Graphical Modeling Research Articles

An accurate estimation algorithm for structural change points of multi-dimensional stochastic models

In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multi-dimensional stochastic Graphical Modeling model based on multivariate normal hypothesis is constructed, and the relationship between the Graphical Gaussian model and the linear regression model is determined. The parameters of the multi-dimensional stochastic model are estimated by using the parameter estimation algorithm of the multi-dimensional stochastic model containing intermediate variables. According to the parameter estimation results of the multi-dimensional stochastic model, the structural change point estimation results of the multi-dimensional stochastic model are obtained by using the accurate estimation algorithm of the structural change point based on the MLE identification local drift time. The experimental results show that the proposed algorithm has higher estimation accuracy of structural change points than the control algorithms, which shows that it can effectively estimate the structural change points of multi-dimensional random models and has higher practicability.

Graphic Transfer Matrix Method for Kinetostatic and Dynamic Analyses of Compliant Mechanisms

Abstract Kinetostatic and dynamic analyses of compliant mechanisms with complex configurations continue to be an attractive issue for obtaining a process-concise and result-accurate solution. In this paper, the transfer matrix method (TMM) is improved for a unified linear kinetostatics and dynamic modeling of compliant mechanisms with complex serial-parallel configurations in an oriented graphic way. In detail, the transfer matrices of typical building blocks commonly used in compliant mechanisms are summarized and derived. Then, a graphic transfer matrix modeling procedure capturing both the kinetostatics and dynamics of general compliant mechanisms is introduced. The displacement amplification ratio, input/output stiffness, parasitic error, natural frequencies, and frequency response of a typical compliant microgripper and a planar parallel three-degrees-of-freedom (3DOF) nanopositioner are calculated with such a graphic transfer matrix method. The advantages of the proposed modeling method lie in its convenience and uniformity in formulating both the kinetostatic and dynamic behaviors of a class of compliant mechanisms with distributed and lumped compliances in a transfer matrix manner, which has minimal DOF and is easily programmed.

A Novel Approach for Steady State Calculations of VSC-HVDC Connected PMSG Based Offshore Wind Farms Integrated into Multi-Machine Systems

Offshore wind farms equipped with Direct Drive Permanent Magnet Synchronous Generators (DD-PMSG) are drawing increased attention due to their advantage over other variable speed technologies. VSC-HVDC links are considered the most suitable option for transferring power to the onshore system. The integration of VSC-HVDC connected DD-PMSG based offshore wind farms into multi-machine systems is explored in this paper. A novel approach for power flow and initial condition calculations is proposed to facilitate dynamic analysis of the system. For three cases of the most commonly specified quantities of the wind farm, efficient methods have been described. The cases comprise combinations of data like the total output of the wind farm, the number of wind turbines, the wind speed, or the output of individual wind turbine, which are frequently given in literature. This approach enables the user to build the dynamic model of the system in any basic graphical dynamic modeler and numerical computational software without requiring power system toolboxes or electromagnetic transient packages. The proposed methods are highly effective for studies focusing primarily on the dynamic aspects and controls of the system. Case studies and simulations are conducted to verify the proposed technique.

Professional Author's Training Methods Computer-Graphic Modeling for Participants of the Advanced Engineering School

The article presents a research view on the features of the organization of the process of teaching computer-graphic modeling for participants in the educational process within the framework of an advanced engineering school. It is shown that this direction is not innovative and the creative teams of university engineering schools have already gained some experience in quickly solving typical problems in the process of advanced training of a highly qualified specialist. At the same time, the authors of the article draw attention to circumstances that significantly limit the achievement of ambitious goals pursued in the implementation of pilot educational programs. The application of the participants of the advanced engineering school to bridge the gap between theory and practice looks especially declarative, given that the formation of even basic skills (spatial thinking) for many members of elite engineering training groups requires significant time costs. Today, in Russian universities, this problem is solved by traditional methodological methods as part of the teaching of the discipline "Engineering Geometry and Computer Graphics". The author's methods of teaching computer-graphic modeling outlined in the article are addressed to innovative teachers who realistically assess their capabilities when developing their own design educational products for students who have made a conscious choice in favor of an advanced engineering school. The article was written based on the results of approbation of the author's pedagogical initiatives in the educational process of the university (Perm Polytechnic University).

Genetic modeling of olive fruit using clustering technique.

Data mining is the process of analyzing a quantity of data and finding the relationships between them. Moreover, summarizing them to obtain graphic models, which are recognizable and beneficial to their users by utilizing a set of automated tools to extract knowledge out of its reservoirs without making prior assumptions about what knowledge, might be. The study is a step toward clarifying the principle of the fuzzy clustering algorithm on both practical and theoretical levels. The theoretical section of the research handled with the notion of data clustering and its different types, in addition an illustration of the FCM method. Whereas the practical part, dealt with selection of neighbor FCM method. Furthermore, its application to olive fruits, which were chosen due to the nutritional economic and commercial properties of these olive fruits, depending on some of the properties available in them because those properties belong to more than one variety at the same time, and the detection of ambiguities states when distinguishing between them. As well as knowing, the similarities between them and their complexity to determine the extent of convergence between the olive types. Python language was used to implement the practical side and the algorithm proved highly efficient in determining the genetic characteristics of the olive fruits in the research sample.

Load of a semi-car having the dismountable roof of composites

Purpose. Creation and substantiation of the measures for adopting the supporting structure of the multipurpose semi-car to carry cargoes that need weather protection. Methodology. For the adaptation of supporting constructions of semi-cars to carriage of cargoes that need weather protection, it was proposed to apply a removable roof manufactured from composites. As the application of a removable roof enlarges the volume of the container of the semi-car, the authors defined the load on its construction with the help of mathematical modeling. The authors see semi-car as a system which includes three components – a supporting construction having a roof with two carts. The Runge-Kutta method was used to calculate the main dynamic parameters of the semi-car. These defined dynamic parameters were accounted during calculation of the strength of the dismountable roof. Graphic spatial modeling of a semi-car with a dismountable roof was performed in the SolidWorks software kit. The calculation of solidity is implemented by the method of ultimate elements using the SolidWorks Simulation program kit. The original frequencies and forms of vibrations of the supporting construction of the semi-car are performed in this research. Findings. The outcomes of a mathematical model of the dynamic loading of the supporting construction of the semi-car having a dismountable roof set that the obtained dynamic parameters are not higher than the allowable values. The acceleration of the semi-car supporting construction in the gravity center made approximately 4.5 m/s2. The vertical dynamic ratio amounts to 0.53. The car motion is evaluated as “excellent”. It was found that when the vertical load acts on the dismountable roof, the maximum tension emerging inside is 173.2 MPa. When concentrated load affects the dismountable roof, the biggest tensions are focused on the zones of its attachment to the upper strapping of the casing, and makes 205.7 MPa. It can be seen from the modal analysis that the first original frequency of the semi-car casing is equal to 9.6 Hz, which is more than the allowable value – 8 Hz. Originality. Models for determining the loading of the supporting construction of the semi-car having a dismountable roof from composites within the general modes of loading operation are given in this study. Practical value. The study results allow expanding the applicability of semi-cars by adapting them to the carriage of cargoes, which need protection from weather effects and can increase the performance of railroad carriage.

Estimating the Global Spread of Epidemic Human Monkeypox with Bayesian Directed Acyclic Graphic Model.

A "Public Health Emergency of International Concern (PHEIC)" monkeypox outbreak was declared by the World Health Organization on 23 June 2022. More than 16,000 monkeypox cases were reported in more than 75 countries across six regions as of July 25. The Bayesian SIR (Susceptible-Infected-Recovered) model with the directed acyclic graphic method was used to estimate the basic/effective reproductive number (R0/Re) and to assess the epidemic spread of monkeypox across the globe. The maximum estimated R0/Re was 1.16 (1.15-1.17), 1.20 (1.20-1.20), 1.34 (1.34-1.35), 1.33 (1.33-1.33) and 2.52 (2.41-2.66) in the United States, Spain, Brazil, the United Kingdom and the Democratic Republic of the Congo, respectively. The values of R0/Re were below 1 after August 2022. The estimated infectious time before isolation ranged from 2.05 to 2.74 days. The PHEIC of the global spreading of human monkeypox has been contained so as to avoid a pandemic in the light of the reasoning-based epidemic model assessment.

Upaya Meningkatkan Motivasi Belajar Materi Sistem Klasifikasi Makhluk Hidup dengan Model Pembelajaran Kooperatif Jigsaw

Pontianak 3 Public Middle School, especially in class VIIF, there are indications that students are less interested in science subjects. This is based on the results of the questionnaire that the researcher distributed in this class, before the class action was carried out. 70% of students in class VIII F consider science as a difficult subject, with lots of foreign and uninteresting terms. That's why researchers try to apply variations of the TIP (Know-Want-Learn) Jigsaw Assisted Graphic Cooperative Learning model to overcome this. This learning model is quite interesting, because it is assisted by the TIP Graphic Manager which is accompanied by colorful pictures and worksheets which can bring up students' ideas in the learning process. The results of observations during the study showed that students began to appear enthusiastic when the jigsaw type cooperative learning model assisted by the TIP graphic controller was applied in class VII F. Students were asked to communicate the results of their understanding during the learning process. When studying in groups all students can complete assignments on time. And the result, 92% of students from 33 students can achieve the KKM score. While the results of the pretest, there were 29 students who were able to achieve the KKM or 81%. There was an increase of 13%. Based on this research, the researchers suggest that science teachers at SMPN 3 Pontianak try to apply a jigsaw type cooperative learning model assisted by a TIP graphical controller when learning with material on classification systems in living things. The creativity of extraordinary students will be seen when they present their skills. Besides being able to increase learning motivation, this method can also be used for habituation which can foster creativity besides that this method is also to train students to be confident in communicating so that students can express their ideas when the learning process takes place.

Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning

Efficient and feasible anomaly detection scheme that could utilize data collected by supervisory-control-and-data-acquisition (SCADA) system is essential for wind turbines, which could greatly increase the amount of available condition monitoring data, and improve the power generation efficiency and reduce maintenance costs. While it is also very challenging as condition estimation with such massive field data is difficult to tackle, especially the SCADA data is not labeled in most cases. This work presents an unsupervised anomaly detection framework for wind turbines incorporating physical-statistical feature fusion and graph neural networks (GNNs), realizing dimensionality reduction, temporal dependence extraction, and latent nonlinear correlation capture of high-dimensional data. Firstly, graphical modeling for SCADA data of wind turbines is presented. Secondly, the physical-statistical feature fusion is implemented via local-global mutual information maximization. Finally, anomaly detection is realized with an energy-based method to learn patterns in the updated nodes' feature matrix. The results show that i) the features designed by physical information can reasonably represent equipment's state and reduce the information-redundancy, ii) the time-series based graph structure can effectively express the dataset structure information and extract temporal dependence, iii) and the anomaly detection model can fully use the physical-statistical information and local-global information, which outperforms comparison methods.

Разработка интегрированной пространственно-временной базы данных, характеризующей последствия изменения климата

Currently, at the global and regional (national) levels, the expert community, as well as statesmen, have prioritized the task of practical implementation of the Sustainable Development Goals (characterizing the dynamics of a development of various parameters of socio-ecological and economic systems). Achieving these goals requires serious analytical work based on a deep and comprehensive analysis of a processes taking place in various spheres, as well as the formation of an appropriate information platform, including a set of databases that adequately describe changes taking place in various spheres of state activity. However, today, in Russian practice, this tool is practically absent, which significantly complicates the qualitative and quantitative analysis, assessment and forecasting of the processes of adaptation of Russian regions (including the population living in them) to consequences of global climate change. The purpose of this study is to form a database of indicators characterizing process of adaptation of socio-ecological and economic systems of a northern regions of Western Siberia (The Khanty-Mansiysk Autonomous Okrug and Yamalo-Nenets Autonomous Okrug) to global climate change. The projected database should become the basis for creating an open information resource for a wide range of users when they solve analytical and predictive tasks related to a socio-economic assessment of an impact of global climate warming on permafrost. To achieve the objectives of the study, a set of methods was used: theoretical generalization and comparison, formalization, algorithmization, structuring and grouping, economic and statistical methods (including correlation and regression analysis), cartographic method, graphical modeling method. As a result, we have formed a database representing a single, periodically updated repository containing spatio-temporal data sets characterizing processes and phenomena, on the one hand, socio-ecological and economic systems, on the other – climate change. Further work with the database assumes, on the basis of available quantitative data and methods of mathematical statistics, the identification of elements of the ecological, socio-economic systems of the KhMAO and YaNAO vulnerable to global climate change, as well as forecasting the dynamics of their (systems) transformation.

PELATIHAH DASAR PENGGUNAAN APLIKASI MICROSOFT OFFICE DI MTS INSAN KREASI

Microsoft Word socialization is a word processing activity using the Windows operating system which is reliable enough to provide convenience for users. Besides being easy to operate, the variations and styles of letters use a full graphic model. In Microsoft Word, the use of commands is usually more than one. You can do this through the menus and toolbars in application programs or via the mouse and keyboard. Microsoft Word is one of the members of Microsoft Office that is often used in terms of the most sophisticated word processing today. The current use of Microsoft Word is only part of the functions that are used, there are still many that have not been used optimally. Many people who type only by relying on the ability of their fingers actually Microsoft Word already provides features that can make us even faster in typing. The results of this socialization add knowledge and a spirit of independence to the rest and female students. It is hoped that the knowledge gained at Community Service this time will be able to provide new enthusiasm for us in sharing knowledge and motivation and contributing to the younger generation, both within the community and family.

FedGR: Federated Graph Neural Network for Recommendation Systems

Social recommendation systems based on the graph neural network (GNN) have received a lot of research-related attention recently because they can use social information to improve recommendation accuracy and because of the benefits derived from the excellent performance of the graph neural network in graphic data modeling. A large number of excellent studies in this area have been proposed one after another, but they all share a common requirement that the data should be centrally stored. In recent years, there have been growing concerns about data privacy. At the same time, the introduction of numerous stringent data protection regulations, represented by general data protection regulations (GDPR), has challenged the recommendation models with conventional centralized data storage. For the above reasons, we have designed a flexible model of recommendation algorithms for social scenarios based on federated learning. We call it the federated graph neural network for recommendation systems (FedGR). Previous related work in this area has only considered GNN, social networks, and federated learning separately. Our work is the first to consider all three together, and we have carried out a detailed design for each part. In FedGR, we used the graph attention network to assist in modeling the implicit vector representation learned by users from social relationship graphs and historical item graphs. In order to protect data privacy, we used FedGR flexible data privacy protection by incorporating traditional cryptography encryption techniques with the proposed “noise injection” strategy, which enables FedGR to ensure data privacy while minimizing the loss of recommended performance. We also demonstrate a different learning paradigm for the recommendation model under federation. Our proposed work has been validated on two publicly available popular datasets. According to the experimental results, FedGR has decreased MAE and RMSE compared with previous work, which proves its rationality and effectiveness.

Bayesian statistics study of a sustainable dissolution of cobalt‐bearing minerals from Cu‐Co ores

AbstractThe present paper discusses the dissolution of cobalt‐bearing minerals from a copper‐cobalt ore using probabilistic models where a priori and a posteriori knowledge of leaching are used to predict the dissolution of cobalt‐bearing minerals in a sulphuric acid medium in the presence of a reducing agent. Priorly, the dissolution of cobalt‐bearing minerals depends on their mineralogy, leading to the use of FeSO4 as a reducing agent for the trivalent (Co3+) form of cobalt (CoOOH). A posteriori, the dissolution of Co3+ is improved by the presence of ferrous ions, resulting from the dissolution of Fe‐bearing minerals, including Fe from Co(Fe)OOH. The results showed that the predictive‐oriented probabilistic graphic models based on the Bayesian approach, in combination with the design of the experiment data, made it possible to model the leaching of cobalt‐bearing minerals. The results from the design of the experiment using the experimental tree methodology associated with the optimization of the multiple responses in a multiple input for a multiple output set‐up derived the following optimized parameters: 60°C for the temperature (T), 850 rpm for the agitation, 40% for the solid percentage, 1.5 for the pH, and 4 g/L for the concentration of the Fe2+ ion. The cobalt dissolution yield obtained was 89.95%. The analysis of the dependence between the random variables only (P(Fe2+|T), P(pH|T), and P(Fe2+|pH)) and the dependence between the random variables and the responses (P(Co‐yield|pH, Eh)) allowed the construction of two Bayesian networks, respectively, with and without posterior knowledge. For the Bayesian network with posteriori knowledge, the {5–2} structure was found to be the most appropriate arrangement. The model predicted a cobalt yield value, and the experimental value indicated a correlation coefficient (R2) of 0.861.

Development and Finite Element Analysis of a Cantilever Beam Apparatus used in Strength of Materials Laboratory

The development of a cantilever beam apparatus for the teaching of undergraduates students in Strength of Materials laboratory is successfully presented. This development of the laboratory apparatus is targeted at building a local content in the production of equipment. It tends to eliminate the dependence on foreign technology which appears to be challenging in the face of scarce foreign exchange. The laboratory apparatus was designed using engineering formulae to determine the maximum bending moment, maximum shear stress, factor of safety and the Von Misses stress of the beam to be749.95Nm, 11.26Mpa, 2.36 and 112.28Mpa respectively. The apparatus was designed to have a strength to weight ratio of 5.3Mpa/N. A graphical modeling was carried out using AutoCAD and Finite Element ANSYS Workbench which yielded an equivalent Von Misses stress of the apparatus to be 115Mpa. Keywords: Cantilever beam, apparatus,Von Misses stress, finite element and materials DOI: 10.7176/JIEA/13-1-01 Publication date: February 28 th 2023

Time Series Prediction Method of Metrics of Dispatching Automation System Based on AI Platform

In view of the shortcomings of manual operation mode and passive anomaly perception in the current dispatching automation system, this paper provides a time series prediction method for the metrics of the dispatching automation system based on the artificial intelligence platform. Firstly, based on the collected metrics of the dispatching automation system, the time series prediction algorithm of metrics based on Long Short-Term Memory(LSTM) and LightGBM(LGB) combined model is studied. Then, the algorithm is integrated into a one-stop graphical interactive modeling tool and the model training process is drawn. Through the task is automatic split and distributed model training is automatically carried out on a regular basis. Then, the AI model service sharing technology based on Kubernetes is used to realize the model service release and automatic update, and the platform interactive development component is used to realize automatic online prediction and rolling iterative update. Finally, the actual data of the system metrics is used for example analysis, and the results show that the proposed method can realize the advance prediction of the system metrics, and realize the whole process automatic integration model training and prediction based on the artificial intelligence platform, and effectively improve the model training efficiency, meet the real-time and security requirements of the dispatching automation system, and is more suitable for engineering application scenarios.

Multiple-Walrasian Auction Mechanism for Tree Valuation Service in NFV Market

As a new and effective technology, network function virtualization (NFV) manages network nodes to improve service performance for the next-generation network. Despite its promised effectiveness, efficiently deploying the functions to different consumers is the major difficulty presented in NFV. In this article, the multiple-Walrasian auction graphic model combined with virtual network functions of different bundled tree nodes based on Vickrey–Clarke–Grove (VCG) payment is proposed to maximize the social effectiveness of NFV. It is the first work to define the virtualized service as a tree valuation in the NFV market. To solve this problem, novel algorithms, including the valuation structure algorithm and auction strategy, are implemented along with the NFV to schedule network resources. With all theories considered, we conducted a comprehensive simulation to verify the tree valuation mechanism. The results confirmed that the tree valuation mechanism outperforms the backpack auction model and reserve auction model with respect to social welfare.

Designing Reconfigurable Cyber-Physical Systems Using Unified Modeling Language

Technological progress in recent years in the Cyber-Physical Systems (CPSs) area has given designers unprecedented possibilities and computational power, but as a consequence, the modeled CPSs are becoming increasingly complex, hierarchical, and concurrent. Therefore, new methods of CPSs design (especially using abstract modeling) are needed. The paper presents an approach to the CPS control part modeling using state machine diagrams from Unified Modelling Language (UML). The proposed design method attempts to combine the advantages of graphical notation (intuitiveness, convenience, readability) with the benefits of text specification languages (unambiguity, precision, versatility). The UML specification is transformed using Model-Driven Development (MDD) techniques into an effective program in Hardware Description Language (HDL), using Concurrent Finite State Machine (CFSM) as a temporary model. The obtained HDL specification can be analyzed, validated, synthesized, and finally implemented in Field Programmable Gate Array (FPGA) devices. The dynamic, partial reconfiguration (a feature of modern FPGAs) allows for the exchange of a part of the implemented CPS algorithm without stopping the device. But to use this feature, the model must be safe, which in the proposed approach means, that it should possess special idle states, where the control is transferred during the reconfiguration process. Applying the CFSM model greatly facilitates this task. The proposed design method offers efficient graphical modeling of a control part of CPS, and automatic translation of the behavior model into a synthesizable Verilog description, which can be directly implemented in FPGA devices, and dynamically reconfigured as needed. A practical example illustrating the successive stages of the proposed method is also presented.

Capitalization of Tourist Resources in the Post-COVID-19 Period—Developing the Chorematic Method for Oltenia Tourist Destination, Romania

The paper aims at adapting the knowledge regarding the capitalization of tourist resources to the method of tourist choremas in the Oltenia region, Romania, thus creating a tool for planning and organizing the geographical space for sustainable development. The tourism choremas method uses the graphic and cartographic modelling of the territory of the South-West Oltenia region, and in a post-COVID-19 period it is important that the research can help in increasing the productivity in tourism, which can lead to higher incomes for managers in the tourism sector and for the locals from different rural/disadvantaged area of the region. GIS (Geographical Information Systems) represents a key element for the future development of tourism in the virtual environment, given the fact that many travellers use virtual electronic systems to see the tourist destination, surroundings, protected areas, accommodation, tourist routes, recreation and entertainment areas (zoos, hunting areas), etc. Choremes of the Oltenia region were created using ArcGIS 10.1 software, showing different entities and a series of attributes (descriptive elements) marked on the created maps as follows: elements of the natural environment (landforms altimetry, hydrographic network, lakes, national parks and reserves, waterfalls); man-made environment (roads, landmarks—churches, monasteries, museums); or conventional delimitations (county boundary, the boundaries of the two areas—Northern and Southern Oltenia). Four successive stages were conducted in order to create the choremes using the GIS software: (1) creating the database for the analysis of the studied destination; (2) processing the statistical data for Northern and Southern Oltenia; (3) the interpretation of the data obtained in correlation with the specialized literature on the capitalization of tourism resources and forms of tourism existing in the region; and (4) cartographic representation. The chorematic maps are represented at the scale of the geographical territory where the study analysis is carried out. The representation of physico-geographical elements, but also of touristic elements, was represented by lines, points and polygons, using the vector and raster spatial data model. The chorematic method proves to be innovative and up-to-date especially for the organization of the tourist space, for the sustainable development of the region and for the tourism research activity in the area.

Bayesian Modeling of the Mnemonic Similarity Task Using Multinomial Processing Trees.

The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings. We develop new cognitive models, based on the multinomial processing tree framework, for two versions of the MST. The models are implemented as generative probabilistic models and applied to behavioral data using Bayesian graphical modeling methods. We demonstrate how the combination of cognitive modeling and Bayesian methods allows for flexible and powerful inferences about performance on the MST. These demonstrations include latent-mixture extensions for identifying individual differences in decision strategies, and hierarchical extensions that measure fine-grained differences in the ability to detect lures. One key finding is that the availability of a "similar" response in the MST reduces individual differences in decision strategies and allows for more direct measurement of recognition memory.

(Retracted) Multidimensional graphic art design method based on visual analysis technology in intelligent environment

In the last few years, the artificial intelligence technology has provided unique methods for design analysis in the art field. With the development of China’s economy and cultural prosperity, graphic design has changed people’s lives greatly. Under the concept of modern scientific and technological creation, the variety, scale, and plasticity of graphic design art are constantly improving, and the diversification of design elements is becoming the development trend. We designed a graphic art element recognition model based on single shot multibox detector (SSD) method through deep learning of visual processing technology. This method can automatically identify various elements in multidimensional graphic art works, thus helping artists and learners analyze an art work better. In this method, we take the SSD structure as the main model backbone and use the improved attention mechanism module feature pyramid transformer to replace the original feature fusion module, inject long-distance dependency into the model, and improve the accuracy of object detection. In addition, we use the public dataset to make the relevant image target detection dataset. Different object detection evaluation metrics are used to evaluate the proposed methods, and several existing methods are selected for comparative experiments. Compared with YOLO V5 object detection model, our method improves 0.53%, 0.67%, 1.33%, and 1.28% on pixel accuracy, mean pixel accuracy, average recall, and mean intersection over union, respectively. The proposed algorithm has a great contribution to the performance improvement of object detection and the auxiliary analysis of multidimensional works of art.