuable accuracy and efficiency, through this proposed multi-software cooperation automated optimization process. The method proposed in this paper can achieve the requirement of accurate programmatic solution of heat source model parameters, reducing the dependence of simulation efficiency and accuracy on researchers' experience.

Application of Iterative Elastic SH Reverse Time Migration to Synthetic Ultrasonic Echo Data

The ultrasonic echo technique is widely used in non-destructive testing (NDT) of concrete objects for thickness measurements, geometry determinations and localization of built-in components. To improve ultrasonic imaging of complex concrete structures, we transferred a seismic imaging technique, the Reverse Time Migration (RTM), to NDT in civil engineering. RTM, in contrast to the conventionally used synthetic aperture focusing technique (SAFT) algorithms, considers all wavefield types and thus, can handle complex wave propagations in any direction with no limit on velocity variations and reflector dip. In this paper, we focused on the development, application and evaluation of a two-dimensional elastic RTM algorithm considering horizontally polarized shear (SH) waves only. We applied the elastic SH RTM routine to synthetic ultrasonic echo SH-wave data generated with a concrete model incorporating several steps and circular cavities. As these features can often be found in real-world NDT use cases, their imaging is extremely important. By using elastic SH RTM, we were able to clearly reproduce almost all reflectors inside the concrete model including the vertical step edges and the cross sections of the cavities. We were also capable to show that more features could be mapped compared to SAFT, and that imaging of complex reflectors could be sharpened compared to elastic P-SV (compressional-vertically polarized shear) RTM. Our promising results illustrate that elastic SH RTM has the potential to significantly enhance the reconstruction of challenging concrete structures, representing an important step forward for precise, high-quality ultrasonic NDT in civil engineering.

Nanoarchitectonics on Electrosynthesis and Assembly of Conjugated Metallopolymers.

In contrast to edge-on and face-on orientations, end-on uniaxial conjugated polymers have the theoretical possibility of providing a macroscopic crystalline film. However, their fabrication is insurmountable due to sluggishly thermodynamic equilibrium states. Herein, we report the programmatic pathway to fabricate nanoarchitectonics on end-on uniaxial conjugated metallopolymers by surface-initiated simultaneous electrosynthesis and assembly. Self-assembled monolayer (SAM) with bottom-up oriented electroactive molecules as a temple allows orientation, stacking, and reactive addition of monomers triggered by switching alternative redox reactions as well as crystallization of small molecules. Repeating the same reaction can repair the unreactive site on the SAM and dynamically and statistically ensure maximum iterative coverage with ideal linear coefficients between optical or electrical responses and iterative times. The resulting nanoarchitectonics on uniaxially assembled end-on polymers over centimeter-sized areas have a subnanometer-uniform morphology and exhibit ultrahigh modulus as well as an inorganic indium tin oxides and the highest conductance among conjugated molecular monolayers. Their memristive devices provide quantitative electrical and optical responses as a function of molecular length, bias, and iterative junctions. Precise processing of nanoarchitectonics as an electrically assisted assembly or printing technique can present sophisticated optoelectric functions and dimensional batch-to-batch consistency for micro-sized organic materials and electronics.

Nanoarchitectonics on Electrosynthesis and Assembly of Conjugated Metallopolymers

AbstractIn contrast to edge‐on and face‐on orientations, end‐on uniaxial conjugated polymers have the theoretical possibility of providing a macroscopic crystalline film. However, their fabrication is insurmountable due to sluggishly thermodynamic equilibrium states. Herein, we report the programmatic pathway to fabricate nanoarchitectonics on end‐on uniaxial conjugated metallopolymers by surface‐initiated simultaneous electrosynthesis and assembly. Self‐assembled monolayer (SAM) with bottom‐up oriented electroactive molecules as a temple allows orientation, stacking, and reactive addition of monomers triggered by switching alternative redox reactions as well as crystallization of small molecules. Repeating the same reaction can repair the unreactive site on the SAM and dynamically and statistically ensure maximum iterative coverage with ideal linear coefficients between optical or electrical responses and iterative times. The resulting nanoarchitectonics on uniaxially assembled end‐on polymers over centimeter‐sized areas have a subnanometer‐uniform morphology and exhibit ultrahigh modulus as well as an inorganic indium tin oxides and the highest conductance among conjugated molecular monolayers. Their memristive devices provide quantitative electrical and optical responses as a function of molecular length, bias, and iterative junctions. Precise processing of nanoarchitectonics as an electrically assisted assembly or printing technique can present sophisticated optoelectric functions and dimensional batch‐to‐batch consistency for micro‐sized organic materials and electronics.

Ant colony optimization-based adjusted PID parameters: a proposed method

The ant colony algorithm (ACA) is a heuristic algorithm that resolves the optimality problem by simulating an ant’s foraging process, which finds the shortest path. The connotation of the ACA is to find the optimal solution. The Proportional Integral Derivative (PID) parameter tuning is an essential tool in the control field and includes three parameters, Kp, Ki, and Kd, to achieve the best control effect. Besides, tuning the PID parameters is closely related to finding the “optimal” solution that can be attained based on the feasible combination of the two. This article transforms the PID parameter tuning problem into an ACA that finds the optimal solution called ACA-based PID parameters tuning. Furthermore, PID control is simulated by setting the parameters of ACA, such as ant colony size, iteration times, nodes, paths, path evaluation criteria, pheromone concentration, heuristic function, weight factor, and decision function. Eventually, the two PID controller parameter tuning strategies are compared and analyzed, and the advantages and disadvantages of each are obtained. Compared with the 4:1 attenuation curve method, the proposed method can significantly reduce the MP score of the overshoot of the system, increase the time, and improve the dynamic and steady-state performance of the system, but reduce the steady-state error of the system. Therefore, the feasibility and effectiveness of the proposed method is verified.

CRUSTAL THICKNESS BENEATH UTAKO ABUJA USING RECEIVER FUNCTION ANALYSIS

Understanding the Mohorovicc discontinuity (Moho depth) is crucial in unraveling the Earth’s subsurface structure. In this research, we present the report of the crustal thickness and the ratio of the primary wave to the shear wave velocities (p-to-s) beneath the seismic station located at the Nigerian Geological Survey Agency (NGSA) Abuja using Receiver Function Analysis (RFA). The primary objective of this study is to determine the depth of the Moho boundary and gain insights into the geological properties of the Earth’s crust and upper mantle beneath the receiver station. This analysis focused on teleseismic earthquakes that were recorded at the station between 2020 and 2021, with epicentere distances between 30o and 90o and magnitudes 5.5 or greater. The P wave Receiver Function (PRF) was modeled from iterative time domain deconvolution using the converted phases (Ps) at the Moho interface.

The use of thematic context-based deep learning in discourse expression of sports news

Sports news is a type of discourse that is characterized by a specific vocabulary, style, and tone, and it is typically focused on conveying information about sporting events, athletes, and teams. Thematic context-based deep learning is a powerful approach that can be used to analyze and interpret various forms of natural language, including the discourse expression of sports news. An application model of sign language and lip language recognition based on deep learning is proposed to facilitate people with hearing impairment to easily obtain sports news content. First, the lip language recognition system is constructed; next, MobileNet lightweight network combined with Long-Short Term Memory (LSTM) is used to extract lip reading features. ResNet-50 residual network structure isadopted to extract the features of sign language; finally, the convergence, accuracy, precision and recall of the model are verified respectively. The results show that the loss of training set and test set converges gradually with the increase of iteration times; the lip language recognition model and the sign language recognition model basically tend to be stable after 14 iterations and 12 iterations, respectively, suggesting a better convergence effect of sign language recognition. The accuracy of sign language recognition and lip language recognition is 98.9% and 87.7%, respectively. In sign language recognition, the recognition accuracy of numbers 1, 2, 4, 6 and 8 can reach 100%. In lip language recognition, the recognition accuracy of numbers 2, 3 and 9 is relatively higher. This exploration can facilitate hearing-impaired people to quickly obtain the relevant content in sports news videos, and also provide help for their communication.

Active Distribution Network Reactive Power Optimization Based on an Improved Genetic Particle Swarm Optimization Algorithm

As the distributed generation is gradually connected to the distribution network, it will lead to an increase in node voltage deviation and network loss. The active distribution network reactive power optimization approach based on an improved genetic particle swarm optimization algorithm is proposed in this paper to increase voltage quality and system efficiency. First, a reactive power optimization model for active distribution networks that has the least possible active power loss and voltage variation is developed. Then, the genetic idea is applied to the particle swarm optimization algorithm to improve it and address the issues of the long iteration time of the genetic algorithm and the tendency of the particle swarm optimization algorithm to settle into local optimal solutions. Finally, the enhanced genetic particle swarm optimization algorithm described in this paper is validated using the enhanced IEEE-33 node system. The findings indicate that this strategy can significantly shorten computation times while also increasing calculation precision.

A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP

In view of the known problems of parameter sensitivity, local optimum, and slow convergence in the ant colony optimization (ACO), we aim to improve the performance of the ACO. To solve the traveling salesman problem (TSP) quickly with accurate results, we propose a fully parallel ACO (FP-ACO). Based on the max–min ant system (MMAS), we initiate a compensation mechanism for pheromone to constrain its value, guarantee the correctness of results and avoid a local optimum, and further enhance the convergence ability of ACO. Moreover, based on the compute unified device architecture (CUDA), the ACO is implemented as a kernel function on a graphics processing unit (GPU), which shortens the running time of massive iterations. Combined with the roulette wheel selection mechanism, FP-ACO has powerful search capabilities and is committed to obtaining better solutions. The experimental results show that, compared with the effective strategies ACO (ESACO) that runs on CPU, the speed-up ratio of the proposed algorithm reaches 35, and the running time is less than that of the max–min ant system-roulette wheel method-bitmask tabu (MMAS-RWM-BT) that runs on GPU. Furthermore, our algorithm outperforms the other two algorithms in the speed-up ratio and less runtime, proving that the proposed FP-ACO is more suitable for solving TSP.

Relevance gradient descent for parameter optimization of image enhancement

Machine learning plays a pivotal role in constructing learning models and leveraging abundant image data for feature extraction, which is essential for achieving accurate and efficient image enhancement through optimization algorithms. Traditional gradient descent methods are confined to optimizing independent parameters, while recent algorithms neglect the interdependence among parameters, resulting in prolonged iteration times for each method. To address this issue, we propose a novel Correlation-Based Gradient Descent (RGD) algorithm tailored for enhancing low-light images. RGD leverages inter-iteration results for mutual parameter transmission and optimization, employing convergence or oscillation criteria to select the final enhanced image and find the optimal parameter combination for image enhancement, thereby enhancing parameter optimization efficiency. Specifically, each iteration is divided into multiple operations, with each operation adopting different enhancement schemes. During each iteration, the results of different operation parameters are shared, which facilitates the discovery of optimal parameters for low-light image enhancement. Subsequently, corresponding enhancement operations are performed based on this parameter combination to optimize standard indicators. Our experiments on medical and natural low-light scenarios demonstrate that our RGD method achieves a significant reduction in iteration time, with at least a 17% decrease compared to exhaustive loop methods.

Enhanced Feature Extraction Network Based on Acoustic Signal Feature Learning for Bearing Fault Diagnosis.

The method of acoustic radiation signal detection not only enables contactless measurement but also provides comprehensive state information during equipment operation. This paper proposes an enhanced feature extraction network (EFEN) for fault diagnosis of rolling bearings based on acoustic signal feature learning. The EFEN network comprises four main components: the data preprocessing module, the information feature selection module (IFSM), the channel attention mechanism module (CAMM), and the convolutional neural network module (CNNM). Firstly, the one-dimensional acoustic signal is transformed into a two-dimensional grayscale image. Then, IFSM utilizes three different-sized convolution filters to process input image data and fuse and assign weights to feature information that can attenuate noise while highlighting effective fault information. Next, a channel attention mechanism module is introduced to assign weights to each channel. Finally, the convolutional neural network (CNN) fault diagnosis module is employed for accurate classification of rolling bearing faults. Experimental results demonstrate that the EFEN network achieves high accuracy in fault diagnosis and effectively detects rolling bearing faults based on acoustic signals. The proposed method achieves an accuracy of 98.52%, surpassing other methods in terms of performance. In comparative analysis of antinoise experiments, the average accuracy remains remarkably high at 96.62%, accompanied by a significantly reduced average iteration time of only 0.25 s. Furthermore, comparative analysis confirms that the proposed algorithm exhibits excellent accuracy and resistance against noise.

High Accuracy and Wide Range Recognition of Micro AR Markers with Dynamic Camera Parameter Control

This paper presents a novel dynamic camera parameter control method for the position and posture estimation of highly miniaturized AR markers (micro AR markers) using a low-cost general camera. The proposed method captures images from the camera at each cycle and detects markers from these images. Subsequently, it performs iterative calculations of the marker’s position and posture to converge them to a specified accuracy while dynamically updating the camera’s zoom, focus, and other parameter values based on the detected marker’s depth distances. For a 10 mm square micro AR marker, the proposed system demonstrated recognition accuracy better than ±1.0% for depth distance and 2.5∘ for posture angle, with a maximum recognition range of 1.0 m. In addition, the iterative calculation time was 0.7 s for the initial detection of the marker. These experimental results indicate that the proposed method and system can be applied to the precise robotic handling of small objects at a low cost.

Integration of a Gas Model Into CFD Analysis for the Simulation of Turbine Exhaust Flows with High Steam Loads

Abstract The Water-Enhanced Turbofan (WET) is a promising future propulsion concept to reduce aero engine emissions. In the WETengine, a heat exchanger uses turbine exhaust heat in order to generate superheated steam out of liquid water. For evaporator design, CFD simulations are necessary since correlation-based predictions have a high uncertainty during preliminary design. A common way of modeling steam loaded flows is the integration of gas models into CFD analysis. However, to the author's knowledge, there is no gas model published that accounts for the exact gas composition of turbine exhaust flows with high steam loads and is commonly used by low- and high-fidelity methods. Therefore, a gas model predicting the thermodynamic behavior of the turbine exhaust flow considering high steam loads is presented and integrated into an existing CFD solver. The approach is able to incorporate the implemented gas model into the CFD simulation by two methods: runtime and offline. The offline method has a computational advantage in iteration time compared to the runtime integration. As demonstration case, a single two dimensional cylinder is considered. A variation of the steam loading of the flow shows a significant effect on local properties and therefore on local and average heat transfer. Increasing the steam loading up to 40 % results in an increase of the average Nusselt number of 17 %.