Special Regularization Research Articles

Investment Market Trend Forecasting and Strategy Optimization based on Big Data Analytics

The financial market is an important part of the global economy, and there exists a certain special internal regularity in its changes. With the rapid development of big data technology, the huge amount of data in the financial market provides new opportunities for the prediction of investment trends and strategy optimization. Based on big data analytics technology, combined with machine learning models, the study explores how to construct investment market trend prediction models through effective data preprocessing and feature engineering, and evaluates and compares the performance of different prediction models. How to accurately predict the direction of the financial market will be an important reference value for relevant practitioners as well as researchers. Traditional financial time series research mainly focuses on linear trend fitting based on statistical knowledge, however, the trend of the financial market is not limited to pure mathematical and rational function curves, the financial market is affected by a combination of various factors from all walks of life. Big data analytics has significant application potential in the investment market, providing investors with more scientific and accurate decision support.

Review of Some Modified Generalized Korteweg–de Vries–Kuramoto–Sivashinsky Equations (Part II)

In part I of this work to appear in Foudations-MDPI 2024, some existence and uniqueness results for the solutions of some equations were reviewed, such as the Korteweg–de Vries equation (KdV), the Kuramoto–Sivashinsky equation (KS), the generalized Korteweg–de Vries–Kuramoto–Sivashinsky equation (gKdV-KS), and the nonhomogeneous boundary value problem for the KdV-KS equation in quarter plane. The main objective of this paper is to review some results of the existence of global attractors for the evolution equations with nonlinearity of the form N(ux), where ux denotes the derivative of u with respect to x, focusing in particular on the Kuramoto–Sivashinsky equation in one and two dimensions. In order to illustrate the general abstract results, we have chosen to discuss in detail the existence of global attractors for the Kuramoto–Sivashinsky (KS) equation in 1D and 2D. Once a global attractor is obtained, the question arises whether it has special regularity properties. Then we give an integrated version of the homogeneous steady state Kuramoto–Sivashinsky equation in Rn. This work ends with a change from rectangular to polar coordinates in the three-dimensional KS equation to give an energy estimate in this case.

Study of special adaptation mechanisms and morphological regularities of broiler chickens against the background of pharmacological prevention combined pre-slaughter, heat and transport stress in industrial poultry farming

The study was conducted to investigate the adaptation mechanisms of broiler chickens against the background of pharmacological prevention which includes combined pre-slaughter, heat and transport stress in industrial poultry farming. In summer, from clinically healthy Ross 308 chickens three groups of 95648 ± 931 chickens were formed on the principle of analogy. The first group was a control group and it was fed with complete feed. The second group was the first experimental group and it was fed with complete feed which included the anti-stress supplementary feed PIK-antistress at a dose of 1270 g per 1 ton of feed 5 days before slaughter. The third group was the second experimental group and it was fed with the anti-stress supplementary feed PIK-antistress which included L-carnitine at a dose of 1700 g per 1 ton of feed 5 days before slaughter. 38 days later broiler chickens were slaughtered. 3 main periods were commonly studied: the period after fasting, the period after transporting the chickens to the slaughter site and the period just before slaughter. Adaptation processes were developing during the slaughter of broiler chickens in summer. The processes are characterized by increasing the level of the ratio of heterophils to blood lymphocytes on average 2.8 times. The use of the complex food additive PIK-antistress and PIK-antistress in combination with L-carnitine can reduce the activity of stress-implementing mechanisms and increase the survivability of chickens by 3.20 and 4.58 %, respectively and the European productivity index by 32 and 45 units. All the data obtained are consistent with the morphometric studies of the adrenal glands while against the background of the use of anti-stress prophylaxis an increase of core area was noted by 43.0 % and 36.0 % and respectively against the background of a decrease of the area of the cytoplasm of the cortical cords by 11.2 % and 22.8 %, respectively, which indicates stabilization of metabolic processes in the cell.

Nonlinear wave model of towed system and numerical method for its calculation

Towed systems have found wide practical applications. Notable underwater systems for the long-distance extraction of minerals (concretions) from the ocean floor extend over 5-10 km. The existing mathematical models for solving dynamic problems of such systems in various environments are not entirely accurate regarding the diversity of wave processes. This necessitates the development of refined wave models. This article presents a new quasi-linear mathematical model describing the nonlinear four-mode dynamics of a towed system in a spatially inhomogeneous field of mass and surface forces. It is described by a nonlinear system of twelve first-order partial differential equations. The principles of boundedness and hyperbolicity are satisfied. The validation of the two-mode reduction of the model is based on the numerical solution of the problem of the propagation of two waves: longitudinal and configurational. Using a numerical algorithm and a program based on the finite difference method, a comparison of two difference schemes – Crank-Nicolson and Euler – was conducted. The main limitations for applying the finite difference method used for numerical modeling of wave propagation and reflection in a towed system are the peculiarities of the defining quasi-linear equations, which are related to the necessity of simultaneous computation of variables responsible for fast and slow processes. For such systems of equations, the term "singularly perturbed system of equations" is used. These perturbations result from the significant differences in the propagation speeds of longitudinal and configurational waves at the physical level. Consequently, it is necessary to employ special time-stepping regularization and filtering methods for numerical results. This imposes certain restrictions on the ability to model real processes and on the accuracy of the obtained results, necessitating the use of implicit difference schemes and high-frequency filtering.

Monitoring hydrodynamic vessel performance by incremental machine learning using in-service data

ABSTRACT An adaptive machine learning framework is established for an implicit determination of the performance degradation of a ship due to marine growth, i.e. biofouling. The framework is applied in a case study considering telemetry data of a cruise ship operating predominantly in the Caribbean Sea. The dataset encompasses seven years including three dry-docking intervals and several in-water cleaning events. The COVID-19 period receives special focus due to the drastic change in the operational profile. A main outcome of the study is a comparison of the derived performance estimate to the corresponding results of the industry standard ISO 19030. Additional aspects of the present study include the use of special regularization techniques for incremental machine learning and the increase of transparency through the implementation of prediction intervals indicating model uncertainty. Overall, it is found that the developed machine learning framework shows good agreement with the industry standard underlining its plausibility.

Investigation on the influence of seal clearance leakage on the rotating stall characteristics for a centrifugal pump

The occurrence and development of centrifugal pump rotating stall is closely related to disturbance in the incoming flow. The seal clearance leakage is an inherent disturbance in the impeller incoming flow of centrifugal pumps. In order to investigate the seal clearance leakage influence on the rotating stall characteristics, the flow field numerical simulation of a centrifugal pump under rotating stall flow conditions with and without seal clearance are carried out. It is found that under the 0.4Q0 rotating stall condition with seal clearance leakage, the “λ” special shaped high-velocity region distribution along the blade leading edges are strengthened, which lead to larger scale of stall vortices. And the periodicity of the separation vortex evolution is more obvious under the condition with leakage. For a specific stall vortex development process, at the initial format stage, there is a notable disturbance characteristic of medium frequencies band that of 2–3 times the rotation frequency in the pressure fluctuation near the blade leading edges, which is significantly different from that under the condition without leakage. The vorticity analysis shows that this disturbance is closely related to the leakage induced vortices. Affected by the seal clearance leakage disturbance, the stall rotating propagation frequency is reduced from 0.143 times the impeller rotational frequency to 0.103 times. Meanwhile, the fluctuation intensity in the impeller channels is greatly enhanced. Therefore, by judging the special fluctuation frequencies, regularity, and intensity trend in the monitoring signal, it can not only be used to guide the safe operation of centrifugal pumps under off-design conditions but also serve as the basis for judging the wear of the seal, which has important practical significance.

Stability of dynamic fluid transport simulations

Pipeline transport is an efficient method for transporting fluids in energy supply and other technical applications. While natural gas is the classical example, the transport of hydrogen is becoming more and more important; both are transmitted under high pressure in a gaseous state. Also relevant is the transport of carbon dioxide, captured in the places of formation, transferred under high pressure in a liquid or supercritical state and pumped into underground reservoirs for storage. The transport of other fluids is also required in technical applications. Meanwhile, the transport equations for different fluids are essentially the same, and the simulation can be performed using the same methods. In this paper, the effect of control elements such as compressors, regulators and flaptraps on the stability of fluid transport simulations is studied. It is shown that modeling of these elements can lead to instabilities, both in stationary and dynamic simulations. Special regularization methods were developed to overcome these problems. Their functionality also for dynamic simulations is demonstrated for a number of numerical experiments.

Intermittency and Lower Dimensional Dissipation in Incompressible Fluids

In the context of incompressible fluids, the observation that turbulent singular structures fail to be space filling is known as “intermittency”, and it has strong experimental foundations. Consequently, as first pointed out by Landau, real turbulent flows do not satisfy the central assumptions of homogeneity and self-similarity in the K41 theory, and the K41 prediction of structure function exponents ζp=p/3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\zeta _p={p}/{3}$$\\end{document} might be inaccurate. In this work we prove that, in the inviscid case, energy dissipation that is lower-dimensional in an appropriate sense implies deviations from the K41 prediction in every p-th order structure function for p>3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p>3$$\\end{document}. By exploiting a Lagrangian-type Minkowski dimension that is very reminiscent of the Taylor’s frozen turbulence hypothesis, our strongest upper bound on ζp\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\zeta _p$$\\end{document} coincides with the β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-model proposed by Frisch, Sulem and Nelkin in the late 70s, adding some rigorous analytical foundations to the model. More generally, we explore the relationship between dimensionality assumptions on the dissipation support and restrictions on the p-th order absolute structure functions. This approach differs from the current mathematical works on intermittency by its focus on geometrical rather than purely analytical assumptions. The proof is based on a new local variant of the celebrated Constantin-E-Titi argument that features the use of a third order commutator estimate, the special double regularity of the pressure, and mollification along the flow of a vector field.

Boundary contact problems with regard to friction of couple-stress viscoelasticity for inhomogeneous anisotropic bodies (quasi-static cases)

Abstract In this paper, quasi-statical boundary contact problems of couple-stress viscoelasticity for inhomogeneous anisotropic bodies with regard to friction are investigated. We prove the uniqueness theorem of weak solutions using the corresponding Green’s formulas and positive definiteness of the potential energy. To analyze the existence of solutions, we equivalently reduce the problem under consideration to a spatial variational inequality. We consider a special parameter-dependent regularization of this variational inequality which is equivalent to the relevant regularized variational equation depending on a real parameter, and study its solvability by the Galerkin approximate method. Some a priori estimates for solutions of the regularized variational equation are established and with the help of an appropriate limiting procedure, the existence theorem for the original contact problem with friction is proved.

Deep continual hashing for real-world multi-label image retrieval

Deep Hashing has achieved great success in large-scale image retrieval due to binary code’s storage and computation efficiency. However, its learning paradigm under real-world environments is less studied, and most existing approaches are developed in the closed scenario, e.g., simple and unchanging semantics. When images of new classes emerge, they have to retrain the model on all history training datasets, but the constant data uploading makes this impractical. This paper proposes a novel method, called continual deep semantic hashing (CDSH), for learning binary codes of multi-label images with increasing classes. The CDSH consists of two hashing networks. One learns to hash the increasing semantics of data, i.e., label, into the semantic codes and accumulates label-code pairs as long-term knowledge, incorporating empirically verified loss and designed special regularization to ensure encoding old labels unchanged. The other learns to map images to the corresponding semantic code from a probabilistic view and solid knowledge via retaining history exemplar samples and projecting model gradients. We theoretically prove this improves the probability of old data’s code unchanged after the model is updated. Extensive experiments on four widely used image datasets demonstrate that the CDSH method can continually learn hash functions and yield state-of-the-art retrieval performance.

A Systematic Literature Review on Binary Neural Networks

This paper presents an extensive literature review on Binary Neural Network (BNN). BNN utilizes binary weights and activation function parameters to substitute the full-precision values. In digital implementations, BNN replaces the complex calculations of Convolutional Neural Networks (CNNs) with simple bitwise operations. BNN optimizes large computation and memory storage requirements, which leads to less area and power consumption compared to full-precision models. Although there are many advantages of BNN, the binarization process has a significant impact on the performance and accuracy of the generated models. To reflect the state-of-the-art in BNN and explore how to develop and improve BNN-based models, we conduct a systematic literature review on BNN with data extracted from 239 research studies. Our review discusses various BNN architectures and the optimization approaches developed to improve their performance. There are three main research directions in BNN: accuracy optimization, compression optimization, and acceleration optimization. The accuracy optimization approaches include quantization error reduction, special regularization, gradient error minimization, and network structure. The compression optimization approaches combine fractional BNN and pruning. The acceleration optimization approaches comprise computing in-memory, FPGA-based implementations, and ASIC-based implementations. At the end of our review, we present a comprehensive analysis of BNN applications and their evaluation metrics. Also, we shed some light on the most common BNN challenges and the future research trends of BNN.

Retracted: An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal.

[This retracts the article DOI: 10.1155/2022/3408501.].

A Binocular Vision Application in IoT: Realtime Trustworthy Road Condition Detection System in Passable Area

The structural information detection of road conditions, which is adopted for improving driving comfort, patrol inspection, road maintenance, and accident rescue. In order to improve the trustworthiness of road condition detection, a real-time artificial intelligence road detection system based on binocular vision sensors is investigated in this article. The system is deployed on the low-power edge computing platform, which can upload the processing results to the cloud through the Internet-of-Things devices. The authors use binocular disparity information and image-based lightweight deep segmentation network to enhance the detection robustness and accuracy in the industrial Internet-of-Things application scenarios. Considering the small training dataset, a special data labeling regularization and training strategy have also been proposed for training this network. In addition, we employ multiframes feature matching and measurement data filtering to enhance the measurement accuracy. The experimental results demonstrate that our monocular–binocular fusion framework is robust and efficient.

Parcelamento tributário como determinante da agressividade fiscal em empresas brasileiras abertas

ABSTRACT The aim of this article was to study the impact of tax installment plans on the tax aggressiveness of Brazilian listed companies. Despite the relevance and timeliness of the topic, there are no studies that examine the use of special tax installment plans as a determinant of the tax aggressiveness of Brazilian listed companies. Tax installment plans have been used repeatedly by Brazilian companies and the number of adherents has grown significantly over time, where this resource can be understood by companies from the perspectives of tax regularization and tax savings. The research contributes to the scientific knowledge on the subject by providing evidence on the determinants of tax aggressiveness, and it is believed that the government can use the results to improve the offer of tax installment plans and the understanding of tax legislation, such as through the development of restrictions on access to installment plans, by allocating them to companies in real financial difficulties, as well as through improvements in its inspection process, considering that companies that pay taxes in installments are more tax aggressive. The data were obtained from explanatory notes, through the Ministry of the Economy’s access to information service, and from the Economatica® database. The Special Tax Regularization Program (PERT) 2017 was used as a reference for tax installment plans, and tax aggressiveness was measured using the effective tax rate (ETR), ETR Cash, and book tax difference (BTD). The period analyzed is from 2017 to 2020, and the statistical method applied was multiple linear regression for panel data with random effects. The results show that companies that choose to pay their taxes in installments tend to take a more aggressive tax position. The evidence contributes to extending the theory of the determinants of tax aggressiveness and can help the government in its inspections and in improving tax installment offers and tax benefits.

ON THE PROPAGATION OF REGULARITY OF SOLUTIONS TO THE NONLINEAR FIFTH ORDER EQUATION OF KDV TYPE

We investigate special regularity of solutions to the initial value problem associated to the nonlinear fifth order equation of KdV type. The main results show that for datum $u_{0} \in H^{s}(\mathbf{R}), F(u) \in C^{s+2}(\mathbf{R})$ with $s\geq5$, whose restriction belongs to $H^{l}((x_{0},\infty))$ and $H^{l+2}((x_{0},\infty))$ respectively, for some $l \in \mathbb{Z}^{+}$ and $x_{0}\in \mathbf{R}$, then the restriction of the corresponding solution $u(\cdot,t)$ belongs to $H^{l}((b,\infty))$ for any $b\in\mathbf{R}$ and any $t\in (0,T)$. Thus, this type of regularity travels with infinite speed to its left as time evolves. To a certain extent, our results complement the previous studies on the related aspects, and deepen the understanding of such properties for the dispersion equation.

Life cycle of COVID-19 Progression in a Given Region

The study focuses on revealing the typical patterns of COVID-19 spread. The identification of regularities is based on conceptual processing data, in which a sample is approximated by model functions expressing a specific phenomenological concept of the process in question. The reconstruction of desired parameters is hindered by insufficient information on the growth features, the limited volume of the required experimental data, and incomplete elucidation of the phenomenological properties of the objects required to formulate complex models. The investigation addresses these limitations using the Verhulst equation, which expresses the general factors of object growth and adequately describes the growth dynamics in the context of significant theoretical uncertainties regarding an object under study. The need for identifiability and numerical stability of the model parameter estimates is also addressed, and a special regularization approach is applied to obtain an adequate and stable picture of the virus’s evolutionary dynamics in the context of significant uncertainties. The results demonstrate that the growth of the viral population can be integrally described by two variable parameters of the Verhulst equation. Interpreting the reconstructed functions indicates the existence of the typical pattern of seven stages of COVID-19 spread. The possibility of radically managing the number of infected persons is established. The proposed processing of coronavirus cases is of practical value for the preclinical diagnosis and control of virus spread in any geographic area, such as a district, city, local region, or country.

On the minimum problem for non-quasiconvex vectorial functionals

We consider functionals of the form F(u)=∫Ωf(x,u(x),Du(x))dx,u∈u0+W01,r(Ω,Rm),\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\begin{aligned} {\\mathcal {F}}(u) = \\int _{\\Omega } f(x, u(x), D u(x))\\,dx, \\quad u\\in u_0 + W_0^{1,r}(\\Omega ,{\\mathbb {R}}^m), \\end{aligned}$$\\end{document}where the integrand f:Omega times {mathbb {R}}^mtimes {mathbb {M}}^{mtimes n} rightarrow {mathbb {R}} is assumed to be non-quasiconvex in the last variable and u_0 in W^{1,r}(Omega ,{mathbb {R}}^m) is an arbitrary boundary value. We study the minimum problem by the introduction of the lower quasiconvex envelope {overline{f}} of f and of the relaxed functional F¯(u)=∫Ωf¯(x,u(x),Du(x))dx,u∈u0+W01,r(Ω,Rm),\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\begin{aligned} \\overline{{\\mathcal {F}}}(u) = \\int _{\\Omega } {\\overline{f}}(x, u(x), D u(x))\\,dx, \\quad u\\in u_0 + W_0^{1,r}(\\Omega ,{\\mathbb {R}}^m), \\end{aligned}$$\\end{document}imposing standard differentiability and growth properties on {overline{f}}. In addition we assume a suitable structural condition on {overline{f}} and a special regularity on the minimizers of overline{{mathcal {F}}}, showing that under such assumptions {mathcal {F}} attains its infimum. Futhermore, we study the minimum problem for a class of functionals with separate dependence on the gradients of competing maps by the use of integro-extremality method, proving an existence result inspired by analogous ones obtained in the scalar case (m=1). This last argument does not require the special regularity assumption mentioned above but the usual notion of classical differentiability (almost everywhere).

An efficient gradient method with approximately optimal stepsizes based on regularization models for unconstrained optimization

It is widely accepted that the stepsize is of great significance to gradient method. An efficient gradient method with approximately optimal stepsizes mainly based on regularization models is proposed for unconstrained optimization. More specifically, if the objective function is not close to a quadratic function on the line segment between the current and latest iterates, regularization model is exploited carefully to generate approximately optimal stepsize. Otherwise, quadratic approximation model is used. In addition, when the curvature is non-positive, special regularization model is developed. The convergence of the proposed method is established under some weak conditions. Extensive numerical experiments indicated the proposed method is very promising. Due to the surprising efficiency, we believe that gradient methods with approximately optimal stepsizes can become strong candidates for large-scale unconstrained optimization.

An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal.

Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MIT-BIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. Then, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random sampler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. The obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion) based on the Association for the Advancement of Medical Instrumentation (AAMI) is 99.93%, and 99.96% for the proposed Deep-SR model and Deep-NSR model, which demonstrate that the effectiveness of the proposed models has surpassed the state-of-the-art models and also expresses the higher model generalization. The received results with model size suggest that the proposed CNN models especially Deep-NSR could be more useful in wearable devices such as medical vests, bracelets for long-term monitoring of cardiac conditions, and in telemedicine to accurate diagnose the arrhythmia from ECG automatically. As a result, medical costs of patients and work pressure on physicians in medicals and clinics would be reduced effectively.

Structural Formation, Evolution, and Genetic Mechanisms of Fault in Controlling Hydrocarbon Migration of Unconventional Rocks: A Case Study of Zhuangnan Fault

Zhuangnan fault zone was affiliated with Gubei subsag of the Zhanhua sag in Jiyang Depression, Bohai gulf basin. Zhuangnan fault zone has two functions as the main east-west strike-slip accommodation structure. That is, on the one hand, Zhuangnan fault zone inherited the rules of development of regional tectonic. On the other hand, the present structure framework of Zhanhua sag was reconstructed by Zhuangnan fault zone. In the study, there are four main seismic reflectors: T0, T1, T2, and T6. They are the basis for researching the planar and vertical features of Zhuangnan fault zone. The structural formation, evolution, and genetic mechanisms of Zhuangnan fault zone are studied from three aspects of geometry, kinematics, and dynamics. In geometry, by the closure interpretation of 43 south-north interpretation sections and 24 east-west interpretation sections, the result indicates that Zhuangnan fault zone has its special regularity and characteristic which has three-piece planar characterization (eastern part, middle part, and western part). Three types of plane combination forms are determined: arched, linear, and “S” curved type. The plane combination structure styles of the Zhuangnan fault zone mainly include feather row, horsetail, grid, diagonal, and parallel style. These planar structural features play an important role in indicating the fault zoning of Zhuangnan fault zone. Based on the plane and section structural styles of Zhuangnan fault zone and the analysis of dynamic evolution, it is found that Zhuangnan fault zone has a succession relationship between deep and shallow fault systems. From tectonic evolution and regional dynamic point of view, this paper discusses the activity law of the Zhuangnan fault zone and the boundary faults on the east and west sides of Jiyang Depression, which are Changdi fault, Wuhaozhuang fault, and Chengnan fault, respectively. This study provides a new and more reasonable explanation for the unique structural characteristics of Zhuangnan fault zone and further confirms the important role and regulation mechanism of Zhuangnan fault zone.