Dynamic Parameter Optimization Research Articles

More is better? The impact of predictor choice on the INE oil futures volatility forecasting

This paper aims to address the predictor choice issue in forecasting volatility of INE oil futures by a comprehensive comparative study with a large number of predictive variables and applying machine learning models along with their interpretability tools. The main finding is that the selection of predictors is crucial for improving volatility forecasting accuracy, but it is not always the case that including more predictive variables leads to better forecasting results, even for machine learning models. Specifically, this paper has five major findings: (1) A few variables can significantly improve forecasting accuracy independently, but their contribution is limited. (2) Increasing the number of predictors from specific categories (market sentiment indicators, crude oil futures prices from other exchanges, and energy market indicators) helps to enhance forecasting accuracy. (3) Low-frequency variables have a weak effect on improving the daily volatility. (4) Ensemble tree models perform better than traditional machine learning models based on variable selection with dynamic parameter optimization, even without much parameter tuning. The above findings still hold true under a series of robustness tests and economic value assessments. These findings provide substantial evidence for addressing the issues of model and variable choice in crude oil futures volatility forecasting.

RL-SeqISP: Reinforcement Learning-Based Sequential Optimization for Image Signal Processing

Hardware image signal processing (ISP), aiming at converting RAW inputs to RGB images, consists of a series of processing blocks, each with multiple parameters. Traditionally, ISP parameters are manually tuned in isolation by imaging experts according to application-specific quality and performance metrics, which is time-consuming and biased towards human perception due to complex interaction with the output image. Since the relationship between any single parameter’s variation and the output performance metric is a complex, non-linear function, optimizing such a large number of ISP parameters is challenging. To address this challenge, we propose a novel Sequential ISP parameter optimization model, called the RL-SeqISP model, which utilizes deep reinforcement learning to jointly optimize all ISP parameters for a variety of imaging applications. Concretely, inspired by the sequential tuning process of human experts, the proposed model can progressively enhance image quality by seamlessly integrating information from both the image feature space and the parameter space. Furthermore, a dynamic parameter optimization module is introduced to avoid ISP parameters getting stuck into local optima, which is able to more effectively guarantee the optimal parameters resulting from the sequential learning strategy. These merits of the RL-SeqISP model as well as its high efficiency are substantiated by comprehensive experiments on a wide range of downstream tasks, including two visual analysis tasks (instance segmentation and object detection), and image quality assessment (IQA), as compared with representative methods both quantitatively and qualitatively. In particular, even using only 10% of the training data, our model outperforms other SOTA methods by an average of 7% mAP on two visual analysis tasks.

Dynamic performance and parameter optimization of a half-vehicle system coupled with an inerter-based X-structure nonlinear energy sink

Dynamic performance and parameter optimization of a half-vehicle system coupled with an inerter-based X-structure nonlinear energy sink

Dynamic Parameter Optimization of High-Speed Pantograph Based on Swarm Intelligence and Machine Learning

Good pantograph–catenary interaction quality is a fundamental premise for ensuring stable and reliable current collection of high-speed trains, and the optimization of dynamic parameters of high-speed pantographs provides an effective approach to improve the current collection quality of the pantograph–catenary system. In this paper, with the objective of minimizing the standard deviation of the pantograph–catenary contact force, the multi-parameter joint optimization for pantograph at different filtering frequencies and running speeds was carried out by using swarm intelligence optimization algorithm and artificial neural network method. First, the selection operator in genetic algorithm (GA) was introduced into crow search algorithm (CSA), and the selective CSA was proposed, which can effectively improve the solution accuracy and convergence rate of multi-parameter optimization. Second, a radial basis function (RBF) neural network was used to construct a surrogate model of the standard deviation of contact force with respect to the running speed and pantograph dynamic parameters, and a method for optimizing the upper limit of mapping interval of the decision variables by the selective crow search algorithm (SCSA) was proposed, which effectively improves the generalization ability of the surrogate model. Finally, by combining the surrogate model and SCSA, optimization iterations for a total of 630 combined conditions such as cut-off frequency, running speed and pantograph dynamic parameters were conducted, and an optimization method for high-speed pantograph dynamic parameters with universal applicability was proposed.

Performance Improving of Wind Power Generation Systems Through Parameter Optimization and Dynamic Analysis of the Speed-Regulating Differential Transmission

Abstract Hybrid drive wind power generation systems (WPGSs) equipped with speed-regulating differential mechanisms (SRDMs) have emerged as a promising solution for integrating large-scale wind energy into the power grid without the need for partially or fully rated converters. This article presents a comprehensive study on the dynamic analysis and parameter optimization of the SRDM-based transmission, with the aim of providing a sound foundation for the design and performance improvement of hybrid drive WPGSs. This study first formulates the kinematics, power flow, and mechanical efficiency of the SRDM and then proposes an effective parameter configuration model for optimizing the speed ratios of the key link units. The objective function is set as the minimum peak power required for speed regulation by the SRDM. Furthermore, to deal with the unique mechanical features such as dual power inputs, continuously variable transmission, and time-varying steering mechanism, an appropriate nonlinear dynamic modeling method of the SRDM transmission is developed. The torsion–translation vibration equations are derived and solved using the Runge–Kutta numerical integral method, considering randomly changing wind speed inputs and time-varying internal/external excitations. Results reveal that the sun gear experiences severe vibrations with the maximal and average vibration displacements of 0.563 mm and 0.112 mm, respectively, in the circumferential direction, while the planet gear exhibits complex frequency responses. Finally, specialized case studies are demonstrated to verify the proposed approaches, showing the satisfactory on-grid operating performance of the proposed SRDM-based WPGSs.

Dynamic Analysis and Parameter Optimization of the Cutting System for Castor Harvester Picking Devices

Our study aimed to identify a design which would reduce cutting resistance during the harvesting of castor. This paper presents a theoretical study of the wave-type disc cutter, which plays an important role in castor harvesting. Based on the SPH–FEM coupling algorithm, a combined orthogonal rotation experiment was performed to study the effects of disc cutter thickness, edge angle, disc cutter rotation speed, and feeding speed on the maximum cutting force. The response surface method was used to achieve an optimal combination of all the test factors. Mathematical modeling of the maximum cutting force and influencing factors was utilized to obtain the optimal parameters for a cutting system consisting of wave-type disc cutters. The optimal results were obtained with a computer-simulated disc cutter rotation speed of 844.2–942.1 r/min, a feeding speed of 0.89–1.01 m/s, a disc cutter thickness of 2.71–3.15 mm, and an edge angle of 29.2–33.9°. Under these conditions, the maximum cutting force was less than 50 N. Finally, the experimental data and numerical computer simulation results were compared using cutting performance test verification. The analysis found that the test results and simulation results were largely consistent. Therefore, the simulation model was judged to be effective and reasonable.

Reinforcement Learning for Stock Prediction and High-Frequency Trading With T+1 Rules

The high-frequency trading framework for the price trend prediction model and trading strategy has been a popular approach for T+0 trading in the stock market. The prediction model is used to predict price trends, and the trading strategy is used to determine the price and volume of the order. Most trading strategies consist of multiple trading logic associated with certain tuning parameters. These parameters significantly affect the profitability of high-frequency trading frameworks. There are two main disadvantages of this framework: 1) the price trend prediction model can not adapt to the current market data distribution, and 2) the trading strategy can not adapt to the current market conditions automatically. Thus, the framework cannot always maintain positive revenue. To address this problem, we propose a novel dynamic parameter optimization algorithm based on reinforcement learning for stock prediction and trading, and to generate an adaptive trading framework. First, we use a rolling model training method for stock price trend prediction. Second, we regard each set of strategy parameters as action and devise an inverse reinforcement learning algorithm for the reward function to accurately estimate the reward of each action. Because of the T+1 trading rules of the Chinese stock market, we consider the constraint of limited short position in the reward function. Finally, a reward-enhanced upper confidence bound (UCB) selection algorithm is proposed to automatically optimize the parameters of the trading logic in real-time trading. The experimental results show that our method achieves competitive performance in the Chinese stock market.

Monocular vision-based multiinformation map construction framework for field terrain

Three-dimensional (3D) spatial data, terrain type, and physical properties of field terrain are very important information for mobile robots in trajectory planning, force adjustment, motion control, dynamic parameter optimization, and so on. Obtaining the information in advance through vision can provide a prior perception to help make reasonable decisions and improve security and adaptability. We present the first monocular-vision-based multiinformation map construction framework for field terrain. The current state-of-the-art physical properties inference algorithm is promoted in illumination robustness, time consumption, and terrain richness. Our improvement makes it possible to apply physical properties inference to the real-time mapping of field terrain. An improved monocular vision and inertia fused localization scheme is used to introduce the gravity direction. Depending on the obtained terrain segmentation and camera pose, an unsupervised monocular scale-consistent terrain dense depth estimation method with comprehensive geometric constraints is designed. Finally, the components are systematically combined to form a complete framework, and the ambiguity of inference results between multiview frames is addressed. Comprehensive experiments are conducted to verify the effectiveness of each component and the performance of the overall framework. Our multiinformation map construction framework can output a gravity-aligned 3D RGB map, terrain map, friction property map, and stiffness property map at once with high accuracy.

Modeling and Design Optimization of a New Piezoelectric Inchworm Actuator with Screw Clamping Mechanisms.

A new piezoelectric inchworm actuator with screw clamping mechanisms has been developed recently for the wing folding mechanism of a small unmanned aircraft where the actuator power density is a great concern. Considering that the prototype actuator was designed just with engineering intuition and the performance optimization through experimental developments would take a vast amount of cost and time, a mathematical model was developed to investigate the actuator's critical design parameters and optimize its presently undesirable performance. Based on the lumped parameter method reported previously, and taking full account of the detailed modeling of the complex actuator housing and the actual nonlinear behaviors from the high-force contact and friction occurring at the screw-nut interface, as well as the output performance of the main drive elements including the piezoelectric stack and hollow ultrasonic motors (HUSMs), this model was built and then was experimentally verified for its accuracy and availability. Finally, nine design parameters were studied for their individual effect on the actuator's output using the proposed model. The simulation results indicate that the performance can be considerably improved by performing a slight modification to the prototype, and the dynamic modeling and parameter optimization methods used in this study can also serve as a useful reference for the design of similar piezoelectric inchworm actuators with intermittent clamping behaviors.

TPE-CatBoost: An adaptive model for soil moisture spatial estimation in the main maize-producing areas of China with multiple environment covariates

Maize is one of the major crops in China. The soil water content (SWC) in the root zone of maize is a critical indicator that guides agricultural production decisions and can affect national food security. However, a daily-scale, high-precision, spatial estimation method for SWC in China's main maize-producing areas has not been well researched. Therefore, we developed a spatial estimation model for SWC with a dynamic parameter optimization mechanism termed TPE-CatBoost. It combines the CatBoost algorithm as the core fitting framework with the efficient tree-structured Parzen estimator (TPE) algorithm to achieve a dynamic hyperparameter optimization based on covariate characteristics. Daily measured multi-depth SWC data at 175 stations from 2015 to 2019 were used as the reference truth, and 18 items of information, including soil physical and chemical properties, daily meteorological conditions, and spatial location information, were obtained from Google Earth Engine and considered as covariates. Model training was performed using the leave-one-out cross-validation method. Estimation error differences were investigated in four dimensions: time, space, depth, and the model. Our key results are as follows: (1) by combining all covariates, the highest estimation accuracy could be obtained at any soil depth, with a mean absolute error (MAE) within [6.06%, 6.94%]. The top five mean importance scores of covariates were latitude, soil pH, bulk density, DEM, and dewpoint temperature; (2) the MAE for all years remained within [4.66%, 9.34%], with higher errors in June; (3) the MAE for each province remained within [3.5%, 8.29%], with errors decreasing from north to south; and (4) compared with GIS-based spatial interpolation methods (inverse distance weighted, ordinary Kriging, and empirical Bayesian Kriging [EBK]), artificial intelligence (AI) algorithms combining environmental covariates (XGBoost, CatBoost, and TPE-CatBoost) could achieve better estimation accuracy. In particular, TPE-CatBoost performed well, with an improvement of 15.1% over EBK. We also demonstrated that TPE-CatBoost was susceptible to changes in the covariate gain capacity under extreme weather conditions using the SHapley Additive exPlanation (SHAP) algorithm. Visual mapping of single-day spatial estimation results in ArcGIS showed high consistency in distribution trends compared with the Soil Moisture Active Passive (SMAP) product.

Dynamic Temperature Modulation Measurement of VOC Gases Based on SnO2 Gas Sensor

Pure SnO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is widely used to detect VOC gases due to its high stability. Unfortunately, there is a problem of poor selectivity, especially for gases such as ketones and alcohols, which have a similar high sensitivity. In this paper, dynamic temperature modulation technology was used to solve this problem. Rectangular wave and sine wave, typical voltage jump waveform and voltage smooth waveform, were used as heating voltage waveform. It was introduced in detail that the optimization of dynamic temperature modulation parameters based on response time and power consumption. According to the different heating waveform parameters, we obtained the dynamic response data of six groups of sensors to five concentration gradients of acetone, butanone, n-propyl alcohol and isopropyl alcohol. It was used to evaluate the performance of heating waveform parameters by support vector machine (SVM) and principal component analysis (PCA) combined with K-nearest neighbor (KNN) algorithm. Both data analysis methods showed that the recognition effect was better when the SnO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sensor was heated by sine wave. In order to reduce power consumption, the heating waveform was determined as 0-6 V sine wave. Then the mechanism was analyzed in dynamic temperature modulation of the sensor. The voltage smooth waveform reached the stepless response of multiple temperature points by traversing the temperature range. More characteristics can be produced by experiencing more types proportion of adsorbed oxygen species within the cycle. It provided a new research idea for semiconductor gas sensor dynamic temperature modulation technology.

Self-Adapting Handover Parameters Optimization for SDN-Enabled UDN

Increasing the deployment density of small base stations (SBS) is a key method designed to satisfy high data traffic in 5th generation mobile network (5G). However, a large number of SBSs in such ultra-dense network (UDN) may cause ping-pong handovers (HOs), accompanied by increased delay and HO failure. In addition, because of the separation of control and data signaling in 5G, the HO procedure must be performed in both layers. In this paper, we introduce an SDN-based intelligent dynamic HO parameter optimization strategy to minimize both HO failures and ping-pong HOs together. The goal of the proposed strategy is to reduce the HO failure rate and redundant HO (i.e. ping-pong HO) while enabling user equipment (UE) to make full use of the benefits of dense deployment of BSs. Simulation results present that the method proposed in this paper effectively suppresses the ping-pong effect and keeps it at a low level in all of the investigated scenes. In addition, compared with the other algorithms, the HO failure rate is significantly reduced and the throughput of UE is greatly increased, especially in the case of high BS density. Therefore, the benefits of intensive BS deployment are retained.

Robust Optimization of Dynamic Fit Parameters for Injection Mechanism in Squeeze Casting Based on Six-sigma Frame and Bayesian Kriging Metamodel

Robust Optimization of Dynamic Fit Parameters for Injection Mechanism in Squeeze Casting Based on Six-sigma Frame and Bayesian Kriging Metamodel

Research on Commercial Bank Risk Early Warning Model Based on Dynamic Parameter Optimization Neural Network

Based on the background of big data, it is necessary to study the dynamic parameter optimization of the commercial bank risk model neural network. Several customer information attribute groups that have an impact on loan customer rating are selected, and the existing customer data are used to train the network model of the attribute group and customer default rate, so that it can predict the customer’s default rate according to the newly entered loan customer information and then predict whether the customer defaults. Based on a neural network model, this article constructs the credit risk early warning model of science and technology bank, makes an empirical test, and puts forward relevant countermeasures and suggestions to control the credit risk of bank. This article establishes a warning model of commercial banks by using a neural network. Taking the bank as an empirical sample, the constructed neural network model is used. Finally, the error of the model is small and the early warning results are satisfactory. The experimental results show that the proposed risk early warning model can accurately predict the customer default rate, so as to warn the defaulting customers. In the whole process, there are few human intervention factors and a high degree of intelligence, which reduces the operational risk.

Analysis of Dynamic Behavior of Spray Boom under Step Excitation

Dynamic behavior modeling and parameter optimization of the spray boom based on multi-body vibration were developed to study the influence of the boom sprayer’s motion in a complex working environment on the dynamic behavior of the spray boom. First, a geometric continuous model of spray boom vibration was established considering the influence of the boom sprayer body and boom suspension. Then, the dynamic behavior of the spray boom under step excitation was studied on a SWAN3WP-500 self-propelled boom spray experimental platform. The time-domain response of spray boom movement was characterized using a multi-channel dynamic test system, and the simulation results of the geometric continuous model were verified. The correlation coefficient between the simulated and experimental results is 0.917, and the maximum relative error is 12.16%. Finally, the effects of boom sprayer speed, spray boom length, and spray boom cross-section shape on spray boom vibration were analyzed through the geometric continuous model. (1) The amplitude and period of vibration at the extremity of the spray boom gradually decline with a rise in boom sprayer speed. (2) The amplitude and period of vibration at the extremity of the spray boom gradually increase when the unilateral spray boom is shorter than 5 m but slowly decrease when it is longer than 5 m. To avoid excessive static elastic deformation of the spray boom, the length of the unilateral spray boom should be controlled within 6 m. (3) A reasonable cross-sectional shape can improve the spray boom vibration, and the spray boom with a type B cross-section can better inhibit the vertical vibration of the spray boom under the same cross-sectional area.

Dynamic behavior and seismic performance of base-isolated structures with electromagnetic inertial mass dampers: Analytical solutions and simulations

The newly-developed electromagnetic inertial mass damper (EIMD) consisting of an inerter element and a damping element can significantly enhance the seismic performance of frame structures. However, whether an EIMD can enhance the seismic performance of a base-isolated structure (BIS) remains largely unknown. This paper presents a comprehensive theoretical study of the BIS with an EIMD, stressing on the dynamic behavior, parameter optimization, and seismic performance. Based on a simplified two-degree-of-freedom model, we derive the closed-form solutions of the dynamic characteristic parameters, modal participation factors, and dynamic amplification factors. A parametric study is conducted to investigate the effects of the EIMD parameters on the dynamic behavior of the system, through which the seismic response control mechanism of the EIMD in BIS is comprehensively analyzed. When the BIS subjected to a total of 20 earthquake records, the parameters of the EIMD are optimized for minimizing the interstory drift of the superstructure, the absolute acceleration response of the superstructure, and the relative displacement response of the base floor, individually. The effectiveness of the EIMD in enhancing the seismic performance of base-isolated structures is validated via numerical simulation of a five-story isolated building structure. Under a typical earthquake ground motion, the peak relative displacement of the base floor is reduced by up to 56.57% in comparison to traditional BIS; whereas the peak relative displacement and the absolute acceleration of the superstructure are reduced by up to 60.00% and 52.11%, respectively. Both analytical and numerical results demonstrate that the EIMD can significantly reduce the dynamic responses of both the base floor and the superstructure.

GENERAL METHODS OF OPTIMIZA¬TION OF KINEMATIC AND DYNAMIC PARAMETERS OF THE MACHINE UNIT

In steady-state operation, the kinematic and dynamic characteristics of the machine unit are periodic functions, as there is a periodic change in the forces and the transfer function of the drive mechanism. Accordingly, the task of finding methods to optimize the characteristics of the machine unit is mainly to reduce the amplitude of oscillations of the speed of the link by increasing the total moment of inertia by installing a flywheel on the shaft of the link, which increases the weight of the mechanism and its inertia.&#x0D; To eliminate the corresponding shortcomings of additional flywheels, it is necessary in the process of preliminary design of the drive of the executive body of the machine unit to select such a standard gearbox, the swing torque of which would correspond to the additional flywheel, which is difficult to implement. It is more expedient to design new transmission mechanisms, the moments of inertia of rotating masses which could perform the function of a flywheel. The corresponding design principle makes it possible to solve the problem in the first approximation, but sometimes with great difficulty, so it is necessary to pay attention to the machine unit as a whole, and not just the mechanical transmission.&#x0D; Based on the basic mathematical model of the machine unit using the principle of mass and force reduction, three variants of dynamic models were obtained depending on the choice of the link of construction, in order to optimize the kinematic and force parameters of the machine unit.&#x0D; As a result of the analysis of dynamic models it is concluded that the most optimal solution of problems of optimization of kinematic and dynamic parameters of the machine unit is the complex approach which consists in designing not the drive scheme and the machine unit as a whole. That is, using only a standard electric motor, and the executive body of the machine and the mechanical transmission to design as a whole. This approach will provide the optimal solution to almost any problem posed to engineers and designers by modern production.

МАТЕМАТИЧЕСКАЯ МОДЕЛЬ ВЗАИМОДЕЙСТВИЯ ШНЕКОВЫХ РАБОЧИХ ОРГАНОВ ЛЕСОПОЖАРНЫХ ГРУНТОМЕТАТЕЛЬНЫХ МАШИН С НАПОЧВЕННЫМ ПОКРОВОМ

The paper provides an overview of research on the working processes of screw working bodies of technological machines. It is noted that at present such important issues in the theory of auger working bodies as the required number of auger turns, the required position of the auger spiral in relation to the center, etc. have not been fully resolved, since the solution of these issues can provide an increased productivity of the tool. A structural and technological scheme of a forest fire machine with multifunctional modules is proposed, which consists of auger working bodies, which can be changed modularly with a screw metal thread for a brush, depending on the area and type of soil, the rotor of the thrower, with the ability to drive the cutters-throwers and auger working bodies both from the power take-off shaft of the tractor, and using a hydraulic motor, a guide casing. A mathematical model of an auger working body with a hydraulic drive has been compiled for removing the ground cover with forest litter when extinguishing forest fires with a ground gun, so that combustible materials do not fall into the fire zone together with the soil flow from the rotor-thrower. The working process of the hydraulic drive of the auger working bodies of a forest fire ground-sweeping machine is described by a system of differential equations, including the equations of translational and rotational movements of the auger working body and the equation of the flow rate of the working fluid. The problem of optimization of kinematic and dynamic parameters of auger working bodies of forest fire ground-sweeping machine is set

Optimization of dynamic absorber parameters for protecting the machines against the vibrations transmitted through their base

The correct functioning of machines could be affected by the vibrations transmitted through their base from other devices. The first possible protection measure is to support these machines on elastic elements with damping. The present work presents a protection solution which consists in the attachment of a dynamic absorber with viscous friction to the considered machine, in order to diminish the amplitudes of the vibrations caused by base movement. The mathematical model is developed for the real case when the base vibrations are not transmitted to the machine, but to the elastic supporting elements. The analytical expression of machine vibration amplitude in steady state is obtained. This amplitude could be diminished by an accurate choice of the three dynamic absorber parameters (mass, elastic constant, coefficient of viscous friction). An optimization program conceived in MATLAB provides the values of these parameters which lead to the minimum amplitude of machine vibrations.

Dynamic Characteristics and Key Parameter Optimization of Mechanical Automatic Vertical Drilling Tools

Mechanical automatic vertical drilling tools (MAVDT) have gradually gained attention as a drilling tool that can achieve active correction in harsh working environments such as high temperature and high pressure. The gravity sensing mechanism can sense the deviation and convert the deviation signal into the control signal to drive the actuator to correct the deviation. It is the core component of the mechanical automatic vertical drilling tool. This paper analyzes the force on the gravity sensing mechanism based on the structural analysis of the mechanical automatic vertical drilling tool. Then, the general dynamic equation of the gravity sensing mechanism is established based on D’Alembert principle. The critical response position where the acceleration value is zero is used as the research object to complete the preliminary design and analysis of the sensing mechanism. Through analysis, it can be found that there is a conflict between the response accuracy and control stability of the gravity sensing mechanism. High response accuracy often means poor control stability. For the gravity sensing mechanism with definite structural parameters, there is a limit value of the allowable friction coefficient. When the friction coefficient of the thrust bearing exceeds the limit value, the gravity sensing mechanism cannot achieve the inclination response no matter how big the inclination angle and deflection angle are. The friction coefficient between the disc valves and the force between the disc valves of the gravity sensing mechanism have a linear effect on the performance of the mechanism, and the smaller the deviation angle, the greater the influence coefficient of the force or the friction coefficient between the disc valves on the length of the gravity sensing mechanism. During the process of dynamic swing, the dynamic stable position of the gravity sensing mechanism is related to the relative relationship between the restoring force of the mechanism and the friction damping. To be precise, it is related to the potential energy zero point and the speed zero point during the gravity sensing mechanism swing process before it reaches the dynamic stable position.