Historical Measurements Research Articles

Stochastic MPC based double-time-scale voltage regulation for unbalanced distribution networks with distributed generators

With the increasing penetration of renewable energy resources, the uncertainty of renewable energy resources output introduces challenges to voltage control in distribution networks (DNs). To solve these problems, this paper proposes a double-time-scale voltage control scheme for unbalanced DNs based on stochastic model predictive control (SMPC) to coordinate multiple voltage control devices. In fast-time-scale control, the active and reactive power outputs of distributed generators (DGs) are optimized to minimize voltage deviation, active power curtailment and reactive power fluctuation. In the slow-time-scale control, optimal tap changes of on-load tap changers (OLTC), step voltage regulators (SVR), and switched capacitor banks (CBs) are calculated to reduce voltage deviation and tap operations. Additionally, in the fast-time-scale control, the SMPC-based scheme adopts a scenario generation approach to represent the uncertainty of DG output. Considering the temporal correlation of the DG output, different prediction scenarios and scenario probabilities are obtained by the empirical cumulative distribution function fitted by the historical measurement data. In this paper, the effectiveness of the proposed double-time-scale voltage control scheme is verified by the unbalanced IEEE123 node system. The case study demonstrates that the proposed voltage scheme can effectively keep the three-phase voltages in unbalanced DN within the secure operation range and achieve better voltage profiles.

A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems

Inverse heat transfer problems are important in numerous scientific research and engineering applications. This paper proposes a network-based method utilizing the nonlinear autoregressive with exogenous inputs (NARX) neural network, which can achieve real-time identification of thermal boundary conditions for nonlinear transient heat transfer processes. With the introduction of the NARX neural network, the proposed method offers two key advantages: (1) The proposed method can obtain inversion results using only surface temperature time series. (2) The heat flux can be estimated even when the state equation of the system is unknown. The NARX neural network is trained using the Bayesian regularization algorithm with a dataset comprising exact surface temperature and heat flux data. The neural network takes current and historical surface temperature measurements as inputs to calculate the heat flux at the current time. The capability of the NARX method has been verified through numerical simulation experiments. Experimental results demonstrate that the NARX method has high precision, strong noise resistance, and broad applicability. The composition of the input data, the surface temperature measurement noise, and the boundary heat flux shape have been studied in detail for their impact on the inversion results. The NARX method is a highly competitive solution to inverse heat transfer problems.

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm.

Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

Regression-enhanced Entrotaxis as an autonomous search algorithm for seeking an unknown gas leakage source

Rapid localization of hazardous gas leak sources is a vital task regarding the security of human society. Over the past few decades, autonomous search using mobile sensing devices equipped with gas sensors has become a promising field. Cognitive search strategies, such as Infotaxis and Entrotaxis, have been successfully used to search an unknown source autonomously in turbulence. However, they are time-consuming and may not be efficient because all source parameters (locations and strength) are estimated via particle filtering, especially when the prior information of source strength is rarely known. To address this inefficiency, this paper proposes a hybrid autonomous source searching approach, named as regression-enhanced Entrotaxis, which incorporates the Entrotaxis algorithm with the regression methods. Only the position of leak source is inferred by particle filtering, while the strength is estimated by the least squares based on the historical measurements. Through this way, the searching space in particle filtering is converted from 3-dimensions (source position in x–y plane and source strength) to 2-dimensions (source position in x–y plane), speeding up the computation significantly. Then, the reward function according to maximum entropy sampling principles is utilized to guide the robot’s next move. The performance of the proposed method is compared to that of the Entrotaxis algorithm. The results of Monte Carlo simulations demonstrate that fewer particles will be used to reach a specific success rate, saving time in the search process. Besides, the proposed strategy relies on less prior information regarding source strength, which is more in line with the actual scenario. In addition, the proposed approach is adaptable to a broader search region.

High-throughput digital quantification of Alzheimer disease pathology and associated infrastructure in large autopsy studies.

High-throughput digital pathology offers considerable advantages over traditional semiquantitative and manual methods of counting pathology. We used brain tissue from 5 clinical-pathologic cohort studies of aging; the Religious Orders Study, the Rush Memory and Aging Project, the Minority Aging Research Study, the African American Clinical Core, and the Latino Core to (1) develop a workflow management system for digital pathology processes, (2) optimize digital algorithms to quantify Alzheimer disease (AD) pathology, and (3) harmonize data statistically. Data from digital algorithms for the quantification of β-amyloid (Aβ, n = 413) whole slide images and tau-tangles (n = 639) were highly correlated with manual pathology data (r = 0.83 to 0.94). Measures were robust and reproducible across different magnifications and repeated scans. Digital measures for Aβ and tau-tangles across multiple brain regions reproduced established patterns of correlations, even when samples were stratified by clinical diagnosis. Finally, we harmonized newly generated digital measures with historical measures across multiple large autopsy-based studies. We describe a multidisciplinary approach to develop a digital pathology pipeline that reproducibly identifies AD neuropathologies, Aβ load, and tau-tangles. Digital pathology is a powerful tool that can overcome critical challenges associated with traditional microscopy methods.

Confluence simulations based on dynamic channel parameters in the grasslands lacking historical measurements

Technology has greatly promoted ecohydrological model development, but runoff generation and confluence simulations have fallen behind in ecohydrological model development due to limited innovations. To fully understand ecohydrological processes and accurately describe the coupling between ecological and hydrological processes, a distributed ecohydrological model was constructed by integrating multisource information into distributed dynamic process model (DDPM). We mainly describe runoff generation and convergence modules. Based on the improved HBV model and degree-3 h factor method, runoff generation and snow routines were constructed for semiarid grassland basins. In view of meandering and variable steppe river channels and steep hydrological relief characteristics, a confluence module was constructed; the 1-km bend radius equivalent concept was innovatively proposed to unify river channel bend degrees. The daily runoff simulation validation results obtained using two datasets were R2 = 0.947 and 0.932, NSE = 0.945 and 0.905, KGE = 0.971 and 0.680. In the 3-hour flood simulations, the DDPM could better restore small long-distance water flows than the confluence method that did not consider actual river lengths or bend energy losses; the DDPM more accurately simulated the flood peak arrival time than the confluence method that did not consider overflow. The simulated mainstream overflow frequency increased by 0.84/10 years, and significant interaction periods of 10 to 13 years occurred with local precipitation, ecological status, and global climate change. An approximately 2-year lag occurred in the global climate change response. This study helps us further understand and reveal the ecohydrological processes of steppe rivers in semiarid regions.

Applying Historical Measurement Methods to Study the Interaction between Cultural Identity and Historical Development

Abstract This paper aims to explore the correlation and influencing factors between cultural identity and historical development using the historiometric method. Meanwhile, a genetic algorithm is used to collect historical data and quantitative indicators of cultural identity, which is then combined to construct a mathematical model of historical development and cultural identity. The optimization of the model involves using genetic algorithms to find the best match and optimal solution. The historical measures score up to 9.5 due to the significant impact of cultural identity on historical development. This study provides new methods and perspectives for a deeper understanding of the interaction between cultural identity and historical development and is of great significance in promoting the development of historical research and cultural identity research.

Regional to global assessments of ocean transparency dynamics from 1997 to 2019

Water transparency, often quantified using the Secchi disk depth (Zsd), is a key parameter for assessing the water quality and ecological health in marine environments. However, a limited understanding of global Zsd dynamics over the past two decades exists. To assess the variations of ocean transparency over multiple timescales, the Ocean Colour Climate Change Initiative (OC-CCI) datasets are used to retrieve monthly global Zsd products during 1997–2019. On regional to global scales, significant Zsd variations on seasonal, interannual, and long-term timescales are identified. Seasonal and interannual variations of Zsd in most open oceans are found to be closely linked to phytoplankton dynamics and climate change. In the coastal zones, the Zsd variability is attributed to various physical and environmental factors, including seasonal monsoons, river outflow, sediment resuspension, eutrophication, etc. Moreover, the climatological seasonal Zsd variations of 22 typical ocean areas and the associated influencing factors are investigated on regional scales. The long-term trends of Zsd suggest the widespread expansion of oligotrophic waters within the North Pacific, North Atlantic, and South Indian Ocean gyres, indicating ongoing ocean desertification. The OC-CCI Zsd datasets can be used to build a merged centennial time series of Zsd in marine optics by linking to historical measurements. By relying on satellite-derived products, global and time-varying images offer a more comprehensive understanding of regional to global Zsd dynamics.

ATIN: Attention-embedded time-aware imputation networks for production data anomaly detection

Effective identification of anomalous data from production time series in the oilfield affects future analysis and forecasting. Such time series is often characterized by irregular time intervals due to uneven manual sampling, and missing values caused by incomplete measurements. Therefore, the identification task becomes more challenging. In this paper, an Attention-Embedded Time-Aware Imputation Network (ATIN) with two sub-networks is proposed for this task. First, Time-Aware Imputation LSTM (TI-LSTM) is designed for modeling irregular time intervals and incomplete measurements. It decays the long-term memory component as the producing well conditions may be varied during the water cut stage. Second, Attention-Embedding LSTM (ATEM) is designed to improve the effectiveness of anomaly detection. It focuses on the correlation between the last and historical measurements in a given sequence. Comparison experiments with several state-of-the-art methods, including mTAN, GRU-D, T-LSTM, ATTAIN, and BRITS are conducted. Results show that the proposed ATIN performs better in accuracy, F1-score, and area under curve (AUC).

Data-Driven Nonparametric Probabilistic Optimal Power Flow: An Integrated Probabilistic Forecasting and Analysis Methodology

With large-scale integration of renewable energy such as wind power, probabilistic analysis of optimal power flow becomes crucial for the decision-making of power systems. This paper proposes a novel data-driven integrated probabilistic forecasting and analysis (IPFA) methodology for the nonparametric probabilistic optimal power flow (N-POPF), which internalizes the probabilistic forecasting and nonparametric distributional description forms into uncertainty analysis. The proposed IPFA methodology fully utilizes the uncertainty analysis method to guide the model-free nonparametric probabilistic forecasting of wind power, and then conducts the N-POPF analysis effectively based on the uncertainty information contained in historical data. A comprehensive uncertainty evaluation criterion based on point estimate method and differential entropy is proposed to assess both the inherent uncertainty and uncertainty influence of input random variables. Then a model-free multivariate probabilistic forecasting method is established to directly support the solving of N-POPF problems with similar historical measurements. Finally, with deterministic optimal power flow problems corresponding to the selected historical samples, a weighted combination approach for the power flow results is developed to derive the quantiles of the output random variables. Comprehensive experiments on IEEE 24-bus and 118-bus test systems validate the superiority of the proposed IPFA methodology in estimation accuracy and computational efficiency.

Vehicle state and parameter estimation based on improved extend Kalman filter

In order to reduce the influence of historical measurement data errors in the process of vehicle state estimation and improve the accuracy of the vehicle state estimation, a limited memory random weighted extended Kalman filter (LMRWEKF) algorithm is proposed. Firstly, a 3-DOF nonlinear vehicle dynamics model is established. Secondly, the limited memory extended Kalman filter is formed by fusing the limited memory filter and the extended Kalman filter. Then, according to the random weighting theory, the weighting coefficients that obey Dirichlet distribution are introduced to further improve the filtering estimation accuracy. Finally, a virtual test based on the ADAMS/CAR is used for the experimental verification. The results show that the error in the longitudinal velocity and the yaw rate is small, especially in the mean value of estimation error of side slip angle which is different in just 0.015 degrees between the virtual test and the simulation result. And also, the results compared with traditional methods indicate that the proposed LMRWEKF algorithm can solve the problem of vehicle state estimation with the performance of noise fluctuation suppression and higher estimation accuracy. The mean absolute error (MAE) and root mean square error (RMSE) are considered to verify the estimation accuracy of the proposed algorithm. And the comparison results indicate that the estimation accuracy of the LMRWEKF algorithm is significantly higher than those of the EKF and DEKF methods.

Monitoring and evaluation of the water quality of the Lower Neches River, Texas, USA

Increasing bacteria levels in the Lower Neches River caused by Hurricane Harvey has been of a serious concern. This study is to analyze the historical water sampling measurements and real-time water quality data collected with wireless sensors to monitor and evaluate water quality under different hydrological and hydraulic conditions. The statistical and Pearson correlation analysis on historical water samples determines that alkalinity, chloride, hardness, conductivity, and pH are highly correlated, and they decrease with increasing flow rate due to dilution. The flow rate has positive correlations with Escherichia coli, total suspended solids, and turbidity, which demonstrates that runoff is one of the causes of the elevated bacteria and sediment loadings in the river. The correlation between E. coli and turbidity indicates that turbidity greater than 45 nephelometric turbidity units in the Neches River can serve as a proxy for E. coli to indicate the bacterial outbreak. A series of statistical tools and an innovative two-layer data smoothing filter are developed to detect outliers, fill missing values, and filter spikes of the sensor measurements. The correlation analysis on the sensor data illustrates that the elevated sediment/bacteria/algae in the river is either caused by the first flush rain and heavy rain events in December to March or practices of land use and land cover. Therefore, utilizing sensor measurements along with rainfall and discharge data is recommended to monitor and evaluate water quality, then in turn to provide early alerts on water resources management decisions.

Incorporation of realistic intrinsic profiles for the Fe Kα and Fe Kβ emission lines in X-ray reprocessor models

ABSTRACT The Fe Kα fluorescent emission line from neutral matter is widely used as a diagnostic tool in X-ray spectroscopy for many astrophysical X-ray sources. The line properties can potentially be used to constrain the geometry, column density, and kinematics of the material in which the line is formed. The intrinsic width and the shape of the intrinsic line profile has been neglected thus far, because it is much smaller than the instrumental broadening in all space-based X-ray detectors before Hitomi. However, the instrumental broadening of X-ray microcalorimeters is comparable to the intrinsic line width. We have performed new calculations based on Monte Carlo simulations of the MYTORUS X-ray reprocessor spectral-fitting model that utilize parametrizations of historical high-precision laboratory measurements of the intrinsic profiles of the Fe Kα and Fe Kβ lines. A publicly available table is provided that can seamlessly replace the existing Fe Kα and Fe Kβ line spectrum in the MYTORUS model. The new table can also be used in a standalone mode (without the MYTORUS model) in order to derive empirical velocity widths, if the line-emitting matter is Compton thin. Neglecting to account for the intrinsic profiles of the Fe Kα and Fe Kβ lines can result in a significant overestimate of the velocity broadening, if the true velocity broadening is less than ${\sim} 2000 \ \rm km \ s^{-1}$. Residual artefacts may also appear in observed line profiles in data with a high signal-to-noise ratio.

Stratigraphy, sedimentology, and paleoclimatic proxies of the Upper Jurassic Morrison Formation of central Montana: Geology of the Intermountain West

Since the discovery of dinosaurs in the late 19th century, the Upper Jurassic Morrison Formation has received considerable scholarly attention. However, the formation in central Montana needed to be sufficiently investigated. Recent dinosaur excavations from exposed Morrison strata on the southern flank of Spindletop dome near the town of Grass Range, Montana, prompted a review of the formation’s geology, paleobotany, and paleoclimate. A review of historical stratigraphic measurements of the formation in Montana reveals a considerable variance in measured thicknesses. Stratigraphic measurements and regional log data indicate that the formation averages 71 m thick across central Montana. A new regional isopach map of the formation from well-log data illustrates a broad distributive fluvial system that migrated from the southwest toward the northeast. The formation is divided into two informal facies in the study area: lower and upper depositional facies. The lower depositional beds represent the mud-rich distal-most distributive fluvial facies that overlies the stranded muddy tidal flat of the Swift Formation. An increased sandstone:mudstone ratio and small isolated fluvial channel and crevasse splay beds indicate that the upper depositional beds represent the slow progression of the distributive fluvial system. However, a review of the regional field stratigraphy and well-log data did not provide a regional correlatable facies change to warrant subdividing the formation into members.The stratigraphic positions and climatic interpretations for lithologic, faunal, and floral paleoclimatic proxies are specified. The compilation of climate proxy data from central Montana demonstrates that the climate in this region was wetter than in southern parts of the Morrison foreland basin. The climatic proxies signify that the environmental conditions were variable during the Late Jurassic in central Montana, displaying changing temperatures with mesic and xeric intervals of unknown duration.

Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States.

In 1987, the United States Environmental Protection Agency recommended installing a mitigation system when the indoor concentration of radon, a well-known carcinogenic radioactive gas, is at or above 148 Bq/m3. In response, tens of millions of short-term radon measurements have been conducted in residential buildings over the past three decades either for disclosure or to initially evaluate the need for mitigation. These measurements, however, are currently underutilized to assess population radon exposure in epidemiological studies. Based on two relatively small radon surveys, Lawrence Berkeley National Laboratory developed a state-of-the-art national radon model. However, this model only provides coarse and invariant radon estimations, which limits the ability of epidemiological studies to accurately investigate the health effects of radon, particularly the effects of acute exposure. This study involved obtaining over 2.8 million historical short-term radon measurements from independent laboratories. With the use of these measurements, an innovative spatial random forest (SRF) model was developed based on geological, architectural, socioeconomical, and meteorological predictors. The model was used to estimate monthly community-level radon concentrations for ZIP Code Tabulation Areas (ZCTAs) in the northeastern and midwestern regions of the United States from 2001 to 2020. Via cross-validation, we found that our ZCTA-level predictions were highly correlated with observations. The prediction errors declined quickly as the number of radon measurements in a ZCTA increased. When ≥15 measurements existed, the mean absolute error was 24.6 Bq/m3, or 26.5% of the observed concentrations (R2 = 0.70). Our study demonstrates the potential of the large amount of historical short-term radon measurements that have been obtained to accurately estimate longitudinal ZCTA-level radon exposures at unprecedented levels of resolutions and accuracy.

Application of the most frequent value method for ^{39}Ar half-life determination

An evaluation method supported by robust statistical analysis was applied to historical measurements of ^{39}Ar half-life. The method, based on the most frequent value (MFV) approach combined with bootstrap analysis, provides a more robust way to estimate ^{39}Ar half-life, and results in T_{1/2}(MFV) = 268.2^{+3.1}_{-2.9} years with uncertainty corresponding to the 68% confidence level. The uncertainty is a factor of 3 smaller than that of the most precise re-calculated ^{39}Ar half-life measurements by Stoenner et al. and a factor of 2.7 smaller than that of the adopted half-life value in nuclear data sheets. Recently, the specific activity of the beta decay of ^{39}Ar in atmospheric argon was measured in several underground facilities. Applying the MFV method to a specific activity of ^{39}Ar from underground measurements results in SA_{{^{39}text {Ar}}/text {Ar}}(text {MFV}) = 0.966^{+0.010}_{-0.018} , , text {Bq/kg}_{text {atmAr}} with uncertainty corresponding to the 68% confidence level. In this paper the method to determine the half-life of ^{39}Ar using the specific activity of ^{39}Ar in atmospheric argon is also discussed.

Few-Shot Learning for WiFi Fingerprinting Indoor Positioning.

In recent years, deep-learning-based WiFi fingerprinting has been intensively studied as a promising technology for providing accurate indoor location services. However, it still demands a time-consuming and labor-intensive site survey and suffers from the fluctuation of wireless signals. To address these issues, we propose a prototypical network-based positioning system, which explores the power of few-shot learning to establish a robust RSSI-position matching model with limited labels. Our system uses a temporal convolutional network as the encoder to learn an embedding of the individual sample, as well as its quality. Each prototype is a weighted combination of the embedded support samples belonging to its position. Online positioning is performed for an embedded query sample by simply finding the nearest position prototype. To mitigate the space ambiguity caused by signal fluctuation, the Kalman Filter estimates the most likely current RSSI based on the historical measurements and current measurement in the online stage. The extensive experiments demonstrate that the proposed system performs better than the existing deep-learning-based models with fewer labeled samples.

Developing basin-specific emission factors for accurate methane inventory and emission reduction strategies in Zonguldak Coal Basin, Turkey: Implications for enhancing safety and sustainability

This study develops basin-specific emission factors using historical coal production and methane emissions data to enhance methane inventories and emission reduction strategies in Turkey's Zonguldak Coal Basin. The basin is known for high levels of coal mine methane (CMM) emissions, posing a serious safety hazard to miners and affecting mine productivity. However, limited data on in-situ parameters related to Coal Mine Methane (CMM) emissions have been available. The study analyzes historical trends in CMM emissions (1980–2020) from the basin's underground coal mines, revealing declining emissions alongside coal production. Over this period, approximately 2.28 billion cubic meters of CMM were released. Continued historical coal production rates suggest a significant 6.63 MWe power generation potential. The investigation into coal production, average mining depth, and coal seam gas content reveal that CMM emissions primarily depend on coal production and mining depth. Utilizing available methane mitigation technologies could prevent an estimated 55.7 million tons of methane annually (equivalent to around 692,000 tons of CO2e). Importantly, the study calculates basin-specific emission factors (ranging from 7.24 m3/t to 21.66 m3/t) based on historical measurements, providing a more accurate regional representation than the Intergovernmental Panel on Climate Change (IPCC) default values. These factors are vital for identifying high-emission areas and guiding emission reduction strategy development. This study addresses a critical data gap in Turkey's estimation of methane emissions from underground coal mining, particularly in the Zonguldak coal basin. It establishes a foundation for future research on methane emissions from coal mines at various scales, contributing to improved understanding, emissions mitigation, safety enhancement, and sustainable coal mining practices.

Inconsistent definitions of GDP: Implications for estimates of decoupling

Efforts to assess the possibilities for decoupling economic growth from resource use and negative environmental impacts have examined their historical relationship, with varying and inconclusive results. This paper shows that ambiguities in the historical measurement arising from definitional changes to GDP are sufficiently large to affect the results. I review the history of structural revisions to GDP using the example of the United States, and on international comparisons of purchasing power parity, and compare decoupling results using GDP vintages reported between 1994 and 2021 for most countries. Between vintages, 10–15% of countries switch between relative decoupling and recoupling from energy or materials on decadal intervals, and up to as many countries as decouple absolutely in an older vintage stop or newly start absolutely decoupling in the newer vintage. GDP vintages also affect environmental Kuznets curve results on absolute decoupling in Grossman and Krueger's seminal paper and accelerate the International Energy Agency's annual global decline in energy intensity by up to −0.2 percentage points. Inconsistencies in economic measurement introduce ambiguity into historical decoupling evidence and model projections into the future. To advance debate, rigorous reporting and sharing of data vintage for subsequent comparison and replication are urgently needed.

Website blocking in the European Union: Network interference from the perspective of Open Internet

AbstractBy establishing an infrastructure for monitoring and blocking networks in accordance with European Union (EU) law on preventive measures against the spread of information, EU member states have also made it easier to block websites and services and monitor information. While relevant studies have documented Internet censorship in non‐European countries, as well as the use of such infrastructures for political reasons, this study examines network interference practices such as website blocking against the backdrop of an almost complete lack of EU‐related research. Specifically, it performs and demonstrates an analysis for the total of 27 EU countries based on three different sources. They include first, tens of millions of historical network measurements collected in 2020 by Open Observatory of Network Interference volunteers from around the world; second, the publicly available blocking lists used by EU member states; and third, the reports issued by network regulators in each country from May 2020 to April 2021. Our results show that authorities issue multiple types of blocklists. Internet Service Providers limit access to different types and categories of websites and services. Such resources are sometimes blocked for unknown reasons and not included in any of the publicly available blocklists. The study concludes with the hurdles related to network measurements and the nontransparency from regulators regarding specifying website addresses in blocking activities.