Stream Data Cleaning for Dynamic Line Rating Application
نویسندگان
چکیده
منابع مشابه
A Wavelet Filtering Application for On-line Dynamic Data Reconciliation
Discrete wavelet transform (DWT) is known for its signal processing ability. In the recent researches, DWT is adopted for signal filtering before executing dynamic data reconciliation. While on-line dynamic data reconciliation is concerned, the computation is heavy duo to the filtering in every time instant. In this article, a shift property of the DWT is indicated and is applied to reduce the ...
متن کاملOn-line outlier detection and data cleaning
Outliers are observations that do not follow the statistical distribution of the bulk of the data, and consequently may lead to erroneous results with respect to statistical analysis. Many conventional outlier detection tools are based on the assumption that the data is identically and independently distributed. In this paper, an outlier-resistant data filter-cleaner is proposed. The proposed d...
متن کاملOn-Line Nonlinear Dynamic Data Reconciliation Using Extended Kalman Filtering: Application to a Distillation Column and a CSTR
Extended Kalman Filtering (EKF) is a nonlinear dynamic data reconciliation (NDDR) method. One of its main advantages is its suitability for on-line applications. This paper presents an on-line NDDR method using EKF. It is implemented for two case studies, temperature measurements of a distillation column and concentration measurements of a CSTR. In each time step, random numbers with zero m...
متن کاملDYNG: Dynamic Online Growing Neural Gas for stream data classification
In this paper we introduce Dynamic Online Growing Neural Gas (DYNG), a novel online stream data classification approach based on Online Growing Neural Gas (OGNG). DYNG exploits labelled data during processing to adapt the network structure as well as the speed of growth of the network to the requirements of the classification task. It thus speeds up learning for new classes/labels and dampens g...
متن کاملStatic Optimisation vs. Dynamic Evaluation for Data Stream Processing
The work presented in this dissertation offers the quantitive comparison between two different execution frameworks for queries over data streams. The fist framework is the static one. Its optimiser decides the execution plan, and it orders the operators according to it. Then, it schedules the incoming data through these operators. The plan is fixed and it cannot change throughout the processin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Energies
سال: 2018
ISSN: 1996-1073
DOI: 10.3390/en11082007