Weight Coefficients Research Articles

Основные особенности климатических условий зимнего сезона 2023/2024 гг. по данным мониторинга и прогнозов

The main results of the comprehensive analysis of the Northern Hemisphere large-scale atmospheric circulation features are presented for the 2023/2024 winter season. Skill scores of the consensus forecast for the 2023/2024 Northern Eurasia winter season are discussed in the context of reproducing the temperature and humidity regime. The qualitative analysis of the multimodel forecast from WMO’s Lead Center and the consensus forecast issued by NEACOF for seasonal anomalies in air temperature and precipitation for the winter of 2023/2024 resulted in the conclusion of the superior accuracy of the consensus forecast. This forecast was based on the data of three Russian models SL-AV, MGO, and INM with equal weighting coefficients.

Harnessing physics-guided neural networks for tailoring the orbital angular momentum spectrum in second harmonic generation

The management of orbital angular momentum (OAM) in frequency conversion processes is essential for numerous applications such as quantum and classical optical communications. This paper presents a wavefront modulation approach for the fundamental beam in second harmonic generation (SHG) to efficiently control the OAM spectrum. We employ an inverse design method to derive the necessary wavefront shape of the fundamental beam for achieving a desired SHG OAM spectrum. Specifically, we introduce an efficient inverse design technique based on physics-guided neural networks (PGNNs) that incorporates the coupled equations governing SHG, aimed at tailoring the OAM spectrum of SHG. Utilizing the proposed PGNN, we design the phase pattern for a spatial light modulator (SLM) to shape the wavefront of the fundamental beam. Furthermore, we present a novel loss function, to our knowledge, that effectively links the OAM of the SHG spectrum and efficiency to the SLM phase pattern and crystal temperature, independent of empirical weight coefficients. The proposed PGNN facilitates the purification of the SHG OAM spectrum, even when the fundamental beam comprises mixed Laguerre–Gaussian (LG) modes. Additionally, we demonstrate the generation of desired SHG spectra using the proposed PGNN framework. This study introduces what we believe to be a groundbreaking inverse design method for developing photonic devices with customized functionalities, addressing challenges associated with traditional data-driven deep learning techniques.

Automatic kidney stone identification: an adaptive feature-weighted LSTM model based on urine and blood routine analysis.

Kidney stones are the most common urinary system diseases, and early identification is of great significance. The purpose of this study was to use routine urine and blood detection indices to build a deep learning (DL) model to identify the presence of kidney stones in the early stage. A retrospective analysis was conducted on patients with kidney stones who were treated at West China Hospital of Sichuan University from January 2020 to June 2023. A total of 1130 individuals presenting with kidney stones and 1230 healthy subjects were enrolled. The first blood and urine laboratory data of participants at our hospital were collected, and the data were divided into a training dataset (80%) and a verification dataset (20%). Additionally, a long short-term memory (LSTM)-based adaptive feature weighting model was trained for the early identification of kidney stones, and the results were compared with those of other models. The performance of the model was evaluated by the area under the subject working characteristic curve (AUC). The important predictive factors are determined by ranking the characteristic importance of the predictive factors. A total of 17 variables were screened; among the top 4 characteristics according to the weight coefficient in this model, urine WBC, urine occult blood, qualitative urinary protein, and microcyte percentage had high predictive value for kidney stones in patients. The accuracy of the kidney stone (KS-LSTM) learning model was 89.5%, and the AUC was 0.95. Compared with other models, it has better performance. The results show that the KS-LSTM model based on routine urine and blood tests can accurately identify the presence of kidney stones. And provide valuable assistance for clinicians to identify kidney stones in the early stage.

Working performance of vertical spiral stirred mill based on discrete element method

The main structures and characteristic parameters that affect the working performance of vertical spiral stirred mill were analyzed and studied, and a method for selecting key working parameters was proposed. Based on the discrete element method, a simulation model of vertical spiral stirred mill was established, and the effects of spindle speed, agitator lead and grinding media size distribution on grinding performance were analyzed. A comprehensive grinding performance index was proposed, and orthogonal tests were conducted on each parameter to obtain the quantitative value of the grinding effect, and then the optimal working parameter combination under a specific weight coefficient was obtained, which provided a reference method for the optimal design of the mill.

Soil Erosion Susceptibility Prediction Using GIS-Based Multicriteria Analysis and Worldview-3 Satellite Images

Soil erosion has been identified as a major threat to existing soil deposits, which negatively affects ecosystem sustainability, agricultural production and clean water supply. This study covered the eastern part of Kaštela Bay with the aim of developing a model of susceptibility to soil erosion by applying the GIS-based multicriteria analysis, based on nine determinant criteria categorized by the Jenks method in five classes. The largest representation of soil erosion susceptibility was detected on the slopes of the Kozjak and Mosor massifs. According to the calculated weighting coefficients using the analytic hierarchy process (AHP), the slope was the most impactful criterion with 29.9 % importance. It was followed by hydrological criteria of topographic wetness index, standardized precipitation index and LS-factor, with 13.1 % importance each. After the development of the model, zones of very high and high susceptibility to soil erosion were delineated, with a combined coverage of 33.34 % of the study area. These areas were identified as a potential threat from the future occurrence of negative effects of soil erosion, which should be addressed in the future by land policy managers and government managers.

Comprehensive Preview Decision Method for Direction and Speed of Intelligent Vehicle Based on Rules and Learning

Decision-making is a key part of autonomous driving systems. Human-like decision-making in complex scenarios is important for enhancing drivers’ trust in and acceptance of autonomous driving systems. In order to improve their human-like features and their adaptability to complex scenarios, this paper proposes a comprehensive preview decision method for direction and speed based on rules and learning. First, a decision-making structure including maneuvering ability judgment, motion trajectory prediction, safety judgment, legitimacy judgment, comprehensive performance evaluation, and parameters learning is presented. Second, a learning method for the longitudinal safety distance threshold based on a BP neural network is introduced. Third, inverse reinforcement learning (IRL) is used to learn the comprehensive performance evaluation weight coefficient for different driving styles. Finally, the DJI AD4CHE dataset is processed and used to train the parameters. Car-following and lane-changing scenarios are simulated to verify the effectiveness of decision-making. The simulation results show that the proposed method can reflect human-like decision-making in multiple scenarios.

An adaptive internal mass source wave-maker for short wave generation

The mass source wave-maker is commonly employed for generating water waves in numerical simulations, during which a correct amount of mass is introduced or subtracted from the internal flow region to produce target waves. The method has proven to be effective in producing waves in shallow and intermediate water depths, while its efficiency is declined for short wave generation. The main reason for this efficiency declination is that the internal mass source in deeper water region is not effective to generate short waves with their motions primarily on water surface. In order to overcome this shortcoming, many of the previous numerical treatments have introduced various enhancement factors into the source functions, which are empirically obtained and also violate the law of mass conservation. In this study, we develop a new adaptive internal wave-maker model that can be self-adjusted to suit different wave conditions. The line source starts from the bottom and extends to the computational cell right beneath free surface at each time step. The depth dependent weighting coefficient is introduced to the source function based on the linear wave theory for each wave component. No empirical coefficients are necessary, and the mass conservation is strictly and explicitly enforced. In principle, the method can be applied to all types of linear waves in the entire range of kh. The numerical experiments show that the present method can produce very good results for linear waves with kh up to 16.11, adequate for most of wave conditions in coastal engineering. For generation of fifth-order Stokes waves, the method can be extended straightforwardly for each of five wave components. For irregular waves composed of many linear wave components, different weighting coefficients can be readily calculated for each of them, respectively. As a result, the new model can generate irregular waves with overall better performance of reproducing wave spectrum, whose high-frequency part has been underestimated by previous methods. The numerical experiments also show that the new model can produce better results for focused waves where many linear waves of different frequencies start from the same point with specific phase angles, due to its capability of generating shorter wave components.

Development of the rough Defining Interrelationships Between Ranked criteria II method and its application in the MCDM model

Although numerous methods of multi-criteria decision-making have been developed so far, there are still those that have not been improved by different theories that deal well with uncertainties and inaccuracies that are a normal occurrence in everyday life. The goal of this research is the development of the Rough Defining Interrelationships Between Ranked Criteria II (Rough DIBR II) method and the presentation of its application in the multi-criteria decision-making (MCDM) problem, specifically in human resource management (HRM). The paper presents the subject-developed method and its practical application in the MCDM model with the Rough Additive Ratio Assessment (Rough ARAS) method. In order to check the consistency and validity of the proposed methodology, a sensitivity analysis and a comparative analysis were performed. The conclusions of this research indicate a stable and valid proposed methodology, as well as the possible application of the Rough DIBR II method for defining weighting coefficients of criteria in real-life decision-making problems.

Coordinated strategy for path tracking and dynamic stability considering extreme obstacle avoidance in distributed drive electric vehicles

PurposeThis research aims to investigate the trajectory tracking problem for a four-wheel independent drive autonomous vehicle (4WID) and propose an integrated, coordinated control strategy to address the mutual interference between trajectory tracking and stability control in extreme cases.Design/methodology/approachThe authors establish an adaptive preview model that modifies the preview distance based on vehicle speed. They utilize a three-degrees-of-freedom vehicle model and employ model predictive control to calculate the necessary front wheel angle for trajectory tracking. In terms of longitudinal control, a longitudinal coordinated control mechanism is established to achieve the two conflicting objectives of trajectory tracking accuracy and dynamic stability through early deceleration. A stability controller based on sliding mode control (SMC) is designed, considering tire constraints and tracking the optimal yaw angle and sideslip angle. Furthermore, a lateral coordinated control strategy is developed, considering the weight coefficient of stability control, and the yaw moment is calculated and distributed based on the vehicle torque requirements.FindingsThe proposed integrated, coordinated control strategy successfully addresses the mutual interference between trajectory tracking and stability control in extreme cases for the 4WID vehicle. The strategy achieves trajectory tracking accuracy, dynamic stability and reduced energy consumption while taking into account tire constraints.Originality/valueWe have proposed a cooperative control strategy for the trajectory tracking problem of autonomous driving vehicles. This strategy is different from previous methods in that we have taken into account the integrated dynamic control in both longitudinal and lateral directions, balancing the conflicting control requirements and reducing energy consumption, improving trajectory tracking accuracy and vehicle dynamic stability. We have verified the feasibility of this strategy through joint simulation under different driving conditions.

Optimization of power system parameters for multi-mode hybrid electric vehicles based on regional demand

The performance of the vehicle power system for multi-mode hybrid electric vehicle (M-MHEV) can be improved through meticulous parameter matching and optimization. This paper developed the powertrain parameter design and dynamic performance calculation program based on the structure design, parameter matching calculation, and powertrain system selection. A HOQM of M-MHEV is formulated to ascertain the weight coefficient of vehicle performance indicators according to different regional requirements and a parameter matching and optimization method for power system, employing the Particle Swarm Optimization (PSO) algorithm, is suggested to assess and harmony the vehicle’s performance. Firstly, a house of quality model of M-MHEV is constructed to ascertain the weight coefficient of vehicle performance indicators derived from different regional demands. Additionally, a method is introduced for optimization of power system parameters of M-MHEV based on regional demand, the PSO algorithm is employed to optimize the characteristic parameters. And sensitivity analysis of characteristic parameters is conducted relying on user needs in different regions. The simulation results show that users in the northern and southern regions have different final weight coefficients for vehicle performance indicators and the parameters not only exert a substantial individual influence but also exhibit an interaction effect on the vehicle performance. Finally, a series of comparative simulations, are carried out with two optimization parameters respectively, the simulation outcomes demonstrate that the solution obtained through optimized design parameters solution could markedly enhance the technical parameters of the vehicle. The efficacy and viability of the proposed method based on user needs in different regions are verified. The conclusion provides a useful reference for differentiated design.

Neural Network Algorithm for Training a Mobile Robot in the Task of Following a Target

The article is devoted to the research, development and implementation of an artificial neural network with reinforcement for controlling a mobile robot with a differential drive to solve the task of following a target. The authors of the study present a detailed description of the architecture of the neural network, its training using a deep deterministic gradient policy algorithm, and integration with the robot control system. A distributed neural network structure was selected for specialized generation of controls, for which the authors chose the angular and linear speed of the robot. A system of rules has been synthesized that takes into account changes in the distance between the robot and the target object and tracking of the heading angle in real time. A mathematical model of a robot with a differential drive is considered, on the basis of which a simulation program is implemented for intermediate training of a neural network, as a result of which the initial weighting coefficients are formed. Using this program, you can get by with less energy and time costs at the initial stage of the study. Experiments to test the effectiveness of the developed neural network were carried out in the Gazebo simulation environment using the ROS2 communication interface. The article describes the process of integrating a neural network with a robot control system in a simulation environment, as well as test results and analysis of the obtained data. The results of experiments are presented for three scenarios in which the initial position of the robot is the same, the remaining parameters are generated according to the rules described by the authors. The effectiveness of using such a solution for trajectory planning has been confirmed. The research contributes to the field of autonomous systems and demonstrates the potential of artificial reinforcement neural networks in robotics.

Clutch Torque Optimization for Heavy-Duty Vehicles Starting Based on Intelligent Identification of Driver’s Starting Intention, Vehicle Mass, and Road Grade

<div>For heavy-duty vehicles equipped with automated mechanical transmission (AMT), the control of automatic clutch torque is crucial during the start-up process. However, the difficulty of controlling clutch torque is exacerbated by differences in driver’s starting intentions, changes in vehicle mass, and road gradient. Therefore, this article proposes the clutch starting torque optimization strategy based on intelligent recognition of driver’s starting intention, vehicle mass, and road gradient. First, an intelligent recognition strategy is proposed based on the combination of data-driven and onboard transmission control unit (TCU) algorithms, which improves the accuracy of recognizing the driver’s intention to start as well as the vehicle mass and road gradient. Based on the vehicle’s historical state data information, the predictive model is trained offline using a long–short-term memory (LSTM) network to obtain predicted parameter identification results, which are then used to calibrate the computed values of the onboard TCU algorithm. Second, the clutch torque optimization strategy is designed based on the driver’s starting intention, while considering the effects of road gradient and vehicle mass on the clutch starting resistance torque. The weight coefficients of the objective performance function are adjusted according to the driver’s starting intention, and the Pontryagin’s minimum principle (PMP) is used to solve the clutch target torque. Finally, offline data training and real-vehicle testing are performed. The results show that the optimization strategy can effectively reduce the friction work and the degree of impact during the starting process, minimize the clutch slipping time, and improve the smoothness of vehicle starting and driving comfort.</div>

Comprehensive evaluation of the implementation of close-type county medical alliance in Shandong Province using entropy weight TOPSIS method and non-integer rank sum ratio method

BackgroundTo improve the capacity of county medical and health services, China encourages all localities to pilot the close-type county medical alliance. In Shandong Province, medical alliances have been piloted in 47 counties, ranking first in the country. The objective of this study is to comprehensively evaluate the implementation of close-type county medical alliance in Shandong Province, identify the differences between different county regions, and analyze the reasons, so as to provide a reference for the construction of a new county medical and health service system with clear goals, powers and responsibilities, and division of labor.MethodsThe implementation of the close-type county medical alliance was comprehensively evaluated in 47 national pilot counties in Shandong Province using entropy weight TOPSIS method and non-integer rank sum ratio method. Variance analysis was used for comparison of the comprehensive evaluation results.ResultsThe weight coefficient of evaluation indicators was highest for information interconnection, at 18.06%, and lowest for orderly referral of patients, at 3.64%. There was no difference in results of the comprehensive evaluation of entropy weight TOPSIS method and non-integer rank sum ratio method. Comprehensively order the implementation status of each pilot county according to the relative paste progress, 13 counties Y5, Y11, Y14, Y16, Y20, Y25, Y26, Y27, Y28, Y32, Y33, Y40 and Y42 were ranked highest, whereas county Y37 was ranked lowest. Non-integer rank sum ratio method graded counties into three grades: excellent, good and average. Kruskal-Wallis nonparametric test showed that the difference between the grades was statistically significant (H\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:=\\:$$\\end{document}37.099, p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:<\\:$$\\end{document}0.001). Variance analysis based on comprehensive evaluation results showed that implementation status was not correlated with the county economic development level, the level of health resources input and the medical service ability of the lead hospital.ConclusionsOur findings indicated that the implementation of the close-type county medical alliance is significantly different between pilot counties, with a marked differentiation within the same urban area. Therefore, effective measures are recommended to reduce this gap, including promoting informatization empowerment of the county medical community, strengthening government responsibility and improving policy effectiveness.

Enhancing older care services: a comprehensive internet + community home indicator system

ObjectiveConstructing a demand-oriented Internet + community home care service indicator system, starting from the actual needs of the elderly, and quantifying the demand into specific service contents that can be provided, so as to realize the effective supply of services. It aims to provide a theoretical basis for the government to formulate relevant aging policies and provide a reference basis for promoting the innovation and development of the Internet + elderly care model.MethodsBased on existence needs, relationship needs and growth needs abbreviated as “Existence, Relatedness, Growth (ERG)” demand theory, through the discussion in the group meeting, form the expert consultation questionnaire of the demand-oriented Internet + community home care service indicator system, select experts in related fields to conduct two rounds of Delphi expert consultation, summarize the experts’ opinions to determine the indicator system items, and use the Analytic Hierarchy Process (AHP) method to construct the judgment matrix to derive the weighting coefficients of the indicators, and establish the ultimate demand-oriented Internet + community home care service indicator system.ResultsThe effective recovery rates for the two rounds of expert consoulation questionnaires were 100%, with expert authority coefficients of 0.852 and 0.868, respectively. The Kendall coefficients for the second round of expert views varied from 0.226 to 0.431, with coefficients of variation for all indexes less than 0.25.The finalized demand-oriented Internet + community home care service indicator system includes 3 first-level indicators, 10 s-level indicators, and 46 third-level indicators. The consistency ratios of indicator judgment matrices at all levels are less than 0.10, suggesting that the hierarchical analysis findings are consistent, implying that the weight coefficient distribution is appropriate.ConclusionThe demand-oriented Internet + community home care service Indicator System, which was created through expert consultation and AHP method, has reasonable content and is more dependable as an evaluation tool for reliably assessing demand for elderly servic.

Improved PEG-Based Construction of Analog Fountain Codes.

This paper proposes an improved progressive edge-growth (PEG) construction of analog fountain codes (AFCs). During edge selection, it simultaneously allocates weight coefficients in descending order. Analysis shows that our proposed construction reduces the probability of large weight coefficients involved in harmful short cycles. Simulation results indicate that it has good block error rate (BLER) in short block length regime.

Increasing energy efficiency and flow coefficient by determining fluid behaviours with computer aided design and CFD analysis in isolation valves

In this study, it was aimed to convey theoretical and practical development of the double eccentric butterfly valve, which would be the first in the sector, with highly efficient, saves energy, has a long life, and requires less maintenance. For this purpose, a prototype with lower weight and higher flow coefficient has been developed by the Computational Fluid Dynamics (CFD) method. Thanks to the use of computer aided design and simulation techniques, environmentally friendly and highly efficient valves have been produced, improving competitive conditions and an innovative product has been obtained in the sector. The design, CFD analysis, and casting simulation were carried out by Solidworks, ANSYS, and Anycasting computer programmes respectively. At the same time, as a result of this study, a design suitable for an innovative production technique that leads to less energy and less waste has been obtained. Innovative material selection and casting techniques were used by optimising the total material weight and valve cost.

Numerical investigations of aerodynamic characteristics in highly loaded tandem cascades with end-bend

Tandem cascades and end-bend blades can reduce compressor losses and promote energy conservation. In this study, a vortex model tailored for tandem cascades is proposed. Numerical investigations are conducted to elucidate the influence of end-bend heights and angles on aerodynamic characteristics. A quantitative assessment of the loss contribution for each vortex is provided based on loss weight coefficients. The findings demonstrate that end-bend angles exert a more pronounced impact on aerodynamic characteristics than end-bend heights. Although the implementation of end-bend increases the loss of the front blade trailing edge shedding vortex, it reduces losses by weakening the passage vortex and the rear blade trailing edge shedding vortex, resulting in a net decrease of the total loss. The end-bend blade optimizes the loading profile near the endwall, characterized by an expanded low-pressure zone behind the gap. A comparative analysis is performed among four tandem cascades: straight, curved, end-bend, and combined curved-end-bend. The correlation between losses, vortices, and topological structures is established by using topological analysis together with the vortex model. It is a crucial research methodology for elucidating the mechanisms of loss generation and vortex dynamics in tandem cascades with end-bend.

Research on entropy weight variation evaluation method for wind power clusters based on dynamic layered sorting

This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes. A dynamic layered sorting allocation method is also proposed. The proposed evaluation method considers the power-limiting degree of the last cycle, the adjustment margin, and volatility. It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time, and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results. A case study of a large-scale wind power base in Northwest China was conducted. The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods. The results show that the three scoring trends are the same, and that the proposed evaluation method is closer to the average level of the latter two, demonstrating higher accuracy. The proposed allocation method can reduce the number of adjustments made to wind farms, which is significant for the allocation and evaluation of wind power clusters.

Multi-swarm multi-tasking ensemble learning for multi-energy demand prediction

The increasing uptake of renewable energy sources leads to more uncertainties of energy scheduling, resulting in more difficulties of energy demand management in smart grid. Accurate forecasting plays a significant role in energy management, and in some cases it may involve predictions of different consumers or geographic regions. Traditionally, these prediction problems have been solved independently, ignoring the potential shared knowledge among them that may help facilitate the overall performance. In this manuscript, we propose a multi-swarm multi-tasking ensemble learning (MSMTEL) framework for solving the energy demand forecasting across multiple cities. The proposed method comprises single-task pretraining, multi-task optimization (MTO), and ensemble learning (EL). For each prediction task, several subtasks are generated based on predefined parameters in the forecasting model. Each subtask utilizes an independent deep neural network (DNN) as the predictor, which is pretrained individually. Subsequently, we modify the dynamic multi-swarm particle swarm optimization (DMS-PSO) as a customized multi-swarm PSO (CMS-PSO) algorithm for implementing MTO. Each subswarm in CMS-PSO focuses on finding the optimal model knowledge (pretrained DNN weights and biases) of source tasks (all subtasks) to be reused by the target subtask. Finally, instead of choosing the best forecasting among the subtasks, results of subtasks over the same energy prediction problem are combined to yield the EL result, wherein weight coefficients determining the contributions of subtasks to EL are optimized by PSO. MSMTEL is evaluated against single-task learning (STL) and MTO to demonstrate its superior performance.

A flexible camera calibration method for pose vision measurement system of roadheader

Cameras used for visual measurement in underground roadway construction sites are difficult to calibrate. Therefore, this study proposed an on-site camera calibration method that does not require manual intervention. First, the virtual point and line features on the plane target are constructed using pole-polar constraints. Second, a homography model based on point-and-line features is established. Finally, a combined objective function based on the dynamic weight coefficient is proposed to optimise the calibration results further. Relevant simulations and experiments showed that the maximum internal parameter error obtained using the proposed method was 0.463 %, and the maximum position estimation error of the roadheader was 30 mm. In addition, the results show that this method is superior to other methods in terms of calibration accuracy and robustness, solving the difficulty of camera calibration in underground roadway construction sites and guaranteeing high-precision pose vision measurement.