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Accurate electrical phase identification (PI) is essential for efficient grid management, yet existing research predominantly focuses on high-frequency smart meter data, not adequately addressing phase identification with low sampling rates using energy consumption data. This study addresses this gap by proposing a novel method that employs a fully connected neural network (FCNN) to predict household phases from energy consumption data. The research utilizes the IEEE European Low Voltage Testing Feeder dataset, which includes one-minute energy consumption readings for 55 households over a full day. The methodology involves data cleaning, preprocessing, and feature extraction through recursive feature elimination (RFE), along with splitting the data into training and testing sets. To enhance performance, training data are augmented using a generative adversarial network (GAN), achieving an accuracy of 91.81% via 10-fold cross-validation. Additional experiments assess the model's performance across extended sampling intervals of 5, 10, 15, and 30 min. The proposed model demonstrates superior performance compared to existing classification, clustering, and AI methods, highlighting its robustness and adaptability to varying sampling durations and providing valuable insights for improving grid management strategies. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Earthquake Sciences is the property of China Earthquake Administration, Institute of Geophysics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The applicability of a new location program HYPOSAT6 in Shanghai and its adjacent areas is analyzed using 22 catalog earthquakes recorded by Shanghai Seismic Network and some shared seismic stations in adjacent provinces from 2020 to 2022 as test data. The results show that the HYPOSAT6 location program is basically consistent with the catalog results when locating earthquakes in Shanghai and its adjacent areas, but the results are slightly deviated when locating small and micro earthquakes in the marginal sea area. At the same time, when distant locating earthquakes outside the network with unclear P-wave initial phase, the positioning results are poor. In terms of magnitude determination, the HYPOSAT6 location program can improve the problem of small magnitude of near earthquakes in Shanghai seismic network to a certain extent. However, there is also a phenomenon that the magnitude calculated by the location of distant earthquakes outside the network with unclear records is small. In addition, the HYPOSAT6 location program can automatically identify the actual seismic phase type of the first arrival phase position marked by the seismic analyst. This feature is different from the traditional location program, which is helpful to reduce the error caused by the difference of seismic phase identification and improve the positioning accuracy.

ABSTRACT Manganese oxides/oxyhydroxides (MnOx) are based on Mnn+O6 and Mnn+O4 polyhedra arranged such as to form compact, channel or layered structures. In geology, they are precious archives of past redox conditions for palaeoclimatic reconstructions; in material sciences, they are used for a variety of applications, from pigments to environmental remediation and energy storage. Thus, the fast, remote and non‐destructive identification of MnOx is critical in several disciplines. Micro‐Raman spectroscopy is often used for this purpose, although a systematic characterization of their stability under the laser beam is still lacking. In this work, we present our results on the behaviour of the most common MnOx having compact and channel structures when a 532‐nm laser with intensity between ~23 μW/μm2 and ~36.8 mW/μm2 is used. The compact structures of manganosite (NaCl‐like) and hausmannite (spinel‐like) are stable up to ~36.8 mW/μm2. The stability of oxides with channel structures depends on channel size, charge of channel cations and valence state of Mn. Hausmannite is the final degradation product of all MnOx with channel structures, irrespective of the starting phase. Pyrolusite, manganite, hollandite and romanéchite are relatively stable under the laser beam, and the transition to the spinel structure occurs above 2.5 mW/μm2 while the degradation of cryptomelane and todorokite starts ~226 μW/μm2. The analysis of MnOx thus needs very accurate experimental conditions to avoid misleading and incorrect phase identifications. Based on our data, we propose an analytical protocol for a proper characterization of these minerals via Raman spectroscopy. [ABSTRACT FROM AUTHOR]

In recent years, there is a significant interest from the crystallographic and materials science communities to have access to raw diffraction data. The effort in archiving raw data for access by the user community is spearheaded by the International Union of Crystallography (IUCr) Committee on Data. In materials science, where powder diffraction is extensively used, the challenge in archiving raw data is different to that from single crystal data, owing to the very nature of the contributions involved. Powder diffraction (X‐ray or neutron) data consist of contributions from the material under study as well as instrument specific parameters. Having raw powder diffraction data can be essential in cases of analysing materials with poor crystallinity, disorder, micro structure (size/strain) etc. Here, the initiative and progress made by the International Centre for Diffraction Data (ICDDR) in archiving powder X‐ray diffraction raw data in the Powder Diffraction FileTM (PDFR) database is outlined. The upcoming 2025 release of the PDF‐5+ database will have more than 20800 raw powder diffraction patterns that are available for reference. [ABSTRACT FROM AUTHOR]

Nitrocarburising was an effective technique to enhance the surface strength of iron-based alloys and has been widely used in engineering. However, an unfavourable issue was the potential development of spottiness defects on specimen surfaces due to nitrocarburising treatment. This paper focused on the nitrocarburised 40Cr steel shafts with surface spottiness defects in production conditions. Optimisation of the production process was conducted by pre-cleaning the furnace with organic solvents before nitrocarburising treatment to address spottiness defects on the workpiece surfaces. The microhardness values of the specimens were measured, and surface characteristics were analysed using scanning electron microscope, energy-dispersive spectroscopy, and X-ray diffractometer. The surface microhardness results indicated that the specimens with spottiness defects displayed lower levels, which implied that spottiness defects were detrimental to surface microhardness. The main elements distributed on the surface of the specimens were iron, oxygen, nitrogen, and carbon. The phase identification results proved that the nitrocarburised surface layers were mainly composed of Fe3O4, Fe3C, and Fe4N. The spottiness defects were most likely caused by the accumulation of iron oxides, which hindered the diffusion of nitrogen and carbon atoms in the nitrocarburising process. As a result, the Fe3O4 levels on the surface of the optimised-treated specimens decreased, while the Fe3C and Fe4N content increased. This research served as valuable insights for enhancing the visual appearance and properties of alloy steels post nitrocarburising treatment. [ABSTRACT FROM AUTHOR]

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

Microstructure degradation and phase transformations are critical concerns in nickel-based superalloys during thermal exposure. Understanding the phase transformation mechanism requires the detailed mapping of the distribution of each phase at different degradation stages and in various precipitation sizes. However, differentiating between phases in large areas, typically on the scale of millimeters and often relying on scanning electron microscopy (SEM) techniques, has traditionally been a challenging task. In this study, we present a novel and efficient phase mapping method that leverages multiple imaging detectors and modes in SEM. This approach allows for the relatively rapid and explicit differentiation and mapping of the distribution of various phases, including MC, M23C6, γ′, and η phases, as demonstrated in a typical superalloy subjected to aging experiments at 800 °C. [ABSTRACT FROM AUTHOR]

The continuous scanning laser Doppler vibrometry (CSLDV) technique is usually used to evaluate the vibration operational deflection shapes (ODSs) of structures with continuous surfaces. In this paper, an extended CSLDV is demonstrated to measure the non-continuous surface of the bladed disk and to obtain the ODS efficiently. For a bladed disk, the blades are uniformly distributed on a given disk. Although the ODS of each blade can be derived from its response data along the scanning path with CSLDV, the relative vibration direction between different blades cannot be determined from those data. Therefore, it is difficult to reconstruct the complete vibration mode of the whole blade disk. In order to measure the complete ODS of the bladed disk, a method based on ODS frequency response functions (ODS FRFs) has been proposed. While the ODS of each blade is measured by designing the suitable scanning paths in CSLDV, an additional response signal is obtained at a fixed point as the reference signal to identify the relative vibration phase between the blade and the blade of the bladed disk. Finally, a measurement is performed with a simple bladed disk and the results demonstrate the feasibility and effectiveness of the proposed extended CSLDV method. [ABSTRACT FROM AUTHOR]

In unit cell simulations, identification of ordered phases in block copolymers (BCPs) is a tedious and time-consuming task, impeding the advancement of more streamlined and potentially automated research workflows. In this study, we propose a scattering-based automated identification strategy (SAIS) for characterization and identification of ordered phases of BCPs based on their computed scattering patterns. Our approach leverages the scattering theory of perfect crystals to efficiently compute the scattering patterns of periodic morphologies in a unit cell. In the first stage of the SAIS, phases are identified by comparing reflection conditions at a sequence of Miller indices. To confirm or refine the identification results of the first stage, the second stage of the SAIS introduces a tailored residual between the test phase and each of the known candidate phases. Furthermore, our strategy incorporates a variance-like criterion to distinguish background species, enabling its extension to multi-species BCP systems. It has been demonstrated that our strategy achieves exceptional accuracy and robustness while requiring minimal computational resources. Additionally, the approach allows for real-time expansion and improvement to the candidate phase library, facilitating the development of automated research workflows for designing specific ordered structures and discovering new ordered phases in BCPs. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate distribution system models are becoming increasingly critical for grid modernization tasks, and inaccurate phase labels are one type of modeling error that can have broad impacts on analyses using the distribution system models. This work demonstrates a phase identification methodology that leverages advanced metering infrastructure (AMI) data and additional data streams from sensors (relays in this case) placed throughout the medium-voltage sector of distribution system feeders. Intuitive confidence metrics are employed to increase the credibility of the algorithm predictions and reduce the incidence of false-positive predictions. The method is first demonstrated on a synthetic dataset under known conditions for robustness testing with measurement noise, meter bias, and missing data. Then, four utility feeders are tested, and the algorithm’s predictions are proven to be accurate through field validation by the utility. Lastly, the ability of the method to increase the accuracy of simulated voltages using the corrected model compared to actual measured voltages is demonstrated through quasi-static time-series (QSTS) simulations. The proposed methodology is a good candidate for widespread implementation because it is accurate on both the synthetic and utility test cases and is robust to measurement noise and other issues.

Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-to-stand phase using two inertial sensors. First, we defined the sit-to-stand transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum transfer phase, the extension phase, and the stable standing phase based on the preprocessed acceleration and angular velocity data. We then employed a threshold method to recognize the initial sitting and the stable standing phases. Finally, we designed a novel CNN-BiLSTM-Attention algorithm to identify the three transition phases, namely, the flexion momentum phase, the momentum transfer phase, and the extension phase. Fifteen subjects were recruited to perform sit-to-stand transition experiments under a specific paradigm. A combination of the acceleration and angular velocity data features for the sit-to-stand transition phase identification were validated for the model performance improvements. The integration of the CNN, Bi-LSTM, and Attention modules demonstrated the reasonableness of the proposed algorithms. The experimental results showed that the proposed CNN-BiLSTM-Attention algorithm achieved the highest average classification accuracy of 99.5% for all five phases when compared to both traditional machine learning algorithms and deep learning algorithms on our customized dataset (STS-PD). The proposed sit-to-stand phase recognition algorithm could serve as a foundation for the control of wearable exoskeletons and is important for the further development of intelligent wearable exoskeleton rehabilitation robots.

[Introduction] The correct topology information recorded by the power supply department can help the staff monitor the power grid information, analyze the faults, and optimize the operation of the power grid to meet the needs of lean and intelligent management of low-voltage distribution networks. At present, the addition of various new types of electricity-using equipment and users has caused the low-voltage distribution network structure to show a continuous change in characteristics, and the line maintenance cost is greatly increased. [Method] Therefore, the identification method of low-voltage distribution network attribution relationship based on density clustering was proposed. First, the effective voltage data collected by smart meters were extracted to generate a high-dimensional time-series voltage matrix. Then, the t-distributed Stochastic Neighbor Embedding algorithm (t-SNE) and Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) were applied to cluster the voltage data to achieve identification of low-voltage distribution network attribution relationship. Finally, the actual data of a low-voltage distribution network in Sanya City, Hainan Province were analyzed, and the proposed method is compared with other mainstream topology identification methods. [Result] The analysis results show that the proposed method can achieve more than 95% of identification accuracy, which is higher than other mainstream topology identification methods. [Conclusion] The proposed method is effective and advantageous in solving such problems, and can provide reference for practical engineering applications and offer a different research idea in the field of topology identification of low-voltage distribution network.

Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-to-stand phase using two inertial sensors. First, we defined the sit-to-stand transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum transfer phase, the extension phase, and the stable standing phase based on the preprocessed acceleration and angular velocity data. We then employed a threshold method to recognize the initial sitting and the stable standing phases. Finally, we designed a novel CNN-BiLSTM-Attention algorithm to identify the three transition phases, namely, the flexion momentum phase, the momentum transfer phase, and the extension phase. Fifteen subjects were recruited to perform sit-to-stand transition experiments under a specific paradigm. A combination of the acceleration and angular velocity data features for the sit-to-stand transition phase identification were validated for the model performance improvements. The integration of the CNN, Bi-LSTM, and Attention modules demonstrated the reasonableness of the proposed algorithms. The experimental results showed that the proposed CNN-BiLSTM-Attention algorithm achieved the highest average classification accuracy of 99.5% for all five phases when compared to both traditional machine learning algorithms and deep learning algorithms on our customized dataset (STS-PD). The proposed sit-to-stand phase recognition algorithm could serve as a foundation for the control of wearable exoskeletons and is important for the further development of intelligent wearable exoskeleton rehabilitation robots. [ABSTRACT FROM AUTHOR]

Microstructure degradation and phase transformations are critical concerns in nickel-based superalloys during thermal exposure. Understanding the phase transformation mechanism requires the detailed mapping of the distribution of each phase at different degradation stages and in various precipitation sizes. However, differentiating between phases in large areas, typically on the scale of millimeters and often relying on scanning electron microscopy (SEM) techniques, has traditionally been a challenging task. In this study, we present a novel and efficient phase mapping method that leverages multiple imaging detectors and modes in SEM. This approach allows for the relatively rapid and explicit differentiation and mapping of the distribution of various phases, including MC, M23C6, γ′, and η phases, as demonstrated in a typical superalloy subjected to aging experiments at 800 °C.

The present investigation adds new information about the cubic Sb2O3 and orthorhombic Sb2O3 and SbO2 phases to the scientific society. In this work, the Sb2O3 (commercial) powder was ball-milled in a tungsten carbide (WC) jar using WC balls with different diameters at 300 ​rpm for several time intervals (1, 3, 5, 10, 20, and 30 ​min and 1, 2, 5, 10, 20, and 30 ​h) and reported their findings of structural, thermal, optical, and morphology. Interestingly, the ball-milled powder undergoes phase transformation from cubic Sb2O3 into orthorhombic Sb2O3 and SbO2 in a short duration (3–30 ​min) of ball milling. While the 0, 1, and 3 ​min ball milled samples preserve the cubic Sb2O3 structure, the 5 ​min ball milled sample exhibits a nearly single-phase orthorhombic Sb2O3 structure. Mixed phases of orthorhombic Sb2O3 and SbO2 phases are seen for the 10–20 ​min ball milled samples. For a 30 ​min ball milled sample, an orthorhombic SbO2 phase is observed. Furthermore, the 1, 3, 5, 10, 20, and 30 ​h ball milled samples retain the orthorhombic SbO2 phase. Maximum weight loss of 34.4 ​% is noted for the commercial Sb2O3 powder, whereas the 1 and 3 ​min of ball-milled samples reveal the weight loss of 9.3 and 4.7 ​%, respectively. The other ball-milled samples exhibit both weight loss and weight gain in the thermogravimetric analysis (TGA) curves. The Raman features of ball-milled orthorhombic SbO2 are quite different from those of other types of antimony-based oxides. Bar/bundle-shaped and spherical-shaped with agglomerated particles are seen for the commercial Sb2O3 phase and ball-milled (1 and 30 ​h) SbO2 phase samples.

To solve the problem of inaccurate user phase identification, the paper proposes a new algorithm based on improved cloud model and adaptive segmented voltage algorithm. Firstly, the new algorithm uses improved cloud model to calculate the digital features of station area and users' voltage sequences quickly. Secondly, the paper uses the adaptive segmentation voltage algorithm to divide the full voltage sequences into three parts automatically to add local features into phase identification. Finally, the paper calculates cosine similarity between each segmented voltage cloud model to identify users' voltage phase. The analysis based on station data and field verification shows that the new algorithm has not only improved the calculation efficiency by 41% compared with traditional user phase identification algorithm, but also increased the difference in identification results between different phases by 1000 times. In the final result, the accuracy of the new algorithm is 95%. The new algorithm has more obvious differentiation and higher accuracy. The analysis results based on the actual engineering data also prove the feasibility and effectiveness of the new user phase identification algorithm. [ABSTRACT FROM AUTHOR]

The precipitation of struvite from wastewater is a potential alternative for the recovery of nutrients, especially phosphorus, which is an essential macronutrient for agriculture but can be harmful to the environment when improperly disposed of in water bodies. In addition, struvite has elements of great added value for agricultural activity (P, N, and Mg) and is, therefore, considered a sustainable alternative fertilizer. In its formation process, several intervening physicochemical factors may be responsible for the production yield levels. Optimization processes can help to define and direct the factors that truly matter for precipitation. In this context, a sequential design of experiments (DOE) methodology was applied to select and optimize the main struvite precipitation factors in wastewater. Initially, a screening was performed with eight factors with the aid of Plackett–Burman design, and the factors with a real influence on the process were identified. Then, a Doehlert design was used for optimization by applying the response surface methodology and the desirability function. The results were used to identify the optimal points of the pH (10.2), N/P ratio (≥4), and initial phosphorus concentration (183.5 mg/L); these values had a greater effect on phosphorus recovery and the production of struvite, which was confirmed through thermochemical analysis of the decomposition of its structure by differential scanning calorimeter—glass transition temperature (DSC-TG) and phase identification by X-ray diffraction (XRD). The determination of the best synthesis conditions is an enormous contribution to the control of the process because these conditions lead to better yields and higher levels of phosphorus recovery. [ABSTRACT FROM AUTHOR]

X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the "machine learning X-ray diffraction" search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability, interpretability of the results and model generalizability and robustness. Additionally, future research will likely incorporate more domain knowledge and physical constraints, integrate with quantum physical methods, and apply techniques like real-time data analysis and high-throughput screening to accelerate the discovery of tailored novel materials. [ABSTRACT FROM AUTHOR]

Accurate information for consumer phase connectivity in a low-voltage distribution network (LVDN) is critical for the management of line losses and the quality of customer service. The wide application of smart meters provides the data basis for the phase identification of LVDN. However, the measurement errors, poor communication, and data distortion have significant impacts on the accuracy of phase identification. In order to solve this problem, this paper proposes a phase identification method of LVDN based on stepwise regression (SR) method. First, a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN. Second, the SR algorithm is used to identify the consumer phase connectivity. Third, by defining a significance correction factor, the results from the SR algorithm are updated to improve the accuracy of phase identification. Finally, an LVDN test system with 63 consumers is constructed based on the real load. The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.

Phase identification is the problem of determining the phase connection of loads in a power distribution system. In modern times, utility operators will generally accomplish this using smart meter data that requires some form of feature engineering to achieve practical phase identification using data-driven methods. Feature engineering is essential for voltage magnitude data containing noise, seasonality, and trend. We present crucial components of a feature engineering pipeline to perform linear denoising with Singular Value Decomposition, filtering of the denoised data to remove the seasonality and trend, and fuse multiple meter channels. We use the results of the feature engineering to perform phase label correction, a subproblem of phase identification. To evaluate techniques, the authors generate a synthetic dataset from the meshed IEEE 342-Node test feeder circuit with the 2021 Electric Reliability Council of Texas load profiles. Our results show that denoising is quite effective for improving phase identification accuracy in the presence of measurement noise. We present new insight into filtering voltage measurement data to improve accuracy and eliminate the need to determine salient frequencies. We also present the application of a data channel fusion technique that is novel to the phase identification literature. This technique enhances phase identification in cases where both wye and delta-connected loads are present.

Abstract The identification accuracy of low‐voltage distribution consumer–transformer relationship and phase are crucial to three‐phase unbalanced regulation and error correction in consumer–transformer relationships. However, owing to the rapid increase in the number of consumers and the upgrade of the feed lines for low‐voltage distribution systems, the timely update of the consumer‐transformer relationship and phase information of consumers is challenging. This influences the accuracy of the basic information of the power grid. Thus, this study proposes a low‐voltage distribution network consumer–transformer relationship and phase identification method based on anomaly detection and the clustering algorithm. First, the improved fast dynamic time warping distance based on the filter search between voltage sequences is used to measure the similarity between voltage curves. Subsequently, an abnormal consumer detection method based on the local outlier factor is used to identify consumers with mismatched consumer‐transformer relationships by determining the local outlier factor scores of voltage curves. Furthermore, the phase information of normal consumers is identified through clustering by fast search and find of density peaks. Finally, the proposed method is validated using case studies of practical low‐voltage distribution systems in China. The proposed method can effectively improve phase identification accuracy and maintain high adaptability in various data environments.

Specialty and fine chemicals are often manufactured in multipurpose batch production plants. Compared to continuous production, these plants offer increased flexibility at the cost of operational complexity. A recipe defines the sequence and process parameters of different batch phases that are needed to transform raw materials into the desired product. In some plants detailed information about the executed recipe is not always captured by data acquisition systems. Knowledge of these phases is essential for optimizing quality and throughput. State‐of‐the‐art data‐driven machine learning techniques can recognize recurrent patterns in noisy time series data, enabling automatic labeling of batch phases based on widely available sensor data. In this review paper, we provide an overview of several machine learning approaches that can be used in an industrial setting. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quantitative phase analysis (QPA) of slags is complex due to the natural richness of phases and variability in sample composition. The number of phases frequently exceeds 10, with certain slag types (EAF, BOF, blends, stainless) having extreme peak overlap, making identification difficult. Another convolution arises from the variable crystallite sizes of phases found in slag, as well as the mixture of crystalline and amorphous components specific to each slag type. Additionally, polymorphs are common because of the complexity of the steelmaking and slag cooling processes, such as the cation-doped calcium aluminum silicate (Ca3Al2O6, C3A, Z = 24) supercell in LMF slag. References for these doped variants may not exist or in many cases are not known in advance, therefore it is incumbent on the analyzer to be aware of such discrepancies and choose the best available reference. All issues can compound to form a highly intricate QPA and have prevented previous methods of QPA from accurately measuring phase components in slag. QPA was performed via the internal standard method using 8 wt% ZnO as the internal standard and JADE Pro's Whole Pattern Fitting analysis. For each phase, five variables (lattice parameters, preferred orientation, scale factor, temperature factor, and crystallite size) must be accounted for during quantitation, with a specific emphasis on not refining crystallite sizes for iron oxides and trace phases as they are inclined to over-broaden and interact with the background to improve the goodness of fit (R / E value). Preliminary investigations show somewhat reliable results with the use of custom file sets created within PDF-4+ specifically targeted toward slag minerals to further regulate and normalize the analysis process. The objective of this research is to provide a standard protocol for collecting data, as well as to update methodologies and databases for QPA, to the slag community for implementation in a conventional laboratory setting. Currently, Whole Pattern Fitting "Modified" Rietveld block refinement coupled with the addition of a ZnO internal standard gives the most accurate QPA results, though further research is needed to improve upon the complex issues found in this study of the QPA of slags. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The influence of the trace elements Pb, Bi, and Sn on the microstructure and mechanical properties of Sr-modified and grain-refined Al-10.8%Si-2%Cu-based alloys was investigated in both as-cast and heat-treated conditions. The results show that individual addition of Pb has no significant effect on the microstructure of the Al-10.8%Si-2%Cu alloy in both as-cast and heat-treated conditions. The addition of Bi counteracts the modification effect of Sr, leading to a noticeable coarsening of the eutectic Si particles, whereas tin precipitates as β-Sn on pre-existing phases, i.e., Al2Cu and Fe-based intermetallics. For comparison, the study included A356.2 and B319.2 alloys as well. Both Sn and Bi react with Mg with no tendency to react with transition elements. In this case, Sn precipitates in the form of β-Sn (rounded particles) or Mg2Sn (Chinese script). During solution heat treatment, β-Sn particles tend to melt at 232 °C. In contrast, Mg2Sn resists melting (778 °C). With the addition of Sn to the A356.2 alloy, a large proportion of the Si in the Mg2Si phase is replaced by Sn, which thus changes its composition to Mg2Si0.2Sn0.8. The combined addition of Pb and Bi to the modified grain-refined Al-11%Si-2%Cu alloy provides better mechanical properties in the as-cast and artificially aged conditions than is provided by a combined addition of Bi and Sn. Addition of high percentage of Sn of the order of 1% or higher may cause reduction in the alloy strength as a result of precipitation of a significant amount of soft β-Sn phase particles. Long un-modified Si platelets accelerate the crack propagation. [ABSTRACT FROM AUTHOR]

In this work, an AA5183 alloy plate was successfully deposited by low-power cold metal transfer technology. The forming defects, microstructural characteristics, and mechanical properties were investigated. The results show that the number of defects increases gradually along the building direction of the deposited plate. X-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy, electron probe microanalysis, electron backscatter diffraction, and transmission electron microscopy were employed to study the distribution of alloying elements, deposited microstructural characteristics, and the crystal structure of intermetallic compounds in the Al alloy plate. The tensile samples perpendicular to the building direction presented greater tensile strength and superior plasticity compared to those parallel to the deposition direction. The average UTS was 327 ± 0.65 MPa and the average EL was 30.6 ± 2.0%. The UTS of conventionally forged 5083-H32 (Al-Mg4.5) alloy is 324 MPa; the UTS of extruded 5083-H116 (Al-Mg4.5) alloy is 305 MPa. Further, the strength of our prepared plate reaches the value needed for industrial applications of the 5083 Al alloy. The differences in the strength and plasticity of the samples assessed under multiple sampling methods were analyzed based on a synergistic strength–ductility mechanism. [ABSTRACT FROM AUTHOR]

Earthquake detection and phase identification are fundamental and challenging tasks in observational seismology. Deep learning has achieved considerable progress in these two tasks. To overcome the limitations of existing methods, mainly because of the lack of large labeled seismic datasets and the separation of detection and identification tasks, we introduced the continuous wavelet transform (CWT)- convolutional neural networks (CNN) Few-shot learning Earthquake model (CCFE), a deep learning model for simultaneous earthquake detection and phase identification. CCFE can perform few-shot learning with minimal labeled seismic data by utilizing continuous wavelet transform and lightweight convolutional neural networks with fewer layers. We tested our model in the Huoshan area of southern China and found that CCFE outperformed both traditional and published representative deep learning models for detection and identification in this area and that combining detection and identification tasks enhances the performance of each task separately. We found 76% more earthquakes using CCFE than the manual catalog across 15 days of continuous data from the Huoshan region. In regions with low seismicity, CCFE can aid in enhancing earthquake monitoring capacity.

X-ray diffraction (XRD) is a proven, powerful technique for determining the phase composition, structure, and microstructural features of crystalline materials. The use of machine learning (ML) techniques applied to crystalline materials research has increased significantly over the last decade. This review presents a survey of the scientific literature on applications of ML to XRD data analysis. Publications suitable for inclusion in this review were identified using the “machine learning X-ray diffraction” search term, keeping only English-language publications in which ML was employed to analyze XRD data specifically. The selected publications covered a wide range of applications, including XRD classification and phase identification, lattice and quantitative phase analyses, and detection of defects and substituents, as well as microstructural material characterization. Current trends in the field suggest that future efforts pertaining to the application of ML techniques to XRD data analysis will address shortcomings of ML approaches related to data quality and availability, interpretability of the results and model generalizability and robustness. Additionally, future research will likely incorporate more domain knowledge and physical constraints, integrate with quantum physical methods, and apply techniques like real-time data analysis and high-throughput screening to accelerate the discovery of tailored novel materials.

In the present work, the effect of Nd/Sr partial replacement on the crystallographic, morphological, electrical, and superconducting properties of Bi1.8Pb0.35Sr1.9-yNdyCa2.2Cu3Ox materials is studied with the aid of powder X-ray diffraction (XRD), dc electrical resistivity versus temperature (ρ-T), scanning electron microscope (SEM), and electron dispersive X-ray (EDX) measurements. The crystal structure of new produced materials is defined in the tetragonal crystal system with the P4/mmm space group. According to the experimental results observed, the quantity of characteristic Bi-2223 superconducting phase is found to reduce regularly with the enhancement of Nd nanoparticles in the crystal system, confirming that the replacement of Nd impurity at the Sr site causes an increase in the characteristic Bi-2212 superconducting phase. Similarly, the reductions of average crystallite size and c-lattice cell parameter as well as the systematic increment in the a-axis length verify that the partial Nd/Sr substitution deteriorates the Bi-2223 superconducting phase. Moreover, the experimental results display that the offset superconducting transition temperature ( T c offset ) is observed to decrease dramatically with increasing the concentration level of Nd impurity. In this context, the maximum T c offset parameter is noticed to be about 104.3 K for the pure Bi-2223 superconducting ceramic sample. This may be related to the degradation in the strength quality of transcrystalline regions, intergrain boundary couplings, and especially crystallinity quality. Furthermore, the SEM images demonstrate that the increment of Nd foreign impurities in the bulk Bi-2223 superconducting materials damages the flaky layers of platelet-like shape for the superconducting grains. [ABSTRACT FROM AUTHOR]

Because of the crosstalk of neutral line signals in adjacent distribution transformers, the phase attribution relationship between users and transformer is not clear, which brings difficulties to the collection of users' electricity consumption information, file management, and power systems business development. Accordingly, this paper proposes a phase identification method for distribution network users based on dynamic phasor measurement, which can effectively identify all phase meter devices under the same station area. Firstly, considering that the signal on the power line contains complex frequency components and interference, the time‐varying phasor is approximated by the Taylor series. Secondly, by combining the discrete Fourier transform results of two adjacent data windows and calling the offline calculation matrix, accurate phasor measurements can be obtained. Phase shift processing is to obtain the phase angle of the bus line on the low‐voltage side of the transformer and the user at the same reporting time. In addition, the phase angle distortion caused by the impedance of the transmission line under different currents is also considered, so we make phase angle compensation to decrease the influence of the impedance of the line on the voltage phase angle. Simulation results and field experiments show that the proposed method can achieve a recognition success rate of 100% under frequency deviation conditions. Under the dynamic modulation and harmonic interference conditions, the recognition success rate could achieve 94.83% and 96.55%, respectively, it is much higher than that of the voltage correlation analysis method. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Seismic data obtained from seismic stations are the major source of the information used to forecast earthquakes. With the growth in the number of seismic stations, the size of the dataset has also increased. Traditionally, STA/LTA and AIC method have been applied to process seismic data. However, the enormous size of the dataset reduces accuracy and increases the rate of missed detection of the P and S wave phase when using these traditional methods. To tackle these issues, we introduce the novel U-net-Bidirectional Long-Term Memory Deep Network (UBDN) which can automatically and accurately identify the P and S wave phases from seismic data. The U-net based UBDN strongly maintains the U-net's high accuracy in edge detection for extracting seismic phase features. Meanwhile, it also reduces the missed detection rate by applying the Bidirectional Long Short-Term Memory (Bi-LSTM) mode that processes timing signals to establish the relationship between seismic phase features. Experimental results using the Stanford University seismic dataset and data from the 2008 Wenchuan earthquake aftershock confirm that the proposed UBDN method is very accurate and has a lower rate of missed phase detection, outperforming solutions that adapt traditional methods by an order of magnitude in terms of error percentage. [ABSTRACT FROM AUTHOR]

Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of d spacings and related intensities to the measured data. Most automated approaches apply some iterative procedure for the search/match process but fail to be generally reliable yet without the manual validation step of an expert. Recent advances in the field of machine and deep learning have led to the development of algorithms for use with diffraction patterns and are producing promising results in some applications. A limitation, however, is that thousands of training samples are required for the model to achieve a reliable performance and not enough measured samples are available. Accordingly, a framework for the efficient generation of thousands of synthetic XRD scans is presented which considers typical effects in realistic measurements and thus simulates realistic patterns for the training of machine- or deep-learning models. The generated data set can be applied to any machine- or deep-learning structure as training data so that the models learn to analyze measured XRD data based on synthetic diffraction patterns. Consequently, we train a convolutional neural network with the simulated diffraction patterns for application with iron ores or cements compounds and prove robustness against varying unit-cell parameters, preferred orientation and crystallite size in synthetic, as well as measured, XRD scans.

The connection relationship of distribution network topology is of great significance for the maintenance and fault diagnosis of distribution network, and scheduled power outage optimization. At present, the verification of topological documents mainly relies on on-site inspection, which consumes a lot of manpower and material resources and is inefficient. Therefore, an efficient method for topology verification of low-voltage substation areas is required. Given this background, a model for error correction and user access phase identification of low-voltage stations based on multi-dimensional voltage data collected by smart meters is presented in this paper, which can provide a certain reference for topology identification and line troubleshooting of low-voltage substations. First, the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm and the Principal Component Analysis (PCA) performs dimensionality reduction on the original load data to solve the problem of redundancy caused by the high dimension of the original voltage data set. Second, the Local Outlier Factor (LOF) algorithm is used to identify abnormal samples in the voltage data set. Then, the spectral clustering method is used to cluster the dimensionality-reduced load data to realize the phase identification of single-phase users in the low-voltage station area. Finally, the real data of a certain area in Haining, Zhejiang Province of China are used as simulation cases for demonstrating. The results of the case studies show that the model proposed in this paper is feasible and effective.