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The battery energy storage systems (BESSs) have been increasingly installed in the power system, especially with the growing penetration rate of the renewable energy sources. However, it is difficult for BESSs to be profitable due to high capital costs. In order to boost the economic value of BESSs, this paper proposes a hierarchical energy management system (HiEMS) to aggregate multiple BESSs, and...
This paper presents a novel fault diagnosis model for oil-immersed power transformers based on dissolved gas analysis. The model is rooted on the theories of rough set and support vector machine. A fitness function based on attribute dependence is developed to identify fault features to improve classification accuracy of transformer fault samples by using Genetic Algorithm. To get improved classification...
Recurrent neural network language models (RNNLMs) are becoming increasingly popular for speech recognition. Previously, we have shown that RNNLMs with a full (non-classed) output layer (F-RNNLMs) can be trained efficiently using a GPU giving a large reduction in training time over conventional class-based models (C-RNNLMs) on a standard CPU. However, since test-time RNNLM evaluation is often performed...
Read-Optimized databases are well suited for read intensive Data Warehouse applications. In addition, data in these applications grow rapidly and hence need a dynamically scalable environment like Cloud. Cloud provides a flexible environment where user can load data, execute queries and scale resources on demand. However, cloud has its own challenges. To reduce the inter-node communication during...
During the past several years, there has been a significant amount of research conducted simultaneous multiple resources scheduling problem (SMRSP) Intelligence manufacturing based on meta-heuristics, such as genetic algorithms (GAs), simulated annealing (SA) particle swarm optimization(PSO), has become a common tool to find satisfactory solutions within reasonable computational times in real settings...
There are many factors affect the accuracy of project duration forecasting, the lack of relative information and the complexity of the project are two major aspects. To overcome these constraints and establish a feasible forecasting model, this paper presents an improved method to forecast the project duration, which combines the earned schedule and artificial neural network. We adopt the artificial...
We entered the 10th Annual PhysioNet/Computers in Cardiology Challenge to predict which intensive care patients would experience an acute hypotensive episode (AHE) using physiologic data prior to the occurrence of the AHE. An AHE was defined through mean arterial blood pressure (ABP). We took a pragmatic approach to the Challenge. We explored six basic indices derived from ABP data near the forecast...
We introduce an improved technique for selectively quantifying cardiac sympathetic and parasympathetic nervous function based on the identification of the heart rate (HR) baroreflex impulse response from non-invasive cardio-respiratory measurements. We have tested the technique with respect to 24 humans breathing randomly or spontaneously under selective pharmacological autonomic blockade. Our results...
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