Modeling Method Research Articles

H ∞ prescribed tracking performance-based fractional-order sliding mode control for disturbed discrete-time systems: an LMI approach

This paper presents a novel approach to the design of a discrete-time Fractional-Order Sliding Mode Control (FSMC) system for Single Input Single Output (SISO) applications, utilising the Autoregressive with exogenous input (ARX) method for system modelling. The proposed methodology emphasises the Prescribed Performance Control (PPC) to ensure minimal tracking errors through a transformation of tracking errors based on a specified performance function. A Fractional-Order Quasi-Sliding Mode Control (FQSMC) is subsequently developed to maintain the tracking error within predetermined bounds, effectively addressing the chattering phenomenon commonly associated with the traditional sliding mode control. The integration of fractional calculus enhances the smoothness of the output while ensuring accurate tracking, making it ideal for real-world applications. Additionally, a novel Linear Matrix Inequality (LMI) observer is introduced to bolster the system's resilience against uncertainties and disturbances without complicating the computational process. The effectiveness of the proposed Prescribed Performance Fractional-Order Quasi-Sliding Mode Control (PP-FQSMC) is validated through extensive MATLAB simulations and experimental studies conducted on a Tank system. Results demonstrate that the PP-FQSMC significantly outperforms traditional methods, with maximum tracking errors of 1.6 × 10−4 compared to 0.2 for the PP-QSMC and a Root Mean Square Tracking Error (RMSTE) of 1.3 × 10−3 compared to 1.2 × 10−4 for the PP-QSMC, ensuring strong tracking performance and stability. The findings indicate that the proposed controller effectively mitigates chattering in control signals while maintaining a smooth output. It showcases its potential for practical applications in environments characterised by model inaccuracies, time delays, and external disturbances.

Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences

Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determination. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation without sequence guidance often suffers from errors caused by cross-chain interaction or noise densities, which would accumulate and mislead the subsequent steps. Here, we present EModelX, a fully automated cryo-EM protein complex structure modeling method, which achieves sequence-guiding modeling through cross-modal alignments between cryo-EM maps and protein sequences. EModelX first employs multi-task deep learning to predict Cα atoms, backbone atoms, and amino acid types from cryo-EM maps, which is subsequently used to sample Cα traces with amino acid profiles. The profiles are then aligned with protein sequences to obtain initial structural models, which yielded an average RMSD of 1.17 Å in our test set, approaching atomic-level precision in recovering PDB-deposited structures. After filling unmodeled gaps through sequence-guiding Cα threading, the final models achieved an average TM-score of 0.808, outperforming the state-of-the-art method. The further combination with AlphaFold can improve the average TM-score to 0.911. Analyzes conducted by comparing some EModelX-built models and PDB structures highlight its potential to improve PDB structures. EModelX is accessible at https://bio-web1.nscc-gz.cn/app/EModelX.

Donating or collecting? Differential impact mechanisms of knowledge sharing through enterprise social media on employees' innovative work behavior

PurposeIn the digital age, enterprise social media (ESM) has been broadly used to break the barrier of space and time for knowledge sharing involving two activities—knowledge donating and knowledge collecting. Yet, hardly any study has thus far explored how and when knowledge donating and knowledge collecting through ESM impact employees’ innovative work behavior (IWB). Therefore, drawing on the social exchange theory, this study aims to introduce relational social capital as a mediator and performance climate as a moderator to examine differential impact mechanisms of knowledge donating and knowledge collecting through ESM on employee IWB.Design/methodology/approachFirst, we collected 225 survey data from employees who used ESM at work. Then, according to the two-stage procedure of the partial least square structural equation modeling method, we established and analyzed a moderated mediation model based on the context of ESM.FindingsResults demonstrate that knowledge donating through ESM has a positive relationship with IWB, but knowledge collecting through ESM has no such relationship. Through the mediation of relational social capital, both knowledge donating and knowledge collecting through ESM have indirect positive relationships with IWB, but knowledge collecting through ESM has a greater one. Moreover, when employees perceived a relatively high-performance climate, the positive relationships of knowledge donating and knowledge collecting through ESM with IWB via relational social capital would be stronger.Practical implicationsOn the one hand, organizational managers should not only focus on different innovation benefits caused by knowledge donating and knowledge collecting through ESM but also should realize differences in the direct/indirect effect of knowledge donating and knowledge collecting through ESM on IWB. On the other hand, employees need to proactively enhance their innovative ability by considering knowledge donating/collecting channels (e.g. ESM), gaining successful competitiveness. Moreover, both managers and employees should realize that different innovation benefits of knowledge sharing activities through ESM can be further strengthened when establishing a high-performance climate.Originality/valueDifferent from prior literature on knowledge sharing or ESM, this study contributes to analyzing the differential effects of two dimensions (i.e. knowledge donating and knowledge collecting) of knowledge sharing on employees' IWB in the context of ESM.

Visual Modeling Method for Ship Piping Network Programs in Engine Simulators

Visual Modeling Method for Ship Piping Network Programs in Engine Simulators

Soft sensor modeling method and application based on TSECIT2FNN-LSTM

To address the issue of low accuracy in soft sensor modeling of key variables caused by multi-variable coupling and parameter sensitivity in complex processes, this paper introduces a TSK-type-based self-evolving compensatory interval type-2 fuzzy Long short-term memory (LSTM) neural network (TSECIT2FNN-LSTM) soft sensor model. The proposed TSECIT2FNN-LSTM integrates the LSTM neural network with the interval type-2 fuzzy inference system to address long-term dependencies in sequence data by utilizing the gate mechanism of the LSTM neural network. The TSECIT2FNN-LSTM structure learning algorithm uses the firing strength of the network rule antecedent to decide whether to generate new rules to improve the rationality of the network structure. TSECIT2FNN-LSTM parameter learning utilizes the gradient descent method to optimize network parameters. However, unlike other interval type-2 fuzzy neural network gradient calculation processes, the error term in the LSTM node parameter gradient of TSECIT2FNN-LSTM is propagated backwards in the time dimension. Additionally, the error term is simultaneously transferred to the upper layer network to enhance network prediction accuracy and memory capabilities. The TSECIT2FNN-LSTM soft sensor model is utilized to predict the alcohol concentration in wine and the nitrogen oxide emission in gas turbines. Experimental results demonstrate that the proposed TSECIT2FNN-LSTM soft sensing model achieves higher prediction accuracy compared to other models.

Research on Chebyshev Graph Convolutional Neural Network Modeling Method for Rotating Equipment Fault Diagnosis under Variable Working Conditions

Research on Chebyshev Graph Convolutional Neural Network Modeling Method for Rotating Equipment Fault Diagnosis under Variable Working Conditions

ALS-Based, Automated, Single-Tree 3D Reconstruction and Parameter Extraction Modeling

The 3D reconstruction of point cloud trees and the acquisition of stand factors are key to supporting forestry regulation and urban planning. However, the two are usually independent modules in existing studies. In this work, we extended the AdTree method for 3D modeling of trees by adding a quantitative analysis capability to acquire stand factors. We used unmanned aircraft LiDAR (ALS) data as the raw data for this study. After denoising the data and segmenting the single trees, we obtained the single-tree samples needed for this study and produced our own single-tree sample dataset. The scanned tree point cloud was reconstructed in three dimensions in terms of geometry and topology, and important stand parameters in forestry were extracted. This improvement in the quantification of model parameters significantly improves the utility of the original point cloud tree reconstruction algorithm and increases its ability for quantitative analysis. The tree parameters obtained by this improved model were validated on 82 camphor pine trees sampled from the Northeast Forestry University forest. In a controlled experiment with the same field-measured parameters, the root mean square errors (RMSEs) and coefficients of determination (R2s) for diameters at breast height (DBHs) and crown widths (CWs) were 4.1 cm and 0.63, and 0.61 m and 0.74, and the RMSEs and coefficients of determination (R2s) for heights at tree height (THs) and crown base heights (CBHs) were 0.55 m and 0.85, and 1.02 m and 0.88, respectively. The overall effect of the canopy volume extracted based on the alpha shape is closest to the original point cloud and best estimated when alpha = 0.3.

A Novel Wiener‐Type Dynamic Neural Network Method for Large Signal Modeling of Power Amplifiers

ABSTRACTA novel Wiener‐type dynamic neural network (Wiener‐type DNN) method for large signal modeling of power amplifiers (PAs) is proposed in this paper. In this method, the proposed model structure for the PA contains two Wiener‐type DNNs to describe the input impedance and the amplification efficiency, respectively. The Wiener‐type DNN includes a simplified linear dynamic equation module and a nonlinear static equation module. For the simplification process of the linear dynamic equation, it is proposed to process fundamental frequency components of the large signal harmonic data of the PA by vector fitting. The neural network is used to implement the static equation in the Wiener‐type DNN. The formulas for training the proposed Wiener‐type DNN model of the PA using large signal data are derived. The training algorithm of the PA model is proposed to improve the training efficiency. A Motorola MOSFET PA example and a Freescale lateral double‐diffused MOSFET PA example are present to validate the proposed Wiener‐type DNN method. For the Motorola MOSFET PA, a dynamic neural network (DNN) model and a time‐delay deep neural network (TDDNN) are established for comparison. For the Freescale PA, the DNN model is used for comparison experiment. The comparison figures show that the Wiener‐type DNN model has better convergence properties than the DNN model in the nonlinear region of the PA. The established accurate PA model is conducive to the design of subsequent communication circuits.

Применение имитационного моделирования в процессах постройки судна

This article provides an example of the application of the method of simulation modeling of ship construction processes in AnyLogic software. The process is briefly described and the logistics of building a barge produced at shipyards is characterized, and a map of the production process is prepared. In conclusion, three possible modernization options were proposed, which consist in technological, organizational improvements and a variant combining both of these solutions. AnyLogic software was used for a comparative analysis of the proposed options in relation to the process in the basic version. This method allows you to obtain accurate data for making informed decisions without the need for physical tests. The use of simulation modeling is a key tool for optimizing shipbuilding processes and further development of the industry.

Reliable comparison of multi-directional mechanical properties for biomimetic circle arc honeycombs

Abstract Compared to straight honeycombs, curved honeycombs derived from biomimetic strategies typically exhibit superior mechanical properties and a broader tunable range. Consequently, there has been a surge in research focusing on curved honeycombs. However, few studies have concurrently designed multiple honeycombs for reliable comparisons, and little attention has been given to isotropy. In this study, circle arc walls were introduced to replace the straight walls of five classical honeycombs, thereby creating representative curved honeycombs for analysis. The numerical modeling method in ANSYS software was validated by experimentally testing 3D printed specimens of circle arc honeycombs. At the same mass and under loading conditions in different directions, it was found that the Young's modulus, yield strength, Poisson's ratio, and energy absorption of circle arc hexagon (CAH), circle arc square (CAS), circle arc triangle (CAT), circle arc re-entrant (CAR), and circle arc double-V (CAD) were assessed. Both experimental and numerical results revealed that CAH demonstrates superior energy absorption while maintaining a high degree of isotropy, characterized by all isotropic factors below 0.1 in mechanical properties. Notably, CAD exhibits a Negative Poisson's ratio (NPR) effect across all loading directions. These results provide benchmarks for future studies on curved honeycombs and guide further research in this field.

High-precision optical modeling method for galvanometer-driven dual-camera systems

To build a galvanometer-driven dual-camera sensing system, it is important to accurately correlate the wide-view image coordinates with the pan-tilt mirror angles for adjusting the incident light path of the zoom-in camera. Existing optical modeling methods assume sufficient target distance and simplify dual-camera optical centers as coincident. However, this simplification is not valid in many practical cases and might cause severe system malfunctions, such as complete loss tracking of important targets. To address this problem, we propose a novel approach, to the best of our knowledge, to facilitate high-precision optical modeling and calibration of galvanometer-driven dual-camera systems. The proposed method takes into consideration the dual-camera optical center misalignment issue and builds a model for accurate estimation and rectification of target localization errors under various optical configurations. Qualitative and quantitative experimental results demonstrate the superiority of our method, improving the performance of galvanometer-driven dual-camera systems for high-precision optical sensing applications.

Thermal-deformation behaviors of the primary sealants in double, triple, and multi glazed insulating glass units

Polyisobutylene (PIB), commonly used as the primary sealant of double, triple, and multi glazed insulating glass units (IGUs), provides the key moisture barrier function and determines the expected lifespan of the IGUs and even the entire glass curtain wall systems (GCWSs). Slipping and debonding of the PIB, caused by temperature changes, have resulted in numerous instances of premature failure of building envelopes. However, available research is inadequate in accurately evaluating the service environment of IGUs and thermal-deformation behaviors of this energy-saving building material. This paper presented a simplified method for analyzing the thermal environment of IGUs, considering outdoor air temperature, solar radiation, wind speed, and angle to the horizontal. A numerical modeling method was proposed and validated with the heat transfer tests. Three finite element (FE) models were built and utilized for the precise analysis of temperature-induced deformations of double, tripled, and quadruple glazed IGUs. Simplified calculation formulas for the maximum thermal deformation of PIB in the X and Y directions under different temperature conditions were obtained, taking a new concept “cavity-to-pane ratio” into consideration. Finally, the relationship between the PIB’s temperature-induced deformation and its probability was proposed. The results show that the IGUs in Beijing suffer more than 22 % of their time in harsh service conditions where the difference between indoor and outdoor temperatures exceeds 20 °C. For DIGUs, the maximum temperature-induced deformations of the PIB in X (along the short side of the panel) and Y (along the long side of the panel) directions are 0.142 and 0.214 mm, respectively, corresponding to shear strains of 28.4 % and 42.8 % for the PIB with a thickness of only 0.5 mm. For TIGUs, the maximum deformations increase to 0.159 and 0.239 mm, corresponding to shear strains of 31.8 % and 47.8 %. For QIGUs and the other multi-glazed IGUs, these values can increase to 36.8 % and 55.4 % or more. The methodology proposed in this paper aims to lay the groundwork for further addressing premature failure issues and optimizing edge bond constructions of multi-glazed IGUs.

Gated recurrent unit (GRU)-based deep learning method for spectrum estimation and inverse modeling in plasmonic devices

Gated recurrent unit (GRU)-based deep learning method for spectrum estimation and inverse modeling in plasmonic devices

PENGARUH CUSTOMER RELATIONSHIP, STRATEGIC SUPPLIER, INFORMATION SHARING & POSTPONEMENT TERHADAP ORGANIZATIONAL PERFORMANCE YANG DIMEDIASI INBOUND DAN OUTBOUND INNOVATION

Technological advancements and the growth of e-commerce have increased the complexity of supply chain management, making it more vulnerable to disruptions. The COVID-19 pandemic has highlighted this vulnerability, leading organizations to reassess traditional risk management approaches and emphasize collaborative partnerships to enhance supply chain value. This study aims to evaluate the impact of Customer Relationship (CR), Quality of Information Sharing (QIS), Strategic Supplier Partnership (SSP), Postponement (P), Top Management Knowledge Value (TMKV), and Knowledge Sharing Practice (KSP) on Organizational Performance (OP), with Inbound and Outbound Open Innovation (IOI and OOI) as mediating variables. Data was collected from 164 professionals in Jakarta using the Purposive Sampling method through online questionnaires on Google Forms. Data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The results of the study show that all independent variables positively affect organizational performance, although not all of these effects are significant. This indicates that effective integration between Supply Chain Management strategies and Open Innovation can improve organizational performance. TMKV has a significant positive impact on KSP, KSP also has a significant positive impact on IOI, IOI has a significant positive impact on OP, and KSP mediated by IOI is proven to have a significant positive effect in supporting OP, while OOI mediation is not proven. This study is not intended to generalize a theory due to the limitations in sample size, industry, and geographical area.

Digitalization of the Electoral Process in Russia: Legal Aspects

Introduction. Digitalization is a distinctive feature of development of the modern society, which implies intensive implementation of the new technologies into the different spheres of life, including the electoral process. In Russia, the development of measures efficiently ensuring the suffrage of the citizens, the improvement of the legal regulation of the electoral process are being carried out for more than thirty years. However, the dynamics of automation and digitalization of the electoral procedures often outrides their theoretical justification and legal comprehension. The works of Russian and foreign authors study the problems of the electoral process digitalization, the electronic forms of manifesting the civic activity and political engagement. A number of researchers consider the ongoing changes to be inevitable and call for a revision of the electoral process basic fundamentals, others propose to limit this process indicating on the impossibility to ensure the absolute information security. The paper aims at analysing the main stages of automation and digitalization of the electoral process in Russia, identify the problems associated with implementation of the new technologies and develop recommendations for the efficient transformation of the electoral system in the context of digitalization.Materials and Methods. To ensure the relevance and reliability of the obtained results, the general and specific scientific methods of cognition were used in the research: the dialectical method, the method of juridical comparativistics, as well as the content analysis of the regulatory legal acts and historical, systemic, logical and other methods. Various methods of interpreting the legal norms were used to identify the regularities in their application. The content analysis of the regulatory legal acts and the logical method made it possible to identify the electoral process digital transformation features. The method of modeling enabled formulation of the proposals on improving the legislation.Results. The main stages of automation and digitalization of the electoral process have been studied. Their features and problems have been identified. The need for transformation of the legal regulation, capable of efficient ensuring the suffrage of the citizens in the new conditions, has been substantiated.Discussion and Conclusion. Based on the results of the research, the recommendations have been developed for efficient legal regulation of the main stages of the electoral process in the context of digitalization. The theoretical conclusions formulated by the authors are proposed to be used for further research and improvement of the current electoral law of the Russian Federation.

Enhancing precision in 3D printing for highly functional printing with high-speed vision

Abstract3D printing has revolutionized product design and manufacturing across various industries by enabling the creation of complex geometries with minimal waste. Despite its advancements, 3D printing still faces significant challenges, including spatial constraints and process control limitations. This paper introduces novel approaches to improve the functionality and precision of material extrusion 3D printers for the fused deposition modeling method, particularly for additional printing tasks such as printing on existing objects or continuing a print on a relocated object without prior knowledge of its position or even the environment is changed. We introduce a compensation system integrating a high-speed vision system for robot arms to address these challenges. Our system employs a three-step pose estimation process—fast point feature histograms (FPFH)-based, corner-based, and sub-pixel edge-based methods—to ensure high accuracy in restoring the position of printed pieces for additional printing tasks on a given object. Experimental results demonstrate substantial improvements in printing precision, with the system achieving sub-millimeter and sub-pixel accuracy. These advancements not only eliminate work area constraints but also enhance the adaptability and reliability of 3D printing processes.

Effectiveness Evaluation Method Using System of Systems Architecture Description of Aircraft Health Management in Aircraft Maintenance Program

This study proposes a system modeling method for aircraft maintenance program development that adopts condition-based maintenance using aircraft health management (AHM) based on a systems engineering approach, which considers AHM as a system of systems. The metamodel is tailored on the basis of the Unified Architecture Framework (UAF) and the NASA Systems Modeling Handbook for Systems Engineering. It is described using the modeling tool "Balus 2.0" (Levii, Inc). The applicability and effectiveness of a maintenance program adopting AHM is analyzed on the basis of the Maintenance Steering Group-3 (MSG-3), and its effectiveness is evaluated using the proposed system modeling method. The proposed method considers the uncertainty of the aircraft maintenance environment related to airline operations in addition to the uncertainty of the aircraft system. The effectiveness of the proposed system is investigated through a sample problem that considers a tire system using a pressure monitoring system as AHM based on the MSG-3 approach. Finally, the limitations of the proposed method are discussed.

Three-dimensional airborne electromagnetic forward modeling based on multiscale hexahedral finite-element method

Slow forward modeling is the main factor that restricts the practical use of three-dimensional (3D) inversion and interpretation of airborne electromagnetic (AEM) data. To improve the modeling efficiency in 3D AEM, we propose a new multiscale finite-element (MsFE) method based on unstructured hexahedral meshes. Compared with the traditional 3D AEM forward modeling, the main advantage of our newly developed method is that it can simulate complex underground structures in the earth quickly. Since we can fit the earth's topography or the anomalous bodies underground using a small number of hexahedral grids, we can quickly model them using MsFE. The main idea of the MsFE forward modeling method is to construct an interpolation operator between a coarse and a dense mesh and use the interpolation operator to map the conventional FE coefficient matrix to the MsFE coefficient matrix and thus reduce the number of unknowns in the modeling process. This will vastly reduce the scale of the linear equations system. We validate our method by simulating a typical mountain peak model and demonstrate its effectiveness by simulating numerous synthetic models and a model from Voisey Bay's Ovoid sulfide deposit, Canada.

Modelling and optimization of TPMLMs with slotted stators based on Bayesian DNN

AbstractThe Permanent Magnet Linear Motor (TPMLM) is widely used in different industrial fields. TPMLMs with slots and iron cores have high power density, but their thrust fluctuations and copper losses are significant. Due to the nonlinearity and saturation of magnetic circuits, their electromagnetic models are complex and the accuracy of numerical methods is very inferior. Substantially accurate modelling is crucial for motor optimisation design. In this paper, a data‐driven modelling method based on Bayesian optimisation deep neural network (DNN) is proposed to improve the accuracy of the electromagnetic field. The finite element (FE) modelling under different structural parameters is analysed and provides a training dataset for DNN. Then, a multi‐objective optimisation problem for the slotted TPMLM is carried out based on the multi‐objective black hole algorithm. Compared to the original design, the average thrust of TPMLM increased by 49.37%, the thrust fluctuation percentage decreased by 9.59%, and the coil copper consumption percentage decreased by 2.64%. The results show that the improved DNN model has very high modelling accuracy, providing a new way for motor design and optimisation.

МЕТОДИ ОБЛІКУ З МЕТОЮ СКЛАДАННЯ ФІНАНСОВОЇ ЗВІТНОСТІ: НАЦІОНАЛЬНИЙ ТА МІЖНАРОДНИЙ АСПЕКТИ

The article examines accounting methods for the purpose of financial reporting using the example of national and global application of rules and standards. General research methods were used: analysis, synthesis, comparison and grouping, schematic display of results. The research methodology takes into account a generalization of domestic and foreign features of accounting methods and methods for preparing financial statements, scientific analysis of standards of accounting methods, fundamental provisions of economic theory, accounting and reporting. A comparison of accounting approaches according to NP(S)BU 1 "General Requirements for Financial Reporting" and International Financial Reporting Standards IAS 1 "Presentation of Financial Statements" was carried out to assess the state of national accounting methods with world practice. Separate measures are proposed to systematize methods for preparing financial statements. The need to take into account the characteristics and essence of individual species using the modeling method is substantiated, which will allow generating reporting in conditions close to international standards. The results of the study can be useful for further approximation of the conditions and features of the application of accounting and reporting methods according to national to international standards. The study analyzed and summarized the state of regulatory regulation of the application of accounting methods for the preparation of financial statements, taking into account national and international aspects. It has been determined that the accounting method is a system of interrelated methods and techniques for displaying the value of objects. Accounting methods are general scientific and specific, and it is the latter that are the object of regulatory regulation at both the national and international levels. It is noted that national accounting standards should take into account all methods that contribute to the preparation of financial statements close to international standards. For the current state of accounting science and practice, taking into account the achievements of foreign standards of accounting and reporting, the most practical direction of improving methods is the use of the modeling method. The issues of accounting and reporting need further study through the prism of the elements of the accounting method reflected in the accounting policy.