Traditional Estimation Methods Research Articles

Parallel Complementary Virtual Arrays Algorithm for Direction of Arrival (DOA) Estimation

This Paper discusses the challenges faced by previous method 2D Direction of Arrival (DOA) systems, such as low degrees of freedom, poor resolution, and significant estimation errors in scenarios with small snapshots. In response to these issues, the present method proposes a low-complexity 2D Direction of Arrival (DOA) estimation algorithm based on a parallel complementary virtual array.
 The algorithm utilizes two mutually parallel complementary linear arrays to generate a virtual array, addressing the limitations of traditional parallel arrays. It constructs an extended matrix with enhanced 2D angular degrees of freedom using covariance and cross-covariance matrices. The final step involves obtaining automatic matching 2D angle estimates through Singular Value Decomposition (SVD) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT).
 In comparison to traditional 2D DOA estimation methods, the proposed algorithm better exploits the information from the array's received data. It can identify more incoming signals, offering high resolution without the need for 2D linear search or angle parameter matching. Importantly, it demonstrates effective estimation even in scenarios with low Signal-to-Noise Ratio (SNR) and small snapshots. Experimental simulation results validate the effectiveness and reliability of the proposed algorithm.

Lightweight 2D Human Pose Estimation Based on Joint Channel Coordinate Attention Mechanism

Traditional human pose estimation methods typically rely on complex models and algorithms. Lite-HRNet can achieve an excellent performance while reducing model complexity. However, its feature extraction scale is relatively single, which can lead to lower keypoints’ localization accuracy in crowded and complex scenes. To address this issue, we propose a lightweight human pose estimation model based on a joint channel coordinate attention mechanism. This model provides a powerful information interaction channel, enabling features of different resolutions to interact more effectively. This interaction can solve the problem of human pose estimation in complex scenes and improve the robustness and accuracy of the pose estimation model. The introduction of the joint channel coordinate attention mechanism enables the model to more effectively retain key information, thereby enhancing keypoints’ localization accuracy. We also redesign the lightweight basic module using the shuffle module and the joint channel coordinate attention mechanism to replace the spatial weight calculation module in the original Lite-HRNet model. By introducing this new module, we not only improve the network calculation speed and reduce the number of parameters of the entire model, but also ensure the accuracy of the model, thereby achieving a balance between performance and efficiency. We compare this model with current mainstream methods on the COCO and MPII dataset. The experimental results show that this model can effectively reduce the number of parameters and computational complexity while ensuring high model accuracy.

Estimating adult age categories in commingled skeletons with Transition Analysis 3.

We estimate adult age frequencies from Unar 1 and Unar 2, two late Umm an-Nar (2400-2100 BCE) tombs in the modern-day Emirate of Ras al-Khaimah, United Arab Emirates. These collective tombs each contained hundreds of skeletons in commingled, fragmented, and variably cremated states. Previous studies placed the vast majority of this mortuary community in a generalized "adult" category, as have most analyses of similar tombs from this period. We sought to test how adult age estimation methods compare in identifying young, middle, and old-age individuals in commingled assemblages. We employed Transition Analysis 3 (TA3) and traditional age estimation methods to generate adult age frequencies for each tomb. We compared these frequencies between tomb contexts as well as by method. Unar 1 and Unar 2 had similar adult age frequencies within each method, but TA3 age frequencies included significantly more middle and older adult individuals than those generated by traditional methods. These results support findings of earlier iterations of transition analysis in regard to sensitivity in old adult age estimation, compared with traditional methods. Our findings indicate a potential use of TA3 in reconstructing age frequencies and mortality profiles in commingled skeletal assemblages. Increasing our understanding of everyday life in the distant past necessitates better understandings of adult age, and here, we illustrate how age estimation method choice significantly changes bioarchaeological interpretations of aging in Bronze Age Arabia. Adult age estimation using TA3 revealed significantly more middle and older adults than traditional methods in two commingled tombs. Similar mean maximum likelihood point estimates by side and across skeletal elements were found between tombs.

Monocular endoscopy images depth estimation with multi-scale residual fusion

BackgroundMonocular depth estimation plays a fundamental role in clinical endoscopy surgery. However, the coherent illumination, smooth surfaces, and texture-less nature of endoscopy images present significant challenges to traditional depth estimation methods. Existing approaches struggle to accurately perceive depth in such settings. MethodTo overcome these challenges, this paper proposes a novel multi-scale residual fusion method for estimating the depth of monocular endoscopy images. Specifically, we address the issue of coherent illumination by leveraging image frequency domain component space transformation, thereby enhancing the stability of the scene's light source. Moreover, we employ an image radiation intensity attenuation model to estimate the initial depth map. Finally, to refine the accuracy of depth estimation, we utilize a multi-scale residual fusion optimization technique. ResultsTo evaluate the performance of our proposed method, extensive experiments were conducted on public datasets. The structural similarity measures for continuous frames in three distinct clinical data scenes reached impressive values of 0.94, 0.82, and 0.84, respectively. These results demonstrate the effectiveness of our approach in capturing the intricate details of endoscopy images. Furthermore, the depth estimation accuracy achieved remarkable levels of 89.3 % and 91.2 % for the two models’ data, respectively, underscoring the robustness of our method. ConclusionsOverall, the promising results obtained on public datasets highlight the significant potential of our method for clinical applications, facilitating reliable depth estimation and enhancing the quality of endoscopy surgical procedures.

State-wide forest canopy height and aboveground biomass map for New York with 10 m resolution, integrating GEDI, Sentinel-1, and Sentinel-2 data

Investigating the quantity of forest aboveground biomass (AGB) is crucial for understanding the role forests play in the global carbon cycle. The canopy height model (CHM) is a critical component in estimating AGB, as it provides a three-dimensional representation of the tree canopy. Traditional CHM estimation methods are time-consuming, labor-intensive, and expensive, particularly at large-scales. Remote sensing is a cost-effective and efficient alternative approach, providing valuable information over large areas in a timely manner. The Global Ecosystem Dynamics Investigation (GEDI) onboard the International Space Station is a space-based light detection and ranging (LiDAR) system designed to collect information on vertical structures of vegetation. One major problem with the collection of GEDI data is that it provides limited information over discrete ground samples, also known as footprints, and thus do not provide wall-to-wall gridded height products. The objective of this study was twofold: a) to integrate the GEDI LiDAR footprint heights with Sentinel-2 multispectral imagery to generate a 10 m wall-to-wall CHM map of New York State (NYS), USA for the year 2019 and b) to improve our previously generated AGB map (both accuracy and resolution) of NYS for the year 2019 by fusing Sentinel-2 multispectral, Sentinel-1 synthetic aperture radar (SAR), and the produced CHM. To generate the 10 m CHM map, the GEDI footprints height measurements were extrapolated using Sentinel-2 imagery and a random forest model. The CHM that was produced was assessed by using GEDI footprints that were not part of the training phase and were therefore independent (extrapolated). Comparing our 10 m CHM with the available global 30 m CHM map provided by Potapov et al. (2021) over NYS shows significant improvement not only in terms of spatial resolution, but also in terms of accuracy. The root mean square error (RMSE) of our 10 m CHM is 4.4 m while this value is 7.49 m for the 30 m CHM over NYS. Similarly, the R2 value for the 10 m CHM map is 0.74, while that of the 30 m CHM is 0.46. Finally, the integration of produced 10 m CHM, Sentinel-1, and Sentinel-2 datasets were utilized to create a 10 m AGB map of NYS with the RMSE of 39.49 Mg/ha, and R2 of 0.65. The results demonstrate the potential of integrating GEDI, Sentinel-1, and Sentinel-2 data for providing a valuable tool for large-scale mapping of forest canopy structure and biomass, which can help to inform forest management and carbon accounting efforts.

URBAN 3D RECONSTRUCTION OF VHR SAR IMAGES USING ITERATIVE OPTIMIZATION ALGORITHM AND LAYOVER FIXED-ORDER MODEL

Abstract. Synthetic Aperture Radar (SAR) Tomography (TomoSAR) is a three-dimensional SAR imaging technique that uses multiple passes to process complex SAR images and obtain three-dimensional spatial scattering information to derive the elevation scattering distribution. Due to its own shortcomings, the elevation obtained by the traditional spectral estimation method has low resolution in the elevation direction and is affected by noise. The imaging algorithm based on compressed sensing can achieve super-resolution reconstruction in the elevation direction while reducing the number of observations. However, the CS algorithm still faces challenges when applied to real-world tomographic SAR imaging. In particular, it often requires numerous iterations to achieve satisfactory results, which significantly reduces its processing efficiency in large-scale tomography. To address the above issues, in this paper, we proposed an urban 3D reconstruction of VHR SAR images using an iterative optimization algorithm and layover fixed-order model. The iterative optimization algorithm and the layover fixed-order model consist of two parts: The TomoSAR imaging equation is solved by the two-step iterative shrinkage/thresholding (TwIST) algorithm, and the number of scatterers K is estimated by the Bayesian Information Criterion (BIC). In this paper, the effectiveness of TwIST-BIC in TomoSAR imaging in urban areas is verified with real TerraSARX data. By comparing with the OMP algorithm based on matching tracking and the FISTA algorithm based on gradient descent. The TWIST-BIC method is less complex, converges faster, and combines both execution speed and super-resolution, which can effectively solve the processing efficiency problem in large-area tomography and acquire high-resolution tomographic analysis.

Establishment of a Lithium-Ion Battery Model Considering Environmental Temperature for Battery State of Charge Estimation

Accurate estimation of the state-of-charge (SOC) is essential to prevent overcharging and over-discharging of lithium-ion batteries. However, traditional SOC estimation methods exhibit significant errors under large temperature variations due to the strong temperature dependence of battery characteristics. To enhance the accuracy of SOC estimation, this study proposes a second-order RC equivalent circuit model with temperature correction. By considering the influence of temperature on model parameters, the model’s accuracy is improved by adjusting the estimated parameters. A polynomial coefficient data table for model parameters is established to expedite the computation time of the SOC estimation process. Finally, the temperature-corrected model is combined with an Adaptive Extended Kalman Filter (AEKF) algorithm for SOC estimation. The results of the Dynamic Stress Test (DST) condition experiments show that the temperature correction model can improve the accuracy of SOC estimation under different temperature conditions. It has a more lower SOC estimation error than the model without temperature correction.

Code observation-specific biases estimation for the modernized multi-GNSS and multi-frequency signals: An approach considering signal distortion biases

With the continuous development of global navigation satellite systems (GNSS) modernization, the code bias presented in the form of observation-specific biases (OSB) is more flexible and effective when processing modern multi-frequency and multi-GNSS signals, and therefore widely accepted by the GNSS community. However, the newly reported signal distortion biases (SDB) are ignored in the existing OSB estimation methods, which may affect the reliability of the final OSB estimation. In addition, the existing studies about SDB estimation are independent of OSB estimation, and thus a set of SDB products is generated independent of OSB products, which undoubtedly brings more complicated correction operations to GNSS users. In view of it, this study proposes an approach to code OSB estimation for multi-frequency and multi-GNSS arbitrary signals by reparameterization, which considers the SDB parameter. The aim of this study is to generate a set of code OSB products that take into account SDB parameters for the GNSS users to use. Due to considering the influence of SDB on raw code observations, the OSB estimation obtained by the proposed method is related to specific satellite-receiver pairs, which makes it different from the traditional OSB estimation methods. To achieve this, an extended model for multi-frequency and multi-GNSS arbitrary signal is first established based on the undifferenced and uncombined model, in which the SDB parameter is taken into account by re-parameterization. Based on the proposed model, three types of code bias that contain the OSB information can be obtained for the subsequent OSB estimation. Then, the three types of code bias in the first step are used as virtual observations for multi-frequency and multi-GNSS arbitrary signal OSB estimation through a two-step method. The proposed method is validated with a whole month of real measured data from 418 stations in MGEX and GA CORS to generate OSBs for GPS, GLONASS, and Galileo. The comparison between the estimated OSBs and the OSB products from CAS indicates the reliability of the proposed method. Besides, all types of OSB for four receiver groups are generated based on the proposed method to analyze the relationship between OSB and receiver groups. Results indicate that the estimated OSBs are related to receiver groups, and specific differences exist between different satellites and GNSS signals. It also proves that the OSB estimation is biased by the inconsistencies of the deviation in different receiver groups, which is more obvious for GLONASS.

A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries

Accurate estimating the state of health (SOH) and state of charge (SOC) is crucial for ensuring the reliable and safe operation of lithium-ion batteries. Traditional methods for the joint estimation of SOC and SOH typically rely on separate models, resulting in a decoupling of their relationship. Moreover, the current convolutional and recurrent-based deep models overlook the inherent connection between local features and global temporal features. These limitations not only hinder the extraction of combined feature information relevant to SOC and SOH during the charging process, but also increase computational complexity and diminish estimation accuracy. To solve these problems, this study proposes a novel SOC–SOH Estimation Framework (SSEF). The framework achieves parameter sharing by segmented training, effectively accounting for the intrinsic coupling between SOC and SOH. This enables a unified joint estimation of the two variables, leading to a substantial enhancement in efficiency. Additionally, a novel charging encoder that alternates between Temporal Convolutional Network and Bidirectional Gated Recurrent Unit is designed. It captures local temporal information and long-term dependencies related to SOC and SOH during charging. SSEF enables precise SOC and SOH estimation for whole-life-cycle lithium-ion batteries, enhancing accuracy and efficiency compared to prevalent methods.

State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions.

CrossFuse-XGBoost: accurate prediction of the maximum recommended daily dose through multi-feature fusion, cross-validation screening and extreme gradient boosting.

In the drug development process, approximately 30% of failures are attributed to drug safety issues. In particular, the first-in-human (FIH) trial of a new drug represents one of the highest safety risks, and initial dose selection is crucial for ensuring safety in clinical trials. With traditional dose estimation methods, which extrapolate data from animals to humans, catastrophic events have occurred during Phase I clinical trials due to interspecies differences in compound sensitivity and unknown molecular mechanisms. To address this issue, this study proposes a CrossFuse-extreme gradient boosting (XGBoost) method that can directly predict the maximum recommended daily dose of a compound based on existing human research data, providing a reference for FIH dose selection. This method not only integrates multiple features, including molecular representations, physicochemical properties and compound-protein interactions, but also improves feature selection based on cross-validation. The results demonstrate that the CrossFuse-XGBoost method not only improves prediction accuracy compared to that of existing local weighted methods [k-nearest neighbor (k-NN) and variable k-NN (v-NN)] but also solves the low prediction coverage issue of v-NN, achieving full coverage of the external validation set and enabling more reliable predictions. Furthermore, this study offers a high level of interpretability by identifying the importance of different features in model construction. The 241 features with the most significant impact on the maximum recommended daily dose were selected, providing references for optimizing the structure of new compounds and guiding experimental research. The datasets and source code are freely available at https://github.com/cqmu-lq/CrossFuse-XGBoost.

Computational and experimental assessment of health risks of fine particulate matter in Nanjing and Yangzhou, China.

Fine particulate matter (PM2.5) is a major air pollutant in most cities of China, and poses great health risks to local residents. In this study, the health effects of PM2.5 in Nanjing and Yangzhou were compared using computational and experimental methods. The global exposure mortality model (GEMM), including the results of a cohort study in China, was used to estimate the disease-related risks. Premature mortality attributable to PM2.5 exposure were markedly higher in Nanjing than that in Yangzhou at comparable levels of PM2.5 (8191 95% CI, 6975-9994 vs. 6548 95% CI, 5599-8049 in 2015). However, the baseline mortality rate was on a country-level and the age distribution was on a province-level, traditional estimation method could not accurately represent the health burdens of PM2.5 on a city-level. We proposed a refined calculation method which based on the actual deaths of each city and the disease death rates. Conversely, similar concentrations of PM2.5 exposure resulted in higher actual deaths per million population in Yangzhou (1466 95% CI, 1266-1746) than that in Nanjing (1271 95% CI, 1098-1514). Health risks of PM2.5 are associated with the generation of reactive oxygen species, among which hydroxyl radial (·OH) is the most reactive one. We then collected these PM2.5 samples and quantified the induced ·OH. Consistently, average ·OH concentration in 2015 was higher in Yangzhou than that in Nanjing, again indicating that PM2.5 in Yangzhou was more toxic. The combination of computational and experimental methods demonstrated the complex relationship between health risks and PM2.5 concentrations. The refined estimation method could help us better estimate and interpret the risks caused by PM2.5 exposure on a city-level.

Flow resistance at lowland and mountainous rivers

Abstract This study initially examines the various sources of flow resistance in sand-bed (lowland) and gravel-bed (mountainous) rivers along with the limitations of traditional estimation methods. The nondimensional hydraulic geometry approach, relating dimensionless flow discharge (q *) to the Darcy-Weisbach friction factor (f), has demonstrated good performance for both river types, covering shallow to moderately deep flows. However, accuracy in estimating f is affected by simplifications like assuming uniform and deep flow, neglecting bed load transport and vegetation effects, which require further evaluation. To address these issues, the proposed method is evaluated using data from four sand-bed rivers in Slovakia (with vegetation), and three gravel-bed rivers in Iran (dominated by cobbles and boulders). Bedforms prove to be significant resistance sources in all studied rivers. The approach yields separate predictors for each river type, showing a satisfactory agreement between observed and calculated values within a maximum deviation of ±20% error bands. These predictors are further validated using field data and established equations from rivers with similar physiographic characteristics. Results indicate the method performs well in predicting flow resistance in sand-bed rivers, slightly overestimating overall (+40%). It effectively captures riverbed features and vegetation influence under small-scale roughness conditions. However, the predictor’s validity for gravel-bed rivers is somewhat limited due to high variability in water-surface profiles, making it challenging to accurately capture flow dynamics under large-scale roughness conditions. Addressing complex characteristics of gravel-bed riverbeds, including boulders and local energy extraction, is crucial for improving the estimation of water-surface profile variations and flow resistance using the hydraulic geometry approach.

Mapping hailstorm damage on winter wheat (Triticum aestivum L.) using a microscale UAV hyperspectral approach

Hailstorms pose a direct threat to agriculture, often causing yield losses and worsening farmers’ agricultural activity. Traditional methods of hail damage estimation, conducted by insurance field inspectors, have been questioned due to their complexity, partial subjectivity, and lack of accounting for spatial variability. Therefore, remote sensing integration in the estimation process could provide a valuable aid. The focus of this study was on winter wheat (Triticum aestivum L.) and its response to damage in the near-infrared (NIR) spectral region, with a particular emphasis on the study of brown pigments as a proxy for yield damage estimation and mapping. An experiment was conducted during two cropping seasons (2020–2021 and 2021–2022) at two sites, simulating hail damage at critical flowering and milky stages using a specifically designed prototype machinery with low, medium, and high damage gradients compared to undamaged conditions in plots with a minimum of 400 m2 area. After the damage simulation, hyperspectral visible-NIR reflectance was measured with Unmanned Aerial Vehicle (UAV) flights, and measurements of chlorophyll and of leaf area index (LAI) were contextually taken. Final yield per treatment was recorded using a combine. An increase in absorbance in the NIR region (780–950 nm) was observed and evaluated using a spectral mixture analysis (SMA) after selecting representative damaged and undamaged vegetation spectra to map the damage. The abundance of damaged endmember pixels per treatment resulted in a good relationship with the final yield (R2 = 0.73), identifying the most damaged areas. The absorbance feature was further analysed with a newly designed multispectral index (TAI), which was tested against a selection of indices and resulted in the highest relationship with the final yield (R2 = 0.64). Both approaches were effective in highlighting the absorbance feature over different dates and development stages, defining an effective mean for hailstorm damage mapping in winter wheat.

Reconstructing subglacial lake activity with an altimetry-based inverse method

Abstract Subglacial lake water-volume changes produce ice-elevation anomalies that provide clues about water flow beneath glaciers and ice sheets. Significant challenges remain in the quantitative interpretation of these elevation-change anomalies because the surface expression of subglacial lake activity depends on basal conditions, rate of water-volume change, and ice rheology. To address these challenges, we introduce an inverse method that reconstructs subglacial lake activity from altimetry data while accounting for the effects of viscous ice flow. We use a linearized approximation of a Stokes ice-flow model under the assumption that subglacial lake activity only induces small perturbations relative to a reference ice-flow state. We validate this assumption by accurately reconstructing lake activity from synthetic data that are produced with a fully nonlinear model. We then apply the method to estimate the water-volume changes of several active subglacial lakes in Antarctica by inverting data from NASA's Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) laser altimetry mission. The results show that there can be substantial discrepancies (20% or more) between the inversion and traditional estimation methods due to the effects of viscous ice flow. The inverse method will help refine estimates of subglacial water transport and further constrain the role of subglacial hydrology in ice-sheet evolution.

An Improved GOMP Sparse Channel Estimation for Vehicle-to-Vehicle Communications

Reliable channel estimation is critical for wireless communication performance, especially in vehicle-to-vehicle (V2V) communication scenarios. Aiming at the major challenges of channel tracking and estimating as the highly dynamic nature of vehicle environments, an improved generalized orthogonal matching pursuit (iGOMP) is proposed for V2V channel estimation. The iGOMP algorithm transforms the channel estimation problem into a sparse signal recovery problem and replaces the classical inner product criterion with the Dice atom matching criterion. Additionally, the atomic weak progressive selection method is integrated to avoid the suboptimal selection of atoms from the redundant dictionary using the inner product criterion. The proposed iGOMP method can achieve optimal channel estimation by iterating feedback results. Simulation results demonstrate that the iGOMP method has superior estimation accuracy, mean square error (MSE), and bit error rate (BER) performance compared with traditional channel estimation methods in V2V communications.

A Novel Algorithm for the Retrieval of Chlorophyll a in Marine Environments Using Deep Learning

Chlorophyll a (Chla) is a crucial pigment in phytoplankton, playing a vital role in determining phytoplankton biomass and water nutrient status. However, in optically complex water bodies, Chla concentration is no longer the primary factor influencing remote sensing spectral reflectance signals, leading to significant errors in traditional Chla concentration estimation methods. With advancements in in situ measurements, synchronized satellite data, and computer technology, machine learning algorithms have become popular in Chla concentration retrieval. Nevertheless, when using machine learning methods to estimate Chla concentration, abrupt changes in Chla values can disrupt the spatiotemporal smoothness of the retrieval results. Therefore, this study proposes a two-stage approach to enhance the accuracy of Chla concentration estimation in optically complex water bodies. In the first stage, a one-dimensional convolutional neural network (1D CNN) is employed for precise Chla retrieval, and in the second stage, the regression layer of the 1DCNN is replaced with support vector regression (SVR). The research findings are as follows: (1) In the first stage, the performance metrics (R2, RMSE, RMLSE, Bias, MAE) of the 1D CNN outperform state-of-the-art algorithms (OCI, SVR, RFR) on the test dataset. (2) After the second stage, the performance further improves, with the metrics achieving values of 0.892, 11.243, 0.052, 1.056, and 1.444, respectively. (3) In mid- to high-latitude regions, the inversion performance of 1D CNN\SVR is superior to other algorithms, exhibiting richer details and higher noise tolerance in nearshore areas. (4) 1D CNN\SVR demonstrates high inversion capabilities in water bodies with medium-to-high nutrient levels.

Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning

In this study, a deep learning-based probabilistic remaining useful life (RUL) prediction model is proposed to improve the strong prior limitations of traditional probabilistic RUL prediction methods through a flexible prior distribution and strategy for sequential optimization of hyperparameters with regularization factor. It enables output richer probabilistic lifetime density distributions and confidence intervals with various parameters and overcome the problem of poor accuracy of short RUL predictions to some extent. Eventually, the model is effectively validated on a benchmark dataset, and the experimental results show that the probabilistic lifetime prediction model with optimized prior distribution parameters significantly improves prediction performance and demonstrates good learning performance and robustness of test results compared with traditional point estimation methods and parameter-free models. This study informs maintenance decisions and reliability assessments in engineering systems and guides the research and application of probabilistic-based prediction methods in deep learning framework.

Distributional Regression for Data Analysis

The flexible modeling of an entire distribution as a function of covariates, known as distributional regression, has seen growing interest over the past decades in both the statistics and machine learning literature. This review outlines selected state-of-the-art statistical approaches to distributional regression, complemented with alternatives from machine learning. Topics covered include the similarities and differences between these approaches, extensions, properties and limitations, estimation procedures, and the availability of software. In view of the increasing complexity and availability of large-scale data, this review also discusses the scalability of traditional estimation methods, current trends, and open challenges. Illustrations are provided using data on childhood malnutrition in Nigeria and Australian electricity prices.

Deep learning based channel estimation method for mine OFDM system

In this paper, we present a channel estimation approach based on deep learning to solve the problem that the orthogonal frequency division multiplexing (OFDM) system channel estimation algorithm cannot accurately obtain the channel state information in the complex environment of the mine, resulting in system performance degradation. First, LS channel estimation matrix is considered as a low-resolution image and the actual channel state information is considered as a high-resolution image. Then the optimization of the LS channel estimation matrix is achieved by the FSRCNN image super-resolution algorithm. We validate the effectiveness of the proposed algorithm by conducting experiments in different channel environments, different number of pilots, and mismatched signal-to-noise ratio scenarios. The simulation results show that the proposed scheme is much better than the traditional LS channel estimation method and the DFT-LS channel estimation method, and the accuracy of the proposed scheme approaches that of the MMSE channel estimation method when the number of pilots is low.