Model Construction Method Research Articles

Research on the constitutive model of low-damage corn threshing based on DEM

The constitutive model of corn has an intense influence on the mechanical structure design and machine performance. In this paper, a creep property formula of corn-based on the Burgers model was proposed, which described the change process of the stress–strain trait of corn grain with time. Then, the mathematical model of the “corn-mechanical” interaction system was then established based on the Hertz-Mindlin no-slip model. The kinematic and dynamic formulas of corn ear under the action of threshers were deduced, and the “corn-corn” contact characteristics based on Hertz-Mindlin with bonding model were analyzed. Moreover, a new discrete element model construction method of the corn ear was proposed through particle filling and splicing methods. The simulation experiment was carried out in EDEM to verify the constitutive model and discrete element model of corn ear. The results showed that the particle filling model could better explain the mechanism of corn ear threshing and obtain the force of each corn grain during threshing. The average velocity of corn ear was 55.2 m/s, the maximum value was 216.7 m/s, and the minimum value was 16.2 m/s, while the average normal force was 201.7 N, the maximum force was 342.0 N, and the minimum force was 104.2 N respectively, it verified the feasibility and accuracy of models. Finally, the field experiment was carried out to verify the simulation results, and the corn grain broken rate was 2.16%. These results fill the blank of fundamental theoretical research on the design of corn ear threshing devices and provide a reference for the optimal design of other crop production equipment.

Meso fracture characteristics of granite and instability evolution law of surrounding rock in deep cavern

In order to analyze the influence of meso-structure and meso-parameters on deep granite characteristics, a construction method of variable radius proportional clump model was proposed with particle flow method. The clump particle flow structure was constructed which suited the mechanical characteristics of granite. The deep cavern numerical calculation model of gradual particle density was constructed using the variable radius proportional clump model construction method, and the macroscopic fracture law of deep cavern surrounding rock was analyzed. The results show that meso parameters have lower effects on the compressive and tensile ratios of particle structures in the ball and clump models. It is also found that clump structure is greatly influenced by particle proportion and size while ball model is only slightly influenced by particle size. Furthermore, the compressive and tensile strength curves and fracture modes of numerical simulations and laboratory tests are in good agreement. In addition, the calculated results of the tunnel after simulated excavation are very close to the engineering practice. There are obvious shear failure areas on the right vault and left wall of the tunnel, and the shape and depth of the fracture area are basically the same.

Novel automatic model construction method for the rapid characterization of petroleum properties from near-infrared spectroscopy

Petroleum fuels play an important role in economic society, and near-infrared analysis has been widely used in the characterization of petroleum fuels due to its effectiveness and efficiency. However, near-infrared spectra are high dimensional data that require high computational cost, thereby complicating the practical application. Previous model construction methods are also restricted to pre-designed model structure, thus limiting the model construction ability. To solve these problems, this paper proposes an automatic model construction algorithm that increases the diversity of model structure with linear-in-parameter representation and optimizes the model structure with genetic programming. The proposed automatic construction algorithm is verified on datasets from literature and experiments with real petroleum products compared with Partial Least Square (PLS) and Support Vector Machine Regression (SVR). The proposed method outperforms PLS and SVR in overall predictive accuracy and reduces the involved variable number by at least 70% during the model construction process. Good performance in prediction accuracy, model complexity, and interpretability on the rapid characterization of different petroleum products can assist refinery plant in product control and management during production.

Research on the evaluation system of operations behavioral norms for off-campus tutoring institutions based on AHP

<abstract> <p>Against the background of the current high level of development of off-campus tutoring institutions and the implementation of the "Double Reduction" policy of education, the establishment of a scientific evaluation index system for of the management behavioral norms is a powerful guarantee for the supervision of off-campus tutoring behavioral norms. The construction of the evaluation index system is mainly based on the content of policy supervision of off-campus tutoring. The existing supervision tools use the system model construction and index weight calculation method of the analytic hierarchy process (AHP) for reference. An evaluation system of operations behavioral norms for off-campus tutoring institutions is constructed, which consists of four dimensions: "operations foundation, organizational construction, operations process, and operations results". The evaluation system of behavioral norms for off-campus tutoring institutions includes "4 first-level indicators, 11 second-level indicators and 34 third-level indicators". It implements a scientific dynamic evaluation and monitoring of the degree of behavioral norms for off-campus tutoring. Therefore, it provides a decision-making basis for promoting off-campus tutoring institutions to improve and standardize operations and promote the effective achievement of the "Double Reduction" goal.</p> </abstract>

Construction of a photosynthetic rate prediction model for greenhouse strawberries with distributed regulation of light environment.

In winter and spring, for greenhouses with larger areas and stereoscopic cultivation, distributed light environment regulation based on photosynthetic rate prediction model can better ensure good crop growth. In this paper, strawberries at flowering-fruit stage were used as the test crop, and the LI-6800 portable photosynthesis system was used to control the leaf chamber environment and obtain sample data by nested photosynthetic rate combination experiments under temperature, light and CO2 concentration conditions to study the photosynthetic rate prediction model construction method. For a small-sample, nonlinear real experimental data set validated by grey relational analysis, a photosynthetic rate prediction model was developed based on Support vector regression (SVR), and the particle swarm algorithm (PSO) was used to search the influence of the empirical values of parameters, such as the penalty parameter C, accuracy ε and kernel constant g, on the model prediction performance. The modeling and prediction results show that the PSO-SVR method outperforms the commonly used algorithms such as MLR, BP, SVR and RF in terms of prediction performance and generalization on a small sample data set. The research in this paper achieves accurate prediction of photosynthetic rate of strawberry and lays the foundation for subsequent distributed regulation of greenhouse strawberry light environment.

Model Construction Based on Real-Time System Identification Method (Proposal of a Model Construction Method for Anomaly Detection of Control Systems)

Model Construction Based on Real-Time System Identification Method (Proposal of a Model Construction Method for Anomaly Detection of Control Systems)

Multi-Scale Progressive Digital Terrain Model Construction Method Based on Backpack LiDAR Point Cloud

Multi-Scale Progressive Digital Terrain Model Construction Method Based on Backpack LiDAR Point Cloud

Online Recognition Model Construction Method of Driver Emotion Based on Physiological Feature Mapping

Online Recognition Model Construction Method of Driver Emotion Based on Physiological Feature Mapping

Construction of integration model for regional sub-metre high-resolution remote sensing geographic information

In order to overcome the problem of poor data coordination existing in the existing geographic information integration model, a new regional sub-metre high score remote sensing geographic information integration model construction method is proposed. The application of distributed integration technology to build a regional sub-metre level high score remote sensing geographic information integration model. Joint processing of images, in the integrated environment of GIS and RS, transform the independent variables in the data into a vector grid integrated structure, establish a joint index format based on B+ tree index and quadtree index, complete the RS and GIS model build. Experimental results show that the model can apply street, city name, and place type keywords, with better integrated indexing effect and higher data coordination.

Research on load prediction model construction method of the tire condition monitoring system

Research on load prediction model construction method of the tire condition monitoring system

Stitching and Geometric Modeling Approach Based on Multi-Slice Satellite Images

Time delay and integration (TDI) charge-coupled device (CCD) is an image sensor for capturing images of moving objects at low light levels. This study examines the model construction of stitched TDI CCD original multi-slice images. The traditional approaches, for example, include the image-space-oriented algorithm and the object-space-oriented algorithm. The former indicates concise principles and high efficiency, whereas the panoramic stitching images lack the clear geometric relationships generated from the image-space-oriented algorithm. Similarly, even though the object-space-oriented algorithm generates an image with a clear geometric relationship, it is time-consuming due to the complicated and intensive computational demands. In this study, we developed a multi-slice satellite images stitching and geometric model construction method. The method consists of three major steps. First, the high-precision reference data assist in block adjustment and obtain the original slice image bias-corrected RFM to perform multi-slice image block adjustment. The second process generates the panoramic stitching image by establishing the image coordinate conversion relationship from the panoramic stitching image to the original multi-slice images. The final step is dividing the panoramic stitching image uniformly into image grids and employing the established image coordinate conversion relationship and the original multi-slice image bias-corrected RFM to generate a virtual control grid to construct the panoramic stitching image RFM. To evaluate the performance, we conducted experiments using the Tianhui-1(TH-1) high-resolution image and the Ziyuan-3(ZY-3) triple liner-array image data. The experimental results show that, compared with the object-space-oriented algorithm, the stitching accuracy loss of the generated panoramic stitching image was only 0.2 pixels and that the mean value was 0.799798 pixels, achieving the sub-pixel stitching requirements. Compared with the object-space-oriented algorithm, the RFM positioning difference of the panoramic stitching image was within 0.3 m, which achieves equal positioning accuracy.

Automatic Evaluation System for Piano Performance Based on the Internet of Things Technology under the Background of Artificial Intelligence

Ubiquitous sensors cover many areas of modern society. As the sensor network matures, various applications based on the Internet of Things are setting off a new revolution in all aspects of social life. In order to in-depth study whether the Internet of Things technology can be used in the automatic evaluation of piano performance, this article uses artificial system comparison method, database establishment method, and model construction method to collect samples, analyze the automatic evaluation model, and streamline the algorithm, and based on these foundations, a practical automatic evaluation system for piano performance was created. However, the role of this article does not stop there. There are also a variety of algorithm-like models and the construction of technical models. First, the practicality of the created model is studied, and it is found that the traditional manual recognition rate is about 52%, while the recognition rate of the system in this paper is more than 90%, and the average recognition time of the system is 1.1 s. The start-up process and recognition process time of other systems are all no more than 6 s, indicating the superior performance of the system. On this basis, select the classic piano textbook: Thompson’s Simple Piano Tutorial. From it, select representative pieces as test samples. We can find that the overall F-measure value is above 90%, and the average F-measure value is 96.8%; the system performance test is good and can provide accurate evaluation results for piano learners. The results show that the number of identifications and missing numbers of the system are not much different from those of manual identification, which further proves its superiority. It is basically realized that starting from the Internet of Things technology, a system model that can automatically evaluate most piano repertoires has been designed.

Estimating cotton leaf nitrogen by combining the bands sensitive to nitrogen concentration and oxidase activities using hyperspectral imaging

Oxidase activities (OA) are highly correlated with the nitrogen concentration in crop leaves. To improve the universality and inter-annual repeatability of the model for estimating cotton leaf nitrogen concentration (LNC), a method for model construction was proposed based on the combination of the bands sensitive to LNC with the bands sensitive to OA. In this plot experiment, 320 and 250 sets of hyperspectral data of cotton leaves in seedling stage, bud stage, initial flowering stage, full flowering stage, and boll setting stage were collected in 2019 and 2020, respectively by using hyperspectral technology, and the LNC and OA were also measured in indoor biochemical experiments. Then, successive projection algorithm (SPA) was used to analyze the LNC and OA-sensitive bands in the original spectrum and five kinds of spectral conversions in 2019, to construct the partial least squares regression (PLSR) and principal component regression (PCR) models. Finally, the accuracy of the models were verified using the spectral data in 2020. The results showed that the selection of LNC and OA-sensitive bands could greatly reduce the collinearity and redundant information among bands. The accuracy of the models based on the LNC and OA-sensitive bands in all stages were higher than those of the models based on the LNC-sensitive bands. The optimal was the model based on the malondialdehyde (MDA), peroxidase (POD), and LNC sensitive bands in full flowering stage, with determination coefficients (R2) of 0.846, root mean squared error (RMSE) of 3.081, and residual prediction deviation (RPD) of 2.975. The universality and inter-annual repeatability of the optimal model were significantly improved, with R2 increasing by 12.24%-79.89% and RMSE reducing by 19.80%-72.52%, compared with those of the model based on the LNC-sensitive bands. Besides, the accuracy and stability of PLSR models were significantly higher than those of PCR models. In conclusion, the combination of LNC and OA-sensitive bands could obviously improve the accuracy and universality of the LNC estimation model. This study provides a new method for improving the accuracy and universality of crop nitrogen estimation model.

Modeling the heterogeneity response induced by the cascade collisions of glass-ceramics

An effective molecular dynamics model to describe heterogeneous materials like glass-ceramics is proposed. Based on the model, we find that the velocity direction of the primary knock-on atom (PKA) is more prone to deflect sharply in glassy phases (GP) than that in crystalline phases (CP). And the high-stress region in CP is more concentrated, while the distribution of the high-stress region in GP is relatively discrete. Typical topologies of the Frenkel pairs (FPs) show that the direction of FPs propagation has an obviously turn at the boundary. In the hot peak period, the off-site atoms induced by the cascade collisions in CP could rearrange to avoid the number of defects coming to a high point. However, once the distorted clusters form in GP, the dislocated atoms of the distorted clusters are hard to move back to their site due to the high energy barriers. The annihilation of defects is also blocked in GP. The derived results could help to further understand the evolution mechanism of the heterostructure and defects at the nanoscale. And the method of model construction could contribute to more exciting simulations.

Risk factors for lethal outcome in tuberculosis­associated immune reconstitution inflammatory syndrome with tuberculous lesions of the central nervous system

Objective — to study the relation of death in tuberculosis-associated immune reconstitution inflammatory syndrome (TB-IRIS) and central nervous system (CNS) tuberculosis with the following factors: 1) baseline CD4+ T-lymphocyte count, cells/µL, at the beginning of treatment; 2) the level of viral load at the beginning of treatment; 3) resistance to antimycobacterial drugs (R, HR, HR + others) or its absence at the beginning of treatment; 4) age of patients; 5) gender of patients.
 Materials and methods. 55 cases of neurological TB-IRIS were analyzed. These patients were treated and observed during the 1 year in anti-tuberculosis facilities in Kyiv and Kyiv region in 2017—2019. All patients received ART in accordance with the HIV treatment protocols adopted in Ukraine. Treatment of tuberculosis was carried out taking into account the susceptibility of the pathogen to antimycobacterial therapy according to the standard of tuberculosis treatment adopted in Ukraine. The logistic regression model construction method was used to analyze. A multifactor model for predicting the risk of death in neurological TB-IRIS was built.
 Results and discussion. After calculations, two factor signs were identified, associated with the risk of death: the baseline level of CD4+ lymphocytes in 1 μl of blood at the beginning of treatment and the level of viral load (copies of HIV RNA in 1 μl) at the beginning of treatment.
 Conclusions. It was found that the risk of death in TB-IRIS with tuberculous lesions of the CNS is significantly associated (p < 0.05) with the following factors: 1) the level of CD4+ lymphocytes in 1 μl at the beginning of treatment; 2) the level of viral load at the beginning of treatment (copies of HIV RNA in 1 μl).

Change monitoring and assessment of health capacity of Iraq due to COVID-19 by application of GIS techniques

Infectious disease distribution is fundamentally a spatial process; thus, geospatial data, technology and analysis methods play an essential role in analysing and controlling the 2019 Coronavirus Disease (COVID-19) pandemic. As a result of this virus, there is a rapid increase in the number of infected and deaths distributed in different regions of Iraq. Social functioning, human health, life, human production, and also international relations are extremely threatened by COVID-19. To fight this virus, Geographic Information Systems (GIS) can play an important role in many aspects such as preventing the spread and the evaluating the required measures. GIS is an advanced technique which has a complete method for database, form construction, model construction, and map production geographical health gaps and social weaknesses review, and communicating illness state or return-to-normal operations in hospitals. The use of the GIS has the ability to improve the location and usability of personal security installations, ventilators, hospital beds and other objects. However, strengthen production of GIS to boost our ability to respond rapidly in the face of pandemics that are on the verge of spreading. This study summarizes the main uses of GIS technologies in the global health care sector, highlighting applications related to the modelling and analysis of parasitic diseases. It also allows investigators to connect health public and climate data so that relations of health-related variables and environmental risk factors at various geographical quantities can be analysed.

Methods of constructing models and optimizing the operating modes of a chemical engineering system for the production of benzene in a fuzzy environment

The study object: the chemical engineering system for production of benzene and optimization of the system operation modes based on modeling. An approach to the effective solution of problems of optimization of operating modes of real chemical engineering systems was proposed. Since such systems are usually multicriterial and characterized by the fuzziness of initial information, an approach to the development of their models and optimization of their operating modes in a fuzzy environment was proposed. The essence of this approach lies in the construction of mathematical models and optimization of system operation modes based on the system analysis methodology using available information of deterministic, statistical, and fuzzy nature. Statements of the problems of optimization by means of chemical engineering systems in a fuzzy environment have been obtained by modifying various principles of optimality for working in a fuzzy environment. Based on a modification of the principles of maximin and Pareto optimality, a heuristic algorithm for solving the formulated optimization problem was proposed based on the use of knowledge and experience of decision-makers. The proposed method of model construction and an optimization algorithm were implemented in practice when constructing models of benzene and rectification columns of a chemical engineering system of production of benzene when formulating and solving the problem of optimizing their operation modes in a fuzzy environment. Analysis and comparison of optimization results allow us to conclude about the effectiveness of the proposed fuzzy approach to solving optimization problems in a fuzzy environment. As a result of optimization of the benzene production process, the benzene yield increased by 1.45 thousand t or by 1.1 %, the raffinate output volume increased by 0.4 thousand t in conditions of upholding constraints on benzene quality. The proposed approach makes it possible to assess the degree of upholding of fuzzy constraints

On the topographic entity-oriented digital elevation model construction method for urban area land surface

Human activity transforms a land surface into a complex surface where artificial and natural landforms coexist and continuous and emergent landforms merge. In this background, the problems of conventional digital elevation models (DEMs), such as morphological distortion, complicated updates, and lack of information, are increasingly prominent. This study proposes a new idea of DEM construction based on the concept of geographic ontology. First, landforms with common features are abstracted into a certain type of topographic entity based on their morphologies and semantics. For each type of topographic entity, a DEM was constructed independently based on the available elevation information and other information about the semantics and spatial relationships. Second, individual DEMs were merged into a complete DEM following certain rules. A 1 km2 area located in the suburb of Nanjing, Jiangsu Province, China, was selected as the experimental area. The effectiveness of the model construction method proposed in this study was verified. The results show that the DEM constructed according to the idea of this study has a significantly better performance than the conventional DEMs. The constructed DEM in this study can well represent ground objects, such as slopes, farmland, and ditches. In particular, the constructed DEM ensures the morphological accuracy of the ground objects.

New boundary of digital twin：A multi-sensory oriented model construction method

New boundary of digital twin：A multi-sensory oriented model construction method

Isogeometric analysis of multi-patch solid-shells in large deformation

In the context of isogeometric analysis (IGA) of shell structures, the popularity of the solid-shell elements benefit from formulation simplicity and full 3D stress state. However some basic questions remain unresolved when using solid-shell element, especially for large deformation cases with patch coupling, which is a common scene in real-life simulations. In this research, after introduction of the solid-shell nonlinear formulation and a fundamental 3D model construction method, we present a non-symmetric variant of the standard Nitsche’s formulation for multi-patch coupling in association with an empirical formula for its stabilization parameter. An selective and reduced integration scheme is also presented to address the locking syndrome. In addition, the quasi-Newton iteration format is derived as solver, together with a step length control method. The second order derivatives are totally neglected by the adoption of the non-symmetric Nitsche’s formulation and the quasi-Newton solver. The solid-shell elements are numerically studied by a linear elastic plate example, then we demonstrate the performance of the proposed formulation in large deformation, in terms of result verification, iteration history and continuity of displacement across the coupling interface. In the context of isogeometric analysis (IGA), after introduction of the solid-shell nonlinear formulation and a fundamental 3D model construction method, we present a non-symmetric variant of the standard Nitsche’s formulation for multi-patch coupling in association with an empirical formula for its stabilization parameter. An selective and reduced integration scheme is also introduced to address the locking syndrome. In addition, the quasi-Newton iteration format is derived as solver, together with a step length control method. The second order derivatives are totally neglected by the adoption of the non-symmetric Nitsche’s formulation and the quasi-Newton solver. The performance of the proposed formulation in large deformation is demonstrated by several examples.