Division Method Research Articles

An Advanced Hardware Implementation of the Trial Division Algorithm for Dividers Utilizing RISC-V ISA

In this paper, we try to design a divider based on RISC-V instruction set architecture (ISA). Since the hardware design of the divider in the ALU is very important for the CPU to implement the arithmetic function. In view of the advantages of the trial division method in the division algorithm and the popularity of the RISC-V ISA, this paper attempts to design a trial division divider utilizing the RISC-V ISA. In this design, the principle of trial division, flow chart, state diagram, RISC-V instructions of division, whole divider module and logic will be designed in detail. To test the function and feasibility, the hardware description is carried out in the Verilog hardware description language (HDL). In this description, four division RISC-V instructions are integrated. Modelsim is used to verify and test the functionality of the divider. The test waveform is used as a result to verify the feasibility of the divider.

Measurement Error Compensation at Strain Area Edge in OFDR Using Local Spectrum Division Method

Measurement Error Compensation at Strain Area Edge in OFDR Using Local Spectrum Division Method

Power allocation optimization strategy for multiple virtual power plants with diversified distributed flexibility resources

AbstractThe virtual power plant integrating the flexible resources in the distribution network can provide additional adjustment capacity for the auxiliary services of distribution network. However, the actual internal situation of distribution network including insufficient adjustable capacity of energy storage, unreasonable power allocation, and voltage overrun leads to the difficulties in optimization scheduling. Therefore, this paper proposes a power allocation optimization strategy of distributed electricity‐H2 virtual power plants (EHVPPs) with aggregated flexible resources. Specifically, a distributed EHVPP division method based on the granular K‐medoids clustering algorithm is proposed to realize the independent autonomy and coordinated interaction between EHVPPs, and in order to quantify the operation and regulation capacity of distributed EHVPPs, an aggregation approach of regulating feasible domains of flexibility resources based on the improved zonotope approximations is developed. Moreover, a power allocation strategy based on the flexibility weight factor is proposed to handle the calculated minimum deviation between the total active output of PV and the dispatching power command, realizing the self‐consistency of distributed EHVPPs. Comparative studies have demonstrated the superior performance of the proposed methodology in economic merits and self‐consistency efficiency.

VISTA: vision improvement via split and reconstruct deep neural network for fundus image quality assessment

AbstractWidespread eye conditions such as cataracts, diabetic retinopathy, and glaucoma impact people worldwide. Ophthalmology uses fundus photography for diagnosing these retinal disorders, but fundus images are prone to image quality challenges. Accurate diagnosis hinges on high-quality fundus images. Therefore, there is a need for image quality assessment methods to evaluate fundus images before diagnosis. Consequently, this paper introduces a deep learning model tailored for fundus images that supports large images. Our division method centres on preserving the original image’s high-resolution features while maintaining low computing and high accuracy. The proposed approach encompasses two fundamental components: an autoencoder model for input image reconstruction and image classification to classify the image quality based on the latent features extracted by the autoencoder, all performed at the original image size, without alteration, before reassembly for decoding networks. Through post hoc interpretability methods, we verified that our model focuses on key elements of fundus image quality. Additionally, an intrinsic interpretability module has been designed into the network that allows decomposing class scores into underlying concepts quality such as brightness or presence of anatomical structures. Experimental results in our model with EyeQ, a fundus image dataset with three categories (Good, Usable, and Rejected) demonstrate that our approach produces competitive outcomes compared to other deep learning-based methods with an overall accuracy of 0.9066, a precision of 0.8843, a recall of 0.8905, and an impressive F1-score of 0.8868. The code is publicly available at https://github.com/saifalkhaldiurv/VISTA_-Image-Quality-Assessment.

A long-term power system state calculation method using sequential power and holomorphic embedding

Traditional Power Flow (PF) calculations struggle to support the data analysis requirements as the power system develops. Therefore, this paper introduces a non-sequential and non-iterative PF calculation method to enhance the computational speed and analytical efficiency of power system state variables over long time scales. Firstly, a Power Sequential Division (PSD) method is proposed, representing the power curve using hierarchical energy with temporal characteristics. Next, based on the Fast and Flexible Holomorphic Embedding (FFHE) method, the paper explores the incremental properties of Voltage Power Series Coefficients (VPSC) and proposes an Incremental Holomorphic Embedding (IHE) method. Finally, by combining PSD and IHE, this study introduces a Power Sequential Flow (PSPF) method for rapid computation of power system state variables over long time scales. Unlike traditional PF methods, PSPF method shifts away from time-series calculations and instead analyzes the distribution of power system state variables over long time scales from an energy perspective. Various test cases are set up to analyze the performance characteristics of IHE and PSPF from multiple perspectives. The results indicate that the proposed methods significantly outperform traditional PF methods in terms of computational speed, adaptability to test cases, and analytical efficiency.

An improved infrared image post-processing method for metals and composites

Post-processing of the infrared thermography image is crucial for nondestructive testing, which will reduce noise and disturbance considerably. In this work, an improved image post-processing method based on the temperature ratio is proposed, namely the Thermal Image Division (TID) method. To validate this method, we carried out experiments on different materials such as composite CFRP (carbon fiber reinforced polymer) plates, 304 stainless steel plates and aluminum alloy samples. The results show the proposed method is pretty useful, significantly improves the signal-to-noise ratio of the image by comparing with other post-processing methods. It’s not only beneficial for identifying the edge of the defect, but also facilitates the detection of smaller defects with deeper depth. The signal-to-noise ratio is improved by nearly 117.5 % from practice. The TID method was applied to the defect size detection of pulse thermography and ultrasonic thermography. The results indicate TID method has good robustness, significantly reduces the error of defect size quantification.

Research on the Markov-Chain State Interval Division Based on Predicted Data Correction

By 2022, the total length of roads in Tibet Autonomous Region reached 121,447 kilometers. Due to the unique geological conditions in Tibet, various natural disasters such as earthquakes, mudslides, landslides, avalanches, and strong winds frequently occur. Along the Sichuan-Tibet Highway alone, over 300 disasters happen each year, significantly impacting the region’s economic development. This study focuses on the complexity and randomness of natural disaster mechanisms and combines Markov chain theory to improve the accuracy of prediction data for mudslides, landslides, and earth subsidence etc. The main method is to modify the state interval of the prediction model parameter-Markov chain based on the distribution of discrete points on the number axis. The following state interval division methods are proposed: (1) If the relative error of the predicted value exceeds 50%, adjust the prediction model. (2) Obtain the lower bound of state E1 by taking the floor value downward. (3) The width of each interval does not need to be uniform. (4) Arrange continuous, dense, and close points on the number line in the same side in batches, and represent a state continuously, dividing it into one suitable interval or batches. Using this method, an improved RMSE of 0.28mm and MAPE of 0.87% were obtained for engineering examples, outperforming other models such as GM(1,1), Verhulst, DGM(2,1) with corresponding RMSE values of 0.86 mm, 0.69 mm, and 1.38 mm, and MAPE values of 2.75%, 2.53%, and 5.99%. The combined prediction results for five sets of data yielded an RMSE of 0.14 mm and MAPE of 0.56%, which are quite close to the results obtained using Markov selection correction with an RMSE of 0.37 mm and MAPE of 1.01%. Furthermore, comparing the four sets of case, the average reduction in RMSE and MAPE is 3.56mm and 1.72%, respectively, demonstrating that this method can further improve the performance of Markov chain prediction.

Anonymous group structure algorithm based on community structure.

A social network is a platform that users can share data through the internet. With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. To mitigate this problem, an anonymous group structure algorithm based on community structure is proposed in this article. At first, a privacy protection scheme model is designed, which can be adjusted dynamically according to the network size and user demand. Secondly, based on the community characteristics, the concept of fuzzy subordinate degree is introduced, then three kinds of community structure mining algorithms are designed: the fuzzy subordinate degree-based algorithm, the improved Kernighan-Lin algorithm, and the enhanced label propagation algorithm. At last, according to the level of privacy, different anonymous graph construction algorithms based on community structure are designed. Furthermore, the simulation experiments show that the three methods of community division can divide the network community effectively. They can be utilized at different privacy levels. In addition, the scheme can satisfy the privacy requirement with minor changes.

Parametric imaging in salivary gland scintigraphy.

Salivary gland scintigraphy (SGS) is an imaging technique to evaluate functional aspects of the salivary glands. First described in 1965, visual analyses of summed images and of time-activity curves generated through regions of interest (ROI) are still the main evaluation tools used in clinical practice. An alternative to ROI-based analysis is the use of parametric images, which are images generated through pixel-by-pixel calculation of parameters from the original frames. In this article, we would like to present some parametric images for SGS studies and how to create and use them. Two images, vascular flow and uptake velocity, were created using the intercept and slope of a linear model of the frames from after the first to fifth minute of acquisition. And two others, excretion fraction and absolute excretion, by subtraction and division methods of the frames before and after sialogogue stimulation. These images allow the visualization of the spatial distribution and heterogeneity of these quantitative parameters, favoring different forms of analysis and helping with image segmentation. After more than a year of using these images in daily routine, our general impression is that they have been very helpful. This article, however, still represents only our early experiences with this technique, and clinical studies are yet needed to better evaluate this method.

A framework for territorial spatial ecological restoration zoning integrating “Carbon neutrality” and “Human-geology-ecology”: Theory and application

Global climate change worsens the imbalance among human environment, geological environment and ecological environment (human-geology-ecology), leading to increasingly prominent ecosystem damage. Constructing a scientifically effective territorial spatial ecological restoration zoning (TSERZ) framework will promote the coordination between nature and human. However, the fundamental role of geological environment in TSERZ has been overlooked, and there is lack of theoretical frameworks and empirical research on TSERZ oriented towards carbon neutrality goal. Therefore, we have proposed a new TSERZ framework by integrating “carbon neutrality” and “human-geology-ecology”, which employs models such as CECM, LCA, IUEMS and ESP to divide primary zones, secondary zones and key areas, and also provides restoration directions for various zones. Taking the typical alpine-gorge region of Yulong County as an example, we conduct empirical research to verify the validity of the TSERZ framework. The findings indicate that, firstly, besides human and ecological factors, geological factors like lithology are indispensable factors for TSERZ, and understanding the dynamic balance relationship of human-geology-ecology is the prerequisite and basis for TSERZ. Secondly, the proposed TSERZ framework emphasizes the fundamental role of carbon source/sink and lithology in TSERZ, constructing the three-level zones division method oriented towards carbon neutrality goal with the research line of “natural background conditions—dominant ecological functions—ecological stress problems”. Thirdly, the framework has been successfully applied to TSERZ in Yulong County, dividing it into 8 primary zones, 17 secondary zones, and 7 types key areas. Given its complex geological conditions, fragile ecological environment and increasingly human interference, Yulong County urgently needs to focus on restoring ecological problems such as rocky desertification and geological disasters to improve carbon sink capacity. This study innovatively constructs a cascading path for TSERZ from theoretical cognition to practical application, which can provide a reference for solving the TSERZ problem in geologically complex regions, especially in alpine-gorge regions.

Urban functional area building carbon emission reduction driven by three-dimensional compact urban forms’ optimization

Previous optimization approaches with regard to the overall urban form can no longer meet the demand for accurately implementing building energy consumption carbon emission reduction at small spatial scales. This study constructed building energy consumption-oriented urban functional area division method using points of point-of-interest (POI), three-dimensional building and roadway data with urban functional areas as units. The method linked POI to buildings, using building volume as an auxiliary parameter to determine the functional attributes of blocks while taking into account the building operational energy consumption. The residential functional areas, commercial functional areas and industrial functional areas, which account for more than 90% of the total building operation energy consumption and the top three in terms of share, are selected as the analysis objects. Geographic detector was applied to analyze the mechanism of carbon metabolism in the compact spatial patterns of these three functional areas. The case study in Xiamen, China reveals that (1) Three-dimensional building compactness is an important factor influencing building energy consumption(normalized difference vegetation index q-value is 0.227); (2) the difference in energy consumption between different compactness classes in residential and commercial functional areas is significant, while not in industrial functional areas; (3) according to the three-dimensional building compactness optimization path of this study, the building operation energy consumption in central Xiamen City could be optimized to reduce by 7% in 2015. Based on the self-created building energy consumption-oriented functional area division method, it is concluded at the functional areas scale that different types of urban functional zones have different optimization methods for three-dimensional building compactness. Such optimization can save construction resources and achieve double carbon more effectively.

A modified cyclic elastoplastic constitutive model considering the variational dynamic recovery term of back stress for FGH95 under asymmetrical cyclic loading

The asymmetric cyclic loading process occurs in aero-engine turbine discs. A constitutive model that accurately describes the cyclic elastoplastic behaviour of the material is important for structural design and low cycle fatigue life prediction of turbine discs. In this paper, the low cycle fatigue test of FGH95 was carried out at 620℃ under 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1.0% and 1.1% strain amplitude with strain ratio equal to 0. The material exhibited cyclic hardening followed by cyclic softening at high strain amplitudes and cyclic softening at low strain amplitudes. The mean stress relaxation rate was similar for each strain amplitude. In addition, the evolution of effective stress and back stress was obtained through the method of internal stress division. Then, the relationship between the change in stress amplitude and the change in internal stress was discussed. The results showed that with cyclic loading, cyclic hardening/softening of FGH95 was affected by the competition mechanism of back and effective stresses. Considering that slip deformation and crystal lattice rotation coexist in plastic deformation, the dynamic recovery term of the Abdel-Karim and Ohno model was used. In order to characterize the different magnitudes of back stress change in materials at different plastic strain intervals, a dynamic recovery term coefficient was introduced to the dynamic recovery term and the critical surface of the back stress. The modified model was used to compare with experimental results. Then, it gives a good description of the material’s mean stress relaxation and strain amplitude variation and gives good agreement on the hysteresis loop.

Machine learning-based estimation of evapotranspiration under adaptation conditions: a case study in Heilongjiang Province, China.

The prediction of evapotranspiration (ET0) is crucial for agricultural ecosystems, irrigation management, and environmental climate regulation. Traditional methods for predicting ET0 require a variety of meteorological parameters. However, obtaining data for these multiple parameters can be challenging, leading to inaccuracies or inability to predict ET0 using traditional methods. This affects decision-making in critical applications such as agricultural irrigation scheduling and water management, consequently impacting the development of agricultural ecosystems. This issue is particularly pronounced in economically underdeveloped regions. Therefore, this paper proposes a machine learning-based evapotranspiration estimation method adapted to evapotranspiration conditions. Compared to traditional methods, our approach relies less on the variety of meteorological parameters and yields higher prediction accuracy. Additionally, we introduce a 'region of evapotranspiration adaptability' division method, which takes into account geographical differences in ET0 prediction. This effectively mitigates the negative impact of anomalies or missing data from individual meteorological stations, making our method more suitable for practical agricultural irrigation and ecosystem water resource management. We validated our approach using meteorological data from 25 stations in Heilongjiang, China. Our results indicate that non-adjacent geographical areas, despite different climatic conditions, can have similar impacts on ET0 prediction. In summary, our method facilitates accurate ET0 prediction, offering new insights for the development of agricultural irrigation and ecosystems, and further contributes to agricultural food supply.

Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin

Abstract In the southwestern Sichuan Basin, the Jurassic Shaximiao Formation encompasses a multitude of working areas, displaying intricate sedimentary traits. Traditional methods of stratigraphic division based on sequence suffer from inherent subjectivity and limitations. This study employs a combined mathematical approach to use the wavelet transform (WT) and the Hilbert–Huang transform (HHT). It decomposes the natural gamma ray (GR) logging curve into energy spectrum plots and wavelet coefficients at different scales, high and low frequency signals at different frequencies, and a set of intrinsic mode function components and residual functions. The study conducted a detailed stratigraphic division of the Jurassic Shaximiao Formation in the southwestern Sichuan Basin using these methods. The WT offers greater resolution for the periodic changes in the base level, whereas the HHT demonstrates a superior correlation with the positions of stratigraphic interfaces. The combined utilization of the continuous wavelet transform, the discrete wavelet transform, and HHT methods has demonstrated encouraging outcomes in the stratigraphic division of the Jurassic Shaximiao Formation. These methods have been shown to enhance the accuracy of stratigraphic division and to reduce the influence of subjective factors. This study presents new insights and approaches for geological data processing, offering significant theoretical and practical implications and novel technical means for oil and gas exploration and development.

Theoretical design and implementation of equal and unequal split ultra-wide band Wilkinson power divider with Chebyshev impedance transform

Abstract In this study, we designed multi-section UWB Wilkinson power dividers for both equal and unequal power divider. The key innovation is successfully matching these power dividers using the Chebyshev method. The proposed method achieves very low insertion losses and high transmission coefficients across the ports. For the three-section equal power divider, Chebyshev polynomials were used to model the reflection coefficient, and the characteristic impedance for each section was calculated symmetrically. The design achieved a fractional bandwidth of 106% for a 20 dB return loss, with an insertion loss of 0.3 dB. For unequal multi-section dividers, power division ratios of 1.5:1 and 2:1 were chosen. Theoretical microwave calculations were used to determine the input and output transmission line impedances. Chebyshev approximation was then applied to design the three-section unequal power dividers. The return losses for these unequal dividers were measured at 17 dB and 15 dB, with fractional bandwidths of 120% and 126%. Both circuits had insertion losses of 0.25 dB. The consistent results from analytical calculations, simulations, and measurements confirm the effectiveness of the Chebyshev method for both equal and unequal power division.

Characteristic investigation of digital control four quadrant electro-hydrostatic actuator with separated hydraulic motor.

The asymmetric electro-hydrostatic actuator (EHA) is a promising distributed hydraulic actuation solution for the more-electric aircraft (MEA). However, the flow asymmetry is a common problem causing the poor position control accuracy and dynamics of EHA. To achieve good flow control in all quadrants and save energy in the assistive quadrants, a digital control four quadrant electro-hydrostatic actuator with a separated hydraulic motor using a novel four-quadrant division principle was proposed in this article. The theoretical model of the proposed EHA has been developed in MATLAB/Simulink and validated in the experiments. The theoretical results indicated that the increased external force allows the proposed EHA to have a constantly and partly linearly and varied motion velocity of the cylinder piston in the resistive and assistive quadrants, and the latter is determined by the specific external forces of 0.5 and 2.8kN, respectively, in the extension and retraction quadrants. Compared with EHA without SHM, in the second and fourth quadrants, the energy dissipation is reduced by 104% and 36.7%, respectively, while the motion velocity of the cylinder piston is reduced by 12.9% and 25.6%, respectively. The theoretical and experimental results indicated that the proposed four quadrants division method effectively corrects the misjudgment of quadrants by using the existing four quadrants division method under the lower external force.

Evaluation of MSA mortar pore structure characteristics in water-saturated curing environment based on Talbot gradation theory

To investigate the impact of aggregate gradation on the pore structure of manufactured sand aggregate (MSA) mortar and its fractal characteristics under water-saturated curing conditions. The static pore structure characteristics of the pore structure of MSA mortar with 10 aggregate gradations were explored. The analysis is based on the Talbot gradation theory, the NMR technique, and fractal theory. The surface area and roughness of aggregate particles contribute to a reduction in the porosity of MSA mortar. The aggregate gradation is quantified using the Talbot index. The correlation characteristics and intrinsic relationships between Talbot index and different pore structure parameters, such as total porosity, pore gradation, pore fractal dimension and permeability of MSA mortar, are analyzed. Two pore division methods are employed to explore the interrelationships among pore types, fractal dimensions and Talbot index. Additionally, A correlation model between Talbot index and permeability is developed to investigate the intrinsic correlation of parameter variations from a microscopic perspective. It is found that aggregate gradation is optimal with a Talbot index of 0.4, leading to the highest number of micropores and the lowest number of larger pores, thereby enhancing resistance to seepage and dissolution. Furthermore, The contribution of pore size, content and type to permeability varies with different aggregate gradations. Overall, this study serves as a valuable guide for the design and durability evaluation of mortar concrete projects in water-saturated curing environments.

A triple population adaptive differential evolution

The Differential Evolution (DE) algorithm is one of the most efficient algorithms for complex numerical optimization. However, the nature of differential mutation and crossover hinders the individuals from a major change and always guides them toward their superior neighbors. There's a lack of useful directional information to help the population escape from early convergence. To solve the above problem, this paper proposes a novel Triple-population-based Adaptive Differential Evolution (TPADE) to enhance the evolutionary efficiency in solving various complex numerical optimization problems. First, a population division method with symmetrical linear reduction is designed to divide the parent population of each iteration into three sub-populations of different sizes, i.e., superior sub-population, medium sub-population, and inferior sub-population. Each sub-population adopts distinct differential mutation and crossover operators to maintain balanced search directions. Second, a superior-trial-preserved selection mechanism is proposed to screen useful directional information to guide the next iteration of evolution. Third, an effective parameter adaptation strategy is designed with the linear population size reduction strategy to avoid redundant search. Experiments are then conducted to show that the TPADE exhibits well performance compared with eleven state-of-the-art DE variants, CEC winners, and their variants on the CEC'2014, CEC'2017, and CEC'2022 benchmark suites. The C++ source code of TPADE can be downloaded from https://github.com/DoubleGong/TPADE.

A reliability analysis method based on the intersection area division of hypersphere and paraboloid

In terms of the approximate failure probability of the limit state function (LSF), First Order Reliability Method (FORM) directly solves the failure probability through the most probable point (MPP), but it can only achieve high accuracy in the LSF with low linearity and non-linearity. Second Order Reliability Method (SORM) performs second-order expansion on the basis of MPP to improve reliability accuracy. However, the approximation may be wrong, and even has a large error in high-dimensional nonlinear problems. In order to get more precise approximation of SORM, this paper proposes an area partition method based on the Intersection Area Division Method (IADM) of Hypersphere and Paraboloid. IADM constructs a hypersphere and an approximate parabolic function of the LSF at MPP. The intersection area of the hypersphere and the approximate paraboloid is defined as the failure region from the geometric of the approximate paraboloid function, and the area of the failure region is calculated by the spherical integral. Finally, the approximation of the failure probability is determined by integrating the ratio of the area to the area of the hypersphere. Several examples proved that IADM is more stable compared with SORM which avoids the generation of wrong solutions and improves the accuracy.

Degradation prediction for mechanical components based on transfer learning and a multistage statistical model

Rolling bearings serve as vital elements in mechanical components; their degradation significantly influences equipment reliability and safety. Previous studies primarily focus on single-stage degradation methods, limiting their applicability for predicting degradation trends across varying conditions and hindering generalization. To address this challenge, we propose a multistage statistical model and a multistage transfer learning based on bidirectional gated recurrent unit with an attention mechanism (MSTL-BGRUA) to predict the multistage degradation trend of bearings under cross-working conditions. Given the nonlinear properties of bearing degradation, we present an online multistage division method to identify degradation characteristics. The degradation features of bearing’s vibration signal are extracted and classified into multiple degradation stages based on a mutation identification policy via the statistical model. Subsequently, a BGRUA model is proposed to balance forward and backward information and assign different weights, achieving accurate prediction for each identified degradation stage. Based on the presented BGRUA, we incorporate an MSTL method for degradation prediction to maintian accuracy under crossing working conditions. Finally, a prognosis framework using both the statistical model and the MSTL-BRGUA model is presented to predict bearing status under different degradation stages. A validation of the presented method is conducted using a vibration dataset, demonstrating its superior predictive performance compared to other advanced degradation prediction methods.