Unmodeled Factors Research Articles

CFD based heat transfer parameter identification of greenhouse and greenhouse climate prediction method

Predicting the greenhouse environment is important for crop yield and energy consumption projections. This study proposes a dynamic greenhouse temperature and humidity modeling method based on integrating CFD simulation and measured data. This method utilizes computational fluid dynamics to analyze the greenhouse's two key heat transfer characteristics. The effect of wind speed and temperature differences on the heat transfer coefficient of the cover is first analyzed under the condition that the inner thermal screen is folded. Secondly, the effect of temperature differences inside and outside the greenhouse, outdoor wind speed, sky temperature, and the internal thermal screen on the heat transfer coefficient between the greenhouse's indoor and outdoor environments is investigated. Based on the thermodynamic parameters of the greenhouse microclimate obtained through CFD simulation, a regression model of the greenhouse climate model parameters with respect to the influencing variables is built using a nonlinear identification algorithm. By analyzing the mechanism of greenhouse environmental changes, this work proposes a universal structure for the agricultural greenhouse climate model and uses identified heat transfer parameters to correct the universal mechanism model of the greenhouse climate. Considering complex environmental dynamics and unmodeled factors, this study proposes a weighted parameter correction method based on adaptive particle swarm optimization (APSO). Finally, the model was validated using climate data from Venlo-type greenhouses in Shanghai. The validation results indicate that the greenhouse climate model can accurately approximate the real greenhouse climate.

Absolute Nodal Coordinate Formulation-Based Shape-Sensing Approach for Large Deformation: Three-Dimensional Beam

In recent years, fiber-optic shape sensing, which means reconstructing the deformation state of structures from strain information measured by fiber-optic sensors, has attracted interest from many fields. Most of the existing shape-sensing research for flexible three-dimensional (3D) slender structures is based on simple strain-curvature transformation, relying on standardized substrates with a circular cross section and particular forms of fiber layouts. To develop a more general shape-sensing method that can be applied to a wider range of practical engineering conditions, in this paper, the strain–displacement relationship of 3D slender structures under the effect of multiple deformation modes coupling is described by introducing absolute nodal coordinate formulation element, and the deformation reconstruction problem is reorganized into a nonlinear optimization problem that can be applied to large deformation and accommodates different cross-sectional shapes and optical fiber layouts. Due to the rapid increase in the calculation difficulties of nonlinear optimization problems with the number of variables, an element-by-element solving strategy is adopted, and nodal degrees of freedom that have less influence on the overall shape of the structure are merged. In addition, in order to address the issue of local strain anomalies caused by unmodeled factors such as section warping, the pointwise matching between the theoretical strains and measured strains is relaxed to an average matching in subregions to capture the overall deformation, improving the robustness and computational efficiency of the solution process. The accuracy and computational performance of the proposed method are verified through numerical simulation and experiment.

Pengaruh Good Corporate Governance, Kinerja Keuangan dan Retention Ratio terhadap Harga Saham

Good Corporate Governance (GCG), Financial Performance, and Retention Ratio are essential factors that can influence stock prices. Good GCG can enhance investor confidence and drive positive financial performance. Strong financial performance can increase the company's value and attract investors to buy the company's shares. A high Retention Ratio indicates that the company can retain quality employees, which can enhance productivity and profitability. This research aims to analyze the influence of GCG, Financial Performance, and Retention Ratio on Stock Prices. The method employed is quantitative. The population used in this study is the LQ45 companies listed on the Indonesia Stock Exchange for the period 2017-2021, totaling 45 companies, and the sample criteria selected 17 companies. Data analysis utilizes descriptive statistics, panel data regression model estimation, classical assumption tests, hypothesis testing, and MRA using Eviews 12. The results indicate a significant influence of the Retention Ratio on Stock Prices, with the statistically higher t-value compared to the t-table (2.663118 > 1.99006). However, variables such as the Board of Directors, Independent Commissioners, Return on Assets (ROA), and Earning Per Share (EPS) do not demonstrate a significant influence on Stock Prices as their t-values are lower than the critical t-value (t-value < 1.99006). The correlation results show that 88.3% of the variation in Stock Prices can be explained by the independent factors examined in this study, while the remaining 11.7% may be influenced by other unmodeled factors. These findings provide valuable insights into understanding the factors affecting company stock prices and underscore the importance of profit retention in the context of a company's market value.

Effects of Differential Magnetic Field/Tensor and Redundant Measurements on Multi-DOF Motion Estimation of a Magnetic Sensing System

This paper presents a multi-DOF motion sensing system consisting of a permanent magnet (PM) and a magnetic tensor sensor (MTS) comprising a 3×3 array of three-axis digital magnetic flux density (MFD) sensors, and the methods to measure their relative MTS-PM position/ orientation in a 3D space. The design enables redundant differential measurements of MFD vectors and gradient tensor components to account for the singularities due to matrix inversion, providing a basis to explore different methods for multi-DOF estimation of the PM position/ pose. Formulated as a two-stage linear least-square (LS) problem to take advantage of the dipole simplicity and exploit its physics revealed by its inverse model to guide the design of a fully connected artificial neural network (ANN) to account for the MTS measurement noise and un-modeled factors, a prototype multi-MTS system capable of 5-DOF motion measurements is developed and evaluated experimentally along with a study analyzing the parametric effects on the estimation accuracy; both stationary and moving sensor scenarios are considered. Enhanced with a fully connected ANN, an accuracy within a root-mean-square error (RMSE) of 40μm spatial position and 0.1° pose can be uniquely obtained without subtracting a predetermined geomagnetic field in both fixed and moving multi-MTS scenarios, representing a significant improvement over the (0.5mm, 1°) RMSE of the single-MTS.

Not going out during the Covid‐19 pandemic? A multilevel geographical analysis of UK Google Mobility Reports, February 2020–December 2021

AbstractThe analysis reported in this paper uses Google Mobility Reports to understand subnational trends in population spatial immobility/mobility in the United Kingdom during 2020 and 2021. Using multilevel modelling, it analyses how spatial mobility changed through time in response to the strictness of government lockdown and the annual seasonal cycle of public holidays, and between places in terms of their population composition as measured by the shares of the highly‐educationally qualified and the self‐employed. The results show that there are no consistent differences between the nations of the United Kingdom; that time spent at home increased with the severity of lockdown; that the share of highly qualified was also a good predictor of staying at home; and that there were major effects from public holidays. The analysis did not explain all the variation between places and dates; it is suggested that this is because of randomisation of the data by Google and unmodelled factors such as tiered restrictions.

Harmonic Current Control of OW-PMSM for Low-Voltage Traction by Nonlinear Disturbance Rejection With Improved Modulation Scheme

The open-winding permanent magnet synchronous motor (OW-PMSM) has been developed, mostly potential for low-voltage traction in electric vehicles. Yet, both the disturbance from vehicle system under driving and the motor with low-reactance (LR) feature expose the limits of traditional controllers that are prone to great current harmonics. Therefore, the current control for LR OW-PMSM becomes challenging. The main objective of this research is to provide a current harmonic control strategy for LR OW-PMSM by the nonlinear lumped disturbance observer (NLDO) combined with improved space vector modulation (SVM) scheme. The existence of nonlinear, unknown, unmodeled factors has been confirmed by current measurement and harmonic analysis in full operation range, and thus is considered in proposed controllers. The NLDO is designed for all the three axes under the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> 0 frame. The SVM algorithm is adjusted to two switching patterns in order to get widened linear voltage modulation range. In addition, in the 3-D voltage space is the possible modulation area investigated. Ultimately, the target LR OW-PMSM has been designed, prototyped, and tested. The proposed NLDO, juxtaposed with ordinary PI and deadbeat controllers, reduces current harmonics on both the motor and inverter sides, possesses high robustness, and is practical for LR motors, according to experimental results.

A New GNSS TEC Neural Network Prediction Algorithm With the Data Fusion of Physical Observation

This work models and predicts anomalous jumps in GNSS total electron content (TEC) caused by geomagnetic disturbance or solar radiation by introducing F10.7 and regional Dst indices, which are used to characterize solar radiation and geomagnetic disturbance status, respectively. We then introduced long-distance constraint stations to restrain ionospheric variations during high solar activity, which reduced the influence of unmodelled factors on prediction accuracy. In our experimental work, Global Ionosphere Map (GIM) TEC data from 11 stations located at different latitudes were used for comparison during maximum (2014) and minimum (2018) of solar cycle 24. Sample data for >27 days were used for learning, and then ionospheric changes for the whole year were predicted using the sliding window method. The results showed that, the standard deviation for the global ionospheric prediction products could be guaranteed to be within 2 TECU. The proximity station constraint method proposed in this paper and the Dst high-order polynomial correction method during geomagnetic disturbance control the prediction error within -4.4 TECU, while the change of adjacent days is -51.4 TECU in 2014. The new algorithm reduced the TEC jump effect in predictions, and the prediction of ionospheric products provided by this algorithm can meet the basic requirements of ionospheric delay correction in high-precision positioning.

Model-Free Adaptive Impedance Control for Autonomous Robotic Sanding

Sanding is a common yet important task in the manufacturing of many wooden objects (e.g. furniture, decoration box), where the coated layer attached on objects is removed after the interaction with sanding belts. Existing sanding operation is heavily dependent on manual works, which is highly labor-intensive and with the low consistency of quality, and the issues of safety and health also arise after continuous working in the noisy and dusty environment. To deal with the aforementioned, this paper presents the development of a new autonomous sanding robot. The autonomous capability of the developed robot is reflected in the whole procedure of sanding. In particular, the CAD model of the target object is automatically constructed with the structured-light technology, and the sanding behavior on the target surface is self-regulated under the desired impedance model. Such feature makes the robot capable of working towards uncertain objects with minimum human involvement. The proposed impedance controller has the model-free advantage, by using the adaptive neural networks (NNs) to compensate the uncertain dynamics and the unknown disturbances online. The stability of the closed-loop system is rigorously proved with Lyapunov methods, and experimental results on different objects are presented to validate the performance of the developed robot. The implementation of the developed robot can systematically address the problems associated with manual works. Note to Practitioners—The current working environment of sanding is not healthy or safe to humans, due to the nature of noise, dust, high-speed sanding belt. By controlling the robot to autonomously perform sanding tasks can keep humans away from such environment and hence systematically address the issues of health and safety. This paper presents a new impedance control method for sanding robot. In impedance control, the control goal is specified as a dynamic relationship between the contact position and the interaction force. In this paper, the contact position is determined by using the 3D vision sensor, while the interaction force is regulated by referring to the human experience. In addition, the influence caused by unmodeled factors (e.g. unknown dynamics of the sanded object) is also dealt with by using the techniques of NNs. Such setting can effectively guarantee the sanding quality and also avoid the physical damage to the sanded object. Therefore, it lays the foundation for the autonomous robotic sanding.

Digital Magnetic Tensor Sensor With ANN Measurement Model for Human Joint Motion Sensing in Sagittal Plane

Motivated by the increased needs of home-based rehabilitation for stroke patients, more and more interest has been drawn towards developing body-fixed sensors for monitoring affected joint motions. Although relatively accurate bone geometries can be obtained by scanning technologies, most human joints are approximated by simple circles and spheres to reduce the highly nonlinear kinematics to a tractable form for motion studies; many human joint-motion sensing challenges remain open. This article presents a novel magnetic tensor sensor (MTS) for noncontact tracing a human joint trajectory and a physics-based measurement model implemented on an artificial neural network (ANN) to account for un-modeled factors. The effects of input configurations and datatypes on measurement accuracy of an MTS/ANN have been numerically investigated with published data and experimentally evaluated on a prototype pantographic exoskeleton worn on a human shank/foot. As demonstrated experimentally, the MTS/ANN system calibrates the sensor intrinsic parameters, accounts for the environmental magnetic effects on the measurements, and can be trained with both offline and user-specific data.

The composition and stability of demographic integration through gentrification

ABSTRACT Gentrification, the increase of land values and resident socioeconomic status in previously low-income neighborhoods, is related to the emergence of demographically mixed White neighborhoods. But questions remain as to what kinds of mixtures (White/Black, White/Hispanic, or White/Asian) gentrification facilitates and how stable they are over time. To address this limitation, this study utilizes Census and American Community Survey data for metropolitan areas from 1980 to 2010. We use a typology of racial/ethnic neighborhoods by composition to determine what kind of demographic integration, if any, results from gentrification and how stable it is over time. Using hybrid fixed effects logistic regression to control for modeled and unmodeled factors, we find gentrification is associated with the emergence of mixed-White-and-Black and mixed-White-and-Hispanic neighborhoods, but not mixed White-and-Asian/Pacific Islander neighborhoods. Using conventional logistic regression, we find gentrification that began in the 1980s is related to the long-term integration of Whites with Hispanics and Blacks.

A Backpropagation Learning Method for Dynamic Parameter Identification of Industrial Robots

Abstract Precise dynamic parameters are of great significance to the model-based controller of industrial robots. The least square (LS) method is widely used to identify the dynamic parameters in reality, but it is sensitive to the measurement noise and may obtain a biased solution. Besides, the nonlinear dynamics are the key and difficult point of identification, such as the nonlinear friction and the coupling effect of adjacent joint, but they are seldom solved simply and effectively. To cope with the above issues, we propose a backpropagation learning (BPL) method for parameter identification. The mean square error between the measured torque and the calculated torque from the dynamic model is taken as the loss function. The gradient of the loss function with respect to the parameters is computed, and the parameters are updated in the negative gradient direction to minimize the loss function until finding the optimal parameters with the minimum loss function. The optimal parameters will not only fit the modeled torque but also compensate for the torque caused by the unmodeled factors, thus improving the parameter accuracy. The proposed method is essentially a supervised learning method, so the impact of measurement noise is reduced by continuous training with a large amount of valid data. The proposed method is verified in an industrial robot platform, and the experimental results show that the proposed method has smaller errors than the weighted least squares (WLS) method and achieves similar accuracy to the semiparametric model (SPM) but has a better generalization ability.

Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV

The underactuated quadrotor unmanned aerial vehicle (UAV) is one of the nonlinear systems that have few actuators as compared to the degree of freedom (DOF); thus, it is a strenuous task to stabilize its attitude and positions. Moreover, an induction of unmodelled dynamic factors and uncertainties make it more difficult to control its maneuverability. In this paper, a model-free based single-dimension fuzzy sliding mode control (MFSDF-SMC) is proposed to control the attitude and positions of underactuated quadrotor UAV. The paper discusses the kinematic and dynamic models with unmodelled dynamic factors and unknown external disturbances. These unmodelled factors and disturbances may lead the quadrotor towards failure in tracking specific trajectory and may also generate some serious transient and steady-state issues. Furthermore, to avoid the problem of gimbal lock, the model is amalgamated with hyperbolic function to resolve the singularity issues dully developed due to Newton Euler’s dynamic modeling. The simulation results performed for MFSDF-SMC using MATLAB software R2020a are compared with conventional sliding mode control, fuzzy-based sliding control and single-dimension fuzzy-based sliding mode control without a model-free approach. The design and implementation of the model-free single dimension-based fuzzy sliding mode control (MFSDF-SMC) with an updated Lyapunov stability theorem is presented in this work. It is observed that MFSDF-SMC produces robust trajectory performance therefore, and the manuscript suggests the experimental setup to test the proposed algorithm in a noisy environment keeping the same conditions. The verification of the equipment used and its effective demonstration is also available for the reader within the manuscript.

A Hybrid Contact Model With Experimental Validation

Abstract This brief paper emphasizes on the experimental study of a hybrid contact model combining a traditional physical-based contact model and a data-driven error model in order to provide a more accurate description of a contact dynamics phenomenon. The physical-based contact model is employed to describe the known contact physics of a complex contact case, while the data-driven error model, which is an artificial neural network model trained from experimental data using a machine learning technique, is used to represent the inherent unmodeled factors of the contact case. A bouncing ball experiment is designed and performed to validate the model. The hybrid contact model can duplicate experimental results well, which demonstrates the feasibility and accuracy of the presented approach.

A Bayesian state-space production model for Korean chub mackerel (Scomber japonicus) stock

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.

Omitted variable bias in studies of plant interactions.

Models of plant-plant interactions underpin our understanding of species coexistence, invasive plant impacts, and plant community responses to climate change. In recent studies, models of competitive interactions failed predictive tests, thereby casting doubt on results of many past studies. We believe these model failures owe at least partly to heterogeneity in unmodeled factors (e.g., nutrients, soil pathogens) that affect both target plants and neighboring competitors. Such heterogeneity is ubiquitous, and models that do not account for it will suffer omitted variable bias. We used instrumental variables analysis to test for and correct omitted variable bias in studies that followed common protocols for measuring plant competition. In an observational study, omitted variables caused competition to seem like mutualism. In a quasi-experiment that partially controlled competitor abundances with seeding, omitted variables caused competition to seem about 35% weaker than it really was, even though the experiment occurred in an abandoned agricultural field where environmental heterogeneity was expected to be relatively low. Despite decades of research, consistently accurate estimates of competitive interactions remain elusive. The most foolproof way around this problem is true experiments that avoid omitted variable bias by completely controlling competitor abundances, but such experiments are rare.

Heterogeneity of epiphytic diatoms in shallow lakes: Implications for lake monitoring

Abstract Current diatom assessments of shallow lakes typically use littoral epiphytic diatoms as bioindicators for eutrophication pressure. However, challenges in establishing robust ecological assessments still exist because of the natural pressures that shallow lakes are subjected to which are difficult to quantify (e.g., water-level fluctuations or wave action). Information on the spatial and temporal variability of epiphytic communities within a shallow lake is limited although important for the development of robust bioassessment protocols. We compared epiphytic diatom taxa and functional group compositions between the littoral and pelagic zone of three least-disturbed shallow lakes in the northeastern part of South Africa along a temporal gradient. In comparison to the pelagic zone, the littoral zone was characterized by almost double in species richness. Temporal variability was significantly higher for littoral diatom communities and littoral water physico-chemistry than those of the pelagic. Overall, water depth was a good predictor of the epiphytic diatom taxa and functional group composition. We also found that pelagic diatom taxa and functional groups correlated better with pelagic physico-chemistry than littoral diatoms to littoral physico-chemistry. This could be attributed to un-modeled factors related to natural physical pressures that will have more of an impact on littoral habitats than less exposed pelagic habitats. Given these findings, using pelagic epiphytic diatoms as a bioindicator in shallow lake monitoring programs could be more efficient than littoral diatoms at providing robust ecological assessments due to their relatively low variability.

Optimal design, modeling and control of a long stroke 3-PRR compliant parallel manipulator with variable thickness flexure pivots

Abstract The variable thickness flexure pivot (VTFP) is a promising flexure hinge to construct long stroke compliant mechanisms with high precision, since it combines both the advantages of the classical flexure pivot and the notched flexure hinge. In this paper, a 3-PRR compliant parallel manipulator (CPM) is proposed by employing the VTFPs to serve as the passive rotational joints. Geometric parameters of the VTFP and the 3-PRR manipulator are optimized by genetic algorithm to obtain the desired motion performance of the CPM. A prototype 3-PRR CPM is fabricated using the optimized results. In order to consider the parasitic rotational center shift of the VTFPs, an accurate inverse kinematic model (AIKM) is established, numerical results show the superiority of the AIKM compared with the rigid inverse kinematic model (RIKM). Moreover, an on-line learning radical basis function neural network (RBFNN) is established to compensate the unmodeled factors of the system, and a disturbance observer (DOB) is designed by utilizing the proposed AIKM and the RBFNN compensator. The observed external disturbances of the system is compensated via a feed-forward compensation strategy. Experiments show that the 3-PRR CPM can achieve micron scale trajectory tracking accuracy over centimeter's motion range and micro-degree rotational tracking accuracy by the proposed control scheme.

Beam-pointing drift prediction in pulsed lasers by a probabilistic learning approach.

In laser systems, it is well known that beam pointing is shifted due to many un-modeled factors, such as vibrations from the hardware platform and air disturbance. In addition, beam-pointing shift also varies with laser sources as well as time, rendering the modeling of shifting errors difficult. While a few works have addressed the problem of predicting shift dynamics, several challenges still remain. Specifically, a generic approach that can be easily applied to different laser systems is highly desired. In contrast to physical modeling approaches, we aim to predict beam-pointing drift using a well-established probabilistic learning approach, i.e., the Gaussian mixture model. By exploiting sampled datapoints (collected from the laser system) comprising time and corresponding shifting errors, the joint distribution of time and shifting error can be estimated. Subsequently, Gaussian mixture regression is employed to predict the shifting error at any query time. The proposed learning scheme is verified in a pulsed laser system (1064nm, Nd:YAG, 100Hz), showing that the drift prediction approach achieves remarkable performances.

Optimal Design and Tracking Control of a Superelastic Flexure Hinge Based 3-PRR Compliant Parallel Manipulator

A major obstacle that restricts the application of notched flexure hinge based compliant mechanism is its limited motion range. This paper presents the optimal design and tracking control of a superelastic flexure hinge based 3-PRR compliant parallel manipulator (CPM) to achieve high precision planar motion within centimeter's translation range and up to 10 degrees' rotational range. Firstly, a novel asymmetric ellipse-parabola (AEP) notch shape is proposed, and the geometric parameters of the AEP superelastic flexure hinge are acquired via multi-objective optimization to obtain desirable transmission performance. Secondly, a nominal inverse kinematic model of the CPM is established, and the dimension parameters of the 3-PRR manipulator are synthesized to maximize the dexterity of the CPM over the regular workspace. Thereafter, a disturbance observer based inverse kinematic control scheme (DOB-IKM) is proposed to suppress the model mismatches and external disturbances of the 3-PRR CPM, where the unmodeled factors of the system are approximated through an online learning radical basis function neural network (RBFNN) and the external disturbances of the CPM is observed and compensated by a disturbance observer (DOB). Finally, a prototype of the 3-PRR CPM is manufactured, and experimental tests show the effectiveness of the proposed control scheme and the superiority of the 3-PRR CPM.

Population structure in genetic studies: Confounding factors and mixed models.

A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to accurately test for association while correcting for population structure is a computational and statistical challenge. Using laboratory mouse strains as an example, our review characterizes the problem of population structure in association studies and describes how it can cause false positive associations. We then motivate mixed models in the context of unmodeled factors.