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This paper focuses on industrial application of start-up vibration signature analysis for novelty detection with experimental trials on industrial gas turbines (IGTs). Firstly, a representative vibration signature is extracted from healthy start-up vibration measurements through the use of an adaptive neuro-fuzzy inference system (ANFIS). Then, the first critical speed and the vibration level at the...
With increasing complexity of today's automotive combustion engines, end-of-line (EOL) testing has become an important method to test assembled engines for production faults. Several hundered measurement signals are evaluated for every EOL test, up to 100% of production volume. The difficulty of finding and maintaining accurate test limits makes EOL testing of complex products interesting as a machine...
In this paper, a Takagi-Sugeno-Kang (TSK) type collaborative fuzzy rule based system is proposed with the help of knowledge learning ability of collaborative fuzzy clustering (CFC). The proposed method split a huge dataset into several small datasets and applying collaborative mechanism to interact each other and this process could be helpful to solve the big data issue. The proposed method applies...
A Distributed Autonomous Neuro-Gen Learning Engine (DANGLE) is proposed in this paper for file type identification. DANGLE is a machine learning tool designed to solve limitations of existing implementation of neural networks, namely excessive training time, fixed architecture and catastrophic forgetting. DANGLE consists of a Gene Regulatory Engine (GRE) and a Distributed Adaptive Neural Network (DANN)...
When a software system starts behaving abnormally during normal operations, system administrators resort to the use of logs, execution traces, and system scanners (e.g., anti-malwares, intrusion detectors, etc.) to diagnose the cause of the anomaly. However, the unpredictable context in which the system runs and daily emergence of new software threats makes it extremely challenging to diagnose anomalies...
The phonetic engine is a system that performs speech signal to symbol transformation. This work describes some issues in the development of an Assamese Phonetic Engine (PE). International phonetic alphabet (IPA) is used as the phonetic unit to transcribe the speech database collected in three different modes, namely, reading, lecture and conversation modes. Only reading mode data is used for training...
This research deals with the creation of an automatic target recognition system used for maritime vessels, for such applications as border patrol, friendly vs. foe id, etc. The paper encompasses robust maritime target feature extraction from 2-d real Inverse Synthetic Aperture Radar images and projects them onto a classifier friendly 1-d space. The image database in use consists of over 2500 images...
This paper presents a data-driven symbolic dynamics-based method for detection of incipient faults in gas turbine engines of commercial aircraft. Detection of incipient faults in such engines could be significantly manifested by taking advantage of transient data (e.g., during takeoff). From this perspective, the fault detection and classification algorithms are built upon the recently reported work...
In this paper, multiple detection engines with multi-layered intrusion detection mechanisms are proposed. The principle is to coordinate the results from each single-engine intrusion alert system, by seamlessly integrating with the multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology-based...
In this paper, we propose an overlapped handwriting input method on handheld devices, which allows users to write continuously without breaks on a single size-restricted writing area. 2 issues have been considered during the implementation of the overlapped input method: previous characters on the background may obstruct the clear viewing of current character and the messy overlapped handwriting is...
With the advent of advanced diesel after-treatment technologies, select catalyst reduction(SCR) becomes dominant technology for the new emission legislation in china. Because of sophisticated NOx sensors are becoming a critical cost challenge to OEMs, open loop SCR is mainly used at present, and which lead to be difficultly adaptive to control emission reduction and calibration workload was heavy...
The thesis, in order to solve the fault diagnosis problem of oil Parameter, adaptive neural network-based fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine, with the construction of ANFIS, by using gradient descent genetic algorithm and optimization of system parameters of neutral network learning algorithm, inputs the fusion data into ANFIS, and introduces...
Fleet maintenance management is geared towards fleet owners who operate a sizable fleet of vehicles and to whom the maintenance and management of these assets is of critical importance. Preventive maintenance (PM) leads to more efficient operations and therefore substantial cost-savings and reduced pollution. A well designed PM program can extend the life of vehicles and equipment and reduce costly...
Existing face recognition approaches are mostly developed based on adult faces which may not work well in distinguishing faces of kids. Especially, baby faces tend to have common features such as round cheeks and chins, so that current face recognition engines often fail to differentiate them. In this paper, we present methods for discriminating baby faces from adult faces, and for training a special...
Research in opinion analysis have drawn a great attention these days. Many of the effective opinion analysis system are based on supervised learning technology. However there lack of annotation sentiment corpora for Chinese opinion analysis. The purpose of our work is try to make use of annotation English corpora, where are rich and reliable to improve opinion analysis in Chinese. We propose a approach...
To deal with any possible cases for training anti-spam machine learning models, it is crucial to design a safe way to shrink the size of training sample set via reducing redundancies with minimal information loss for classification as well as make distribution of samples balanced. Presently, there is no such solution to do so. In this paper, we propose a safe approach to address these problems and...
Support Vector Machine (SVM), based on structural risk minimization principle, is now widely used in pattern recognition, classification and other research fields. It shows better generalization performance than traditional statistical learning theory, especially in small samples. In this paper, some dimensionless parameter is selected as SVM eigenvector, and then support vector machine is applied...
As the rapid development of the Internet, the occurrence of more and more spam mails becomes harmful to users. Content-based spam filtering technologies become the mainstream anti-spam mail methods so far. Support vector machine (SVM), Bayes, windows and KNN are excellent ones of these technologies and they have advantages and disadvantages respectively. The common shortage of content-based methods...
Despite great efforts on the design of ultra-reliable components, the increase of system size and complexity has outpaced the improvement of component reliability. As a result, fault management becomes crucial in high performance computing. The advance of fault management relies on effective failure prediction. Despite years of research on failure prediction, it remains an open problem, especially...
Developing sustainable urban environments is complex as it requires consideration of interacting social, economic and environmental sustain ability factors. The task is made even more difficult by the wide variety of stakeholders (e.g. planners, architects, businesses and the public) that may be involved in the process and the lack of a common language for all to understand. This paper describes a...
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