2,095 research outputs found
Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges
In this study, condition monitoring strategies are examined for gas turbine engines
using vibration data. The focus is on data-driven approaches, for this reason a novelty
detection framework is considered for the development of reliable data-driven models
that can describe the underlying relationships of the processes taking place during an
engine’s operation. From a data analysis perspective, the high dimensionality of features
extracted and the data complexity are two problems that need to be dealt with throughout
analyses of this type. The latter refers to the fact that the healthy engine state data
can be non-stationary. To address this, the implementation of the wavelet transform is
examined to get a set of features from vibration signals that describe the non-stationary
parts. The problem of high dimensionality of the features is addressed by “compressing”
them using the kernel principal component analysis so that more meaningful, lowerdimensional
features can be used to train the pattern recognition algorithms. For feature
discrimination, a novelty detection scheme that is based on the one-class support
vector machine (OCSVM) algorithm is chosen for investigation. The main advantage,
when compared to other pattern recognition algorithms, is that the learning problem is
being cast as a quadratic program. The developed condition monitoring strategy can
be applied for detecting excessive vibration levels that can lead to engine component
failure. Here, we demonstrate its performance on vibration data from an experimental
gas turbine engine operating on different conditions. Engine vibration data that are
designated as belonging to the engine’s “normal” condition correspond to fuels and airto-fuel
ratio combinations, in which the engine experienced low levels of vibration. Results
demonstrate that such novelty detection schemes can achieve a satisfactory validation
accuracy through appropriate selection of two parameters of the OCSVM, the kernel
width γ and optimization penalty parameter ν. This selection was made by searching
along a fixed grid space of values and choosing the combination that provided the highest
cross-validation accuracy. Nevertheless, there exist challenges that are discussed along
with suggestions for future work that can be used to enhance similar novelty detection
schemes
Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews
These days with TV-shows and starred chefs, new kinds of cuisines appear in the market. The main cuisines like French, Italian, Japanese, Chinese and Indian are always appreciated but they are no longer the most popular. The new trend is the fusion cuisine, which is obtained by combining different main cuisines. The opening of a new restaurant proposing new kinds of cuisine produces a lot of excitement in people. They feel the need to try it and be part of this new culture. Yelp is a platform which publishes crowd sourced reviews about different businesses, in particular, restaurants. For some restaurants in Yelp if the kind of cuisine is available, usually, there is a tag only for the main cuisines, but there is no information for the fusion cuisine. There is a need to develop a system which is able to identify restaurants proposing fusion cuisine (novel or unknown cuisines).
This proposal is to address the novelty detection task using Yelp reviews. The idea is that the semi-supervised Machine Learning models trained only on the reviews of restaurants proposing the main cuisine will be able to discriminate between restaurants providing the main cuisine and restaurants providing the novel ones.
We propose effective novelty detection approaches for the unknown cuisine type identification problem using Long Short Term Memory (LSTM), autoencoder and Term-Frequency and Inverse Document Frequency(). Our main idea is to obtain features from LSTM, autoencoder and TF-IDF and use these features with standard semi-supervised novelty detection algorithms like Gaussian Mixture Model, Isolation Forest and One-class Support Vector Machines (SVM) to identify the unknown cuisines.
We conducted extensive experiments that prove the effectiveness of our approaches. The score that we obtained has a very high discrimination power because the best value of AUROC for the novelty detection problem is 0.85 from LSTM. LSTM outperforms our baseline model of TF-IDF and the main motivation is due to its ability to retain only the useful parts of a sentence
AI-assisted patent prior art searching - feasibility study
This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy
NASA SBIR abstracts of 1991 phase 1 projects
The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included
NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 40)
Abstracts are provided for 181 patents and patent applications entered into the NASA scientific and technical information system during the period July 1991 through December 1991. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application
NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 39)
Abstracts are provided for 154 patents and patent applications entered into the NASA scientific and technical information systems during the period Jan. 1991 through Jun. 1991. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application
NASA SBIR abstracts of 1990 phase 1 projects
The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number
Modelling of wireless sensor networks for detection land and forest fire hotspot
Indonesia located in South East Asia countries with tropical region, forest fires in Indonesia is one of big issue and disaster because it happens in almost of every year, this is because of some of region consist of peat land that high risk for fire especially in dry season. Riau Province is one of region that regularly incident of forest fire with affected the length and breadth of Indonesia. Propose development of Wireless Sensor Networks (WSNs) for detection of land and forest fire hotspot in Indonesia as well as one of the main consents in this research, case location in Riau province is at one of the regions that high risk forest fire in dry season. WSNs technology used for ground sensor system to collect environmental data. Data training for fire hotspot detection is done in data center to determine and conclude of fire hotspot then potential to become big fire. The deployment of sensors located at several locations that has potential for fire incident, especially as data shown in previous case and forecast location with potential fire happen. Mathematical analysis is used in this case for modelling number of sensors required to deploy and the size of forest area. The design and development of WSNs give high impact and feasibility to overcome current issues of forest fire and fire hotspot detection in Indonesia. The development of this system used WSNs highly applicable for early warning and alert system for fire hotspot detection
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