18397185
Motor Fault Diagnosis Based on Wavelet Energy and Immune Neural Network
Motor fault diagnosis methods are crucial in acquiring safe and reliable operation in motor drive systems. In this paper, a new method for the motor fault diagnosis is proposed based on wavelet packet transform (WPT) and artificial neural network (ANN). The energy of the vibration signals of motor can be obtained by the multi-decomposition of WPT and used as feature values of ANN inputs for fault diagnosis system. The artificial immune algorithm (AIA) for data clustering is employed to adaptively choose the centers and widths of the hidden layer centers of the radial basis function neural network (RBFNN). The simulation experiment results show the applicability and effectiveness of the proposed method to motor fault diagnosis.
35589713
Time Discretisation Applied to Anomaly Detection in a Marine Engine
An introduction to the problems associated with anomaly detection in a marine engine, explaining the benefits that the SAX representation brings to the field. Despite limitations in accuracy of the SAX representation in comparison with the normalised time series, we conclude that because of the reduction in data points that should be processed SAX should be considered further as a valid and efficient representation. Finally, a continuation of the work to make the approach more viable in the real world is briefly noted based upon Markov Chaining and Support Vector Machines.
37115701
Qualitative classification of descent phases in commercial flight data
Flight data from commercial aircraft in the descent is analysed using one-class classification techniques to identify possible unstable approaches. The method considers snapshots of flight parameters at certain heights in the descent and identifies any abnormalities. Combination rules are used to analyse the flight as a whole and it is found that it is possible to detect abnormal flights with a good degree of accuracy.
37865441
Adapting robot kinematics for human-arm motion recognition
This paper presents a novel method to the analysis of human-arm motion, in particular improving the efficiency of conventional motion recognition algorithms. Contrary to the prior art methods, this research develops a framework for human-arm motion recognition where qualitative normalised templates (QNTs) is proposed to replace the conventional approaches. First of all, the conventional robotic model has been employed to build a generic vision model for a human-arm, that is we utilise the robot kinematics to construct a stick model. Secondly, the qualitative robotic model is adopted to learn and construct the QNTs where human-arm motion is termed as, whose execution is consistent and could be easily characterised by a definite space-time trajectory in configuration space. Finally, classification of the human-arm motion is achieved by comparing the QNTs to the parameters learnt with particle filter based motion tracking algorithm. Experimental evaluation has demonstrated the effectiveness of the proposed method in human-arm motion classification, and our future work is focused on extending the proposed method to recognise complex human motion, e.g. walking and running.
34728116
Travel Speed Prediction Using Fuzzy Reasoning
The speed prediction algorithm introduced in this paper takes advantage of fuzzy systems that are insensitive to random noise, robust to uncertainties, and transparent to interpretation. The proposed algorithm for outlier detection selects the potential outliers based on the density rather than the deviation adopted in conventional approaches. To evaluate the developed system, a seris of experiments conducted on the real world data. The result of the comparison performed to evaluate the outliler detection method proposed reveals the benefit from the consideration of density. The cross validation results indicate the effectiveness of the fuzzy inference system developed.
27826765
Analysing Flight Data Using Clustering Methods
This paper reviews existing forms of density-based, partitional and hierarchical clustering methods in the context of flight data analysis. Advantages and disadvantages are fully explored with a focus on proposing a clustering-based ensemble framework for monitoring flight data in order to search for anomalies during flight operation. Case studies in selected flight scenarios are provided to demonstrate the potential of clustering methods and their integration with reasoning techniques in detecting abnormal flights.
10058829
A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge
Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics of natural languages, word sense disambiguation has been extensively studied in Computational Linguistics. However, existing methods either are brittle and narrowly focus on specific topics or words, or provide only mediocre performance in real-world settings. Broad coverage and disambiguation quality are critical for a word sense disambiguation system. In this paper we present a fully unsupervised word sense disambiguation method that requires only a dictionary and unannotated text as input. Such an automatic approach overcomes the problem of brittleness suffered in many existing methods and makes broad-coverage word sense disambiguation feasible in practice. We evaluated our approach using SemEval 2007 Task 7 (Coarse-grained English All-words Task), and our system significantly outperformed the best unsupervised system participating in SemEval 2007 and achieved the performance approaching top-performing supervised systems. Although our method was only tested with coarse-grained sense disambiguation, it can be directly applied to fine-grained sense disambiguation.
9882695
Rigorously Defining and Analyzing Medical Processes: An Experience Report
This paper describes our experiences in defining the processes associated with preparing and administrating chemotherapy and then using those process definitions as the basis for analyses aimed at finding and correcting defects. The work is a collaboration between medical professionals from a major regional cancer center and computer science researchers. The work uses the Little-JIL language to create precise process definitions, the Propel system to specify precise process requirements, and the FLAVERS system to verify that the process definitions adhere to the requirement specifications. The paper describes how these technologies were applied to successfully identify defects in the chemotherapy process. Although this work is still ongoing, early experiences suggest that this approach can help reduce medical errors and improve patient safety. The work has also helped us to learn about the desiderata for process definition and analysis technologies, both of which are expected to be broadly applicable to other domains.
13017049
Identities for the exponential integral and the complementary error transforms
Abstract In the present paper the authors introduce the exponential integral transform and the complementary error transform, then it is shown that the third iterate of the Laplace transform is the exponential integral transform and a modified third iterate of the Laplace transform is the complementary error transform. Some identities involving these new transforms, the Laplace transform and the Stieltjes transform are given. Using the identities, Parseval–Goldstein type results involving these transforms are proved. Some illustrative examples are also given.
1207779
Experience in Teaching a Management-Oriented Capstone Software Engineering Course
Many computer science departments teach a capstone software engineering course involving the design and implementation of a large-scale project. Such a course is normally taught with students working together in teams; consequently, students must face a number of problems related to project management and team dynamics. Our experience has been that students frequently fail to adequately cope with these organizational problems, and consequently fail to complete their projects at an acceptable level of quality. To prepare students to better face these problems, we have modified our capstone software engineering course to provide equal emphasis in both management and technical areas. Our approach to management topics is formal rather than anecdotal, stressing general principles applied to organizational problems arising specifically from situations that occur frequently in technical organizations. This paper reports on the structure of this course, and provides insight into our initial experience in teaching it.
16581818
A Case Study of Software Process Improvement During Development
We present a case study of the use of a software process improvement method which is based on the analysis of defect data. The first step of the method is the classification of software defects using attributes which relate defects to specific process activities. Such classification captures the semantics of the defects in a fashion which is useful for process correction. The second step utilizes a machine-assisted approach to data exploration which allows a project team to discover such knowledge from defect data as is useful for process correction. We show that such analysis of defect data can readily lead a project team to improve their process during development. >