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Distribution grid simulation is important method to related department for the distribution grid planning, design, testing, operation and experiment. It is common method of the distribution grid protection, measurement and control equipment function test and distribution grid automation system function test. Distribution grid simulation can be divided into real-time simulation and non-real time simulation...
This paper proposes modelling ranked load curves using algebraic polynomials. Starting from the assumption of some characteristic parameters of load profiles: maximum power, minimum power and the energy consumption in a time period, characteristic coefficients of different types of algebraic polynomials such as: linear, parabolic and third-order are obtained. The modelling of real load profile has...
Mesoscopic Traffic Simulation is an important tool in traffic analysis and traffic management support. The balance between traffic modeling details and performance has made Mesoscopic Traffic Simulation one of the key solutions for traffic controllers and policy makers. Mesoscopic traffic simulators offer acceptable speed in simulating normal traffic. However, when traffic prediction and optimization...
Model slicers are tools which provide two services: (a) finding parts of interest in a model and (b) displaying these parts somehow or extract these parts as a new, autonomous model, which is referred to as slice or sub-model. This paper focuses on the creation of editable slices, which can be processed by model editors, analysis tools, model management tools etc. Slices are useful if, e.g., only...
This paper describes a dynamical nonlinear data center model. It is a principle-based continuous time model, aimed at testing and evaluating control and optimization algorithms. The model is based on a module in data center used by RISE, Research Institute of Sweden. The data center modelled is a small scale slab floor data center. It contains four computer-room-air-handling units, two rows of five...
Machine learning algorithms are designed to resolve unknown behaviours by extracting commonalities over massive datasets. Unfortunately, learning such global behaviours can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems and Internet of Things applications. Instead, to make smart decisions,...
The powerful parallel computing capability of modern GPU (Graphics Processing Unit) processors has attracted increasing attentions of researchers and engineers who had conducted a large number of GPU-based acceleration research projects. However, current single GPU based solutions are still incapable of fulfilling the real-time computational requirements from the latest big data applications. Thus,...
In order to know the operation condition before working, and reduce the risk of serious accidents of coal production industry. In this paper, by analyzing the characteristics of the environment of underground mine, using virtual reality technology, combined with the 3d Max modeling techniques and VC++ calling the OpenGL graphics interface techniques, the models are well optimized based on progressive...
This paper deals with the problem of identifying the mathematical model of a dry-clutch transmission system, from input-output data experimentally collected on a real vehicle. The proposed identification procedure is based on a set-membership identification approach, where a-priori information on the structure of the physical model are taken into account in the selection of the model class. Parameter...
The present paper is part of a larger project which aims to monitor and mitigate dust on the PV systems in the city of Arequipa. Its objective is to present a simplified model for simulating PV panels for the design and project maximum power point tracker - MPPT controllers. With an experimental setup, signals of voltage, current, power, irradiance and temperature were acquired. A SEPIC converter...
Modeling low voltage consumption and generation individually is becoming an essential task for DSOs to plan infrastructure investments more efficiently and manage the network more actively in the effort of making grids smarter. In this paper, three different approaches for modeling such individuals is exposed. A quasi-sequential approach which holds the exact distributions of consumption and generation...
In the paper we investigate the performance of parallel deep neural network training with parameter averaging for acoustic modeling in Kaldi, a popular automatic speech recognition toolkit. We describe experiments based on training a recurrent neural network with 4 layers of 800 LSTM hidden states on a 100-hour corpora of annotated Polish speech data. We propose a MPI-based modification of the training...
Following the Service-Oriented Architecture, a large number of diversified Cloud services are exposed as Web APIs (Application Program Interface), which serve as the contracts between the service providers and service consumers. Due to their massive and broad applications, any flaw in the cloud APIs may lead to serious consequences. API testing is thus necessary to ensure the availability, reliability,...
Shape models provide a compact parameterization of a class of shapes, and have been shown to be important to a variety of vision problems, including object detection, tracking, and image segmentation. Learning generative shape models from grid-structured representations, aka silhouettes, is usually hindered by (1) data likelihoods with intractable marginals and posteriors, (2) high-dimensional shape...
To ensure the effectiveness of the RF(Radio Frequency) guided HILS(Hardware-In-the-Loop Simulation) system, the credibility evaluation was necessary. At present, most of the methods were applied in the case of the simulation system input being consistent with the real system input, which is difficult to achieve in some subsystem of the RF guided HILS system. In this paper, the relative complete system...
Major challenges in the simulation of pedestrians are the realistic behaviour of agents, realistic appearance and variety, and how to conveniently define larger crowds of pedestrians. In this paper, pedestrian behaviour in buildings is analysed and structured in order to develop a behavioural model for buildings with high pedestrian flows. Observing pedestrians at a larger train station is used as...
This paper presents an experimental design for building an efficient energy disaggregation system through multi-label classification approach. The proposed system requires a single point measurement of common electrical parameter data at aggregate electric circuit to identify the operating status of multiple appliances. Some multi-label classification algorithms were evaluated to select the best one...
The processing framework of large-scale data is becoming a major concern due to an explosive growth of data intensive applications in the cloud environment, such as MapReduce/Hadoop architecture. Many virtual machines (VMs) are used for processing large-scale data of cloud applications. Therefore, the total completion time of a task is an important index to evaluate the cloud performance. The access...
Compared with distributed graph computation, traditionally single node computation is unfitted in processing large scale graph data. The GAS (Gather, Apply and Scatter) Model is a universal vertex-cut graph computation programming model based on edge-centric programs to support graph algorithms, which process distributed graph computation after graph partition. In this paper, we introduce that three...
The REliable CApacity Provisioning and enhanced remediation for distributed cloud applications (RECAP) project aims to advance cloud and edge computing technology, to develop mechanisms for reliable capacity provisioning, and to make application placement, infrastructure management, and capacity provisioning autonomous, predictable and optimized. This paper presents the RECAP vision for an integrated...
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