Estimation Performance Research Articles

Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms

Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. The network incorporated both a feature correction module and a feature fusion module, significantly enhancing the performance in object detection, segmentation, and trait estimation. The model demonstrated high accuracy in estimating key traits, including fresh weight (fw), dry weight (dw), plant height (h), canopy diameter (d), and leaf area (la), achieving an R2 of 0.9732 for fresh weight. Robustness and accuracy were further validated through 5-fold cross-validation, offering a promising approach for future crop phenotyping.

Performance of Forensic Age Estimation by Aspartic Acid Racemization and DNA Methylation: A Systematic Review

Background Forensic age estimation is not difficult when the body is found in good condition, but in cases of severe decomposition or damage, such as burnt or separated body parts, then the analysis can only be done with bones and teeth. There has been abundant research and development in the field of related biochemistry over the years. Various molecular changes occur in hard tissues and long-lived proteins, such as those in bones and teeth during the physiological process of aging. Aspartic acid racemization and DNA methylation are still the most frequently used age estimation methods because of their advantages in accuracy. Method This study aimed to compare the accuracy of DNA methylation and aspartic acid racemization methods for age estimation. Journal articles were searched in the electronic databases PubMed, Scopus, and Semantic Scholar of 2017-2022 according to PRISMA guidelines. Result Twelve journal articles were eligible for review. The DNA methylation method is quite simple to use because of commercially available methylation kits. Furthermore, the results can be obtained relatively quickly without requiring many samples, and the method is less sensitive to thermal and other damage than the aspartic acid racemization method. Conclusion The aspartic acid racemization method for age estimation has high accuracy, especially in determining age at death. However, temperature and the condition of the teeth affect the racemization of aspartic acid. Given that DNA methylation is generally stable in a wide range of temperatures, it provides a better approach to determining the chronological age even from charred remains.

Fault-Tolerant Control for Quadcopters Under Actuator and Sensor Faults

Fault detection and diagnosis (FDD) methods and fault-tolerant control (FTC) have been the focus of intensive research across various fields to ensure safe operation, reduce costs, and optimize maintenance tasks. Unmanned aerial vehicles (UAVs), particularly quadcopters or quadrotors, are often prone to faults in sensors and actuators due to their complex dynamics and exposure to various external uncertainties. In this context, this work implements different FDD approaches based on the Kalman filter (KF) for fault estimation to achieve FTC of the quadcopter, considering different faults with nonlinear behaviors and the possibility of simultaneous occurrences in actuators and sensors. Three KF approaches are considered in the analysis: linear KF, extended KF (EKF), and unscented KF (UKF), along with three-stage and adaptive variations of the KF. FDD methods, especially the adaptive filter, could enhance fault estimation performance in the scenarios considered. This led to a significant improvement in the safety and reliability of the quadcopter through the FTC architecture, as the system, which previously became unstable in the presence of faults, could maintain stable operation when subjected to uncertainties.

A tactile perception method with flexible grating structural color

Abstract Affordable high-resolution cameras and state-of-the-art computer vision techniques have led to the emergence of various vision-based tactile sensors. However, current vision-based tactile sensors mainly depend on geometric optics or marker tracking for tactile assessments, resulting in limited performance. To solve this dilemma, we introduce optical interference patterns as the visual representation of tactile information for flexible tactile sensors. We propose a novel tactile perception method and its corresponding sensor combining structural colors from flexible blazed gratings with deep learning. The richer structural colors and finer data processing foster the tactile estimation performance. The proposed sensor has an overall normal force magnitude accuracy of 6 mN, a planar resolution of 79 μm, and a contact-depth resolution of 25 μm. This work presents a promising tactile method that combines wave optics, soft materials, and machine learning. It demonstrates well in tactile measurement, and can be expanded into multiple sensing fields.

Research on 3D humanoid robot pose estimation based on HRNet-Epipolar and CRF robot model by multiple view

Purpose This paper aims to present an unmarked method including entire two-dimensional (2D) and three-dimensional (3D) methods to recover absolute 3D humanoid robot poses from multiview images. Design/methodology/approach The method consists of two separate steps: estimating the 2D poses in multiview images and recovering the 3D poses from the multiview 2D heatmaps. The 2D one is conducted by High-Resolution Net with Epipolar (HRNet-Epipolar), and the Conditional Random Fields Humanoid Robot Pictorial Structure Model (CRF Robot Model) is proposed to recover 3D poses. Findings The performance of the algorithm is validated by experiments developed on data sets captured by four RGB cameras in Qualisys system. It illustrates that the algorithm has higher Mean Per Joint Position Error than Direct Linear Transformation and Recursive Pictorial Structure Model algorithms when estimating 14 joints of the humanoid robot. Originality/value A new unmarked method is proposed for 3D humanoid robot pose estimation. Experimental results show enhanced absolute accuracy, which holds important theoretical significance and application value for humanoid robot pose estimation and motion performance testing.

A class of mixed thinning threshold integer-valued autoregressive model for the COVID-19 data

To better capture the features of time series of counts such as the counting of elements of variable character and piecewise phenomenon, this paper proposes a class of mixed-thinning threshold integer-valued autoregressive model where the distribution of the innovation sequence is unknown. Strict stationarity ergodicity property is proved. The unknown parameters in the model are estimated by conditional least squares and modified quasi-likelihood methods, as well as their asymptotic normality, are established for the case that the threshold variable is known. The performances of the estimators are investigated in simulation, which manifests that the modified quasi-likelihood estimation performs better. Finally, a set of COVID-19 data on critical cases imported from outside China is analyzed to illustrate the application of developed model.

RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model

The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect, a vehicle sideslip angle estimation method adopting a robust bias compensation Kalman filter and adaptive weight fusion strategy is proposed. On the basis of the extended Kalman filter algorithm, and with the goals of estimation exactitude and robustness, considering the potential signal deviation, a vehicle state robust deviation compensation Kalman filter estimation algorithm considering bias compensation and residual covariance matrix weighting is proposed. Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.

Event-Triggered Transmission of Sensor Measurements Using Twin Hybrid Filters for Renewable Energy Resource Management Systems

Recently, solar and wind power generation have gained attention as pathways to achieving carbon neutrality, and Renewable Energy Resource Management System (RERMS) technology has been developed to monitor and control small-scale, distributed renewable energy resources. In this work, we present an Event-Triggered Transmission (ETT) algorithm for RERMS, which transmits sensor measurements to the base station only when necessary. The ETT algorithm helps prevent congestion in the communication channel between RERMS and the base station, avoiding time delays or packet loss caused by the excessive transmission of sensor measurements. We design a hybrid state estimation algorithm that combines Kalman and Finite Impulse Response (FIR) filters to enhance the estimation performance, and we propose a new ETT algorithm based on this design. We evaluate the performance of the proposed algorithm through experiments that transmit actual sensor measurements from a photovoltaic power generation system to the base station, demonstrating that it outperforms existing algorithms.

Impact of knowledge of HPV positivity on cervical cytology performance in latin america.

Cervical cytology is recommended by WHO as a triage option in HPV-based cervical cancer screening programmes. We assessed the performance of cytology to detect CIN3+ without and with knowledge of HPV positivity. Women were screened with cytology and HPV across ESTAMPA study centres in Latin America. Screen-positives were referred to colposcopy with biopsy and treatment as needed. Cytology was initially interpreted without knowing HPV results. A subset of cytologies from HPV-positive women were re-interpreted at the same laboratories, with knowledge of HPV status, blinded to previous cytology and histological diagnosis. Performance indicators for cytology to detect CIN3+ without and with knowledge of HPV positivity were estimated. A total of 4,087 women were included, of which 490 had histologically confirmed CIN3 + (455 CIN3 and 35 cancers). Cytology sensitivity without knowledge of HPV positivity for CIN3+ was 47.2% (95% CI: 42.5-51.9), whereas with knowledge of HPV positivity, the sensitivity was higher (58.9%, 95% CI: 54.2-63.5), p < .0001. The specificity without knowledge of HPV was 89.4% (95% CI: 88.2-90.5), while with knowledge of HPV positivity was 78.9% (95% CI: 77.4-80.4), p < .0001. Performance estimates varied by study centre for cytology without knowing the HPV positivity, (range from 32.8% to 61.5% for sensitivity; range 80.7% to 98.6% for specificity). Similarly, performance varied with knowledge of HPV positivity (36.1% to 93.4% for sensitivity; 39.6% to 98.6% for specificity). The increase in sensitivity of cytology with HPV knowledge was limited and highly variable, reinforcing the need for alternative triage methods to support cervical cancer elimination goals.

Generalized Path Integral Energy and Heat Capacity Estimators of Quantum Oscillators and Crystals Using Harmonic Mapping.

Imaginary-time path integral (PI) is a rigorous tool to treat nuclear quantum effects in static properties. However, with its high computational demand, it is crucial to devise precise estimators. We introduce generalized PI estimators for the energy and heat capacity that utilize coordinate mapping. While it can reduce to the standard thermodynamic and centroid virial (CVir) estimators, the formulation can also take advantage of harmonic character of quantum oscillators and crystals to construct a coordinate mapping. The method is not applicable to fluids or systems with imaginary modes such as double-well potentials. This yields harmonically mapped averaging (HMA) estimators, with mappings that decouple (HMAc) or couple (HMAq) the centroid and internal modes. The HMAq is constructed with normal mode coordinates (HMAq-NM) with quadratic scaling of cost or harmonic oscillator staging (HMAq-SG) coordinates with linear scaling. The estimator performance is examined for a 1D anharmonic oscillator and a 3D Lennard-Jones crystal using path integral molecular dynamics (PIMD) simulation. The HMA estimators consistently provide more precise estimates compared to CVir, with the best performance obtained by HMAq-NM, followed by HMAq-SG, and then HMAc. We also examine the effect of anharmonicity (for AO), intrinsic quantumness, and Trotter number. The HMA formulation introduced assumes the availability of forces and Hessian matrix; however, an equally efficient finite difference alternative is possible when these derivatives are inaccessible. The remarkable improvement in precision offered by HMAq estimators provides a framework for efficient PI simulation of more challenging systems, such as those based on ab initio calculations.

Noise‐robust structural response estimation method using short‐time Fourier transform and long short‐term memory

AbstractStructural response estimation based on deep learning can suffer from reduced estimation performance owing to discrepancies between the training and test data as the noise level in the test data increases. This study proposes a short‐time Fourier transform‐based long short‐term memory (STFT‐LSTM) model to improve estimation performance in the presence of noise and ensure estimation robustness. This model enables robust estimations in the presence of noise by positioning an STFT layer before feeding the data into the LSTM layer. The output transformed into the time‐frequency domain by the STFT layer is learned by the LSTM model. The robustness of the proposed model was validated using a numerical model with three degrees of freedom at various signal‐to‐noise ratio levels, and its robustness against impulse and periodic noise was verified. Experimental validation assessed the estimation robustness under impact load and verified the robustness against environmental noise in the acquired acceleration response.

Non-Destructive Estimation of Paper Fiber Using Macro Images: A Comparative Evaluation of Network Architectures and Patch Sizes for Patch-Based Classification

Over the years, research in the field of cultural heritage preservation and document analysis has exponentially grown. In this study, we propose an advanced approach for non-destructive estimation of paper fibers using macro images. Expanding on studies that implemented EfficientNet-B0, we explore the effectiveness of six other deep learning networks, including DenseNet-201, DarkNet-53, Inception-v3, Xception, Inception-ResNet-v2, and NASNet-Large, in conjunction with enlarged patch sizes. We experimentally classified three types of paper fibers, namely, kozo, mitsumata, and gampi. During the experiments, patch sizes of 500, 750, and 1000 pixels were evaluated and their impact on classification accuracy was analyzed. The experiments demonstrated that Inception-ResNet-v2 with 1000-pixel patches achieved the highest patch classification accuracy of 82.7%, whereas Xception with 750-pixel patches exhibited the best macro-image-based fiber estimation performance at 84.9%. Additionally, we assessed the efficacy of the method for images containing text, observing consistent improvements in the case of larger patch sizes. However, limitations exist in background patch availability for text-heavy images. This comprehensive evaluation of network architectures and patch sizes can significantly advance the field of non-destructive paper analysis, offering valuable insights into future developments in historical document examination and conservation science.

SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation

Human pose estimation identifies and classifies key joints of the human body in images or videos. Existing pose estimation methods can precisely capture human movements in real time but require significant computational time and resources, which restricts their usage in specific conditions. Thus, we propose a lightweight pose estimation model—SMS-Net—based on the sequentially stacked structure of the hourglass network. The proposed model uses various lightweight techniques to enable high-speed pose estimation while requiring minimal storage space and computation. Specifically, a shuffle-gated block was introduced to reduce the computational load and number of parameters during the feature extraction process of the encoder composing each hourglass network. A multi-dilation block was used in the decoder to secure the receptive fields of various scales without increasing the computational load. The performance of the proposed model was assessed using the MPII and Common Objects in Context (COCO) datasets used for pose estimation and certain performance metrics and compared with state-of-the-art lightweight pose estimation models. Furthermore, an ablation study was performed to assess the impact of each module on network performance and efficiency. The results demonstrate that the proposed model achieved an improved balance between computational efficiency and performance compared to existing models in human pose estimation. Overall, the study findings can provide a basis for applications in computer vision technology.

Recursive-RARE-based three-dimensional parameter estimation of near-field source considering amplitude attenuation

This paper addresses the issue for near-field (NF) localization considering amplitude attenuation, proposing a recursive rank-reduction (RARE) method for three-dimensional (3-D) parameter estimation of NF sources incident on a symmetrical cross array. The proposed method constructs several one-dimensional (1-D) spectral peak search estimators to obtain two-dimensional angle and range parameters. Initially, using the received data from symmetrical array elements in one axis, a 1-D spectral peak search estimator is constructed. The origins of two types of pseudo peaks within this estimator are analyzed, and then, corresponding pseudo peaks removal methods, namely the initial screening method and the recursive RARE method, are presented to obtain the estimate of the first angle parameter. Subsequently, the estimated results are fed into another 1-D spectral peak search estimator constructed from the original received data to obtain the range parameter. Finally, the same process is applied to the other axis to obtain the second angle and range parameters, followed by a parameter matching operation for 3-D parameters. Compared to existing NF source localization methods, the proposed method more effectively eliminates pseudo peaks, and demonstrates superior parameter estimation performance under conditions of small number of snapshots and low signal-to-noise ratio, as validated by several simulation results.

Neuro-Cognitive Insights Into Engineering Design: Exploring Electroencephalography Predictive Associations With Task Performance

Abstract This paper investigates the relationship between brain activity, measured by electroencephalography (EEG) data, and the performance assessment result of engineering design activities involving different cognitive processes. Employing a novel signal processing pipeline, we analyzed EEG variations of 37 subjects during two design tasks that mostly leverage, respectively, convergent and divergent thinking: the design with morphological table (task and the problem-solving task. The EEG recordings underwent meticulous artifact removal, allowing for a comprehensive investigation into the statistical relationships between frequency bands, channels, and design outcome performance metrics. The developed models linking better design outcomes with brain (de)synchronization demonstrated remarkable accuracy, precision, and recall across performance metrics for both tasks. Notably, the EEG data in theta band measured from the frontal area at both hemispheres and a left parietal/occipital channel were essential for estimating better design performance with brain desynchronization. On the contrary, the model based on brain synchronization produces precise estimations of design performance with alpha band and channels in temporal and parietal areas. These findings highlight EEG variation as a viable proxy for design performance, paving the way for more effective performance prediction models with fewer sensors. Overall, this research contributes to the emerging field of neurocognitive design assessment and underscores the potential for EEG-based predictions in engineering design tasks.

Investigating Model Form Error Estimation for Sparse Data

Computational simulations of dynamical systems involve the use of mathematical models and algorithms to mimic and analyze complex real-world phenomena. By leveraging computational power, simulations enable researchers to explore and understand systems that are otherwise challenging to study experimentally. They offer a cost-effective and efficient means to predict and analyze the behavior of physical, biological, and social systems. However, model form error arises in computational simulations from simplifications, assumptions, and limitations inherent in the mathematical model formulation. Several methods for addressing model form error have been proposed in the literature, but their robustness in the face of challenges inherent to real-world systems has not been thoroughly investigated. In this work, a data assimilation-based approach for model form error estimation is investigated in the presence of sparse observation data. Including physics-based domain knowledge to improve estimation performance is also explored. The Lotka-Volterra equations are employed as a simple computational simulation for demonstration.

Layout optimization and Performance of Large Array of imaging atmospheric Cherenkov Telescope (LACT)

Abstract Large Array of imaging atmospheric Cherenkov Telescope (LACT) is an array of 32 Cherenkov telescopes with 6-meter diameter mirrors to be constructed at the LHAASO site. In this work, we present a study on the layout optimization and performance analysis of LACT. We investigate two observation modes: large zenith angle observations for ultra-high energy events and small zenith angle observations for lower energy thresholds. For large zenith angles (60°), simulations show that an 8-telescope subarray can achieve an effective area of $3 ~\rm km^2$ and excellent angular resolution. For small zenith angles, we optimize the layout of 4-telescope cells and the full 32-telescope array. The threshold of the full array is about $200~\rm GeV$, which is particularly crucial for studying transient phenomena, including gamma-ray bursts (GRBs) and active galactic nuclei (AGNs). This study provides important guidance for the final LACT layout design and performance estimates under different observational conditions, demonstrating LACT's potential for deep observations of ultra-high energy \gray sources and morphological studies of PeVatrons, as well as time-domain \gray astronomy.

Sub-daily precipitation returns levels in ungauged locations: Added value of combining observations with convection permitting simulations

Extreme rainfall events trigger natural hazards, including floods and debris flows, posing serious threats to society and the economy. Accurately quantifying extreme rainfall return levels in ungauged locations is crucial for improving flood protection infrastructure and mitigating water-related risks. This paper quantifies the added value of combining rainfall observations with Convection Permitting Model (CPM) simulations to estimate sub-daily extreme rainfall return levels in ungauged locations. We assess the performance of CPM-informed estimates of extreme return level against a traditional interpolation techniques. We find that kriging methods with external drift outperform inverse distance weighting for both traditional and CPM-informed approaches. We then assess the effectiveness of the two methods under different scenarios of station density. At the highest station density (1/196 km²), traditional interpolation methods outperform the CPM-informed method for durations under 6 h. The performance becomes comparable between 6 and 24 h. For lower station densities (1/400 and 1/800 km²), the CPM-informed method outperforms the traditional method, with average reductions in fractional standard error of 24 %, 13 %, and 8 % for return periods of 2, 10 and 50 years, respectively for a rain gauge density of 1/800 km², and 16 %, 8 %, and 3 % for density of 1/400 km². Information from CPM simulations can thus be useful for estimating sub-daily extreme rainfall events in ungauged sites, particularly in data-scarce areas in which the density of rain gauges is low.

Combining long short-term memory and shape superposition for estimating moving load-induced global responses of bridges

Recently, a method of estimating the global responses of bridges was introduced, by combining a deep neural network (DNN) and shape superposition method (SSM). This DNN-SSM model demonstrated superior performance to the traditional estimation method based on the least-squares method. However, the application and validation of this approach have been restricted to static problems. For bridges, the dynamic effect induced by the moving load requires consideration. Therefore, this study proposes a long short-term memory (LSTM)-SSM model to improve the estimation performance for dynamic global responses. The LSTM-SSM model consists of LSTM layers, followed by fully connected DNN layers and a post-process part based on SSM. Limited displacement, slope, and strain data were used as the inputs, and the global displacement, slope, and strain were estimated. To validate the effectiveness and improvement of the LSTM-SSM model, a numerical model of a cable-stayed bridge was used to compare the proposed model with the previously developed DNN-SSM model. The validation results indicated that the LSTM-SSM model can provide more accurate estimates of the dynamic global response than DNN-SSM models. Finally, a technique for structural safety monitoring based on the proposed method was introduced.

Two New Families of Local Asymptotically Minimax Lower Bounds in Parameter Estimation

We propose two families of asymptotically local minimax lower bounds on parameter estimation performance. The first family of bounds applies to any convex, symmetric loss function that depends solely on the difference between the estimate and the true underlying parameter value (i.e., the estimation error), whereas the second is more specifically oriented to the moments of the estimation error. The proposed bounds are relatively easy to calculate numerically (in the sense that their optimization is over relatively few auxiliary parameters), yet they turn out to be tighter (sometimes significantly so) than previously reported bounds that are associated with similar calculation efforts, across many application examples. In addition to their relative simplicity, they also have the following advantages: (i) Essentially no regularity conditions are required regarding the parametric family of distributions. (ii) The bounds are local (in a sense to be specified). (iii) The bounds provide the correct order of decay as functions of the number of observations, at least in all the examples examined. (iv) At least the first family of bounds extends straightforwardly to vector parameters.