Accurate Estimation Model Research Articles

Estimation of triceps muscle strength based on Mechanomyography

The aim of this study is to establish a reliable and widely applicable muscle strength (MS) estimation model based on the Mechanomyography (MMG). Seven healthy male volunteers were recruited to collect MMG and MS during the isometric contraction of their triceps. For MMG, 18 features were extracted. For the extreme gradient boosting (XGBoost) model and the quadratic polynomial (QP) model, the feature combination with the best estimation result was selected. The MS estimation performance of the XGBoost model and the QP model were compared. The performance of the QP model on the estimation of MS in different frequencies, different fatigue states and time periods was evaluated by using t-test. The results showed that when the number of features exceeds three, the model estimation accuracy has not improved significantly; and there was no significant difference in the estimation result of MS between the two models (p < 0.05), though the QP model was slightly better. The normalized root mean square error (NRMSE) and goodness of fit R of the MS estimation by the QP model were: 0.1343 ± 0.0296 and 0.8273 ± 0.0376. There was no significant difference in the MS estimation results in different conditions (p < 0.05).

Estimation of Small Unmanned Aerial Vehicle Lateral Dynamic Model with System Identification Approaches

Modeling of unmanned aerial vehicle (UAV) with system identification is very important in terms of its model-based effective control. The modeling of UAV is required for aircraft crashes, analyzing autonomous aircrafts, preventing external disturbances, pre-flight analysis. However, since UAV has nonlinear inherent dynamics including inherent chaoticity and fractality, it becomes difficult to obtain a mathematical model under external disturbance. In this study, some of the inherent nonlinear dynamics of UAV are linearized and the model of UAV is obtained by system identification approaches under external disturbance. The linearized lateral dynamics of a fixed wing UAV is used in this study. Further, the flight motion equations applied to fixed wing UAV have been utilized for obtaining the coefficients of lateral model for straight and level flight. The roll angles are calculated using transfer functions for aileron, rudder and deflections inputs. The autoregressive exogenous (ARX), autoregressive moving average with exogenous (ARMAX) and output error (OE) parametric system identification approaches are performed to estimate UAV lateral dynamic system response as using empirical input-output data sets. The accuracy of parametric model estimation and model degrees are compared for different external disturbance effects.

A Novel Method for Assessing the Contribution of Harmonic Sources to Voltage Distortion in Power Systems

This paper presents a method for evaluating the harmonic contributions of multiple harmonic sources to voltage distortion at the point of common coupling (PCC). The proposed method quantitatively represents harmonic contributions based on the equivalent voltage models of harmonic sources. The equivalent voltage models can be estimated using the recursive least square (RLS) algorithm and measured voltage and current at the PCC. The assessment of harmonic contribution heavily depends on the accuracy of equivalent model estimation. Many existing studies have assumed that the equivalent model parameters of harmonic sources are not changed. However, because harmonic source parameters and system operating conditions are not constant in real power systems, this assumption can lead large errors in harmonic contribution assessment. In this paper, we propose a new assessment method based on variable forgetting factor RLS and parameter change detection to solve the problem. The proposed method is also effective for evaluating comprehensive harmonic contributions as well as individual harmonic contributions. The case studies show that the proposed method is superior to the previous one even under parameter changing conditions.

Farmland productivity estimation based on vegetation indexes from remote sensing data

Ensuring food security is a long-term and arduous task. Timely and accurate grasp of grain production capacity information can provide favourable data support for the nation to formulate macroeconomic plans and food policies. With the development of remote sensing technology, it has been widely used in crop yield estimation models. In this paper, the yield of spring maize in Da’an of Jilin province was estimated based on vegetation indexes calculated from Landsat-8 images. The results have shown that the fitting degree and estimation accuracy of yield estimation models at tasselling stage are significantly better than those at milk stage. Among these vegetation indexes, the model based on GNDVI has better fitting degree and estimation accuracy. This paper can provide reference for the post construction evaluation of high standard farmland in China.

A Case Study Examining the Usefulness of Cure Modelling for the Prediction of Survival Based on Data Maturity.

Mixture modelling is increasingly being considered where a potential cure leads to a long life. Traditional methods use relative survival models for frail populations or cure models that have improper survival functions with theoretical infinite lifespans. Additionally, much of the work uses population data with long follow-up or theoretical data for method development. This case study uses life table data to create a proper survival function in a real-world clinical trial context. In particular, we discuss the impact of the length of trial follow-up on the accuracy of model estimation and the impact of extrapolation to capture long-term survival. A review of recent National Institute for Health and Clinical Excellence (NICE) immuno-oncological and chimeric antigen receptor (CAR) T-cell therapy submissions was performed to assess industry uptake and NICE acceptance of survival analysis methods incorporating the potential for long-term survivorship. The case study analysed a simulated trial-based dataset investigating a curative treatment with long-term mortality based on population life tables. The analysis examined three timepoints corresponding to early trial, end-of-trial follow-up and complete follow-up. Mixture modelling approaches were considered, including both cure modelling and relative survival approaches. The curves were evaluated based on the ability to estimate cure fractions and mean life in years within the time span the models are based on and when extrapolating to capture long-term behaviour. The survival curves were fitted with Weibull distributions using non-mixture and mixture cure models. The performance of the cure modelling methods depended on the relative maturity of the data, indicating that care is needed when deciding when the methods should be applied. For progression-free survival, the cure fraction simulated was 15%. The cure fractions estimated using the traditional mixture cure model were 43% (95% confidence interval [CI] 30-57) at the first analysis time point (40 months), 15% (95% CI 12-20) at the end-of-study follow-up (153 months) and 0% (95% CI 0-100) at the end of follow-up. Other standard cure modelling methods produced similar results. For overall survival, we observed a similar pattern of goodness of fit, with a good fit for the end-of-study follow-up and poor fit for the other two data cuts. However, in this case, the estimate of the cure fraction was below the true value in the first analysis data. This case study suggests cure modelling works well with data in which the disease-specific events have had time to occur. Care is needed when extrapolating from immature data, and further information should support the estimation rather than relying on statistical estimates based on the trial alone.

Continuous Description of Human 3D Motion Intent Through Switching Mechanism

Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.

A novel model based on multi-grained cascade forests with wavelet denoising for indoor occupancy estimation

Reliable occupancy estimation is the key to balancing energy use and comfort as well as promoting energy efficiency in buildings. In this study, a novel occupancy estimation model based on CO2 concentration data was proposed. A wavelet denoising method was applied to remove the random noise of the CO2 sensory data, and then a multi-grained scanning cascade forests (GcForest) method was used to estimate the number of occupants. The GcForest model incorporated three different tree-based classifiers in each level, enabling the estimation performance to be enhanced by exploiting the complementarity among the different learning algorithms. To evaluate the effectiveness of the proposed model, in this study a validation experiment was conducted in a university lab office and its results were compared with the support vector machines (SVM), classification and regression trees (CART), and inhomogeneous hidden Markov (IHMM) algorithms. The experimental results show that the wavelet denoising method could filter the noise and preserve the data features of the CO2 concentration. Moreover, the proposed model could achieve higher estimation accuracy, lower mean absolute error, and higher detection accuracy of the occupant presence/absence. Additionally, this model could capture both the first arrival time and the last departure time. Since the maximum depths of the classifiers affected the GcForest model's performance, the results also show that a proper selection of the maximum depth combination could lead to a significant improvement of the model estimation accuracy.

Machine learning models for the estimation of monthly mean daily reference evapotranspiration based on cross-station and synthetic data

AbstractThe estimation of reference evapotranspiration (ET0) is important in hydrology research, irrigation scheduling design and water resources management. This study explored the capability of eight machine learning models, i.e., Artificial Neuron Network (ANN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Spline (MARS), Support Vector Machine (SVM), Extreme Learning Machine and a novel Kernel-based Nonlinear Extension of Arps Decline (KNEA) Model, for modeling monthly mean daily ET0 using only temperature data from local or cross stations. These machine learning models were also compared with the temperature-based Hargreaves–Samani equation. The results indicated that the estimation accuracy of these machine learning models differed in various scenarios. The tree-based models (RF, GBDT and XGBoost) exhibited higher estimation accuracy than the other models in the local application. When the station has only temperature data, the MARS and SVM models were slightly superior to the other models, while the ANN and HS models performed worse than the others. When there was no temperature data at the target station and the data from adjacent stations were used instead, MARS, SVM and KNEA were the suitable models. The results can provide a solution for ET0 estimation in the absence of complete meteorological data.

Redevelopment modeling for land suitability evaluation of the suburb brown-fields using fuzzy logic and GIS, northeastern Iran

The present study aimed to generate a redevelopment model of suitability evaluation in the suburb brown-fields of western Mashhad, northeastern Iran. Redevelopment model assists urban municipalities and environmental agencies through making a cleanup place and a healthy environment. In this regard, using fuzzy logic and GIS, a place making procedure was applied to analyze ten effective spatial variables including elevation heights, slope, aspect, geological units, soil units, land covers, ecological resources, rainfall, proximity to roads, and proximity to water wells. By combining layers using the normalized fuzzy membership functions in GIS, a final map of land suitability evaluation was produced. According to redevelopment modeling, the prone zones of faults and floods hazards were eliminated from the suitability map of the brown-fields. Therefore, fieldwork survey indicted rational requirements of the land suitability to regenerate designable uses and services in order to design an upper hand redevelopment plan to conduct regional and local place makings. Estimation of model accuracy revealed a strong relationship (R2) between suitability classes and real estate inventory using scatter plot equal to 0.83. Hence, the redevelopment model represented an appropriate accuracy in predicting the redevelopment expansion in the brown-fields of the study area.

State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter

Accurate estimation of the battery state of charge (SOC) is of great significance for enhancing its service life and safety. In this study, based on the fractional-order equivalent circuit model of lithium-ion battery, the SOC estimation methods using dual Kalman filter (DKF) and dual extended Kalman filter (DEKF) are simulated and compared, in terms of model accuracy and SOC estimation accuracy. Then, combining the advantages of the DKF and DEKF algorithms, an SOC estimation algorithm based on adaptive double Kalman filter is proposed. This algorithm uses the recursive least squares (RLS) method to update the battery model parameters online in real time, and employs the DKF algorithm to filter the SOC twice to reduce the interferences from the battery model error and the current measurement error. In the experimental studies, the measured SOC values are compared with the estimated SOC values produced by the proposed algorithm. The comparison results show that SOC estimation error of the proposed algorithm is within the range of ±0.01 under most test conditions, and it can automatically correct SOC to true value in the presence of system errors. Thus, the validity, accuracy, robustness and adaptability of the proposed algorithm under different operation conditions are verified.

Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network.

In protein tertiary structure prediction, model quality assessment programs (MQAPs) are often used to select the final structural models from a pool of candidate models generated by multiple templates and prediction methods. The 3-dimensional convolutional neural network (3DCNN) is an expansion of the 2DCNN and has been applied in several fields, including object recognition. The 3DCNN is also used for MQA tasks, but the performance is low due to several technical limitations related to protein tertiary structures, such as orientation alignment. We proposed a novel single-model MQA method based on local structure quality evaluation using a deep neural network containing 3DCNN layers. The proposed method first assesses the quality of local structures for each residue and then evaluates the quality of whole structures by integrating estimated local qualities. We analyzed the model using the CASP11, CASP12, and 3D-Robot datasets and compared the performance of the model with that of the previous 3DCNN method based on whole protein structures. The proposed method showed a significant improvement compared to the previous 3DCNN method for multiple evaluation measures. We also compared the proposed method to other state-of-the-art methods. Our method showed better performance than the previous 3DCNN-based method and comparable accuracy as the current best single-model methods; particularly, in CASP11 stage2, our method showed a Pearson coefficient of 0.486, which was better than those of the best single-model methods (0.366–0.405). A standalone version of the proposed method and data files are available at https://github.com/ishidalab-titech/3DCNN_MQA.

Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning.

Scoring model structure is an essential component of protein structure prediction that can affect the prediction accuracy tremendously. Users of protein structure prediction results also need to score models to select the best models for their application studies. In Critical Assessment of techniques for protein Structure Prediction (CASP), model accuracy estimation methods have been tested in a blind fashion by providing models submitted by the tertiary structure prediction servers for scoring. In CASP13, model accuracy estimation results were evaluated in terms of both global and local structure accuracy. Global structure accuracy estimation was evaluated by the quality of the models selected by the global structure scores and by the absolute estimates of the global scores. Residue-wise, local structure accuracy estimations were evaluated by three different measures. A new measure introduced in CASP13 evaluates the ability to predict inaccurately modeled regions that may be improved by refinement. An intensive comparative analysis on CASP13 and the previous CASPs revealed that the tertiary structure models generated by the CASP13 servers show very distinct features. Higher consensus toward models of higher global accuracy appeared even for free modeling targets, and many models of high global accuracy were not well optimized at the atomic level. This is related to the new technology in CASP13, deep learning for tertiary contact prediction. The tertiary model structures generated by deep learning pose a new challenge for EMA (estimation of model accuracy) method developers. Model accuracy estimation itself is also an area where deep learning can potentially have an impact, although current EMA methods have not fully explored that direction.

An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries

In this paper, an improved state of charge (SOC) estimation method of Lithium-Ion battery is developed based on a cubature Kalman filter (CKF) supported by experimental data. Firstly, a first-order RC model and corresponding fractional order model are established to evaluate the estimation accuracy of different models. Secondly, model parameters are identified through a custom Hybrid Pulse Power Characteristic (HPPC) experiment based on the Sequential Quadratic Programming (SQR) method. Then, a CKF algorithm is used to estimate the battery SOC under different battery models with no prior knowledge of initial SOC. The results show that the proposed CKF method has a better estimate robustness rather than Extended Kalman filter (EKF) and the fractional order model can achieve higher accuracy while it consumes more computing resources compared with equivalent circuit models. SOC estimation error of CKF algorithms is less than 3%. Thirdly, a battery management unit in the loop approach is applied to verify the accuracy of estimation. Last but not least, in order to reduce the estimation error due to battery degradation and battery model errors, a fuzzy controller is constructed to modified the gain coefficient of Kalman. The proposed improved method can minimize the estimation error of SOC by 2%.

Estimation of model accuracy in CASP13

Methods to reliably estimate the accuracy of 3D models of proteins are both a fundamental part of most protein folding pipelines and important for reliable identification of the best models when multiple pipelines are used. Here, we describe the progress made from CASP12 to CASP13 in the field of estimation of model accuracy (EMA) as seen from the progress of the most successful methods in CASP13. We show small but clear progress, that is, several methods perform better than the best methods from CASP12 when tested on CASP13 EMA targets. Some progress is driven by applying deep learning and residue-residue contacts to model accuracy prediction. We show that the best EMA methods select better models than the best servers in CASP13, but that there exists a great potential to improve this further. Also, according to the evaluation criteria based on local similarities, such as lDDT and CAD, it is now clear that single model accuracy methods perform relatively better than consensus-based methods.

A rule-based decision support approach for site selection of Automated Teller Machines (ATMs)

The solution of facility site selection problems is of utmost importance for the existence and future of the businesses. When planning for banking services, banks should ensure their accessibility standards by taking their customers and needs into account. For this reason, the accessibility of the services offered via ATM devices is very important. Contrary to the studies that have been widely done in the literature, in this study, the data mining techniques for determining locations of the ATMs, specifically decision tree algorithms were applied to select the best location among candidates considering new ATM installations. In practice, a data set of a private bank’s ATM’s, which in the boundaries of the districts connected to downtown of Kayseri, was used. A decision support model based on rule-based location detection was created from the data set discussed. The correct classification scales were evaluated by applying different algorithms for decision tree method. Among the decision tree algorithms, model estimation accuracy was found to be 92.96% on average. The results were obtained using STATISTICA 10.0 Software.

IntFOLD: an integrated web resource for high performance protein structure and function prediction.

The IntFOLD server provides a unified resource for the automated prediction of: protein tertiary structures with built-in estimates of model accuracy (EMA), protein structural domain boundaries, natively unstructured or disordered regions in proteins, and protein–ligand interactions. The component methods have been independently evaluated via the successive blind CASP experiments and the continual CAMEO benchmarking project. The IntFOLD server has established its ranking as one of the best performing publicly available servers, based on independent official evaluation metrics. Here, we describe significant updates to the server back end, where we have focused on performance improvements in tertiary structure predictions, in terms of global 3D model quality and accuracy self-estimates (ASE), which we achieve using our newly improved ModFOLD7_rank algorithm. We also report on various upgrades to the front end including: a streamlined submission process, enhanced visualization of models, new confidence scores for ranking, and links for accessing all annotated model data. Furthermore, we now include an option for users to submit selected models for further refinement via convenient push buttons. The IntFOLD server is freely available at: http://www.reading.ac.uk/bioinf/IntFOLD/.

Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery

Battery characterization data is the basis for battery modeling and state estimation. It is generally believed that the higher the sampling frequency, the finer the data, and the higher the model and state estimation accuracy. However, scientific selection strategy for sampling frequency is very important but rarely studied. This paper studies the influence of sampling frequency on the accuracy of battery model and state estimation under four different sampling frequencies: 0.2 Hz, 1 Hz, 2 Hz, and 10 Hz. Then, a function is proposed to depict the relationship between accuracy and sampling frequency, which shows an optimal selection principle. The iterative identification algorithm is presented to identify the model parameters, and state-of-charge (SOC) is estimated via extended Kalman filter algorithm. Experimental results with different operating conditions clearly show the relationship between sampling frequency, accuracy, and data quantity, and the proposed selection strategy has high practical value and universality.

A comparison of longitudinal adhesion models and identification of the dominant adhesion parameter

ABSTRACT Accurately estimating the coefficient of adhesion (CoA) is important to the safety, maintenance, operation and wheel–rail dynamics of railway systems. Studies have reported the influence of various parameters on the CoA and developed subsequent models, but parameter contribution or model estimation accuracy has not been systematically examined. Therefore, this study aims to quantitatively compare the accuracy of Heuristic, Polach and Dynamic estimation models with statistical measures and to investigate the contribution of various parameters on the CoA. Experimental CoA data was collected using a twin-disc rig at 57 and 85 km/h. The percentage root-mean-square error (PRMSE) of each model was then calculated for the rolling, sliding and overall contact regimes at each speed and multiple regression analysis (MRA) was applied to examine the contribution of each parameter. The Dynamic model was found to most accurately estimate the CoA in the sliding contact regime at both speeds due to its ability to capture dynamic variance. The Polach model also provided accurate estimations, but could not capture dynamic variance at the sliding contact regime. Using MRA, the CoA was found to be most dependent on sliding velocity. Overall, this work contributes to increase the prediction accuracy of wheel–rail contact dynamics.

Quadrotor aircraft intelligent system identification experiment design

Aircraft prototyping and modeling is usually associated with resource expensive techniques and significant post flight analysis. The NASA Learn-To-Fly concept targets the replacement of the conventional ground-based aircraft model development and prototyping approaches with an efficient real time paradigm. The work presented herein describes the development of an intelligent excitation input design technique that determines excitation frequencies based on predefined rotational motion dynamic model. The input design is then evaluated on quadcopter unmanned aircraft that utilizes the new multisine input design. In order to minimize flight excursions without compromising the modeling capabilities, multisine input power spectrum is optimized based on the vehicle’s frequency response. The proposed methodology emphasizes excitation of modal frequencies which yields flight data rich information content. The generated optimized multisine input design is utilized for a quadcopter aircraft system identification and the performance is compared to conventional uniform amplitudes design. Simulation results show highly accurate model estimation in all identification results in addition to reduction of induced perturbations and power consumption. Additionally, the generated model prediction capabilities are not compromised after power spectrum optimization. Overall, the proposed technique introduces an efficient and intelligent system identification experiment design that can minimize the time and effort spent during excitation input design.

Optimism Bias Correction in Omics Studies with Big Data: Assessment of Penalized Methods on Simulated Data.

Big Data generated by omics technologies require simultaneous analyses of large numbers of variables. This leads to complex model selection and parameter estimates that show optimism bias. This study on simulated data sets examined optimism-bias correction by penalty regression methods in case-control studies that involve clinical and omics variables. Least absolute shrinkage and selection operator (LASSO)-based methods (LASSO-penalized logistic regression, adaptive LASSO, and regularized LASSO for selection + ridge regression) were evaluated using power, the false positive rate (FPR), false discovery rate (FDR), and by estimated versus theoretical parameter comparisons. The "ordinary" LASSO overcorrects the optimism bias. The adaptive LASSO with LASSO estimation of the weights was unable to provide a sufficient correction. Importantly, the adaptive LASSO with ridge estimation of the weights showed the best parameter estimation. The regularized LASSO selection showed a slight optimism bias that decreased with the increase in the training set size. The optimism bias decreased with the increase of the number of variables selected among truly differentially expressed variables; however, power, FPR, and FDR were correlated. A compromise between model selection and estimation accuracy should be found. These results might prove useful because Big Data analyses are becoming commonplace in omics/multiomics studies in integrative biology, precision medicine, and planetary health.