Improved insensitive to input parameters trajectory clustering algorithm

The existing trajectory clustering （TRACLUS） is sensitive to the input parameters c and MinLns. The parameter value is changed a little, but cluster results are entirely different. Aiming at this vulnerability, a shielding parameters sensitivity trajectory cluster （SPSTC） algorithm is proposed which is insensitive to the input parameters. Firstly, some definitions about the core distance and reachable distance of line segment are presented, and then the algorithm generates cluster sorting according to the core dis- tance and reachable distance. Secondly, the reachable plots of line segment sets are constructed according to the cluster sorting and reachable distance. Thirdly, a parameterized sequence is extracted according to the reachable plot, and then the final trajectory cluster based on the parameterized sequence is acquired. The parameterized sequence represents the inner cluster structure of trajectory data. Experiments on real data sets and test data sets show that the SPSTC algorithm effectively reduces the sensitivity to the input parameters, meanwhile it can obtain the better quality of the trajectory cluster.

Simulator of pyroshock environment and effect rules of its adjustable parameters

During the launching of spacecraft,the on-board devices will undergo a series of pyroshock environments.In order to verify the reliability of these devices under these pyroshock environments,all of them are needed to take the shock test before launching.This paper has carried out an in-depth research on the simulation method of the pyroshock based on the true explosive excitation.In this study,a simulator containing multiple adjustment parameters is presented and the safety is considered by the design of the protective cover.And the working process of this setup is simulated with the explicit dynamic codes LS-DYNA.Whafs more,the effects of the adjustment parameters on the three factors of shock Response Spectrum(SRS)of the resonant board are explored carefully.The rules achieved in this paper are verified by a typical example.The results indicate that the improved simulator can avoid the danger of explosive and make full use of the advantage of actual explosive excitation.And the test condition can be quickly realized at the simulator according to the effect rules of the three adjustable parameters.

Rapid Parameter-Optimizing Strategy for Plug-and-Play Devices in DC Distribution Systems under the Background of Digital Transformation

By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement,information communication,and other fields,the digital DC distribution network can efficiently and reliably access DistributedGenerator(DG)and Energy Storage Systems(ESS),exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play(PnP)operations.However,during device plug-in and-out processes,improper systemparametersmay lead to small-signal stability issues.Therefore,before executing PnP operations,conducting stability analysis and adjusting parameters swiftly is crucial.This study introduces a four-stage strategy for parameter optimization to enhance systemstability efficiently.In the first stage,state-of-the-art technologies in measurement and communication are utilized to correct model parameters.Then,a novel indicator is adopted to identify the key parameters that influence stability in the second stage.Moreover,in the third stage,a local-parameter-tuning strategy,which leverages rapid parameter boundary calculations as a more efficient alternative to plotting root loci,is used to tune the selected parameters.Considering that the local-parameter-tuning strategy may fail due to some operating parameters being limited in adjustment,a multiparameter-tuning strategy based on the particle swarm optimization(PSO)is proposed to comprehensively adjust the dominant parameters to improve the stability margin of the system.Lastly,system stability is reassessed in the fourth stage.The proposed parameter-optimization strategy’s effectiveness has been validated through eigenvalue analysis and nonlinear time-domain simulations.

Measurement of Ion Parameters by Ion Sensitive Probe in ECR Plasma

Ion parameters in electron cyclotron resonance （ECR） microwave plasma were measured by ion sensitive probe and were compared with the electron parameters obtained by double Langmuir probe. The effects of gas pressure and microwave power on the ion temperature and density were analyzed. The spatial distribution of the ion parameters was also investigated by the ion sensitive probes with a tunable radial depth installed on different probe windows along the chamber axis. Results showed that the ion density measured by the ion sensitive probe was in good agreement with the electron density measured by the double Langmuir probe. The influ- ence of gas pressure on the ion parameters was stronger than that of microwave power. With the increase in working pressure, the ion temperature decreased monotonously with a decreasing rate larger than that at higher pressure. The ion density first increased to a peak （42.3~ 101~ cm-3） at 1 Pa and then decreased. The ion temperature and density increased little with the increase in the microwave power from 400 W to 800 W, The plasma far away from the resonant point is found to be radially uniform.

Parameter Optimization of Laser Triangulation Sensor Based On PSD

According to the theory of laser triangulation measurement,it can be used to test not only plane but also curving surface profile. As a non-contact measurement of optic-electro on a single spot,its advantages are high measurement speed,high precision,great anti-jamming,small measurement spot,easy operation,wide application range and so on. Laser triangulation measurement based on PSD makes system perfect with small volume,high sensitivity,low noise,high resolution. The system’s precision is above 0.5%. It shows that with simpler specified PSD software it can be found a convenient way to optimize system design,improve reliability,shrink volume,reduce power and shorten exploitation period.

Influence of multiple structural parameters on interior ballistics based on orthogonal test methods

Influence of multiple structural parameters on the performance of a gun launch system driven by highpressure reactive gases is important for structural design and performance adjustment.A coupled lumped parameter model was utilized to predict the propellant combustion,and a dynamic finite element method was applied to approximate the mechanical interactions between the projectile and the barrel.The combustion and the mechanical interactions were coupled through a user subroutine interface in ABAQUS.The correctness and the capability of the finite element approximations in capturing small structural changes were validated by comparing predicted resistance with experiments.Based on the coupled model,the influence of structural parameters of a medium-caliber gun on the system performance was investigated.In order to reduce the research costs,orthogonal tests were designed to investigate the comprehensive effects of the parameters.According to statistical analysis,the important order of the structural parameters on the launching process was obtained.The results indicate that the influence of the width of the rotating band stands out among the studied parameters in the gun.The work provides a method to investigate the influence of multiple parameters on system performance and gives guidance for controlling the system performance.

Effects of physical parameter range on dimensionless variable sensitivity in water flooding reservoirs

The similarity criterion for water flooding reservoir flows is concerned with in the present paper. When finding out all the dimensionless variables governing this kind of flow, their physical meanings are subsequently elucidated. Then, a numerical approach of sensitivity analysis is adopted to quantify their corresponding dominance degree among the similarity parameters. In this way, we may finally identify major scaling law in different parameter range and demonstrate the respective effects of viscosity, permeability and injection rate.

Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

Parameter sensitivities analysis for classical flutter speed of a horizontal axis wind turbine blade

The parameter sensitivities affecting the flutter speed of the NREL （National Renewable Energy Laboratory） 5-MW baseline HAWT （horizontal axis wind turbine） blades are analyzed. An aeroelastic model, which comprises an aerodynamic part to calculate the aerodynamic loads and a structural part to determine the structural dynamic responses, is established to describe the classical flutter of the blades. For the aerodynamic part, Theodorsen unsteady aerodynamics model is used. For the structural part, Lagrange’s equation is employed. The flutter speed is determined by introducing “V–g” method to the aeroelastic model, which converts the issue of classical flutter speed determination into an eigenvalue problem. Furthermore, the time domain aeroelastic response of the wind turbine blade section is obtained with employing Runge-Kutta method. The results show that four cases （i.e., reducing the blade torsional stiffness, moving the center of gravity or the elastic axis towards the trailing edge of the section, and placing the turbine in high air density area） will decrease the flutter speed. Therefore, the judicious selection of the four parameters （the torsional stiffness, the chordwise position of the center of gravity, the elastic axis position and air density） can increase the relative inflow speed at the blade section associated with the onset of flutter.

CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm

Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.

Important population viability analysis parameters for giant pandas(Aliuropoda melanoleuca)

Population viability analysis(PVA) is a tool to evaluate the risk of extinction for endangered species and aid conservation decision-making.The quality of PVA output is dependent on parameters related to population dynamics and life-history;however,it has been difficult to collect this information for the giant panda(Aliuropoda melanoleuca),a rare and endangered mammal native to China,confined to some 30 fragmented habitat patches.Since giant pandas are long-lived,mature late,have lower reproductive rates,and show little sexual dimorphism,obtaining data to perform adequate PVA has been difficult.Here,we develop a parameter sensitivity index by modeling the dynamics of six giant panda populations in the Minshan Mountains,in order to determine the parameters most influential to giant panda populations.Our data shows that the giant panda populations are most sensitive to changes in four female parameters:initial breeding age,reproductive rate,mortality rate between age 0 and 1,and mortality rate of adults.The parameter sensitivity index strongly correlated with initial population size,as smaller populations were more sensitive to changes in these four variables.This model suggests that demographic parameters of females have more influence on the results of PVA,indicating that females may play a more important role in giant panda population dynamics than males.Consequently,reintroduction of female individuals to a small giant panda population should be a high priority for conservation efforts.Our findings form a technical basis for the coming program of giant panda reintroduction,and inform which parameters are crucial to successfully and feasibly monitoring wild giant panda populations.

Permanent deformation response parameters of asphalt mixtures for a new mix-confined repeated load test

The main objective of this work is to propose new mixture response parameters and to compare correlations with rut depths and sensitivity of permanent deformation response parameters based on field extracted cores and lab-mixed duplicates. A new "mix-confined" test is developed and four new parameters for this test are proposed. Correlation coefficients with rut depths and coefficients of variation (sensitivity) are compared between the four new and two existing parameters. Some parameters are recommended to be used for the newly developed test. The results show that, newly developed test can capture the changes of permanent deformation of asphalt mixtures. Only one new parameter (D1 of Stephen Price model) and one existing parameter (flow number, Fn ) have strong correlations with rut depths of asphalt pavements (R2 greater than 0.7) and have relative small sensitivity (coefficient of variation, COV, less than 30%). For polymer modified asphalt mixtures, the parameter D1 rather than Fn should be used. These findings can be used to check the permanent deformation of asphalt mixture during the mix design.

Parameter sensitivity study of the biogeochemical model in the China coastal seas

In order to develop a coupled basin scale model of ocean circulation and biogeochemical cycling,we present a biogeochemical model including 12 components to study the ecosystem in the China coastal seas（CCS）.The formulation of phytoplankton mortality and zooplankton growth are modified according to biological characteristics of CCS.The four sensitivity biological parameters,zooplankton assimilation efficiency rate（ZooAE_N）,zooplankton basal metabolism rate（ZooBM）,maximum specific growth rate of zooplankton（μ_（20）） and maximum chlorophyll to carbon ratio（Chl2C_m） are obtained in sensitivity experiments for the phytoplankton,and experiments about the parameter μ_（20＇）,half-saturation for phytoplankton NO_3 uptake（K_（NO_3）） and remineralization rate of small detritusN（SDeRRN） are conducted.The results demonstrate that the biogeochemical model is quite sensitive to the zooplankton grazing parameter when it ranges from 0.1 to 1.2 d^（-1）.The K_（NO_3） and SDeRRN also play an important role in determining the nitrogen cycle within certain ranges.The sensitive interval of KNO_3 is from 0.1 to 1.5（mmol/m^3）^（-1）,and interval of SEdRRN is from 0.01 and 0.1 d^（-1）.The observational data from September 1998 to July 2000 obtained at SEATS station are used to validate the performance of biological model after parameters optimization.The results show that the modified model has a good capacity to reveal the biological process features,and the sensitivity analysis can save computational resources greatly during the model simulation.

Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach

Parameter uncertainty is a primary source of uncertainty in ocean ecosystem simulations.The deep chlorophyll maximum(DCM)is a ubiquitous ecological phenomenon in the ocean.Using a theoretical nutrients-phytoplankton model and the conditional nonlinear optimal perturbation approach related to parameters,we investigated the eff ects of parameter uncertainties on DCM simulations.First,the sensitivity of single parameter was analyzed.The sensitivity ranking of 10 parameters was obtained by analyzing the top four specifi cally.The most sensitive parameter(background turbidity)aff ects the light supply for DCM formation,whereas the other three parameters(nutrient content of phytoplankton,nutrient recycling coeffi cient,and vertical turbulent diff usivity)control nutrient supply.To explore the interactions among diff erent parameters,the sensitivity of multiple parameters was further studied by examining combinations of four parameters.The results show that background turbidity is replaced by the phytoplankton loss rate in the optimal parameter combination.In addition,we found that interactions among these parameters are responsible for such diff erences.Finally,we found that reducing the uncertainties of sensitive parameters could improve DCM simulations remarkably.Compared with the sensitive parameters identifi ed in the single parameter analysis,reducing parameter uncertainties in the optimal combination produced better model performance.This study shows the importance of nonlinear interactions among various parameters in identifying sensitive parameters.In the future,the conditional nonlinear optimal perturbation approach related to parameters,especially optimal parameter combinations,is expected to greatly improve DCM simulations in complex ecosystem models.

Sensitivity Analysis of Multi-phase Seepage Parameters Affecting the Clayey Silt Hydrate Reservoir Productivity in the Shenhu Area,South China Sea

Natural gas hydrate(NGH)is an important future resource for the 21st century and a strategic resource with potential for commercial development in the third energy transition.It is of great significance to accurately predict the productivity of hydrate-bearing sediments(HBS).The multi-phase seepage parameters of HBS include permeability,porosity,which is closely related to permeability,and hydrate saturation,which has a direct impact on hydrate content.Existing research has shown that these multi-phase seepage parameters have a great impact on HBS productivity.Permeability directly affects the transmission of pressure-drop and discharge of methane gas,porosity and initial hydrate saturation affect the amount of hydrate decomposition and transmission process of pressure-drop,and also indirectly affect temperature variation of the reservoir.Considering the spatial heterogeneity of multi-phase seepage parameters,a depressurization production model with layered heterogeneity is established based on the clayey silt hydrate reservoir at W11 station in the Shenhu Sea area of the South China Sea.Tough+Hydrate software was used to calculate the production model;the process of gas production and seepage parameter evolution under different multi-phase seepage conditions were obtained.A sensitivity analysis of the parameters affecting the reservoir productivity was conducted so that:(a)a HBS model with layered heterogeneity can better describe the transmission process of pressure and thermal compensation mechanism of hydrate reservoir;(b)considering the multi-phase seepage parameter heterogeneity,the influence degrees of the parameters on HBS productivity were permeability,porosity and initial hydrate saturation,in order from large to small,and the influence of permeability was significantly greater than that of other parameters;(c)the production potential of the clayey silt reservoir should not only be determined by hydrate content or seepage capacity,but also by the comprehensive effect of the two;and(d)time scales need to be considered when studying the effects of changes in multi-phase seepage parameters on HBS productivity.

A Sensitivity Study on Parameterization Scheme of Snow Internal and Interfacial Processes in Snow Model

In order to develop a seasonal snow model of land surface process as accurately as possible for climatic study. it is necessary to fully understand the effects of important snow internal processes and interaction with air and to get an insight into influence of several relevant parameterization schemes with parameters' uncertainty to some degree. Using the snow model (SAST) developed by first author and other one and some useful field observation data, this paper has conducted a series of sensitivity studies on the parameterization schemes. They are relative to compaction process, snow thermal conduction, methodology of layering snow pack and to key parameters such as snow albedo, water holding capacity. Then, based on the results from the sensitivity studies, some useful conclusions for snow cover model improvement are obtained from the analysis of the results.

Input torque sensitivity to uncertain parameters in biped robot

Input torque is the main power to maintain bipedal walking of robot, and can be calculated from trajectory planning and dynamic modeling on biped robot. During bipedal walking, the input torque is usually required to be adjusted due to some uncertain parameters arising from objective or subjective factors in the dynamical model to maintain the pre-planned stable trajectory. Here, a planar 5-link biped robot is used as an illustrating example to investigate the effects of uncertain parameters on the input torques. Kine-matic equations of the biped robot are firstly established by the third-order spline curves based on the trajectory planning method, and the dynamic modeling is accomplished by taking both the certain and uncertain parameters into account. Next, several evaluation indices on input torques are intro-duced to perform sensitivity analysis of the input torque with respect to the uncertain parameters. Finally, based on the Monte Carlo simulation, the values of evaluation indices on input torques are presented, from which all the robot param-eters are classified into three categories, i.e., strongly sensi-tive, sensitive and almost insensitive parameters.

Model development and parameter investigation of a column-type electric power steering system

In this paper, the performance of a column-type electric power steering （EPS） system and vehicle has been studied and a detailed mathematical model for the system has been established. Based on the mathematic model of the optimization design for steering feel, the parameters of the EPS system and vehicle on steering performance have been investigated. Moreover, the effects of the parameters on system stability have been analyzed and compared by the method of absolute sensitivity and the results are given in the end.

Study of petrophysical parameter sensitivity from well log data

Proper analysis of petrophysical parameter sensitivity from well log data can greatly improve the ability to discriminate hydrocarbon-bearing rocks. In this paper we discuss a petrophysical analysis method for the selection and application of higher sensitivity seismic attribute parameters to improve the ability to discriminate fluid and lithology. To better integrate with seismic interpretation, we construct a template to highlight rock physics parameters in sensitivity space, providing guidance for the quantitative seismic interpretation of hydrocarbon-bearing reservoirs.