Error Correlation Research Articles

An Artificial Neural Network approach to assess road roughness using smartphone-based crowdsourcing data

Monitoring road surface conditions is a crucial task for road authorities to develop effective infrastructure maintenance programs. Despite smartphones have been introduced as cost-effective and real-time solution for this purpose, several challenges must be addressed before their real-world application. This study investigates the utilization of smartphone-based crowdsourcing data and Artificial Neural Networks (ANN) to enhance the precision of road surface condition estimation. Initially, data are collected from four different smartphone models mounted in various vehicles, including vertical acceleration, geographic location, and speed. The root mean square of the vertical acceleration data, along with vehicle speed, is then employed as input features for the ANN, while the true International Roughness Index (IRI) values serve as the corresponding output features. Comparative analysis between ANN and regression models based on statistical metrics such as Mean Squared Error (MSE) and Pearson correlation revealed that ANN outperforms regression models. The obtained MSE and Pearson correlation values for ANN (0.56 and 0.91) surpass those of regression models (0.72 and 0.88). Moreover, results indicated that utilizing crowdsourcing smartphone data yielded superior outcomes compared to using a single smartphone for this purpose.

PSV-11 Significance of specialty protein ingredients in feeding programs for broiler chickens on nitrogen utilization and excretion- A meta-analysis

Abstract The utility of specialty protein ingredients (SPI) in feeding programs for broiler chickens is premised on poor utilization of conventional vegetable proteins (CVP). The SPI includes ingredients of animal origin such as processed animal proteins (PAP), insect proteins (IP) and further processed conventional vegetable proteins (PCVP) characterized with greater concentration of crude protein/amino acids, and low anti-nutritional factors. The objective of this study was to apply meta-analysis approach of published studies that incorporated SPI to provide insights into their effectiveness on nitrogen utilization and excretion in broiler chickens. The data were retrieved from Web of Science, PubMed, ScienceDirect, hand searching and cross-referencing, and resulted in 44 publications meeting inclusion criteria such as being a broiler study and treatments diets being isocaloric and isonitrogenous Model development was performed in PROC GLIMMIX of SAS and treated the study as a random effect. The SPI protein source (PAP, IP, and PCVP), bird age (starter 0 to 14 d, grower 14 to 24 d and finisher 25 to 42 d), nitrogen intake and level were the main X variables of interest, while outcome (Y) variables explored included nitrogen excretion, nitrogen retention, nitrogen efficiency, average daily gain (ADG), average daily feed intake (ADFI), and feed conversion ratio (FCR). To address heterogeneity in sample size and errors between treatment means from different studies, the WEIGHT statement in PROC GLIMIXX was employed to weight variables using the inverse of their squared variance. The inclusion or rejection of models developed was based on the evaluation of root mean square prediction error and concordance correlation coefficient. Comparatively, the slopes of PAP, IP and PCVP (soybean meal) linearly reduced and quadratically (P < 0.05) increased ADG during the starter phase against their respective controls. The extent ADG was greatest with intake of PCVP followed by PAP. During the grower phase, ADG was highest for PAP, followed by IP with PCVP being the least (P < 0.05). However, PCVP had the greatest ADG during the finisher phase (P < 0.05). On nitrogen excretion, provision of PAP during the stater phase increased the nitrogen excretion while it reduced the excretion during the grower and finisher phase (P < 0.05) relative to control. The intake of IP up to 10% inclusion reduced nitrogen excretion only during the starter and grower phase (P < 0.05) when compared with respective controls. The intake of PCVP reduced nitrogen excretion in all growth stages compared with respective controls (P < 0.05). In conclusion specialty proteins (PAP and PCVP) and insect protein at an inclusion level of < 10%, were effective ways of reducing nitrogen excretion while improving growth performance. Combining these strategies may have a synergistic effect.

Near-Surface Air Temperature Estimation Based on an Improved Conditional Generative Adversarial Network.

To address the issue of missing near-surface air temperature data caused by the uneven distribution of ground meteorological observation stations, we propose a method for near-surface air temperature estimation based on an improved conditional generative adversarial network (CGAN) framework. Leveraging the all-weather coverage advantage of Fengyun meteorological satellites, Fengyun-4A (FY-4A) satellite remote sensing data are utilized as conditional guiding information for the CGAN, helping to direct and constrain the near-surface air temperature estimation process. In the proposed network model of the method based on the conditional generative adversarial network structure, the generator combining a self-attention mechanism and cascaded residual blocks is designed with U-Net as the backbone, which extracts implicit feature information and suppresses the irrelevant information in the Fengyun satellite data. Furthermore, a discriminator with multi-level and multi-scale spatial feature fusion is constructed to enhance the network's perception of details and the global structure, enabling accurate air temperature estimation. The experimental results demonstrate that, compared with Attention U-Net, Pix2pix, and other deep learning models, the method presents significant improvements of 68.75% and 10.53%, respectively in the root mean square error (RMSE) and Pearson's correlation coefficient (CC). These results indicate the superior performance of the proposed model for near-surface air temperature estimation.

4D flow MRI velocity uncertainty quantification.

An automatic method is presented for estimating 4D flow MRI velocity measurement uncertainty in each voxel. The velocity distance (VD) metric, a statistical distance between the measured velocity and local error distribution, is introduced as a novel measure of 4D flow MRI velocity measurement quality. The method uses mass conservation to assess the local velocity error variance and the standardized difference of means (SDM) velocity to estimate the velocity error correlations. VD is evaluated as the Mahalanobis distance between the local velocity measurement and the local error distribution. The uncertainty model is validated synthetically and tested in vitro under different flow resolutions and noise levels. The VD's application is demonstrated on two in vivo thoracic vasculature 4D flow datasets. Synthetic results show the proposed uncertainty quantification method is sensitive to aliased regions across various velocity-to-noise ratios and assesses velocity error correlations in four- and six-point acquisitions with correlation errors at or under 3.2%. In vitro results demonstrate the method's sensitivity to spatial resolution, venc settings, partial volume effects, and phase wrapping error sources. Applying VD to assess in vivo 4D flow MRI in the aorta demonstrates the expected increase in measured velocity quality with contrast administration and systolic flow. The proposed 4D flow MRI uncertainty quantification method assesses velocity measurement error owing to sources including noise, intravoxel phase dispersion, and velocity aliasing. This method enables rigorous comparison of 4D flow MRI datasets obtained in longitudinal studies, across patient populations, and with different MRI systems.

Improving GNSS-IR Sea Surface Height Accuracy Based on a New Ionospheric Stratified Elevation Angle Correction Model

Approximately 71% of the Earth’s surface is covered by vast oceans. With the exacerbation of global climate change, high-precision monitoring of sea surface height variations is of vital importance for constructing global ocean gravity fields and preventing natural disasters in the marine system. Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) sea surface altimetry is a method of inferring sea surface height based on the signal-to-noise ratio of satellite signals. It enables the retrieval of sea surface height variations with high precision. However, navigation satellite signals are influenced by the ionosphere during propagation, leading to deviations in the measured values of satellite elevation angles from their true values, which significantly affects the accuracy of GNSS-IR sea surface altimetry. Based on this, the contents of this paper are as follows: Firstly, a new ionospheric stratified elevation angle correction model (ISEACM) was developed by integrating the International Reference Ionosphere Model (IRI) and ray tracing methods. This model aims to improve the accuracy of GNSS-IR sea surface altimetry by correcting the ionospheric refraction effects on satellite elevation angles. Secondly, four GNSS stations (TAR0, PTLD, GOM1, and TPW2) were selected globally, and the corrected sea surface height values obtained using ISEACM were compared with observed values from tide gauge stations. The calculated average Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) were 0.20 m and 0.83, respectively, indicating the effectiveness of ISEACM in sea surface height retrieval. Thirdly, a comparative analysis was conducted between sea surface height retrieval before and after correction using ISEACM. The optimal RMSE and PCC values with tide gauge station observations were 0.15 m and 0.90, respectively, representing a 20.00% improvement in RMSE and a 4.00% improvement in correlation coefficient compared to traditional GNSS-IR retrieval heights. These experimental results demonstrate that correction with ISEACM can effectively enhance the precision of GNSS-IR sea surface altimetry, which is crucial for accurate sea surface height measurements.

Analysis of the Spatial Distribution and Common Mode Error Correlation in a Small-Scale GNSS Network.

When analyzing GPS time series, common mode errors (CME) often obscure the actual crustal movement signals, leading to deviations in the velocity estimates of station coordinates. Therefore, mitigating the impact of CME on station positioning accuracy is crucial to ensuring the precision and reliability of GNSS time series. The current approach to separating CME mainly uses signal filtering methods to decompose the residuals of the observation network into multiple signals, from which the signals corresponding to CME are identified and separated. However, this method overlooks the spatial correlation of the stations. In this paper, we improved the Independent Component Analysis (ICA) method by introducing correlation coefficients as weighting factors, allowing for more accurate emphasis or attenuation of the contributions of the GNSS network's spatial distribution during the ICA process. The results show that the improved Weighted Independent Component Analysis (WICA) method can reduce the root mean square (RMS) of the coordinate time series by an average of 27.96%, 15.23%, and 28.33% in the E, N, and U components, respectively. Compared to the ICA method, considering the spatial distribution correlation of stations, the improved WICA method shows enhancements of 12.53%, 3.70%, and 8.97% in the E, N, and U directions, respectively. This demonstrates the effectiveness of the WICA method in separating CMEs and provides a new algorithmic approach for CME separation methods.

Accurate Global Atmospheric State Analysis Using Objective Error Statistics Including Observation Error Dependence on Water Substance Field

AbstractAtmospheric state analysis is a difficult scientific problem but essential for atmospheric sciences. Data assimilation can generate accurate analyses by integrating information on the atmospheric state using probability density functions (PDFs), where the Gaussian approximation is typically used and PDFs are described by error covariance matrices (ECMs). However, ECMs have been estimated empirically, and dependency of the ECMs on meteorological conditions (flow) is only partially represented. These limit atmospheric state analysis accuracy. This is especially problematic for water substance‐sensitive microwave radiances (WS‐MWRs) because of their strong flow dependence. We objectively estimated ECMs of all data including flow‐dependence of the ECMs of WS‐MWRs. Since the ECM of each data is a component of one ECM representing one joint PDF as a whole, it is theoretically better to objectively estimate ECMs of all data, not just a particular data. For WS‐MWRs, we categorized flow into four using water substance amount and estimating an ECM for each category. Numerical experiments using the new ECMs on an operational global numerical weather prediction system show the followings. The new error standard deviations are generally smaller than those of empirical. Standard deviations and interchannel correlations of observation errors of WS‐MWRs increase with water substance amount. The effects of WS‐MWRs on analysis were approximately doubled. The analysis fields differ systematically such as increase of low‐level clouds over cold oceans. The forecast accuracy improved with 95% statistical significance up to 9%. Both the flow dependence of correlation and variance of WS‐MWRs contributed to the improvement of forecast accuracy.

Bias correction and ensemble techniques in statistical downscaling model for rainfall prediction using Tweedie-LASSO in West Java, Indonesia

Rainfall is a climate element with high variations in space and time scales, so it is not easy to predict. One way to predict rainfall is statistical downscaling (SD). SD can predict local rainfall based on Global Circulation Model (GCM) data. The Decadal Climate Prediction Project (DCPP), one of the GCMs, originates from adjacent grids and experiences multicollinearity problems. Rainfall as a response variable is Tweedie Compound Poisson Gamma (TCPG) distribution data because it has a discrete component (rainfall events) and a continuous component (rainfall intensity), so SD modelling will be carried out using Tweedie-LASSO. This research aims to compare the performance of bias correction and ensemble methods in SD in predicting rainfall in West Java, Indonesia. Bias correction uses Empirical Quantile Mapping (EQM) with CHIRPS data, and the ensemble method uses a stacking technique with Random Forest (Stacking-RF) due to the varied characteristics of DCPP model sources. Evaluation results using Root Mean Square Error Prediction (RMSEP) and correlation coefficient show that bias correction improves single-model performance but not ensemble models. Besides that, ensemble models outperform single models both before and after bias correction. The combination of bias correction and ensemble modelling can be recommended when conducting SD to enhance the prediction capability of rainfall at stations and other areas.

Development of new universal correlations for minimum heat flux point for saturated pool boiling of cryogens

This study addresses the critical need for a reliable database for the minimum heat flux (MHF) point in saturated pool boiling of cryogens. Relying on a comprehensive review of the relatively sparse published literature, a key objective of the study is to amass a MHF database, which is then used to investigate influences of various parameters on MHF, assess the accuracy of published correlations, and develop new correlations specifically tailored to cryogenic fluids. By applying stringent point-by-point evaluation criteria, 165 data points for MHF point temperature (Tmin) and 158 data points for MHF point heat flux (q”min) are aggregated, comprising this study's “Consolidated Database” for MHF. This database includes data for liquid helium (LHe), liquid argon (LAr), liquid hydrogen (LH2), and liquid nitrogen (LN2) and boiling from clean as well as treated (coated or oxidized) surfaces. A total of 9 correlations for Tmin and 10 for q”min are evaluated for accuracy against the new Consolidated Database. Leveraging insights from prior correlation results and trends from the new Consolidated Database, new universal correlations are formulated for both Tmin and q”min. The new correlation for Tmin features Mean Absolute Errors (MAEs) of 9.05 % for clean surfaces and 7.7 % for treated surfaces. Similarly, the new q”min correlation shows MAEs of 21.50 % for clean surfaces and 25.22 % for treated surfaces. While the new correlations represent a significant advancement in the development of predictive tools for cryogens, this study points to a need for more comprehensive experimental investigation of heat transfer aspects of cryogens, which will undoubtedly further improve the robustness and accuracy of the new correlations.

Predicting slump for high‐performance concrete using decision tree and support vector regression approaches coupled with phasor particle swarm optimization algorithm

AbstractThe main focus of this study is to assess the slump characteristics of high‐performance concrete (HPC) using decision tree (DT) and support vector regression (SVR) models. In the first step, the models were solely fed via HPC samples to reproduce the slump rates. By coupling phasor particle swarm optimization (PPSO) to main models, hybrid DT‐PPSO and SVR‐PPSO frameworks, simulate the slump rates accurately. Using the correlation of determination and root mean square error (MAE) metrics for the DT, 96.04 and 5.097 were computed, respectively. SVR was obtained at 92.62 and 6.965, alternatively. In the hybrid approach, DT‐PPSO could improve by 3% and 55% in terms of correlation of determination and root MAE, respectively. DT‐PPSO appeared high‐accuracy model compared to others; however, a single DT had more desirable results than SVR. Overall, the advantages of this study encompass its methodological approach, comparative insights, and practical relevance, offering valuable contributions to the understanding and prediction of mechanical slump in HPC.

Spatially Resolved Uncertainties for Machine Learning Potentials.

Machine learning potentials have become an essential tool for atomistic simulations, yielding results close to ab initio simulations at a fraction of computational cost. With recent improvements on the achievable accuracies, the focus has now shifted on the data set composition itself. The reliable identification of erroneously predicted configurations to extend a given data set is therefore of high priority. Yet, uncertainty estimation techniques have achieved mixed results for machine learning potentials. Consequently, a general and versatile method to correlate energy or atomic force uncertainties with the model error has remained elusive to date. In the current work, we show that epistemic uncertainty cannot correlate with model error by definition but can be aggregated over groups of atoms to yield a strong correlation. We demonstrate that our method correctly estimates prediction errors both globally per structure and locally resolved per atom. The direct correlation of local uncertainty and local error is used to design an active learning framework based on identifying local subregions of a large simulation cell and performing ab initio calculations only for the subregion subsequently. We successfully utilized this method to perform active learning in the low-data regime for liquid water.

Exploratory factor analysis for machining error data of compressor blades based on dimensionality-reduced statistical analysis method

Abstract Various machining errors inevitably occur on aero-engine compressor blades, including leading-edge contour error, trailing-edge contour error, camber contour error, and more. The current complexity surrounding the numerous ma-chining error types and their obscure interrelationships imposes immense effort for aerodynamic analysis and hin-ders overall error control. Thus, elucidating error correlations to achieve error dimensionality reduction is imperative. This study pioneers a dimensionality reduction approach via factor analysis to conduct a comprehensive statistical analysis of 13 types of blade machining errors. The proposed technique can categorize the 13 errors into three groups, each dominated by a distinct common factor. To validate the accuracy of the extracted factors, the factor scores of the three identified latent factors are computed. Cluster analysis is then performed on the factor scores, and the clustering results are visualized by t-Distributed Stochastic Neighbor Embedding (t-SNE). Furthermore, boot-strap resampling establishes the 95% confidence intervals for the factor scores. Capitalizing on the grouping struc-ture uncovered by factor analysis, multiple linear regression models are built for the errors within each group, and then, a preliminary conjecture is made about the potential key error types for each group of errors based on the re-gression coefficients. This hypothesis is then evidenced by the statistical analysis of cross-section profile error data of 28 blades. The present work can not only optimize machining processes but also relax tolerance requirements and diminish the effort of aerodynamic analysis.

The neural basis of swap errors in working memory

When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once-such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally and theoretical explanations have been proposed, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms by analyzing neural population recordings from monkeys performing two multistimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location or will be shown at the corresponding location. Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results provide evidence that swap errors emerge during selection of correctly remembered information from working memory, and highlight this selection as a crucial-yet surprisingly brittle-neural process.

Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups

Spinal deformities, including adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD), affect many patients. The measurement of the Cobb angle on coronal radiographs is essential for their diagnosis and treatment planning. To enhance the precision of Cobb angle measurements for both AIS and ASD, we developed three distinct artificial intelligence (AI) algorithms: AIS/ASD-trained AI (trained with both AIS and ASD cases); AIS-trained AI (trained solely on AIS cases); ASD-trained AI (trained solely on ASD cases). We used 1612 whole-spine radiographs, including 1029 AIS and 583 ASD cases with variable postures, as teaching data. We measured the major and two minor curves. To assess the accuracy, we used 285 radiographs (159 AIS and 126 ASD) as a test set and calculated the mean absolute error (MAE) and intraclass correlation coefficient (ICC) between each AI algorithm and the average of manual measurements by four spine experts. The AIS/ASD-trained AI showed the highest accuracy among the three AI algorithms. This result suggested that learning across multiple diseases rather than disease-specific training may be an efficient AI learning method. The presented AI algorithm has the potential to reduce errors in Cobb angle measurements and improve the quality of clinical practice.

Assessing screw length impact on bone strain in proximal humerus fracture fixation via surrogate modelling.

A high failure rate is associated with fracture plates in proximal humerus fractures. The causes of failure remain unclear due to the complexity of the problem including the number and position of the screws, their length and orientation in the space. Finite element (FE) analysis has been used for the analysis of plating of proximal humeral fractures, but due to computational costs is unable to fully explore all potential screw combinations. Surrogate modelling is a viable solution, having the potential to significantly reduce the computational cost whilst requiring a moderate number of training sets. This study aimed to develop adaptive neural network (ANN)-based surrogate models to predict the strain in the humeral bone as a result of changing the length of the screws. The ANN models were trained using data from FE simulations of a single humerus, and after defining the best training sample size, multiple and single-output models were developed. The best performing ANN model was used to predict all the possible screw length configurations. The ANN predictions were compared with the FE results of unseen data, showing a good correlation (R2 = 0.99) and low levels of error (RMSE = 0.51%-1.83% strain). The ANN predictions of all possible screw length configurations showed that the screw that provided the medial support was the most influential on the predicted strain. Overall, the ANN-based surrogate model accurately captured bone strains and has the potential to be used for more complex problems with a larger number of variables.

Further Psychometric Evaluation of the Eight-Item Hospice Philosophy Scale: Results From a National Sample of Interdisciplinary Hospice Clinicians.

This study examined the psychometric properties of the eight-item Hospice Philosophy Scale (HPS-8) through confirmatory factor analysis; differential item functioning by age, gender, race, and professional discipline; and internal consistency reliability. We administered the HPS-8 to a national convenience sample of 471 interdisciplinary hospice clinicians. Confirmatory factor analysis results supported a one-factor model with an error correlation between two similarly worded items, χ2(19) = 48.38, p < .001 (RMSEA = .06, SRMR = .03, CFI = .98, TLI = .97). "Multiple indicators, multiple causes" model results indicated differential item functioning by age, race, and/or professional discipline on five items. However, subsequent uncorrected and differential item functioning-corrected models detected no statistically significant HPS-8 mean differences by grouping variables. Composite reliability results (CR = .82) demonstrated acceptable internal consistency reliability. Our results support the HPS-8 as a valid and reliable measure of attitudes toward the hospice philosophy of care in hospice clinicians.

Summarizing multiple aspects of triple collocation analysis in a single diagram

With the ongoing expansion of global observation networks, it is expected that we shall routinely analyze records of geophysical variables such as temperature from multiple collocated instruments. Validating datasets in this situation is not a trivial task because every observing system has its own bias and noise. Triple collocation is a general statistical framework to estimate the error characteristics in three or more observational-based datasets. In a triple colocation analysis, several metrics are routinely reported but traditional multiple-panel plots are not the most effective way to display information. A new formula of error variance is derived for connecting the key terms in the triple collocation theory. A diagram based on this formula is devised to facilitate triple collocation analysis of any data from observations, as illustrated using three aerosol optical depth datasets from the recent Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). An observational-based skill score is also derived to evaluate the quality of three datasets by taking into account both error variance and correlation coefficient. Several applications are discussed and sample plotting routines are provided.

Psychometric Analysis of the Heart Failure Somatic Perception Scale in Japanese Patients With Heart Failure.

Patients with heart failure (HF) experience a wide variety of symptoms. Appropriate recognition of symptoms is important in HF care. The Heart Failure Somatic Perception Scale (HFSPS) measures the presence of HF symptoms and the degree to which physical symptoms are bothersome. The aim of this study was to assess the validity and reliability of the Japanese version of the HFSPS. Confirmatory factor analysis was used to assess structural validity. Construct validity was assessed using Spearman's rank correlation coefficient to evaluate the association between HFSPS total and subscale scores and global physical health on the Patient-Reported Outcomes Measurement Information System. Internal consistency was assessed using the model-based internal consistency for the HFSPS as a whole and Cronbach α for the subscales. Participants were 315 Japanese outpatients (72.1% male), with a mean age of 72.9 ± 12.9 years. The result of confirmatory factor analysis was an adequate model fit by adding error correlations. Construct validity was significant for the correlation with global physical health of the Patient-Reported Outcomes Measurement Information System. The model-based internal consistency was 0.95. Cronbach αs for each subscale were 0.88 for dyspnea, 0.60 for chest discomfort, 0.77 for early and subtle symptoms, and 0.77 for edema. The findings support the use of the HFSPS in a more diverse population, suggesting that it is a reliable and valid instrument in Japanese patients with HF. The HFSPS may provide an accurate assessment of the symptoms experienced by patients with HF in daily life in future educational intervention studies to improve symptom perception and coping behaviors.

A spatiotemporal comparison of interobserver error in vegetation sampling

AbstractQuestionsWe asked how interobserver error in sampling vegetation (excluding trees) varied over time, space and habitat type; determined whether there were any obvious correlates of observer error; and evaluated evidence of bias among observers.LocationNine national park units in the Midwestern USA.MethodsWe quantified observer error in the context of a long‐term monitoring program employing three observers, evaluating interobserver error across 11 locations in the Midwestern USA over five years. The vegetation (excluding trees) was sampled independently by two teams of observers at prairie and woodland locations (n = 94 plots total).ResultsTotal pseudoturnover ranged between 20.2% and 22.1% at prairie locations, and between 16.8% and 28.6% at woodland locations. The overlooking component of pseudoturnover accounted for 75% or more of total pseudoturnover, with misidentification and cautious components each contributing 19% or less of the total, depending on location. The percentage of comparisons in which both observers recorded the same cover class ranged from 71.3% to 78.5% at the prairie locations and 56.9% to 85.6% at woodland locations. When observers did not agree on cover class, they were off by more than one class less than 6% of the time. Overlooking error was more likely to occur for species with less cover, while estimation error was more likely to occur for species with greater cover. A bias existed among observers, as the least experienced observer recorded 6.2%–11.8% more species than the other two observers. Interobserver bias also existed for rates of estimation error, as one observer consistently recorded higher cover classes.ConclusionsObserver error is a pervasive aspect of vegetation sampling. Continued training and experience yielded limited increases in precision. Elements of the sampling design probably reduced observer error to a certain degree, although some level of interobserver error in vegetation surveys is unavoidable.

Temporal feature decomposition fusion network for building energy multi-step prediction

Accurate building energy prediction methods have become a key factor in achieving energy-saving goals. Traditional methods for building energy multi-step prediction often use recursive or direct strategies to address time series prediction problems, which may neglect the data sequence correlation and result in the cumulative error. To solve the above problem, this paper proposed a Temporal Feature Decomposition Fusion Network (TFDFNet) model for building energy consumption multi-step prediction, with an encoder-decoder architecture. In the encoder, feature fusion layers is employed to consider the influence of different feature sequences on the predicted sequence. Through the decomposition of historical load data sequences, different hierarchical sequence structures are constructed to enhance the interpretability and predictability of the data. In the decoder, a simple and efficient network is constructed using MLP to decode the encoded information and obtain the prediction results. Experimental results show that, the proposed model achieves higher prediction accuracy and more stable convergence than the other five comparable methods, which also indicates the potential of achieving excellent building energy consumption multi-step prediction results with a simple model design.