YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Dynamic response of multi-scale structure in flexible pavement to moving load

In order to study the dynamic responses in the microstructures of the pavement structure, the multi-scale modeling subjected to moving load is analyzed using the discrete element method （DEM）. The macro-scale discrete element model of the flexible pavement structure is established. The stress and strain at the bottom of the asphalt concrete layer under moving load are calculated. The DEM model is validated through comparison between DEM predictions and the results from the classical program. Based on the validated macro-scale DEM model, the distribution and the volumetric fraction of coarse aggregate, mastics and air voids at the bottom of the asphalt layer are modeled, and then the multi-scale model is constructed. The dynamic response in the microstructures of the multi-scale model are calculated and compared with the results from the macro model. The influence of mastic stiffness on the distribution of dynamic response in the microstructures is also analyzed. Results show that the average values and the variation coefficient of the tensile stress at the aggregate-mastic interface are far more than those within the mastics. The dynamic response including stress and strain distributes non-uniformly in both mastics and the interface. An increase in mastic stiffness tends to a uniform distribution of tensile stress in asphalt concrete.

Study on the coupling calculation method for the launch dynamics of a self-propelled artillery multibody system considering engraving process

The launch dynamics theory for multibody systems emerges as an innovative and efficacious approach for the study of launch dynamics,capable of addressing the challenges of complex modeling,diminished computational efficiency,and imprecise analyses of system dynamic responses found in the dynamics research of intricate multi-rigid-flexible body systems,such as self-propelled artillery.This advancement aims to enhance the firing accuracy and launch safety of self-propelled artillery.Recognizing the shortfall of overlooking the band engraving process in existing theories,this study introduces a novel coupling calculation methodology for the launch dynamics of a self-propelled artillery multibody system.This method leverages the ABAQUS subroutine interface VUAMP to compute the dynamic response of the projectile and barrel during the launch process of large-caliber self-propelled artillery.Additionally,it examines the changes in projectile resistance and band deformation in relation to projectile motion throughout the band engraving process.Comparative analysis of the computational outcomes with experimental data evidences that the proposed method offers a more precise depiction of the launch process of self-propelled artillery,thereby enhancing the accuracy of launch dynamics calculations for self-propelled artillery.

Deep structure of the Southeast Asian curved subduction system and its dynamic process

The deep structure,material circulation,and dynamic processes in the Southeast Asia have long been an elusive scientific puzzle due to the lack of systematic scientific observations and recognized theoretical models.Based on the deep seismic tomography using long-period natural earthquake data,in this study,the deep structure and material circulation of the curved subduction system in Southeast Asia was studied,and the dynamic processes since 100 million years ago was reconstructed.It is pointed out that challenges still exist in the precise reconstruction of deep mantle structures of the study area,the influence of multi-stage subduction on deep material exchange and shallow magma activity,as well as the spatiotemporal evolution and coupling mechanism of multi-plate convergence.Future work should focus on high-resolution land-sea joint 3-D seismic tomography imaging of the curved subduction system in the Southeast Asia,combined with geochemical analysis and geodynamic modelling works.

A process monitoring method for autoregressive-dynamic inner total latent structure projection

As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.

Evolutionary game dynamics of combining the Moran and imitation processes

One of the assumptions of previous research in evolutionary game dynamics is that individuals use only one rule to update their strategy. In reality, an individual's strategy update rules may change with the environment, and it is possible for an individual to use two or more rules to update their strategy. We consider the case where an individual updates strategies based on the Moran and imitation processes, and establish mixed stochastic evolutionary game dynamics by combining both processes. Our aim is to study how individuals change strategies based on two update rules and how this affects evolutionary game dynamics. We obtain an analytic expression and properties of the fixation probability and fixation times(the unconditional fixation time or conditional average fixation time) associated with our proposed process. We find unexpected results. The fixation probability within the proposed model is independent of the probabilities that the individual adopts the imitation rule update strategy. This implies that the fixation probability within the proposed model is equal to that from the Moran and imitation processes. The one-third rule holds in the proposed mixed model. However, under weak selection, the fixation times are different from those of the Moran and imitation processes because it is connected with the probability that individuals adopt an imitation update rule. Numerical examples are presented to illustrate the relationships between fixation times and the probability that an individual adopts the imitation update rule, as well as between fixation times and selection intensity. From the simulated analysis, we find that the fixation time for a mixed process is greater than that of the Moran process, but is less than that of the imitation process. Moreover, the fixation times for a cooperator in the proposed process increase as the probability of adopting an imitation update increases; however, the relationship becomes more complex than a linear relationship.

A wind tunnel simulation of the dynamic processes involved in sand dune formation on the western coast of Hainan Island

The western coast of Hainan Island exhibits a savanna landscape. Many types of sand dunes, including transverse dune ridges, longitudinal dune ridges, elliptical dunes, coppice dunes, and climbing dunes, are widely distributed in the coastal zone. In winter, high-frequency and high-energy NE winds （dominant winds） are prevalent, with a resultant drift direction （RDD） of S35.6°W. In spring, low-frequency and low-energy SW secondary winds prevail, with a RDD of N25.1°E. Wind tunnel simulations revealed that the airflow over the dune surface is the main factor controlling the erosion and deposition patterns of dune surfaces and the morphological development of dunes. In the region＇s bidirectional wind environment, with two seasonally distinct energy levels, the airflow over the surface of elliptical dunes, barchan dunes, and transverse dune ridges will exhibit a transverse pattern, whereas the airflow over longitudinal dunes ridges exhibits a lateral pattern and that over climbing dunes exhibits a climbing-circumfluent pattern. These patterns represent different dynamic processes. The coastal dunes on the western coast of Hainan Island are influenced by factors such as onshore winds, sand sources, coastal slopes, rivers, and forest shelter belts. The source of the sand that supplements these dunes particularly influences the development pattern： when there is more sand, the pattern shows positive equilibrium deposition between dune ridges and dunes; otherwise, it shows negative equilibrium deposition. The presence or absence of forest shelter belts also influences deposition and dune development patterns and transformation of dune forms. Coastal dunes and inland desert dunes experience similar dynamic processes, but the former have more diversified shapes and more complex formation mechanisms.

A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.

MULTI-SCALE GAUSSIAN PROCESSES MODEL

A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a linear basis that is composed of a scale function and its different translations. Finally the distribution of the targets of the given samples can be obtained at different scales. Compared with the standard Gaussian Processes (GP) model, the MGP model can control its complexity conveniently just by adjusting the scale pa-rameter. So it can trade-off the generalization ability and the empirical risk rapidly. Experiments verify the fea-sibility of the MGP model, and exhibit that its performance is superior to the GP model if appropriate scales are chosen.

Dynamics of Ore-Forming Processesof the Stratabound Skarn Copper Depositsof Tongling, Anhui Province

The skarn and ore bodies of the stratabound skarn copper deposits of Tongling, Anhui Province, are both controlled by definite stratigraphic horizons, and they are concordant with the strata. They occur as layers and layer-like bodies in permeable carbonate rocks of the Middle-Upper Carboniferous Huanglong and Chuanshan Formations which are underlain by impermeable shale or siliceous rocks of the Upper Devonian Wutong Formation. The authors study the dynamics of ore-forming processes of the ore deposits with the dynamic model of coupled transport and reaction, and the following results are obtained: The salinity gradient and flow rate of the ore-forming fluids can both promote the mixing and reaction of juvenile water and formation water, and the permeable strata are favourable sites for the intense transport-reaction of mixing and the formation of deposits. (2) As isothermal transport-reaction took place along the bedding of strata, the moving transport-reaction front formed at the contact between the ore-forming fluids and the rocks advanced slowly along the permeable strata, and then stratiform skarn and ore bodies concordant with the strata were formed. (3) The gradient transport-reaction taking place across the isotherms in the cross-bedding direction caused the mineralogical composition to alter gradually from magnesian skarn to sulphide ore bodies.

A Multi-Scale Study on Silicon-Oxide Etching Processes in C_4F_8/Ar Plasmas

A multi-scale numerical method coupled with the reactor,sheath and trench model is constructed to simulate dry etching of SiO_2 in inductively coupled C_4F_8 plasmas.Firstly,ion and neutral particle densities in the reactor are decided using the CFD-ACE＋ commercial software.Then,the ion energy and angular distributions（IEDs and IADs） are obtained in the sheath model with the sheath boundary conditions provided with CFD-ACE＋.Finally,the trench profile evolution is simulated in the trench model.What we principally focus on is the effects of the discharge parameters on the etching results.It is found that the discharge parameters,including discharge pressure,radio-frequency（rf） power,gas mixture ratios,bias voltage and frequency,have synergistic effects on IEDs and IADs on the etched material surface,thus further affecting the trench profiles evolution.

Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

Progressive failure processes of reinforced slopes based on general particle dynamic method

In order to resolve grid distortions in finite element method(FEM), the meshless numerical method which is called general particle dynamics(GPD) was presented to simulate the large deformation and failure of geomaterials. The Mohr-Coulomb strength criterion was implemented into the code to describe the elasto-brittle behaviours of geomaterials while the solid-structure(reinforcing pile) interaction was simulated as an elasto-brittle material. The Weibull statistical approach was applied to describing the heterogeneity of geomaterials. As an application of general particle dynamics to slopes, the interaction between the slopes and the reinforcing pile was modelled. The contact between the geomaterials and the reinforcing pile was modelled by using the coupling condition associated with a Lennard-Jones repulsive force. The safety factor, corresponding to the minimum shear strength reduction factor "R", was obtained, and the slip surface of the slope was determined. The numerical results are in good agreement with those obtained from limit equilibrium method and finite element method. It indicates that the proposed geomaterial-structure interaction algorithm works well in the GPD framework.

Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes

Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution （DE） algorithm and control vector parameteri- zation （CVP） is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods.

Multi-scale calculation of settling speed of coarse particles by accelerated Stokesian dynamics without adjustable parameter

The calculation of settling speed of coarse particles is firstly addressed, with accelerated Stokesian dynamics without adjustable parameters, in which far field force acting on the particle instead of particle velocity is chosen as dependent variables to consider inter-particle hydrodynamic interactions. The sedimentation of a simple cubic array of spherical particles is simulated and compared to the results available to verify and validate the numerical code and computational scheme. The improved method keeps the same computational cost of the order O（NlogN） as usual accelerated Stokesian dynamics does. Then, more realistic random suspension sedimentation is investigated with the help of Mont Carlo method. The computational results agree well with experimental fitting. Finally, the sedimentation of finer cohesive particle, which is often observed in estuary environment, is presented as a further application in coastal engineering.

On-line Dynamic Model Correction Based Fault Diagnosis in Chemical Processes

<Abstract>A novel fault detection and diagnosis method was proposed, using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur. It aims at parameter failures, and the parameters are updated via on-line correction. As it can predict the trend of process and determine the existence of malfunctions simultaneously, this method does not need to design problem-specific observer to estimate unmeasured state variables. Application of the proposed method is presented on one water tank and one aromatization reactor, and the results are compared with those from the traditional method.

A spatial-explicit dynamic vegetation model that couples carbon,water,and nitrogen processes for arid and semiarid ecosystems

Arid and semiarid ecosystems, or dryland, are important to global biogeochemical cycles. Dryland＇s community structure and vegetation dynamics as well as biogeochemical cycles are sensitive to changes in climate and atmospheric composition. Vegetation dynamic models has been applied in global change studies, but the com- plex interactions among the carbon （C）, water, and nitrogen （N） cycles have not been adequately addressed in the current models. In this study, a process-based vegetation dynamic model was developed to study the responses of dryland ecosystems to environmental changes, emphasizing on the interactions among the C, water, and N proc- esses. To address the interactions between the C and water processes, it not only considers the effects of annual precipitation on vegetation distribution and soil moisture on organic matter （SOM） decomposition, but also explicitly models root competition for water and the water compensation processes. To address the interactions between C and N processes, it models the soil inorganic mater processes, such as N mineralization/immobilization, denitrifica- tion/nitrification, and N leaching, as well as the root competition for soil N. The model was parameterized for major plant functional types and evaluated against field observations.

START-UP AND DYNAMIC PROCESSES SIMULATION OF A DISTILLATION COLUMN

A new dynamic non-equilibrium mixing-pool model for simulating start-up and dynamic re-sponse of a distillation column is reported.The proposed model is established on the basis ofconsidering the two dimensional flow/mixing behavior of actual trays in a distillation column.Com-parison is made among the computed results of the start-up time and the dynamic response time bythe proposed and five other typical models.It is found that the computed time for both dynamicprocesses is longer by the model which considers any flow/mixing pattern than by the model withoutsuch concern.The inertia effect of flow/mixing seems to be important and can not be ignored inmodeling the transient process of distillation.The proposed model,which is believed to be suitableto large column,seems somewhat useful in predicting industrial distillation dynamics.

Transient dynamic analysis and comparison on wedging processes of overrunning clutches with different contact surfaces

The effects of contact surface on dynamic wedging behavior of the roller and inner-ring of the overrunning clutch in a dual-turbine torque converter were investigated to reveal the friction self- locking mechanism and dynamic process. Planar strain clutch models including roller, inner-ring and outer-ring were built, and transient wedging process was analyzed with an explicit dynamics meth- od. The modeling of stress and strain distribution and variation of two kinds of contact surfaces show that there are three stages named slipping, wedging and binding respectively during whole wed- ging process. Meanwhile the geometric structures of contact surfaces greatly influence the peak stress and strain distribution of the wedging process of the roller and inner-ring. The load bearing performance of contact surfaces with logarithmic spiral curve is better than that with straight line. Our study provides theoretical foundation for design and further optimization of wedging contact surface of an overrunning clutch in a dual-turbine hydrodynamic torque converter.

Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes

This paper intends to develop suitable methods to provide likely scenarios in order to support decision making for slow dynamic processes such as the underlying of agribusiness. A new method to analyze the short- and long-term time series forecast and to model the behavior of the underlying process using nonlinear artificial neural networks (ANN) is presented. The algorithm can effectively forecast the time-series data by stochastic analysis (Monte Carlo) of its future behavior using fractional Gaussian noise (fGn). The algorithm was used to forecast country risk time series for several countries, both for short term that is 30 days ahead and long term 350 days ahead scenarios.