Robust Estimation Performance Research Articles

Markov modeling on dynamic state space for genetic disorders and infectious diseases with mutations: Probabilistic framework, parameter estimation, and applications

Abstract The emergence and dynamic prevalence of genetic disorders and infectious diseases with mutations pose significant challenges for public health interventions. This study investigated the parameter estimation approach and the application of the dynamic state-space Markov modeling of these conditions. Using extensive simulations, the model demonstrated robust parameter estimation performance, with biases and mean-squared errors decreasing as sample size increased. Applying the model to COVID-19 data revealed distinct temporal patterns for each variant, highlighting their unique emergence, peak dominance, and decline or persistence trajectories. Despite the absence of clear trends in the data, the model exhibited a remarkable accuracy in predicting future prevalence trends for most variants, showcasing its potential for real-time monitoring and analysis. While some discrepancies were observed for specific variants, these findings suggest the model’s promise as a valuable tool for informing public health strategies. Further validation with larger datasets and exploration of incorporating additional factors hold the potential for enhancing the model’s generalizability and applicability to other evolving diseases.

A fast dynamic pose estimation method for vision-based trajectory tracking control of industrial robots

Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector’s pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (±0.11 mm for position and ± 0.05°for orientation).

Direction-of-arrival estimation in closely distributed array exploiting mixed-precision covariance matrices

In this paper, we explore a collaborative direction-of-arrival (DOA) estimation technique that utilizes multiple closely spaced subarrays to maximize the potential of distributed arrays while minimizing communication overhead between the subarrays and the processing center. Each subarray computes its self-covariance matrix using the full-precision data and transmits it, along with a one-bit version of the measured data, to the processing center. The processing center generates one-bit cross-covariance matrices between subarrays, which are combined with full-precision subarray self-covariance matrices to create the mixed-precision covariance matrix of the entire array for source DOA estimation. This approach utilizes the full array aperture and all available degrees of freedom of the entire distributed array. To address missing entries in the full covariance matrix, we employ matrix completion, taking into account its Toeplitz and Hermitian structure. For subarrays that are not positioned on the half-wavelength grid, we propose an iterative DOA estimation method to ensure robust DOA estimation performance. Our proposed approach outperforms scenarios where cross-covariance matrices are unavailable or the entire covariance matrix is not interpolated. With the same communication traffic limitation, it demonstrates superiority over schemes that utilize only full-precision data or only one-bit data.

Enhancing Machine Translation Quality Estimation via Fine-Grained Error Analysis and Large Language Model

Fine-grained error span detection is a sub-task within quality estimation that aims to identify and assess the spans and severity of errors present in translated sentences. In prior quality estimation, the focus has predominantly been on evaluating translations at the sentence and word levels. However, such an approach fails to recognize the severity of specific segments within translated sentences. To the best of our knowledge, this is the first study that concentrates on enhancing models for this fine-grained error span detection task in machine translation. This study introduces a framework that sequentially performs sentence-level error detection, word-level error span extraction, and severity assessment. We present a detailed analysis for each of the methodologies we propose, substantiating the effectiveness of our system, focusing on two language pairs: English-to-German and Chinese-to-English. Our results suggest that task granularity enhances performance and that a prompt-based fine-tuning approach can offer optimal performance in the classification tasks. Furthermore, we demonstrate that employing a large language model to edit the fine-tuned model’s output constitutes a top strategy for achieving robust quality estimation performance.

Enhanced Distributed Consensus Filtering Over Lossy Wireless Sensor Networks With Application to Target Speed Tracking

This paper presents a novel way of distributed consensus filtering with enhanced robustness over wireless sensor networks, where bounded-power disturbances related to modeling uncertainty and white Gaussian noises are included. Sensors are assumed to receive local observations and transmit them to their neighbors through lossy links. A distributed consensus filtering scheme, consisting of an optimal filter, a separated damping term and a consensus tuning parameter, is considered through exchanging prior estimates among neighboring nodes, to achieve the cohesiveness among local estimates together with robust optimal estimation performance. A sufficient and necessary condition is conducted for determining the consensus parameter, leading to a non-conservative design way. Finally, to show the effectiveness of the presented results an experiment of target speed tracking is included.

Hyperspectral-to-image transform and CNN transfer learning enhancing soybean LCC estimation

Leaf chlorophyll content (LCC) is a distinct indicator of crop health status used to estimate nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the monitoring of crop growth. The combined use of hyperspectral and deep learning techniques (e.g., convolutional neural network [CNN] and transfer learning [TL]) can improve the performance of crop LCC estimation. We propose a hyperspectral-to-image transform (HIT) technique for converting canopy hyperspectral reflectance into 2D images. We designed a CNN architecture called LCCNet that fuses the deep and shallow features of CNNs to improve soybean LCC estimation. This study evaluated the combined use of hyperspectral remote sensing (RS), HIT, CNN, and TL techniques to estimate soybean LCC for multiple growth stages. The LCCNet was pre-trained based on a simulated dataset (n = 114,048) from the PROSAIL radiative transfer model (RTM) and used as prior knowledge for this work. The soybean canopy hyperspectral RS dataset (n = 910) was obtained using a FieldSpec 3 spectrometer. The knowledge gained while learning to estimate LCC from PROSAIL RTM was applied when estimating field soybean LCC (Dualex readings). TL was used to enhance the soybean estimation model, called the Soybean-LCCNet (RTM + HIT + CNN + TL) model. We tested the LCC (Dualex readings) estimation performance using (a) HIT + CNN, (b) LCCNet (RTM + HIT + CNN), (c) Soybean-LCCNet (RTM + HIT + CNN + TL), and (d) widely used LCC spectral features + partial least squares regression (PLSR). Four methods were ranked based on their LCC estimation performance: Soybean-LCCNet (R2 = 0.78, RMSE = 4.13 (Dualex readings)) > HIT + CNN (R2 = 0.75, RMSE = 4.41 (Dualex readings)) > PLSR-based method (R2 = 0.61, RMSE = 5.39 (Dualex readings)) > LCCNet (R2 = 0.53, RMSE = 7.11 (Dualex readings)). The main conclusions of this work are as follows: (1) HIT + CNN can provide a more robust LCC estimation performance than the widely used LCC SIs; (2) Fusing the deep and shallow features of CNNs can improve the performance of RS soybean LCC (Dualex readings) estimation; and (3) Soybean-LCCNet can reuse the CNN layer information of a pre-trained LCCNet based on a PROSAIL RTM dataset and improve the soybean LCC estimation performance.

Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model

Based on a global matchup between satellite observations and high performance liquid chromatography (HPLC) measurements, we developed a deep-learning-based model (DL-PPCE model) for globally estimating concentrations of 17 different phytoplankton pigments. The model adopted a fusion architecture of residual and pyramid networks to achieve robust estimation performance. The model inputs include three different data types: essential ocean color parameters, satellite-derived environmental parameters, and the slope of above-surface remote-sensing reflectance (Rrs). We compared the model performances with various input parameters to determine the most effective inputs. The results showed that Rrs in the essential ocean color parameters and sea surface temperature (SST) in the environmental parameters were the most critical input parameters. The estimation of phytoplankton pigment concentrations was validated against HPLC data using the leave-one-out cross-validation method. Except for three pigments, 19′-butanoyloxy-fucoxanthin, prasinoxanthin, and lutein, the estimated pigment concentrations and in-situ observations were strongly correlated for all other pigments (an average relative root-mean-square error of 0.59, R2≥0.60, and regression slopes close to 1). In addition, a time series analysis was performed on the MODIS retrieved global pigment concentrations during 2003–2021 using the established DL-PPCE model to explore the relationship between the distribution of phytoplankton groups and El Niño in the western equatorial Pacific. Our findings revealed that the prokaryotes-dominated area extended eastward from180°E to 150°W during the 2015/2016 El Niño event. From 2003 to 2021, prokaryotic abundance was positively correlated with El Niño intensity (R=0.65,P≪0.01) but negatively correlated with the abundance of the entire phytoplankton community (R=−0.53,P≪0.01). These results demonstrate that the DL-PPCE model presents a novel approach for estimating the concentration of 17 pigments worldwide, and the estimated pigment concentrations are advantageous for analyzing the phytoplankton community dynamics on a large spatiotemporal scale.

Robust DOA Estimation Based on an Exponential Hyperbolic Cosine Adaptive Algorithm

Direction-of-arrival (DOA) estimation methods based on conventional adaptive signal processing are not robust to outliers. A robust DOA estimation technique based on an exponential hyperbolic cosine function (EHCF) under such situations is proposed in this letter. To further enhance the performance of the proposed approach, a variable scale parameter strategy has been incorporated into the proposed scheme. The new methods have been shown to offer improved and robust DOA estimation performance.

Median robust nonlinear weighted total least squares estimator of nonlinear EIV models: three algorithms

To improve robust estimation performance of fully correlated nonlinear error-in-variables model, three median robust estimate methods are discussed on base of Nonlinear Weighted Total Least Squares (NWTLS), namely, median parameter method, median parameter initial value method and median variance method. After the discussion of the median robust estimation theory and the derivation of the calculation formula of the three median robust estimation methods, three iterative algorithms are developed. Through two nonlinear regression examples, four schemes including no artificial outliers, different location of outliers, different degrees of correlation, and different ratios of outliers are adopted, which proves the feasibility and effectiveness of the proposed algorithm. The experimental results show that from the statistical point of view, although the three median methods have different antioutlier effects for different nonlinear models, they are more effective than RNWTLS and NWTLS; the robust estimation results of three median methods are related to the functional model.

Leveraging auxiliary data to improve precision in inverse probability-weighted analyses

PurposeTo demonstrate improvements in the precision of inverse probability-weighted estimators by use of auxiliary variables, i.e., determinants of the outcome that are independent of treatment, missingness or selection. MethodsFirst with simulated data, and then with public data from the National Health and Nutrition Examination Survey (NHANES), we estimated the mean of a continuous outcome using inverse probability weights to account for informative missingness. We assessed gains in precision resulting from the inclusion of auxiliary variables in the model for the weights. We compared the performance of robust and nonparametric bootstrap variance estimators in this setting. ResultsWe found that the inclusion of auxiliary variables reduced the empirical variance of inverse probability-weighted estimators. However, that reduction was not captured in standard errors computed using the robust variance estimator, which is widely used in weighted analyses due to the non-independence of weighted observations. In contrast, a nonparametric bootstrap estimator properly captured the precision gain. ConclusionsEpidemiologists can leverage auxiliary data to improve the precision of weighted estimators by using bootstrap variance estimation, or a closed-form variance estimator that properly accounts for the estimation of the weights, in place of the standard robust variance estimator.

Fusion Poser: 3D Human Pose Estimation Using Sparse IMUs and Head Trackers in Real Time.

The motion capture method using sparse inertial sensors is an approach for solving the occlusion and economic problems in vision-based methods, which is suitable for virtual reality applications and works in complex environments. However, VR applications need to track the location of the user in real-world space, which is hard to obtain using only inertial sensors. In this paper, we present Fusion Poser, which combines the deep learning-based pose estimation and location tracking method with six inertial measurement units and a head tracking sensor that provides head-mounted displays. To estimate human poses, we propose a bidirectional recurrent neural network with a convolutional long short-term memory layer that achieves higher accuracy and stability by preserving spatio-temporal properties. To locate a user with real-world coordinates, our method integrates the results of an estimated joint pose with the pose of the tracker. To train the model, we gathered public motion capture datasets of synthesized IMU measurement data, as well as creating a real-world dataset. In the evaluation, our method showed higher accuracy and a more robust estimation performance, especially when the user adopted lower poses, such as a squat or a bow.

Real‐Time Earthquake Detection and Magnitude Estimation Using Vision Transformer

AbstractWe design a fully automated system for real‐time magnitude estimation based on a vision transformer (ViT) network. ViT is an attention mechanisms, which guides the proposed network to extract the significant features from the input seismic data, leading to robust magnitude estimation performance. We propose to design two separate ViT networks, that is, one for picking the P‐wave arrival time and the other for predicting the earthquake magnitude using a single station. For real‐time application, we pick the P‐wave arrival times and consider them as the reference, based on which the non‐normalized 30‐s (i.e., 1 s before and 29 s after the reference time) three‐component seismograms are used to predict the magnitudes of the corresponding earthquakes. The ViT picking network is first trained and tested using the STanford EArthquake Data set (STEAD) and shows robust picking performance, achieving an average picking error of less than 0.2 s compared to the manual picks. Then, the ViT magnitude estimation network is evaluated using several data sets, including those from California, STEAD repository, and Texas. The ViT demonstrates robust magnitude estimation performance in all these test cases as compared with the benchmark methods. For magnitude estimation, the mean absolute error (MAE) and the standard deviation error (σ) for the testing set of the STEAD data set are 0.112 and 0.164 (as compared with 0.141 and 0.219 for the state‐of‐the‐art MagNet method), respectively. The MAE and σ for the California testing set are 0.079 and 0.120 (as compared with 0.089 and 0.138 for the Magnet method), respectively. As a case study, the new ViT networks are applied to the 24‐hr continuous seismic data of the TexNet‐PB05 station recorded on September 20th. The network successfully picks all the events in the TexNet catalog with a small (<=0.42) magnitude error. The ViT network shows promising magnitude prediction results when tested with 4‐s long seismograms. This highlights its potential in the earthquake early warning (EEW) system for fast and reliable decisions.

Comparison of M, MM and LTS estimators in linear regression in the presence of outlier

In this study, it was aimed to evaluate the performance of different estimators that will be used in regression analysis, which is one of the multivariate statistical methods in the presence of outliers in the data set. Sixth month live weight was estimated with various body measurements for Saanen kids taken from a private farm. In the data set, the use and performance of robust estimators were evaluated because the least squares method did not provide reliable results in the case of outliers. M (for Huber and Tukey bisquare) estimator, MM estimator and LTS estimator were used as robust used in the presence of outliers. MSE, RMSE, rRMSE, MAPE, MAD, R$^{2}$, R$^{2}$ $_{adj}$ and AIC were used as model comparison criteria in the study. As a result of the study, in the case of outlier in the data set, Huber type M estimator can be recommended.

A New Discrete-Time Interval Estimator for Vehicle Side-Slip Angle Estimation

The information about vehicle side-slip angle is essential given its relation to lateral stability and its importance for active safety control systems. Since direct measurement of the side-slip angle is expensive, developing efficient indirect techniques to extract this information from available sensor measurements is essential. This paper proposes a novel, cost-effective indirect strategy for the robust estimation of the side-slip angle using a state observer that relies on a discrete-time Linear Parameter Varying (LPV) lateral vehicle dynamic model. The proposed algorithm relies on a finite-time interval LMI-based observer, which accounts for the uncertainties on the model parameters (cornering stiffness) with known upper and lower uncertainty bounds. The simulation results show the effective and robust estimation performance of the proposed observer under various changes in system parameters. Moreover, comparison results with ℋ∞ set-membership observer for discrete-time LPV systems show the effectiveness of the developed observer.

Maximum likelihood DOA estimation based on improved invasive weed optimization algorithm and application of MEMS vector hydrophone array

<abstract><p>Direction of arrival (DOA) estimation based on Maximum Likelihood is a common method in array signal processing, with many practical applications, but the huge amount of calculation limits the practical application. To deal with such an Maximum Likelihood (ML) DOA estimation problem, firstly, the DOA estimation model with ML for acoustic vector sensor array is developed, where the optimization standard in various cases can be unified by converting the maximum of objective function to the minimum. Secondly, based on the Invasive Weed Optimization (IWO) method which is a novel biological evolutionary algorithm, a new Improved IWO (IIWO) algorithm for DOA estimation of the acoustic vector sensor array is proposed by using ML estimation. This algorithm simulates weed invasion process for DOA estimation by adjusting the non-linear harmonic exponent of IWO algorithm adaptively. The DOA estimation accuracy has been improved, and the computation of multidimensional nonlinear optimization for the ML method has been greatly reduced in the IIWO algorithm. Finally, compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) method and Tuna Swarm Optimization(TSO) algorithm, numerical simulations show that the proposed algorithm has faster convergence rate, improved accuracy in terms of Root Mean Square Error (RMSE), lower computational complexity and more robust estimation performance for ML DOA estimation. The experiment with tracking the orientation of the motorboat by Microelectronic mechanical systems (MEMS) vector hydrophone array shows the superior performance of proposed IIWO algorithm in engineering application. Therefore, the proposed ML-DOA estimation with IIWO algorithm can take into account both resolution and computation. which can meet the requirements of real-time calculation and estimation accuracy in the actual environment.</p></abstract>

흐름 생산 공정문제를 위한 dueling double Q-러닝 기반 강화학습 기법 연구

Flow shop scheduling is an important optimization problem for operating actual manufacturing facilities. In this paper, we propose a novel flow shop scheduling scheme based on the double Q-learning and the dueling architecture. We designed double Q-learning process using two estimation functions rather than a single estimation function to avoid over-estimation problem. In addition, the adaptation of the dueling architecture, which can provide robust policy estimation performance using both the state function and the advantage function, leading to less-variance and efficient learning process. We conducted extensive simulations of flow shop scheduling using multiple datasets with various scheduling scales. From the simulation result, we observed that the proposed scheme outperforms the existing heuristic and reinforcement learning-based scheduling schemes in terms of the final manufacturing time consumption for various flow shop scales.

Comparison of robust logistic regression estimators for variables with generalized extreme value distributions

The aim of this study is to compare the performance of robust estimators in the presence of explanatory variables with Generalized Extreme Value (GEV) distributions in the logistic regression model. Existence of extreme values in the logistic regression model negatively affects the bias and effectiveness of classical Maximum Likelihood (ML) estimators. For this reason, robust estimators that are less sensitive to extreme values have been developed. Random variables with extreme values may be fit in one of specific distributions. In study, the GEV distribution family was examined and five robust estimators were compared for the Fréchet, Gumbel and Weibull distributions. To the simulation results, the CUBIF estimator is prominent according to both bias and efficiency criteria for small samples. In medium and large samples, while the MALLOWS estimator has the minimum bias, the CUBIF estimator has the best efficiency. The same results apply for different contamination ratios and different scale parameter values of the distributions. Simulation findings were supported by a meteorological real data application.

Direction-of-arrival estimation with blind surface impedance compensation for spherical microphone array.

The direction-of-arrival (DoA) estimation from spherical microphone array data requires mode strength compensation for removing the dependency on the array surface impedance. However, in practice, mismatches exist in the actual and modeled surface impedances and degrade the DoA estimation performance. Herein, a non-parametric beamformer that can accurately estimate DoAs under the mode strength mismatch is proposed. The technique utilizes a block-diagonal covariance matrix and order-dependent normalization of spherical harmonic coefficients. The proposed method tested for various surface impedance mismatches and DoAs shows robust estimation performance with reduced dependency on the array surface impedance.

A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding.

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.

Sensorless LC Filter Implementation for Permanent Magnet Machine Drive Using Observer-Based Voltage and Current Estimation.

For pulse width modulation (PWM) inverter drives, an LC filter can cascade to a permanent magnet (PM) machine at inverter output to reduce PWM-reflected current harmonics. Because the LC filter causes resonance, the filter output current and voltage are required for the sensorless field-oriented control (FOC) drive. However, existing sensors and inverters are typically integrated inside commercial closed-form drives; it is not possible for these drives to obtain additional filter output signals. To resolve this integration issue, this paper proposes a sensorless LC filter state estimation using only the drive inside current sensors. The design principle of the LC filter is first introduced to remove PWM current harmonics. A dual-observer is then proposed to estimate the filter output current and voltage for the sensorless FOC drive. Compared to conventional model-based estimation, the proposed dual-observer demonstrates robust estimation performance under parameter error. The capacitor parameter error shows a negligible influence on the proposed observer estimation. The filter inductance error only affects the capacitor current estimation at high speed. The performance of the sensorless FOC drive using the proposed dual-observer is comparable to the same drive using external sensors for filter voltage and current measurement. All experiments are verified by a PM machine with only 130 μH phase inductance.