Mean Square Estimation Error Research Articles

Based on the N2N-SAMP for sparse underwater acoustic channel estimation

IntroductionOrthogonal Frequency Division Multiplexing (OFDM) is widely recognized for its high efficiency in modulation techniques and has been extensively applied in underwater acoustic communication. However, the unique sparsity and noise interference characteristics of the underwater channel pose significant challenges to the performance of traditional channel estimation methods.MethodsTo address these challenges, we propose a sparse underwater channel estimation method that combines the Noise2Noise (N2N) algorithm with the Sparsity Adaptive Matching Pursuit (SAMP) algorithm. This novel approach integrates the N2N technique from image denoising theory with the SAMP algorithm, utilizing a constant iteration termination threshold that does not require prior information. The method leverages the U-net neural network structure to denoise noisy pilot signals, thereby restoring channel sparsity and enhancing the accuracy of channel estimation.ResultsSimulation results indicate that our proposed method demonstrates commendable channel estimation performance across various signal-to-noise ratio (SNR) conditions. Notably, in low SNR environments, the N2N-SAMP algorithm significantly outperforms the traditional SAMP algorithm in terms of Mean Squared Error (MSE) and Bit Error Rate (BER). Specifically, at SNR levels of 0 dB, 10 dB, and 20 dB, the MSE of channel estimation is reduced by 58.95%, 76.08%, and 19.42%, respectively, compared to the SAMP algorithm that selects the optimal threshold based on noise power. Furthermore, the system’s BER is decreased by 12.35%, 26.41%, and 29.62%, respectively.DiscussionThe findings suggest that the integration of N2N and SAMP algorithms offers a promising solution for improving channel estimation in underwater communication channels, especially under low SNR conditions. The significant reduction in MSE and BER highlights the effectiveness of our proposed method in enhancing the reliability and accuracy of underwater communication systems.

GNSS and accelerometer data fusion by variational Bayesian adaptive multi-rate Kalman filtering for dynamic displacement estimation of super high-rise buildings

The integration of Global Navigation Satellite Systems (GNSS) into Structural Health Monitoring (SHM) systems of super high-rise buildings is becoming increasingly prevalent, with a notable trend of combining GNSS with accelerometers to leverage the complementary nature of both technologies for estimating dynamic displacements. However, the multipath errors between the GNSS reference and monitoring stations give rise to the time-varying noises into the process of solving position, resulting in considerable fluctuations in the accuracy of displacement monitoring in complex environments, which impedes the development of GNSS-accelerometer integration techniques in the field of SHM. To address the issue, a variational Bayesian adaptive multi-rate algorithm (VBMRKF) is proposed to rapidly respond the time-varying noise for the GNSS system, thereby facilitating the fusion of GNSS and accelerometer data with greater accuracy. In this algorithm, a convergence criterion is established for approximation of posterior distribution in multi-rate process based on Evidence Lower Bound (ELBO) theory, which reduces the effects of noise accumulation on the accuracy of the approximation processes for the posterior distribution. Furthermore, a noise-sensitive factor is also heuristically proposed, which enables the algorithm to quickly adapt to fluctuations of noise characteristics during multi-rate processes. The numerical simulation results reveal that the algorithm has a higher estimation accuracy for dynamic displacements than the conventional algorithm, and it has a superior adaptability to systems with different frequencies. In instances where the initial settings of noise parameters are highly unreasonable, traditional algorithms even demonstrated a lack of convergence. Conversely, the normalized root mean square errors (NRMSE) of the displacement estimation results obtained by VBMRKF are consistently below 16 %, which also substantiates the robustness of this algorithm. In the presence of time-varying noise, the algorithm demonstrates the capacity to rapidly converge upon the distribution of the noise. Compared to the multi-rate Kalman filtering (MRKF) algorithm, the proposed algorithm achieves the highest reduction of 27.02 % in NRMSE, and compared to around 13.97 % NRMSE of the recent adaptive algorithm, the proposed algorithm also demonstrates significantly superior performance, reducing NRMSE by at least 5.13 %. In addition, regardless of whether the signal is time-varying or not, the algorithm is able to quickly approximate the distribution of noise and produces acceptable displacement estimation results with unreasonable initial settings of the noise parameters, demonstrating its robustness. Finally, the measured accelerometer and GNSS data are employed to successfully determine the two-dimensional dynamic displacement of the 632-metre-high Shanghai Tower at a distance of only 2.1 km from the eye of Typhoon Muifa (202212). A further analysis of the results demonstrates that the estimated displacements are more comprehensive in both time and frequency domains.

Vehicle trajectory extraction with interacting multiple model for low-channel roadside LiDAR

High-precision and consistent vehicle trajectories encompass microscopic traffic parameters, mesoscopic traffic flow characteristics, and macroscopic traffic flow features, which is the cornerstone of innovation in data-driven traffic management and control applications. However, occlusion and trajectory interruption remain challenging in multivehicle tracking under complex traffic environments using low-channel roadside LiDAR. To address the challenge, a novel framework for vehicle trajectory extraction using low-channel roadside LiDAR was proposed. First, the geometric features of the cluster and its L-shape bounding box were used to address the over-segmentation in vehicle detection arising from occlusion and point cloud sparse. Then, objects within adjacent point cloud frames were associated by developing an improved Hungarian algorithm integrated with an adaptive distance threshold to solve the mismatching problem caused by objects entrancing and exiting in a new point cloud frame. Finally, an improved interacting multiple model by considering vehicle driving patterns was deployed to predict the location of missing vehicles and connect the interrupted trajectories. Experimental results showed that the proposed methods achieve 98.76 % of vehicle detection accuracy and 97.40 % of data association precision. The mean absolute error (MAE) and mean square error (MSE) of the vehicle position estimation are 0.2252 m and 0.0729 m2, respectively. The trajectory extraction precision outperforms most of the state-of-the-art algorithms.

Development and Preliminary Evaluation of a Food Literacy Measure for Use With Young People in Canada

BackgroundThis article presents the culmination of a multiyear research project aimed at creating a comprehensive food literacy framework and corresponding measure. Specifically, this article documents the development and validation of a food literacy measure for young people facing social inequities. ObjectivesThis study aimed to identify items to measure 12 attributes of food literacy; test the measure with the identified target groups, considering attribute, face, and content validity, inter-rater reliability and test-retest reliability; and refine the measure. DesignThe study’s design consisted of a 5-phase approach that included drafting the food literacy measure, expert review, cognitive interviews, pilot testing, and validity and reliability testing. Participants and settingSeven international experts provided feedback on the measure and 25 individuals aged 16 to 25 years participated in cognitive interviews. Two hundred fifty-five young people completed the test survey and 147 completed a retest survey 2 weeks later. These surveys identified food literacy factors. To further evaluate the validity of these factors. One hundred ninety-three participants completed a confirmatory test that was used for confirmatory factor analysis to assess the final model’s fit. Interview participants were recruited from local programs and services from across Ontario, Canada, whereas survey participants were recruited from across Canada via social media. The research study was conducted between January 2018 and December 2019. Main outcome measuresThe main outcome measures were validity and reliability scores for a food literacy measurement tool that consisted of 50 questions across 10 attributes of food literacy. Statistical analyses performedInterview analyses were guided by the four stages of cognitive processing. Exploratory factor analysis was used to identify the factors that improved the Cronbach alpha of the food literacy measure. Test-retest reliability was assessed using percent agreement, Cohen kappa, and weighted kappa. Confirmatory factor analysis was used to produce an acceptable final model with a root mean square error of approximation estimate. ResultsThe final food literacy measure consisted of 50 questions addressing 10 food literacy attributes. Exploratory factor analysis showed an improvement in Cronbach alpha when compared with the initial attribute construction. Test-retest reliability showed percent agreement ranging from 64% to 97%, with most items having fair (0.21 to 0.40) to moderate (0.41 to 0.60) kappa values. Confirmatory factor analysis produced an acceptable final model with a root mean square error of approximation estimate of 0.0437. ConclusionsThe food literacy measure is a comprehensive tool for assessing food literacy among young people. Additional research is needed to explore the measure's modularity, its use as an evaluation tool, and its suitability for use with diverse samples, including individuals from varied gender, geographic locations, ethnicities, and cultural backgrounds.

Cauchy-Schwarz bounded trade-off weighting for causal inference with small sample sizes

The difficulty of causal inference for small-sample-size data lies in the issue of inefficiency that the variance of the estimators may be large. Some existing weighting methods adopt the idea of bias-variance trade-off, but they require manual specification of the trade-off parameters. To overcome this drawback, in this article, we propose a Cauchy-Schwarz Bounded Trade-off Weighting (CBTW) method, in which the trade-off parameter is theoretically derived to guarantee a small Mean Square Error (MSE) in estimation. We theoretically prove that optimizing the objective function of CBTW, which is the Cauchy-Schwarz upper-bound of the MSE for causal effect estimators, contributes to minimizing the MSE. Moreover, since the upper-bound consists of the variance and the squared ℓ2-norm of covariate differences, CBTW can not only estimate the causal effects efficiently, but also keep the covariates balanced. Experimental results on both simulation data and real-world data show that the CBTW outperforms most existing methods especially under small sample size scenarios.

Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes

BackgroundThe fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns. It was previously unclear whether unexposed clusters would contribute to the estimation of the intervention effect in a fixed effects analysis. However, recent work has demonstrated that including an unexposed cluster can improve the precision of the intervention effect estimator in a fixed effects analysis of SW-CRTs with continuous outcomes. Still, SW-CRTs are commonly designed with binary outcomes and it is unknown if those previous results extend to SW-CRTs with non-continuous outcomes.MethodsIn this article, we mathematically prove that the inclusion of unexposed clusters improves the precision of the fixed effects intervention effect estimator for SW-CRTs with binary and count outcomes. We then explore the benefits of including an unexposed cluster in simulated datasets with binary or count outcomes and a real palliative care data example with binary outcomes.ResultsThe simulations show that including unexposed clusters leads to tangible improvements in the precision, power, and root mean square error of the intervention effect estimator. The inclusion of the unexposed cluster in the SW-CRT of a novel palliative care intervention with binary outcomes yielded smaller standard errors and narrower 95% Wald Confidence Intervals.ConclusionsIn this article, we demonstrate that the inclusion of unexposed clusters in the fixed effects analysis can lead to the improvements in precision, power, and RMSE of the fixed effects intervention effect estimator for SW-CRTs with binary or count outcomes.

Assimilation of Satellite-Derived Reservoir Storage Data to Improve Global Hydrodynamic Modeling

In this study, we explore the potential of assimilating satellite-derived reservoir storage data into the global-scale hydrodynamic model CaMa-Flood, focusing on the Yangtze River basin. We evaluated three data assimilation (DA) methods: direct assimilation (DIR), anomaly based assimilation (ANO), and normalized assimilation (NOM). Our results show that the DIR method achieved the most significant improvements in reservoir storage and downstream discharge simulations. DIR reduced the average relative root mean square error (rRMSE) of reservoir storage estimates by 80.5%, and increased discharge correlation (ΔCC) by 78.6% in the 14 validated discharge stations. ANO, while effective in certain cases, led to mixed results, with 56.4% of the 39 assimilated dams showing improved storage estimates and a modest 7.8% reduction in average RMSE. NOM had minimal impact, with negligible changes in RMSE or discharge correlation (ΔCC). The direct assimilation method (DIR) consistently outperformed the others, improving both reservoir storage and downstream discharge estimates. However, the magnitude of improvement varied across locations, highlighting the need for the further refinement of DA techniques and input data, especially for regions with complex reservoir operations. Our findings enhance reservoir representation in global hydrodynamic models and improve the predictability of river dynamics and water resource management.

A TMSBL underwater acoustic channel estimation method based on dictionary learning denoising

The shallow sea underwater acoustic channel exhibits a significant sparse multipath structure. The temporally multiple sparse Bayesian learning (TMSBL) algorithm can effectively estimate this sparse multipath channel. However, the complexity of the algorithm is high, the signal-to-noise ratio (SNR) of shallow-sea underwater acoustic communication is low, and the estimation performance of the TMSBL algorithm is greatly affected by noise. To address this problem, an improved TMSBL underwater acoustic channel estimation method based on a dictionary learning noise reduction algorithm is proposed. Firstly, the K-Singular Value Decomposition (K-SVD) dictionary learning method is used to reduce the noise of the received pilot matrix, reducing the influence of noise on the signal. Then, the Generalized Orthogonal Matching Pursuit (GOMP) channel estimation method is combined to obtain a priori information such as the perceptual matrix and hyperparameter matrix for TMSBL channel estimation; and the noise variance is obtained by using the null subcarrier calculation instead of iteratively updating the noise variance in the TMSBL, to improve the estimation accuracy and reduce the algorithmic complexity. Finally, the TMSBL channel estimation method is used to estimate the underwater acoustic channels of different symbols jointly. The simulation results show that the normalized mean square error of the channel estimation of the improved TMSBL method is reduced by about 92.2% compared with the TMSBL algorithm, obtaining higher estimation accuracy; running time is reduced by about 45.6%, and there is also better performance in terms of the running speed, which provides a reference for underwater acoustic channel estimation.

Mobile laser scanning as reference for estimation of stem attributes from airborne laser scanning

The acquisition of high-quality reference data is essential for effectively modelling forest attributes. Incorporating close-range Light Detection and Ranging (LiDAR) systems into the reference data collection stage of remote sensing-based forest inventories can not only increase data collection efficiency but also increase the number of attributes measured with high quality. Therefore, we propose a model-based forest inventory method that uses reference data collected by a car-mounted mobile laser scanning (MLS) system along boreal forest roads. This approach is used for the estimation of diameter at breast height (DBH) and stem volume at the individual tree-level from airborne laser scanning (ALS) data. In addition, we compare the estimates obtained using the proposed method with the ones derived from reference data collected by traditional field inventory of 265 field plots systematically distributed over the study area. The accuracy of the estimates remained comparable regardless of the reference dataset used for estimation of DBH and stem volume. When using the field inventory dataset for model training, the root mean square error (RMSE) of DBH estimates were 4.06 cm (18.8 %) for Norway spruce trees, 6.3 cm (29.6 %) for Scots pine and 8.61 cm (55.9 %) for deciduous trees. Similarly, when evaluating predictions based on the MLS dataset as reference, RMSEs were equal to 3.97 cm (18.4 %) for Norway spruce, 6.12 cm (28.8 %) for Scots pine, and 8.98 cm (58.3 %) for deciduous trees. In general, biases were below 1 cm for most species classes, with the exception of deciduous trees. The accuracy of stem volume also had RMSEs varying across different tree species. For the estimates based on traditional field inventory, the RMSEs were 0.176 m3 (38.8 %) for Norway spruce, 0.228 m3 (52.4 %) for Scots pine and 0.246 m3 (158 %) for deciduous trees. When using the MLS dataset as a reference, the RMSEs were equal to 0.176 m3 (38.8 %), 0.228 m3 (52.4 %), and 0.246 m3 (158 %) for Norway spruce, Scots pine, and deciduous trees, respectively. Car-mounted MLS demonstrated its potential as an efficient alternative for collecting reference data in remote sensing-based forest inventories, which could complement traditional methods.

Weak signal detection in chaotic noise background - based on VMD-EEMD and self-attention mechanisms

Abstract In this paper, a method for detection and estimation of weak harmonic signals in chaotic noise background is proposed. Firstly, the observed signal is smoothed by local weighted regression model to reduce the influence of noise. Then, the smoothed signals are decomposed into the intrinsic mode functions by Variational Mode Decomposition and Ensemble Empirical Mode Decomposition (VMD-EEMD). The components are reconstructed in the phase space, and the one-step prediction is carried out by adding a layer of the self-attention mechanism in the Long Short-Term Memory (LSTM). The one-step prediction error is obtained by summing up the prediction results of the components and the weak harmonic signals will be detected from the one-step prediction error. Finally, the periodogram method is used to detect whether the observed signals contain harmonic signals or not. If the observed signals contain harmonic signals, the amplitude and phase are estimated by the least squares method. Simulation experiments show that the method proposed in this paper has good performance. In detection of single harmonic signal, the Mean Absolute Error (MAE) and Mean Square Error (MSE) of the one-step prediction are 0.0212 and 0.00068 respectively; MAE and MSE of the signal estimation are 0.001977 and 0.0007 respectively. In detection of multiple harmonic signals, MAE and MSE of the one-step prediction are 0. 0248 and 0. 0006 respectively; MAE and MSE of the signal estimation are 0. 0035 and 1.681×10−5 respectively. The method proposed in this paper is able to efficiently detect weak harmonic signals from chaotic noise backgrounds and estimate the harmonic signals.

Multi-task metric learning for optical performance monitoring

In our experiments, applying few shot metric learning for optical performance monitoring (OPM), we set the dataset as 16-way-6-shot. Modulation format identification (MFI) was utilized as a classification task, and optical signal-to-noise ratio (OSNR) estimation was used as a regression task for joint analysis. Multi-task metric learning (MML) used the adaptive weights to balance the weights of the three metric functions, six modulation formats (QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM) are classified correctly with 100 % accuracy after 200 epochs. Furthermore, the lowest mean square error (MSE) of OSNR is 0.431 dB. Then, Ablation experiments compute the corresponding similarity (SIM) for each metric function show that the MSE of MML, SIMLocal+Cosine, SIMCosine+Point, SIMLocal+Point, single-task metric learning (SML) and adaptive multi-task learning (AMTL) is 0.431 dB, 0.572 dB, 0.569 dB, 0.567 dB, 0.637 dB, 1.319 dB, respectively. The proposed model achieves the highest accuracy in MFI and the lowest MSE in OSNR estimation. Finally, when comparing the various metric functions while altering the transmission distance of the optical fiber, it was observed that MML stayed within an acceptable range between 200 km and 800 km. This shows that our algorithm requires only a small number of training samples to create a reasonably good model, offering a new approach to solving problems that arise in optical performance monitoring.

An Aging-Optimized State-of-Charge-Controlled Multi-Stage Constant Current (MCC) Fast Charging Algorithm for Commercial Li-Ion Battery Based on Three-Electrode Measurements

This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estimations and control precisely the charging algorithm. The results of the experiments showed that the Root Mean Square Error (RMSE) of SOC estimation is 1.08%, and the charging time from 0% to 80% SOC is reduced by 30%.

A novel data-driven IBA-ELM model for SOH/SOC estimation of lithium-ion batteries

Lithium-ion batteries (Libs) have become the best power source for electric vehicles due to their high energy density and long cycle life. The state estimation is directly related to the safe and efficient use of Libs. In our previous research, a Bat Algorithm-Extreme Learning Machine (BA-ELM) model was constructed and promising results were achieved for state of health (SOH) estimation. To increase the engineering application value of BA-ELM for SOH/state of charge (SOC) estimation, optimization mechanism of improving the global exploration and local exploitation capabilities of the model was studied. First, three improved methods including a conversion mechanism of Artificial Bee Colony algorithm employing bees and scout bees, a mechanism for adaptive adjustment of the search frequency of bats according to the Euclidean distance difference, and an adaptive inertia weight coefficient method were proposed to construct a novel Improved Bat Algorithm (IBA)-ELM model. Second, a battery life degradation test platform was built to obtain the degradation dataset for Libs. The variation rules of nine battery variables and cycles were revealed. Four high-quality SOH-related features were extracted. Thus, the SOH estimation performance of six models was evaluated. Third, a Libs charge–discharge dataset under driving conditions was collected and processed. Four models were trained based on 72,048 samples from four conditions. Further, SOC estimation tests were conducted on 35,071 samples from completely different conditions. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of SOH estimation by IBA-ELM decreased by 21.93 % and 23.79 %, respectively, compared to those by BA-ELM. The RMSE and MAE of SOC estimation decreased by more than 10 %. Error, radar, and violin plots demonstrate that the IBA-ELM could accurately estimate SOH/SOC in larger scale datasets, simultaneously exhibiting better generalization performance.

Depth accuracy analysis of the ZED 2i Stereo Camera in an indoor Environment

Accurate depth information is crucial for autonomous systems to navigate and interact safely with their surroundings. Passive stereo-vision cameras, such as the ZED 2i, obtain depth information through stereo-image analysis and triangulation. The study measures and assesses the true capabilities of the ZED 2i camera in a real indoor office environment. Furthermore, the study provides a standard test setup to reproduce similar benchmarks with different depth cameras. To achieve the set goals, an experiment was devised and carried out in an office environment to determine the camera depth error and Root Mean Square Error (RMSE) of the depth estimates at different distances using four different image resolutions. The results reveal that the depth error has heavy tails, implying that outliers substantially impact accuracy. Hence, depth errors should not be modeled as normally distributed errors. Moreover, only two out of four resolutions provided the capability of acquiring depth data up to 18 m. These insights provide guidelines for understanding the ZED 2i camera's true capabilities, determining its suitability for different applications and environments, and giving baselines for future tests of other competing sensor units. Furthermore, the study offers a simple, inexpensive, and laboratory space-free, yet effective setup that does not need extensive equipment or complex configurations to facilitate the benchmarking of depth cameras in different working environments.

ANALYSIS OF THE CALCULATION OF REFERENCE EVAPOTRANSPIRATION ACCORDING TO THE DATA OF THE STATE METEOROLOGICAL STATION

Since direct measurement of reference evapotranspiration (ET0) is a complex, time-consuming and expensive process, the most common procedure is to estimate ET0 from climate data. The purpose of this study was to perform reference evapotranspiration calculations based on the data of the state meteorological station Askania-Nova and compare them with the actual ET0 data obtained using an automatic Internet meteorological station. The data for the study were taken from the state meteorological station Askania-Nova (township Askania-Nova, Kakhovsky district, Kherson region, 46.45°N 33.88°E) and the automatic Internet meteorological station iMetos IMT 300 from the company "Pessl Instruments", which is located at the meteorological site of the Askaniysk DSDS (Tavrychanka village, Kakhovsky district, Kherson region, 46.55°N, 33.83°E). Standard evapotranspiration was calculated using the Penman-Monteith method (FAO56-RM). To assess the accuracy of ET0 calculations, mean absolute percent error (MAPE), root mean square error (RMSE) and Standard Error of Estimate (SEE ) were determined. According to the results of the comparison of indicators from two meteorological stations, it was found that the smallest errors are inherent in the daily average and maximum temperature and relative air humidity (MAPE<10%), for the minimum temperature and relative air humidity, the MAPE errors are 18,1 and 13,7%, respectively. The MAPE error for water vapor pressure deficit and solar radiation is 20,2 and 26,3%, respectively. The largest MAPE error of 40,3% was established for wind speed measurements. The average MAPE error between the calculated ET0, based on the meteorological data of the Askania-Nova station, and the actual ET0 data obtained from the automatic Internet meteorological station iMetos is 16,8%, RMSE – 0,65 mm, SEE – 0,56 mm. Applying a coefficient of 0,92 when calculating ET0 reduces the errors of MAPE, RMSE, and SEE by 3,2%, 0,15 mm, and 0,05 mm, respectively, for all calculation periods. For the May-August period, the MAPE error was 10,7%, which brings the calculations close to high accuracy (MAPE <10%). Based on the results of the calculations, it was established that on average over the years of research, the actual ET0 was 68 mm less than the calculated one. The absolute errors of determination of ETc depended on the crop and the average over the years of research ranged from 33 mm (winter wheat) to 68 mm (early tomatoes). The application of the refined value of ET0 in calculations reduces the absolute errors in the determination of c over the years of research, this error did not exceed 6 mm (early tomato). Research results confirm the possibility of using meteorological indicators obtained from state meteorological stations to calculate ET0. To increase the accuracy of calculations, it is necessary to use a refinement coefficient.

A model of the organizational resilience of hospitals in emergencies and disasters

BackgroundIn the health system, hospitals are intricate establishments that offer vital medical services. Their resilience plays a crucial role in mitigating the societal repercussions of disasters. A hospital must possess the capacity to withstand risks, preserve its fundamental structure and operations, and enhance its preparedness by augmenting various capabilities and promptly recovering from the impacts of potential risks. It enables the hospital to attain a heightened level of readiness. Therefore, this study aimed to develop a resilience model tailored for hospitals to navigate crises and disasters effectively.MethodsThis mixed-method study was conducted in 2023 in three phases: (1) Identification of the factors influencing the organizational resilience of the hospital, (2) Evaluation of the influential factors by an expert panel. (3) Following the standardization process, we administered 371 questionnaires to individuals, such as university staff managers and supervisors, nursing managers, and research unit managers. The sample size was determined by multiplying the components by 10, resulting in 360 (10 * 36). Therefore, we selected a sample size of 371 participants. Structural Equation Modeling (SEM) was employed to examine the causal relationships between variables. These steps were performed using SPSS 25.0 and AMOS 22 software. Finally, we identified and presented the final model. We utilized AMOS 22 and applied the SEM to assess the correlation between the variables, with a significance level of 0.05.ResultsFindings indicate that the appropriate modeling identified five dimensions comprising 36 components. These dimensions include vulnerability, preparedness, support management, responsiveness and adaptability, and recovery after the disaster. The model demonstrates a good fit, as indicated by the X2/d indices with a value of 2.202, a goodness of fit index (GFI) of 0.832, a root mean square error of estimation (RMSEA) of 0.057, an adjusted comparative fit index (CFI) of 0.931, and a smoothed fit index (NFI) of 0.901.ConclusionEnhancing hospital resilience is crucial for effective preparedness and response to accidents and disasters. Developing a localized tool for measuring resilience can help identify vulnerabilities, ensure service continuity, and inform rehabilitation programs. The proposed model is a suitable framework for assessing hospital resilience. Key factors include human resource scarcity, hospital specialization, and trauma center capacity. Hospitals should prioritize efficient resource allocation, information technology infrastructure, in-service training, waste management, and a proactive organizational framework to build resilience. By adopting this approach, hospitals can better respond to crises and disasters, ultimately reducing casualties and improving overall preparedness.

Study on Mathematical Models for Precise Estimation of Tire–Road Friction Coefficient of Distributed Drive Electric Vehicles Based on Sensorless Control of the Permanent Magnet Synchronous Motor

In order to reduce the use of wheel angular velocity sensors and improve the estimation accuracy and robustness of the tire–road friction coefficient (TRFC) in non-Gaussian noise environments, this paper proposes a sensorless control-based distributed drive electric vehicle TRFC estimation algorithm using a permanent magnet synchronous motor (PMSM). The algorithm replaces the wheel angular velocity signal with the rotor speed signal obtained from the sensorless control of the PMSM. Firstly, a seven-degree-of-freedom vehicle dynamics model and a mathematical model of the PMSM are established, and the maximum correntropy singular value decomposition generalized high-degree cubature Kalman filter algorithm (MCSVDGHCKF) is derived. Secondly, a sensorless control system of a PMSM based on the MCSVDGHCKF algorithm is established to estimate the rotor speed and position of the PMSM, and its effectiveness is verified. Finally, the feasibility of the algorithm for TRFC estimation in non-Gaussian noise is demonstrated through simulation experiments, the Root Mean Square Error (RMSE) of TRFC estimates for the right front wheel and the left rear wheel were reduced by at least 41.36% and 40.63%, respectively. The results show that the MCSVDGHCKF has a higher accuracy and stronger robustness compared to the maximum correntropy high-degree cubature Kalman filter (MCHCKF), singular value decomposition generalized high-degree cubature Kalman filter (SVDGHCKF), and high-degree cubature Kalman filter (HCKF).

CRT-Based Clock Synchronization for Millimeter-Wave Communication with Asymmetric Propagation Delays

The rapid advancement of millimeter-wave communication technology has presented new challenges for time synchronization, driven by the need for high-speed and low-latency data transmission. Ethernet synchronization technologies, such as Precise Time Protocol (PTP), have emerged to overcome the limitations of point-to-point architecture and enable precise synchronization across multiple devices. A key drawback of conventional PTP is its reliance on symmetric packet exchange delays, which may not hold in real-world scenarios where there is relative mobility between master and slave nodes, leading to asymmetric propagation delays and clock offset errors. To address this issue, a novel clock synchronization protocol that integrates Chinese Remainder Theory (CRT) has been proposed. This protocol enhances conventional PTP by incorporating distance estimation based on CRT using multi-carrier phase measurement. By combining a coarse estimation of one-way propagation delay from conventional PTP with a fine estimation of remainder distance from CRT, the protocol accurately determines the distance between master and slave nodes, reducing motion errors and enhancing clock synchronization accuracy. Simulation results indicate that the Root Mean Square Error (RMSE) of distance estimation remains below 10−5 m, with a corresponding motion time error of approximately 0.01 picosecond.

Using Geostationary Satellite Observations to Improve the Monitoring of Vegetation Phenology

Geostationary satellite data enable frequent observations of the Earth’s surface, facilitating the rapid monitoring of land covers and changes. However, optical signals over vegetation, represented by the vegetation index (VI), exhibit an anisotropic effect due to the diurnal variation in the solar angle during data acquisition by geostationary satellites. This effect, typically characterized by the bi-directional reflectance distribution function (BRDF), can introduce uncertainties in vegetation monitoring and the estimation of phenological transition dates (PTDs). To address this, we investigated the diurnal variation in the normalized difference vegetation index (NDVI) with solar angles obtained from geostationary satellites since the image had fixed observation angles. By establishing a temporal conversion relationship between instantaneous NDVI and daily NDVI at the local solar noon (LSNVI), we successfully converted NDVIs obtained at any time during the day to LSNVI, increasing cloud-free observations of NDVI by 34%. Using different statistics of the time series vegetation index, including LSNVI, daily averaged NDVI (DAVI), and angular corrected NDVI (ACVI), we extracted PTD at five typical sites in China. The results showed a difference of up to 41.5 days in PTD estimation, with the highest accuracy achieved using LSNVI. The use of the proposed conversion approach, utilizing time series LSNVI, reduced the root mean square error (RMSE) of PTD estimation by 9 days compared with the use of actual LSNVI. In conclusion, this study highlights the importance of eliminating BRDF effects in geostationary satellite observations and demonstrates that the proposed angular normalization method can enhance the accuracy of time series NDVI in vegetation monitoring.

ℓ1${\ell }_1$ norm‐based recursive estimation for non‐linear systems with non‐Gaussian noises

AbstractThis study addresses the state estimation problem of discrete‐time non‐linear stochastic systems with non‐Gaussian noises, particularly impulsive noises. Instead of minimizing the mean square error of the state estimate, which tends to excessively focus on outliers caused by non‐Gaussian noises, the norm‐based non‐linear recursive filter (L1KF) is put forward in this paper. Here, minimizing the norm of model errors is actually to pursue the minimum sum of absolute values of all errors, which is equitable to all model errors rather than paying much attention on outliers. To further improve estimation accuracy, a recursive nonlinear smoother (L1KS) is proposed, based on minimizing the norm of model errors. The proposed norm‐based filter and smoother are implemented using unscented transformation for statistical linear regression applied to nonlinear models. Additionally, the computational complexity of the proposed method is analysed. Simulation results of tracking a radar target with impulsive noises demonstrate the effectiveness and robustness of the proposed estimator.