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Modern power systems exhibit an integration of high proportion of renewable energy sources and high proportion of power electronics， commonly referred to as ‘double high’ power systems. The interplay between harmonics， interharmonics， and power frequency components at varying frequencies within the power system can induce wideband oscillations， posing a substantial threat to its secure and stable operation. In response to the challenge of precisely identifying wideband oscillations in the presence of three-phase imbalance， the paper proposes a wideband oscillation detection technology using the FPGA （field programmable gate array） and positive-sequence instantaneous power method. Since the three-phase positive sequence instantaneous power can effectively filter out the three-phase unbalanced components， the method ensures that the result of wideband oscillation detection is not compromised by such imbalances. The incorporation of FPGA’s parallel computing capability significantly enhances the performance of the wideband oscillation detection method. Test results validate the accuracy of this technology in detecting oscillation components during wideband oscillations， effectively addressing misjudgment and insufficient parallel computing capacity in existing technologies.

With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth's crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and storage capabilities for small- and medium-sized teams compared to popular InSAR processing approaches based on cloud computing or supercomputing platforms, and it can be easily deployed on clusters thanks to the integration of various existing computing components. Finally, to demonstrate and evaluate the efficiency and accuracy of P2SBAS, we conducted comparative experiments on a set of 32 TerraSAR images of Beijing, China. The results demonstrate that P2SBAS can fully utilize various computing nodes to improve InSAR processing and can be applied well in large-scale LuTan-1 InSAR applications in the future. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang 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.)

With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth’s crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and storage capabilities for small- and medium-sized teams compared to popular InSAR processing approaches based on cloud computing or supercomputing platforms, and it can be easily deployed on clusters thanks to the integration of various existing computing components. Finally, to demonstrate and evaluate the efficiency and accuracy of P2SBAS, we conducted comparative experiments on a set of 32 TerraSAR images of Beijing, China. The results demonstrate that P2SBAS can fully utilize various computing nodes to improve InSAR processing and can be applied well in large-scale LuTan-1 InSAR applications in the future.

Compressive sensing (CS) method is widely used in synthetic aperture radar (SAR) imaging. However, it has problems such as long computing time and insufficient scalability of computing power. In order to solve the above problems, a distributed imaging method for SAR compressed sensing imaging based on Apache Spark was proposed. First, the compressed data into row vectors according was divided to each row along the azimuth direction, and then performs distance was distributed parallel reconstruction through the Spark distributed computing platform. The result matrix of range imaging was divided into column vectors according to each column, and then the azimuth distributed parallel reconstruction was performed through the Spark distributed computing platform to complete the SAR compressed sensing imaging. By taking advantage of Spark􀆳s memory-based distributed parallel computing, the calculation speed is 1. 9 times that of SAR compressed sensing and 1. 4 times that of MapReduce’s SAR compressed sensing (MR-CS) method. It can be seen that the method in this paper can realize the acceleration of SAR compressed sensing imaging. [ABSTRACT FROM AUTHOR]

The sequential method is easy to integrate with existing large-scale alternating current (AC) power flow solvers and is therefore a common approach for solving the power flow of AC/direct current (DC) hybrid systems. In this paper, a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is developed. The proposed approach is capable of speeding up the entire computation process without compromising the accuracy of result. First, the AC/DC network is intuitively represented by a graph and stored in a graph database (GDB) to expedite data processing. Considering the interconnection of AC grids via high-voltage direct current (HVDC) links, the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow analysis. For each area, the fast-decoupled power flow (FDPF) is employed with node-based parallel computing (NPC) and hierarchical parallel computing (HPC) to quickly identify system states. Furthermore, to reduce the alternate iterations in the sequential method, a new decoupled approach is utilized to achieve a good initial estimate for the New-ton-Raphson method. With the improved initial estimate, the sequential method can converge in fewer iterations. Consequently, the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power system. The performance is verified on standard IEEE 300-bus system, extended large-scale systems, and a practical 11119-bus system in China.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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.)

Abstract Background An important task in the interpretation of sequencing data is to highlight pathogenic genes (or detrimental variants) in the field of Mendelian diseases. It is still challenging despite the recent rapid development of genomics and bioinformatics. A typical interpretation workflow includes annotation, filtration, manual inspection and literature review. Those steps are time-consuming and error-prone in the absence of systematic support. Therefore, we developed GTX.Digest.VCF, an online DNA sequencing interpretation system, which prioritizes genes and variants for novel disease-gene relation discovery and integrates text mining results to provide literature evidence for the discovery. Its phenotype-driven ranking and biological data mining approach significantly speed up the whole interpretation process. Results The GTX.Digest.VCF system is freely available as a web portal at http://vcf.gtxlab.com for academic research. Evaluation on the DDD project dataset demonstrates an accuracy of 77% (235 out of 305 cases) for top-50 genes and an accuracy of 41.6% (127 out of 305 cases) for top-5 genes. Conclusions GTX.Digest.VCF provides an intelligent web portal for genomics data interpretation via the integration of bioinformatics tools, distributed parallel computing, biomedical text mining. It can facilitate the application of genomic analytics in clinical research and practices.

The physiological signal acquisition and analyzing are important for intelligent health services, human–computer interaction and other applications. Due to the computing power limitation of terminal devices, many analyzing methods of physiological signals are in offline mode. However, in many applications, physiological signal should be analyzed in real time. To overcome this problem, a real-time physiological signal acquisition and analysis method based on fractional calculus and stream computing is proposed. Mobile terminals read the physiological data from sensors and upload them to the stream computing platform. A fractal index is used to estimate the physiological status. Based on the stream computing platform, this index is calculated by distributed parallel computing. The experiment results show this method can distinguish the heart health status and reflect driver mental status to some extent. [ABSTRACT FROM AUTHOR]

• Deployed VMS with weak BCs on massively parallel octree-based adaptive meshes. • Developed octree parallelization, efficient matrix assembly, and rapid in-out tests. • Demonstrated the ability to capture drag crisis without any wall treatment. • Demonstrated scalability of the framework up to O (32 K) processors. • Deployed for industrial scale study of the platooning effect of semi-trucks. We present a variant of the immersed boundary method integrated with octree meshes for highly efficient and accurate Large Eddy Simulations (LES) of flows around complex geometries. We demonstrate the scalability of the proposed method up to O (32 K) processors. This is achieved by (a) rapid in-out tests; (b) adaptive quadrature for an accurate evaluation of forces; (c) tensorized evaluation during matrix assembly. We showcase this method on two non-trivial applications: accurately computing the drag coefficient of a sphere across Reynolds numbers 1 − 10 6 encompassing the drag crisis regime; simulating flow features across a semi-truck for investigating the effect of platooning on efficiency. [ABSTRACT FROM AUTHOR]

Fine flow and heat transfer characteristics are critical to nuclear reactor safety. However, a comprehensive CFD calculation of the entire reactor core requires substantial computing resources, restricting the research domain's scope. To address this issue, we propose a Distributed Parallel (DP) computing scheme, tailored specifically for reactor cores using plate-type fuel assemblies. This study focuses on the thermal-hydraulic calculation of the large domain of a reactor core utilizing plate-type fuel assembly, based on the Computational Fluid Dynamics (CFD) method. This innovative solution enables the completion of extensive domain CFD calculations using modestly equipped personal workstations (8 cores, 128G RAM), which traditionally would require supercomputing platforms. To verify the distributed-parallel computing technique, it is successfully applied to all standard, full-length fuel assemblies in the China Advanced Research Reactor (CARR). In the result analysis, it was observed that detailed results could be obtained with reduced computational resources, demonstrating a significant breakthrough for CFD engineering analysis. [ABSTRACT FROM AUTHOR]

Aiming at the problems of real-time defect detection and defect classification requirements for polishing shaft surface defects in production practice, the surface defect detection system for polishing shafts, defect image acquisition, image features, traditional detection methods, etc. were studied, and the treatment methods of shaft surface defect detection were summarized. Two detection systems based on parallel computing were proposed, i.e. cloud-based Spark multi-machine parallel distributed computer cluster detection system and singlemachine multi-core CUDA parallel processing detection system. Through the analysis of Spark architecture multi-machine distributed parallel computer theory, and analysis of the principle of mathematical parallelization combined with CUDA single-machine multi-core parallel operation theory, the experiments were conducted. The results indicate that the single-machine multi-core parallel processing detection system based on CUDA is suitable for real- time detection, and the multi-machine distributed parallel detection system based on Spark is not suitable for real-time detection systems, but is suitable for big data processing systems. [ABSTRACT FROM AUTHOR]

With the increasing volume of remote sensing data, the efficient online data management and service system has become an essential infrastructure for research on massive remote sensing data application. At present, different remote sensing data storage and service application schemes have brought difficulties to the system implementation and application due to the lack of independent intermediate framework design. There is an urgent need for a remote sensing data online distributed management and service frame⁃ work ⁃ independent of data storage and service application, and to reconstruct the implementation mode of data management and service, reduce the difficulty of massive remote sensing image online management and service. Against this background, we design and propose a platform ⁃ independent online distributed management and service framework for remote sensing （RS ⁃ODMS） by abstracting the remote sensing data management and computing model. This framework constructs a remote sensing data distributed hybrid storage model （DHS） and a remote sensing image data distributed parallel computing model （DPC） to improve the remote sensing data management ability and service efficiency. Experimental results show that the prototype system based on RS ⁃ODMS can support an expansion capability of multiple distributed storage backends on remote sensing image management. And it is endowed with high efficiency, stable service ability and extensive applicability, which can provide reference for the online management and service application research of massive remote sensing data. [ABSTRACT FROM AUTHOR]

Clinical magnetic resonance imaging (MRI) scanning requires fast image reconstruction. At present, most image reconstruction algorithms are implemented with central processing unit (CPU)-based processing. However, with massive amount of raw data and increasing complexity of the algorithm, CPU-based processing can be inefficient due to lack of computation parallelism and slow computation speed. For this reason, some advanced algorithms are difficult to implement in clinical practice. In this paper, a new image reconstruction algorithm was designed and implemented, which combined multiple cores CPU with multi-graphic processing unit (GPU) to reconstruct magnetic resonance images by distributed parallel computing on the Gadgetron software platform. Taking the Stack of Star (SOS) reconstruction as an example, it was demonstrated that the proposed method improved the reconstruction speed significantly without the requirement of expensive hardware.

Background: An important task in the interpretation of sequencing data is to highlight pathogenic genes (or detrimental variants) in the field of Mendelian diseases. It is still challenging despite the recent rapid development of genomics and bioinformatics. A typical interpretation workflow includes annotation, filtration, manual inspection and literature review. Those steps are time-consuming and error-prone in the absence of systematic support. Therefore, we developed GTX.Digest.VCF, an online DNA sequencing interpretation system, which prioritizes genes and variants for novel disease-gene relation discovery and integrates text mining results to provide literature evidence for the discovery. Its phenotype-driven ranking and biological data mining approach significantly speed up the whole interpretation process. Results: The GTX.Digest.VCF system is freely available as a web portal at http://vcf.gtxlab.com for academic research. Evaluation on the DDD project dataset demonstrates an accuracy of 77% (235 out of 305 cases) for top-50 genes and an accuracy of 41.6% (127 out of 305 cases) for top-5 genes. Conclusions: GTX.Digest.VCF provides an intelligent web portal for genomics data interpretation via the integration of bioinformatics tools, distributed parallel computing, biomedical text mining. It can facilitate the application of genomic analytics in clinical research and practices. [ABSTRACT FROM AUTHOR]

Message Passing ( MP) and Distributed Shared Memory (DSM) are the two most common approaches to distributed parallel computing. MP is difficult to use, whereas DSM is not scalable. Performance scalability and ease of programming can be achieved at the same time by using navigational programming (NavP). This approach combines the advantages of MP and DSM, and it balances convenience and flexibility. Similar to MP, NavP suggests to its programmers the principle of pivot-computes and hence is efficient and scalable. Like DSM, NavP supports incremental parallelization and shared variable programming and is therefore easy to use. The implementation and performance analysis of real-world algorithms, namely parallel Jacobi iteration and parallel Cholesky factorization, presented in this paper supports the claim that the NavP approach is better suited for general-purpose parallel distributed programming than either MP or DSM.

Three‐dimensional (3D) images have become increasingly popular in practice. They are commonly used in medical imaging applications. In such applications, it is often critical to compare two 3D images, or monitor a sequence of 3D images. To make the image comparison or image monitoring valid, the related 3D images should be geometrically aligned first, which is called image registration (IR). However, IR for 3D images would take much computing time, especially when a flexible method is considered, which does not impose any parametric form on the underlying geometric transformation. Here, the authors explore a fast‐computing environment for 3D IR based on the distributed parallel computing. The selected 3D IR method is based on the Taylor's expansion and 3D local kernel smoothing. It is flexible, but involves much computation. The authors demonstrate that this fast‐computing environment can effectively handle the computing problem while keeping the good properties of the 3D IR method. The method discussed here is therefore useful for applications involving big data. [ABSTRACT FROM AUTHOR]

For studying the cloud computing management platform of human resources, according to the plight of group enterprise human resource management research, a set of human resource management information system based on cloud computing technology is designed, which is used in various fields of group enterprise human resources management, such as personnel management, organization management, salary management, attendance management and so on. The results show that the group level human resource management information system based on cloud computing can help enterprises to complete the task of human resource management efficiently, which reduces the communication costs and improves the management efficiency. In addition, it achieves the group enterprise human resources data search of MapReduce engine under the framework of Hadoop, report data check and so on massive data computing task. In summary, it provides a cloud computing based group human resource management information system achieved based on JAVA programming language, and provides PC terminal, mobile terminal, flat terminal and other multi terminal operating platforms, which has high application value. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department 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 block incomplete-Cholesky (IC) preconditioner is often used for parallel preconditioner in finite-element method (FEM) supported by distributed computing. When many thin elements are included in an analyzed mesh such as magnetic shielding, the convergence characteristic of preconditioned conjugate gradient method would be deteriorated. At that time, IC preconditioner with fill-in makes the convergence characteristic of linear solver much better. This paper demonstrates the validity of block IC with fill-in on linear systems arising in FEM with many flat elements. First, the ability to improve the convergence property using the fill-in strategy is verified by the simulation of magnetic shielding. Next, the parallelization performance of block IC with fill-in is illustrated by using finite-element mesh of laminated core model and induction heating cooker. [ABSTRACT FROM AUTHOR]

Appropriate structural analysis and optimization methods are of great significance for the conceptual design of automotive body-in-white (BIW) structure. This paper simplifies BIW structure as a spatial semi-rigid framed structure to provide early-stage predictions. Then a novel exact transfer stiffness matrix method (TSMM) is proposed for both static and dynamic analyses of three-dimensional semi-rigid framed structures. The matrix storage and equation solution techniques for large-scale engineering structures are also considered. Additionally, a size optimization mathematical model for BIW conceptual structure is formulated and solved by genetic algorithm (GA). Afterwards, to promote the conceptual design of BIW structure, an object-oriented MATLAB toolbox, based on TSMM, is developed. The Unified Modeling Language (UML) and strategy design pattern are employed to facilitate the development of the toolbox. Distributed parallel computing technique is adopted to speed up the former sequential optimization algorithm with simple modifications. Lastly, the validity of this easy-used toolbox is demonstrated by a benchmarking auto-body. [ABSTRACT FROM AUTHOR]

Agent-Based Models (ABMs) assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The Multi-Agent Spatial Simulation (MASS) library provides a framework for simulating ABMs at fine granularity, where agents and spatial data are shared application resources in a distributed memory. We introduce a novel approach to capture ABM provenance in a distributed memory, called ProvMASS. We evaluate our technique with traditional data provenance queries and performance measures. Our results indicate that a configurable approach can capture provenance that explains coordination of distributed shared resources, simulation logic, and agent behavior while limiting performance overhead. We also show the ability to support practical analyses (e.g., agent tracking) and storage requirements for different capture configurations.

The Environmental Scenario Generator (ESG) is a network distributed software system designed to allow a user to interact with archives of environmental data for the purpose of scenario extraction, data analysis and integration with existing models that require environmental input. The ESG uses fuzzy-logic based search tools to allow a user to look for specific environmental scenarios in vast archives by specifying the search in human linguistic terms. For example, the user can specify a scenario such as a "cloud free week" or "high winds and low pressure" and then search relevant archives available across the network to get a list of matching events. The ESG hooks to existing archives of data by providing a simple communication framework and an efficient data model for exchanging data. Once data has been delivered by the distributed archives in the ESG data model, it can easily be accessed by the visualization, integration and analysis components to meet specific user requests. The ESG implementation provides a framework which can be taken as a pattern applicable to other distributed archive systems.

• Load forecasting model based on MapReduce parallel programming framework. • The proposed data cleaning technology can effectively preprocess big data. • A more rapid and accurate load forecasting model under the background of big data. • The model is applied to the actual data set of two distribution networks. Short-term Load Forecasting (STLF) is the basis of smart distribution network system operation, planning, and dispatching. The traditional linear regression prediction method has the problems of slow prediction speed and low prediction accuracy. In order to solve the problem, an improved regression model based on mini-batch stochastic gradient descent is proposed in this paper. Combined with the big data analysis and processing platform, the collected data is conformed, and the parallel computing model Map-Reduce is used to parallelize mini-batch stochastic gradient descent algorithm for improving the processing ability of mini-batch stochastic gradient descent algorithm in big data load forecasting, and shorten load forecasting time. Meanwhile, in order to clean up the duplicated data and bad data generated by the smart meter and sensor before calculation, an adaptive sorted neighborhood method is proposed to detect the repeatedly recorded data, and the K-means clustering method is used to eliminate the noise data.The experimental results show that the parallelized mini-batch stochastic gradient descent algorithm is much faster than the traditional regression analysis algorithm when the data volume is large. The average absolute percentage error of the load forecasting model for Belgium and a transformer station in Baiyin city of Gansu Province in China is 1.902% and 2.058% respectively, which satisfies the requirements of load forecasting. [ABSTRACT FROM AUTHOR]

Aiming at the problem of mass data storage and processing, a cloud storage system based on Hadoop was designed. The system established a cloud storage model on the basis of studying the two core technologies of Hadoop including Hadoop distributed file system and programming model MapReduce. The system optimized the computer storage mode, and increased the efficiency of the network bandwidth and disk in the cluster. At the same time, the MapReduce programming framework design made the system have higher performance of computing ability. Through testing and analysis of Hadoop platform using Linux cluster technology, the results showed that the system had the characteristics of low cost, high efficiency, easy extension, safty and reliability, and could meet the requirement of the mass data processing stably and efficiently. [ABSTRACT FROM AUTHOR]

Weld lines not only detract from an injection-molded part's surface quality, but also significantly reduce its mechanical strength. It is not always easy to completely eliminate weld lines by simply adjusting the relevant injection mold design or the molding conditions. One solution is to prevent the weld lines from forming in regions that are structurally or aesthetically sensitive. The influence of weld lines on the quality of injection-molded parts cannot be overlooked and weld lines should be regarded as an important design constraint, especially for parts with aesthetic concerns. Since precisely predicting the number of weld lines and their positions and lengths is difficult without executing simulation routines, especially when part geometry is considered a design variable, this study adopts an enhanced genetic algorithm, referred to as distributed multi-population genetic algorithm (DMPGA), combining an optimization algorithm and commercial MoldFlow software with a dominance-based constraint-handling technique and a master-slave distributed architecture. MoldFlow obtains relevant data regarding warpage and weld lines and evaluates the corresponding designs. The dominance-based constraint-handling technique handles the weld line design constraint without needing additional penalty factors. Finally, the master-slave distributed architecture reduces the formidable computational time required for injection molding optimization. To illustrate the high viability of DMPGA, this study provides an outer frame of a digital photo frame as an optimization example. The results of this study show that DMPGA cannot only effectively decrease maximum part warpage without violating the weld line constraint, but also conquer hurdles attributed to constraint handling and computational demand. [ABSTRACT FROM AUTHOR]

Abstract: Advanced digital photogrammetry and remote sensing technology produces large terrain datasets (LTD). How to process and use these LTD has become a big challenge for GIS users. Extracting drainage networks, which are basic for hydrological applications, from LTD is one of the typical applications of digital terrain analysis (DTA) in geographical information applications. Existing serial drainage algorithms cannot deal with large data volumes in a timely fashion, and few GIS platforms can process LTD beyond the GB size. High throughput computing (HTC), a distributed parallel computing mode, is proposed to improve the efficiency of drainage networks extraction from LTD. Drainage network extraction using HTC involves two key issues: (1) how to decompose the large DEM datasets into independent computing units and (2) how to merge the separate outputs into a final result. A new decomposition method is presented in which the large datasets are partitioned into independent computing units using natural watershed boundaries instead of using regular 1-dimensional (strip-wise) and 2-dimensional (block-wise) decomposition. Because the distribution of drainage networks is strongly related to watershed boundaries, the new decomposition method is more effective and natural. The method to extract natural watershed boundaries was improved by using multi-scale DEMs instead of single-scale DEMs. A HTC environment is employed to test the proposed methods with real datasets. [Copyright &y& Elsevier]

• Uncertainty of raw FRFs are modelled by multivariate complex-valued Gaussian ratio distribution. • Likelihood function is formulated by embedding the model parameters into the probabilistic model of FRFs. • Vectorization processing is proposed to avoid time-consuming loop operations involved in likelihood function. • Distributed parallel computing scheme is utilized to allow TMCMC stochastic simulation. • Stochastic model updating can achieve much better results than deterministic approaches. This study was devoted to investigating stochastic model updating in a Bayesian inference framework based on a frequency response function (FRF) vector without any post-processing such as smoothing and windowing. The statistics of raw FRFs were inferred with a multivariate complex-valued Gaussian ratio distribution. The likelihood function was formulated by embedding the theoretical FRFs that contained the model parameters to be updated in the class of the probability model of the raw FRFs. The Transitional Markov chain Monte Carlo (TMCMC) used to sample the posterior probability density function implies considerable computational toll because of the large batch of repetitive analyses of the forward model and the increasing expense of the likelihood function calculations with large-scale loop operations. The vectorized formula was derived analytically to avoid time-consuming loop operations involved in the likelihood function evaluation. Furthermore, a distributed parallel computing scheme was developed to allow the TMCMC stochastic simulation to run across multiple CPU cores on multiple computers in a network. The case studies demonstrated that the fast-computational scheme could exploit the availability of high-performance computing facilities to drastically reduce the time-to-solution. Finally, parametric analysis was utilized to illustrate the uncertainty propagation properties of the model parameters with the variations of the noise level, sampling time, and frequency bandwidth. [ABSTRACT FROM AUTHOR]

• A fully distributed and parallel method to solve the UC problem of three-level interactive of TG + ADN + MG is proposed. The proposed method can be implemented in master-slave distributed and parallel schema. • An imprecise Dirichlet model (IDM)-based non-parametric estimation method is presented to better model and tackle the uncertainties of the wind power and load. • To explore the synergistic ability of the TG + ADN + MG coping with uncertainties, the primary and secondary frequency regulation are taken as the means for the TG to cope with uncertainties. Distributed energy resource (DER) including wind power, solar energy and energy storage system (ESS) are connected to the active distribution network (ADN) in various combination ways, which makes the distribution network have interaction. As a bridge connecting the transmission grid (TG) and micro grid (MG), ADN breaks the traditional operation pattern of TG + ADN + MG. Considering the physical connections and shared information among TG, ADN and MG, this paper proposes a decentralized and parallel analytical target cascading (ATC) algorithm for interactive unit commitment (UC) implementation in regional power systems. To explore the synergistic ability of the TG + ADN + MG coping with uncertainties of DER, i.e., wind power, the primary and secondary frequency regulation of TG are implemented to cope with uncertainties. Furthermore, the distributional uncertainty of wind power is well modeled by data driven, which is proposed in our previous work (Zhang et al., 2019) [1]. Both the startup/shutdown variables of the thermal units and the variables in TG + ADN + MG are integrated into the multi-level interactive UC model to optimize simultaneously, thus realizing the optimal goal of the whole network, resources complementary and optimal allocation of power system. An improved 6-bus system is used to test the proposed model, the numerical results show that the proposed decentralized algorithm is a fully parallelized procedure. And it also demonstrates the parallel implementation significantly enhances computations efficiency of the ATC algorithm. [ABSTRACT FROM AUTHOR]

Agent-Based Models (ABMs) assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The Multi-Agent Spatial Simulation (MASS) library provides a framework for simulating ABMs at fine granularity, where agents and spatial data are shared application resources in a distributed memory. We introduce a novel approach to capture ABM provenance in a distributed memory, called ProvMASS. We evaluate our technique with traditional data provenance queries and performance measures. Our results indicate that a configurable approach can capture provenance that explains coordination of distributed shared resources, simulation logic, and agent behavior while limiting performance overhead. We also show the ability to support practical analyses (e.g., agent tracking) and storage requirements for different capture configurations. [ABSTRACT FROM AUTHOR]