Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to...
Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features.
This paper proposes a novel bio-inspired model that quantifies the quality aspect of Wiki content. Unlike the statistical measures, the proposed system automates the quality dispersion mechanism using ant colony's...
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A gradient clustering algorithm, based on the nonparametric methodology of statistical kernel estimators, expanded to its complete form, enabling implementation without particular knowledge of the theoretical aspects ...
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There are many areas where objects with very complex and sometimes interdependent features are to be classified; similarities and dissimilarities are to be evaluated. This makes a complex decision model difficult to c...
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There are many areas where objects with very complex and sometimes interdependent features are to be classified; similarities and dissimilarities are to be evaluated. This makes a complex decision model difficult to construct effectively. This paper presents a Hierarchical Fuzzy Signatures (HFS) approach for improving the effectiveness and efficiency of quality-management system (QMS). The goal is to classify the objectives for a real-world case study. The latter was chosen because it presents a major problem for controlling the quality levels of its production lines. With the use of this fuzzy signature structure, complex decision models in the quality management field should be able to be constructed more effectively. In fact, this study provides a path for designing a tool for decision support to ensure the effectiveness of a corporate QMS.
Phase synchronization and directionality of human EEG data recorded under influence of audio-visual stimulation were investigated. EEG data came from 6 healthy volunteers repeatedly exposed to 20 min stimulation that ...
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The article presents the approach based on the usage of syntactic methods for the static analysis of handwritten signatures. The graph linguistic formalisms applied, such as the IE graph and ETPL(k) grammar, are chara...
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The article presents the approach based on the usage of syntactic methods for the static analysis of handwritten signatures. The graph linguistic formalisms applied, such as the IE graph and ETPL(k) grammar, are characterised by considerable descriptive strength and a polynomial membership problem of the syntactic analysis. For the purposes of representing the analysed handwritten signatures, new hierarchical (two-layer) HIE graph structures based on IE graphs have been defined. The two-layer graph description makes it possible to take into consideration both local and global features of the signature. The usage of attributed graphs enables the storage of additional semantic information (in the form of a set of parameters) describing the properties of individual signature strokes. Information about the shapes of specimen signatures is presented in the form of a language based on random IE graphs and stochastic class ETPL(k) grammars, which makes it possible to take into consideration the natural variability of the shape of specimen signatures. The verification and recognition of a signature consists in analysing the affiliation of its graph description to the language describing the specimen database. Initial assessments display a precision of the method at a average level of about 17%.
In this article author presents novel approach for analyzing the meaning of brain perfusion maps generated with dynamic computer tomography treatment. With these methods it is possible to detect (if exists), describe ...
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In this article author presents novel approach for analyzing the meaning of brain perfusion maps generated with dynamic computer tomography treatment. With these methods it is possible to detect (if exists), describe position, measure, and state prognosis for brain tissues that are affected by ischemic or hemorrhagic lesions. The whole process is driven by number of image processing algorithms, medical knowledge about average perfusion values and knowledge about interpretation of visualized symptoms. The methods was implemented and tested on 75 triplets of medical images acquired from 30 different adult patients (man and woman) with suspicious of ischemia / stroke. Each triplet was consisted of perfusion CBF and CBV map and ¿plain¿ CT image (one of the image from perfusion treatment acquired before contrast arrival became visible). The algorithm response was compared to image description done to each case by radiologist. The hypothesis to verify was if there is any lesions in perfusion map and if the algorithm found correct position, description and prognosis for them (if the algorithm give a wrong answer for any of this condition the case was considered as ¿error¿). Total error rate (the proportion of error instances to all instances) of full automatic detection (without manual correction of position of brain symmetry axis) was 48.0% and total error rate of semi automatic detection results (with correction of position of brain symmetry axis) was 22.7%.
In this paper problems of transport logistics focused on organization of optimal transportation and storage of resources and products are discussed. These problems were reduced to the knapsack and bin packing problems...
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In this paper problems of transport logistics focused on organization of optimal transportation and storage of resources and products are discussed. These problems were reduced to the knapsack and bin packing problems. Different heuristic approaches for the mentioned problems are analyzed. The architecture of the decision support system containing the subsystem for solving optimal cargo loading problems on bases of the algorithms is present.
We present a statistical probing-approach to distributed fault-detection in networked systems, based on autonomous configuration of algorithm parameters. Statistical modelling is used for detection and localisation of...
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ISBN:
(纸本)9781424467488;9780769540214
We present a statistical probing-approach to distributed fault-detection in networked systems, based on autonomous configuration of algorithm parameters. Statistical modelling is used for detection and localisation of network faults. A detected fault is isolated to a node or link by collaborative fault-localisation. From local measurements obtained through probing between nodes, probe response delay and packet drop are modelled via parameter estimation for each link. Estimated model parameters are used for autonomous configuration of algorithm parameters, related to probe intervals and detection mechanisms. Expected fault-detection performance is formulated as a cost instead of specific parameter values, significantly reducing configuration efforts in a distributed system. The benefit offered by using our algorithm is fault-detection with increased certainty based on local measurements, compared to other methods not taking observed network conditions into account. We investigate the algorithm performance for varying user parameters and failure conditions. The simulation results indicate that more than 95% of the generated faults can be detected with few false alarms. At least 80% of the link faults and 65% of the node faults are correctly localised. The performance can be improved by parameter adjustments and by using alternative paths for communication of algorithm control messages.
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