Local Measurement Data Research Articles

Filling data gaps in long-term solar UV monitoring by statistical imputation methods

Knowledge of long-term time trends of solar ultraviolet (UV) radiation on ground level is of high scientific interest. For this purpose, precise measurements over a long time are necessary. One of the challenges solar UV monitoring faces is the permanent and gap-free data collection over several decades. Data gaps hamper the formation and comparison of monthly or annual means, and, in the worst case, lead to incorrect conclusions in further data evaluation and trend analysis of UV data. For estimating data to fill gaps in long-term UV data series (daily radiant exposure and highest daily irradiance), we developed three statistical imputation methods: a model-based imputation, considering actual local solar radiation conditions using predictors correlated to the local UV values in an empirical model; an average-based imputation based on a statistical approach of averaging available local UV measurement data without predictors; and a mixture of these two imputation methods. A detailed validation demonstrates the superiority of the model-based imputation method. The combined method can be considered the best one in practice. Furthermore, it has been shown that the model-based imputation method can be used as an useful tool to identify systematic errors at and between calibration steps in long-term erythemal UV data series.

Observing Schrödinger’s cat with artificial intelligence: emergent classicality from information bottleneck

We train a generative language model on the randomized local measurement data collected from Schrödinger’s cat quantum state. We demonstrate that the classical reality emerges in the language model due to the information bottleneck: although our training data contains the full quantum information about Schrödinger’s cat, a weak language model can only learn to capture the classical reality of the cat from the data. We identify the quantum–classical boundary in terms of both the size of the quantum system and the information processing power of the classical intelligent agent, which indicates that a stronger agent can realize more quantum nature in the environmental noise surrounding the quantum system. Our approach opens up a new avenue for using the big data generated on noisy intermediate-scale quantum devices to train generative models for representation learning of quantum operators, which might be a step toward our ultimate goal of creating an artificial intelligence quantum physicist.

Wind Speed Forecasting Using Wavelet Analysis and Recurrent Artificial Neural Networks Based on Local Measurements in Singida Region, Tanzania

High accuracy wind speed forecasting is essential for wind energy harvest and plays a significant role in wind farm management and grid integration. Wind speed is intermittent in nature, which makes the forecasting to be a big challenge. In the present study, three hybrid single-step wind speed forecasting techniques are proposed and tested by local measurement data in Singida region, Tanzania. The three techniques are based on Wavelet Analysis (WA), Back Propagation (BP) optimization algorithm, and Recurrent Neural Network (RNN). They are referred to as WA-RNN, BP-RNN, and WA-BP-RNN. The model results showed that WA-BP-RNN outperforms the other two proposed techniques, with minimum statistical errors of 0.56 m/s (BIAS), 6.89% (MAPE) and 0.53 m/s (RMSE). Furthermore, the WA-BP-RNN technique has shown highest correlation value of 0.95, which indicates that, the strength of a linear association between the observed and forecasted dataset of the wind speed. In addition, the deployment of the BP optimization algorithm in the proposed technique showed improvements of the model results.

Multi-dimensional characteristics of upward bubbly flows in a vertical large-size square channel

Gas-liquid two-phase flows frequently occur in various flow channels in engineering systems with heat and mass transport processes. The channel geometry affects thermo-fluid behavior generally. Extensive research is done on gas-liquid two-phase flows in circular and annular channels with a wealth of local two-phase flow measurement data. Square channels are used in various engineering disciplines, but there has only been limited experimental research on two-phase flows in square channels with local flow parameter measurements. For two-phase flow analyses in square channels, knowledge of the flow characteristics and model validation against some key parameters, such as the drift-flux parameters and the interfacial area concentration, is crucial. An experiment for upward bubbly flows was conducted in a large square channel with an inside cross-section of 0.136 × 0.136 m. A four-sensor optical probe and a hot-film anemometer were used to measure local two-phase flow parameters at 66 points in an octant symmetric triangular cross-section at three axial locations. The measured two-phase flow parameters were the void fraction, axial gas velocity, axial liquid velocity, interfacial area concentration, and bubble Sauter mean diameter. The flow characteristics through flow development were investigated based on the local measurement data. The local measurement data directly determined the drift-flux parameters through their definitions. The distribution parameters determined through experimentation could be reproduced by the existing correlation for large circular pipes indicating that the flow characteristics in large square channels may be similar to those in large circular pipes. Based on existing drift-flux correlations for large circular pipes, the void fractions in the current experiment could be fairly predicted. Furthermore, the interfacial area concentrations could be predicted by existing correlations with reasonable accuracy.

Advanced Instrumentation and Modeling Framework for Two-Phase Flow

To fully realize the advantages of the two-fluid model, accurate prediction of the interfacial area concentration (IAC) is indispensable. Since conventional flow regime–based IAC correlations are not capable of dynamically describing the evolution of interfacial structure, the interfacial area transport equation (IATE) was developed to close the two-fluid model. In the past 30 years, intensive efforts have been made to improve the prediction performance of IATE and extend the experimental database for the IATE benchmark. Recent efforts of the IATE development and benchmark conducted by the Thermal-hydraulics and Reactor Safety Laboratory at Purdue University are reviewed in this paper. This review covers (1) the development of IATE; (2) the experimental database for IATE modeling, including instrumentation development, local measurement data of adiabatic/diabatic two-phase flow, and annular flow characterization; and (3) implementation and evaluation of IATE in one-dimensional/three-dimensional scenarios. Significant progress has been achieved since 2009, and future works required to advance the modeling of IATE are also suggested.

Unsupervised interpretable learning of phases from many-qubit systems

Experimental progress in qubit manufacturing calls for the development of new theoretical tools to analyze quantum data. We show how an unsupervised machine-learning technique can be used to understand short-range entangled many-qubit systems using data of local measurements. The method successfully constructs the phase diagram of a cluster-state model and detects the respective order parameters of its phases, including string order parameters. For the toric code subject to external magnetic fields, the machine identifies the explicit forms of its two stabilizers. Prior information of the underlying Hamiltonian or the quantum states is not needed; instead, the machine outputs their characteristic observables. Our work opens the door for a first-principles application of hybrid algorithms that aim at strong interpretability without supervision.

Rapid Fault Detection with Periodic Update Feature for Transmission Line Parameters in HVDC Grids

Far distant wind farms are integrated nowadays with the help of Voltage Source Converter (VSC)-based HVDC grids. Rapid fault current is very common in VSC-based HVDC grids and, therefore, quick fault detection and fast executing protection methods are critical for the existence of the HVDC grids. This paper proposes a rise time-based fault detection method using local measurement data and Travelling Wave (TW) characteristics of incoming fault TW. Dominating frequency of the primary wavefront is computed by the rise time of incoming fault TW. With the help of auto-updating transmission line parameters and the dominating frequency of incoming TW, the fault location is identified. The performance of the proposed method has been observed in PSCAD Simulation with a 4-Terminal HVDC test grid in multiple scenarios and compared with the existing fault detection methods on multiple levels. The results show that the proposed method is very fast, and accurate and can complete the whole Fault Location process within a few microseconds just after the Inception of the fault.

Accurate detection technology of super long bored cast-in-place pile concrete pouring thickness based on ultrasonic inclined plane reflection measurement

Aiming at the accurate detection problem of concrete pouring thickness, a solution idea of synchronous data acquisition and concrete thickness detection technology based on ultrasonic inclined plane reflection and vertical reflection of ultrasonic full pulse signal is proposed. Taking full advantage of the acoustic response characteristics of different reflection models, three-dimensional modeling and data visualization optimization scheme are formed. By combining the different depths data and local measurement data, the characteristic parameter extraction technology of different media in the borehole is formed, which lays a foundation for solving the key problem of accurate detection of laitance layer thickness. Finally, a comprehensive technical system is formed, and the technical system is applied to practical engineering. The measurement results are basically consistent with the actual situation. The practical application results show that this technology can realize the three-dimensional imaging and quantitative characterization of the medium state in the borehole during concrete pouring.

Data fusion of local and global sensors within a Global Reference System in aeroplane assembly

Abstract Currently, aeroplane assembly is carried out in rigid, aeroplane-specific assembly lines which causes high costs for reconfiguration. One approach to reduce the costs is to use automated lineless assembly systems. To enable automated reconfiguration of assembly processes, continuous availability of factory-wide position data is required. When providing these data, a challenge is the combination of large-volume, “global” measurement sensors and high-precision, “local” measurement sensors to a single easily interpretable indication. The aim of this research work is the fusion of local and global measurement data within a so-called Global Reference System (GRS). For data fusion, sensors are linked to a reference measurement system covering the entire assembly area. The relationships between the coordinate systems of the individual measurement systems are described using homogeneous transformations. Using Kalman filters, the transformed measurement data are fused into a vector containing information about the pose of aeroplane components. Furthermore, a GUM-compliant statement about the measurement uncertainty of the fused sensor data is made by specifying the covariance matrix. The method is validated using a demonstrator covering the essential aspects of the assembly process. The result is a validated procedure for data fusion and for the determination of the combined measurement uncertainty in a GRS.

Cooperative Navigation for Low-Cost UAV Swarm Based on Sigma Point Belief Propagation

As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method divides the sigma point filter into two steps: the first is to integrate local measurement data; the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, resampling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms.

Estimation of the cross-wind speed from turbulent fluctuations of the image of a diffuse target illuminated by a laser beam

The article presents an optical method for assessing the transverse wind, based on the analysis of turbulent distortions of the image of a diffuse target illuminated by a laser beam. The proposed correlation algorithm for processing video images allows one to assess in real time the crosswind speed using one receiving lens when the target is illuminated in the visible or infrared range. An experimental check of the method on the atmospheric path has been carried out. The optical estimates of the integral wind are compared with the data of independent local measurements of six ultrasonic anemometers located along the sensing path.

Intra-Hour Photovoltaic Generation Forecasting Based on Multi-Source Data and Deep Learning Methods

Global issues pertaining to climate change have necessitated the rapid deployment of new energy sources, such as photovoltaic (PV) generation. In smart grids, accurate forecasting is essential to ensure the reliability and economy of the power system. However, PV generation is severely affected by meteorological factors, which hinders accurate forecasting. Various types of data, such as local measurement data, numerical weather prediction, and satellite images, can reflect meteorological dynamics over different time scales. This paper proposes a novel data-driven forecasting framework based on deep learning, which integrates an advanced U-net and an encoder-decoder architecture to cooperatively process multi-source (time series recording and satellite image) data. The adaption of the neural networks to the data sources and the collaborative learning of both spatial and temporal features boost the model accuracy. The experimental results for 50 real-world PV power stations indicate that the proposed framework features a higher accuracy than that of other baseline models.

Turbulence model assessment and heat transfer phenomena inside a rectangular channel under forced and mixed convection

A very high-temperature gas-cooled reactor (VHTR) is an attractive option for hydrogen production due to its high operating temperature. The reactor cavity cooling system (RCCS), a passive safety system of the VHTR, surrounds the reactor vessel with rectangular riser channels and removes decay heat from it. Under emergency operation conditions such as a decrease in the flow rate of the RCCS, the flow condition inside the RCCS riser can become a mixed convection condition accompanying heat transfer deterioration. To design and operate the VHTR with reliable safety, accurate predictions for various convective heat transfer conditions in RCCS riser channels are required. In this study, by comparing the results of an air flow visualization experiment, the predictability of the turbulence models was assessed for the convective heat transfer and complex flow characteristics near the corner region of the rectangular duct confirmed from the experimental study. In this paper, the heat removal rate through the test section of the visualization experiment was quantified under various convective heat transfer conditions, and local temperature measurement data from the experiment were presented to describe the heat transfer deterioration phenomena inside a rectangular riser in turbulent mixed convection. Linear and nonlinear eddy viscosity models were evaluated by comparing the heat transfer coefficient, local temperature, local flow structure including secondary flow, and turbulence quantities obtained from the experiment. Furthermore, improvements to turbulence models for enhancing the prediction of mixed convection heat transfer in rectangular riser ducts were proposed.

Patient-specific simulation of the blood flow in a proximal femoral artery-graft junction

The paper presents a computational procedure developed for patient-specified blood flow simulation in the proximal anastomosis of femoral-popliteal bypass. The calculations are based on a three-dimensional geometric model constructed from a CT angiography scan of the vascular bed in the anastomotic region and boundary conditions obtained by ultrasound Doppler measurements of the velocity in control sections of the bed. A numerical analysis of 3D pulsating flow structure in a particular model of the proximal anastomosis has been carried out. The model includes sections of the common femoral artery (CFA), the graft, the deep femoral artery and the superficial femoral artery. The Reynolds number based on the CFA inner diameter and the inlet bulk velocity is 880. The sizes and location of stagnant zones, areas with low values of the time-averaged wall shear stress modulus and with high values of the oscillatory shear index are determined. It is shown that the results of the prediction of areas with low values of the time-averaged shear stress modulus are in qualitative agreement with the data of local measurements of neointima thickness formed along the graft wall one year after the operation.

Site selection for solar photovoltaic system installation using analytical hierarchy process model in Azerbaijan

The most suitable sites for solar photovoltaic power installations are determined through a comprehensive assessment of the meteorological, economic and environmental criteria of the energy potential areas. The basic criteria for location selection are evaluated using an analytical hierarchy process method based on multi-criteria decision-making technique for large-scale solar photovoltaic projects. The analytical hierarchy process model is also applied to evaluate areas of high solar potential and factors that are primary criteria for determinate the site suitability index modelling. This method considers various conditions, such as production and technological considerations, which aim to maximise the short-term profit from the project and the efficiency of power generation. In the study, a consistency ratio of suitable localities was determined and proper alternatives for the construction of photovoltaic installations were evaluated. In addition to local meteorology and related satellite measurement data, the country’s radiation values also were compared by converting a digital elevation model data using the tool «Area solar radiation» in GIS. As a result of calculating the site suitability index with the ArcGIS weighted overlay tool, it was concluded that 1.17 % (1016.8 km2) of the country are the most suitable sites for the installation of solar PV systems. These areas mainly include Khizi, Gobustan, Hajigabul, Beylagan, Sharur, Babek and Jeyranchol zones. The total number of locations identified accross the country, classified into 3 categories according to their level of suitability, includes 40 sites. Eight of these high suitability sites, all in Nakhchivan Autonomous Republic, contain 11 % (109.2 km2) of the total potential area. The remaining 32 sites, corresponding to areas with medium and low energy potential, cover 28 % (284.6 km2) and 61 % (623 km2), respectively. When these areas are completely covered with PV panels, it will be possible to fully supply the energy demand of the country with solar energy.

Distributed optimal power flow and the multi-agent system for the realization in cyber-physical system

The requirement for coordination of distributed energy resources in the future power system provides an incentive to move from the current high level of centralized control to a more distributed control paradigm. In this paper, an agent-based distributed optimal power flow is proposed to optimize the operation of the power system. The optimal power flow problem is built in the general consensus optimization formulation in matrix based formulation. The agent is then designed to realize the operation of the multi-agent system in the cyber-physical system. Agents have ability of collecting local measurement data, communicating with neighbor agents and implementing alternating direction method of multiplier. Each agent accesses to limited information but can give decision to solve the global problem. The performance is evaluated on the IEEE 9 bus by using a cyber Hardware-in-the-Loop platform with a cluster of hardware agents, a real-time simulator OPAL-RT and a real communication network.

Fusion of UAV and Terrestrial Photogrammetry with Laser Scanning for 3D Reconstruction of Historic Churches in Georgia

In September 2018, photogrammetric images and terrestrial laser scans were carried out as part of a measurement campaign for the three-dimensional recording of several historic churches in Tbilisi (Georgia). The aim was the complete spatial reconstruction with a spatial resolution and accuracy of approx. 1 cm under partly difficult external conditions, which required the use of different measurement techniques. The local measurement data were collected by two laser scanning campaigns (Leica BLK360 and Faro Focus 3D X330), several UAV flights and two terrestrial image sets. The photogrammetric point clouds were calculated with the image-based modelling programs AgiSoft and RealityCapture taking into account the control points from the laser scans. The mean residual errors from the registrations or photogrammetric evaluations are 4–16 mm, depending on the selected software, size and complexity of the monument and environmental conditions. The best completeness and quality of the resulting 3D model was achieved by using laser scan data and images simultaneously. The article presents recent results obtained with RealityCapture and gives a critical analysis of accuracy and modelling quality.

An error analysis and optimization method for combined measurement with binocular vision

The accurate measurement of surfaces of large aviation components is vital for the assessment of manufacturing and assembly quality of such components. To satisfy the measurement requirement of large-size components, most current researches pay more attention to combined measurement methods utilizing different measuring instruments, but the related researches on error analysis and optimization methods are not taken enough attention. This paper proposes a combined laser-assisted measurement method with feature enhancement techniques, and it also develops an error propagation model of the main factors affecting the overall measurement error in detail. Firstly, the surface of a large-size component is measured by the measurement system at multiple stations. Secondly, a control point coordinate system is established as a bridge to unify all local measurement data into the global coordinate system. To improve the overall measurement accuracy, the pixel extraction error as a key factor causing the overall measurement error is analyzed in detail. Next, the error propagation model is established, and some optimization strategies of layout for minimizing measurement error and transformation error are researched. Finally, experiments are carried out to verify the effectiveness of the proposed method. The results show that the measurement error of the proposed method reaches 0.073% and 0.14% with a 1D standard ruler and a flat plate, respectively.

Distributed Quantiles Estimation of Sensor Network Measurements

A quantile is a value below which random draws from a distribution falls with a given probability. In a centralized setting where the cumulative distribution function (CDF) is unknown, empirical CDF can be used to estimate quantiles after data aggregation. In a distributed sensor network, it is challenging to estimate quantiles, as each sensor observes local measurement data with limited storage and transmission power, which makes it difficult to obtain the global ECDF. This paper proposes consensus-based quantile estimation for such networks, even when communication links are corrupted by independent random noise. The state-values are recursively updated with two steps: a local update based on measurement data and current state and averaging updated states with local nodes. The estimated state sequence is shown to be asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Applications on the distributed estimation of trimmed mean; computation of median, maximum, or minimum values; and identification of outliers through simulation are also provided.

Modified regional biogenic VOC emissions with actual ozone stress and integrated land cover information: A case study in Yangtze River Delta, China

The biogenic volatile organic compounds (BVOCs) emissions are influenced by ambient ozone (O3) concentrations and vegetation cover. In most studies, however, the interaction between O3 and plants has not been considered and there are uncertainties in land cover input and emission factors (EFs) in BVOCs emission estimation, particularly at the regional scale. In this study, an O3 exposure-isoprene (ISOP) response function was developed using meta-analysis, and the EFs of ISOP and land cover inputs were updated by integrating local measurement and investigation data in the Yangtze River Delta (YRD) region. Five different cases were developed to explore the impacts of O3 and input variables on the BVOCs emissions using the Model of Emissions of Gases and Aerosols from Nature (MEGAN). The impacts of those variables on O3 simulation were further examined with air quality modeling. We found that the ISOP emissions were restrained in the city cluster along the Yangtze River during the growing season due to their negative feedback to O3 exposure for deciduous broadleaf forests. The estimation of BVOCs emissions strongly depended on EFs, and the global EFs underestimated the ISOP emissions in July by 37%, mostly in southern YRD. Different land cover datasets with various fractions and spatial distributions of plant function types resulted in a variation of 200–400 Gg in ISOP emissions in July across YRD. Air quality modeling indicated that BVOCs contributed 10%, 12%, and 11% to the 1-h mean, the maximum daily 1-h average, and the maximum daily 8-h average O3 concentrations, respectively, for July across the YRD region. Due to the NOx restriction, the spatial distribution of BVOCs emissions was inconsistent with that of their contribution to O3 formation. The O3 simulation was more sensitive to the changed BVOCs emissions in the area with relatively large contribution of BVOCs to O3 formation.