• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8B
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• pp.701-706
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• 2006

System-level diagnosis plays an important technique for fault detection in multi-processor systems. Efficient diagnosis is very important for real time systems as well as multiprocessor systems. Feng(1) proposed two adaptive diagnosis algorithms HADA and IHADA for hypercube system. The diagnosis cost, measured by diagnosis time and the number of test links, depends on the number and location of the faults. In this paper, we propose an adaptive diagnosis algorithm using the syndrome analysis. This removes unnecessary overhead generated in HADA and IHADA algorithm sand give a better performance compared to Feng's Method.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.665-667
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• 1999

An adaptive algorithm is presented for diagnosis of actuator faults. The concept of unknown input decoupling is combined with an adaptive observer, leading to an adaptive diagnostic observer, which has the robustness property in the presence of an unmeasurable term such as uncertainties. The observation error equation for the adaptive diagnostic observer does not depend on the effect of uncertainties and used to construct an adaptive diagnostic algorithm that provides the estimates of the gains of actuators, which can be obtained directly via the use of the augmented error technique. The simulation results indicate that the proposed algorithm is more realistic in the sense that better robustness properties can be assured without knowledge about uncertainties and is potentially useful in the development of a fault tolerant control system.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.4
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• pp.431-439
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• 2000

In this paper, we propose a system-level diagnosis algorithm for hypercube muti-processors using adaptive cube partition method. Feng[1] proposed a diagnosis algorithm for hypercube multiprocessors which gives a better performance compared to previous researches[2, 3]. But cube partitions in Feng's algorithm are performed without syndrome analysis. Therfore unnecessery overhead is made during cube partitions. In this paper, we propose an adaptive cube partition method which gives better partition through syndrome analysis and reduces diagnosis cost. We give a simulation result for comparisons. We have found that our algorithm shows better performance compared to Feng's method.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.276-280
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• 2003

Somani and Peleg proposed t/k-diagnosable system to diagonse more faults than t(dimension) by allowing upper bounded few number of units to be diagnosed incorrectly. Kranakis and Pelc showed that their adaptive diagnosis algorithm was more efficient than that of any previous ones, assuming that the number of faults does not exceed the hypercube dimension. We propose an adaptive diagnosis algorithm using the idea of t/k-diagnosable system on the basis of that of Kranakis and Pelc's. When the number of faults exceeds t, we allow a fault(k=1, 2, 3) to be diagnosed incorrectly. Based on this idea, we find that the performance of the proposed algorithm is nearly as efficient as any previously known strategies and detect above about double faults.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.5C
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• pp.483-489
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• 2006

Somani and Peleg proposed t/k-diagnosable system to diagonse more faults than t(dimension) by allowing upper bounded few number of units to be diagnosed incorrectly. Kranakis and Pelc showed that their adaptive diagnosis algorithm was more efficient than that of any previous ones, assuming that the number of faults does not exceed the hypercube dimension. We propose an adaptive diagnosis algorithm using the idea of t/k-diagnosable system on the basis of that of Kranakis and Pelc's. When the number of faults exceeds t, we allow a fault(k=1, 2, 3) to be diagnosed incorrectly. Based on this idea, we find that the performance of the proposed algorithm is nearly as efficient as any previously known strategies and detect above about double faults.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.320-326
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• 2008

This paper studies the problem of fault estimation using adaptive fault diagnosis observer. A fast adaptive fault estimation (FAFE) approximator is proposed to improve the rapidity of fault estimation. Then based on linear matrix inequality (LMI) technique, a feasible algorithm is explored to solve the designed parameters. Furthermore, an extension to sensor fault case is investigated. Finally, simulation results are presented to illustrate the efficiency of the proposed FAFE methodology.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.235-238
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• 2000

This article presents an adaptive decision tree algorithm for dynamically reasoning machine failure cause out of real-time, large-scale machine status database. On the basis of experiment using semiconductor etching machine, it has been verified that our model outperforms previously proposed decision tree models.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.125.4-125
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• 2001

The problem of recongurable ight control is investigated, focusing on model reference adaptive control(MRAC) through imprecise fault diagnosis. The method integrates the fault detection and isolation(FDI) scheme with the model reference adaptive control, and can be implemented on-line and in real-time. The algorithm can cope with the fast varying parameters. The Simulation results demonstrate the ability of reconguration to maintain the stability and acceptable performance after a failure.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.500-503
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• 2002

Biomedical signals are ubiquitously contaminated and degraded by background noise which span nearly all frequency bandwidths. This paper proposes the MADF (multiplication free adaptive digital filter) algorithm to cancel the noise. And the convergence characteristics of the algorithm is analyzed. In the experimental results, the MADF algorithm has the advantage in which has superior to a condition of low-frequency and slow data speed. This application gives an important significance in ensuring the objectivity of clinical information and in promoting the representation and the disease diagnosis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1044-1051
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• 2006

System level diagnosis algorithms use the properties of t-diagnosable system where the maximum number of the faults does not exceed 1. The existing diagnosis algorithms have limit when dealing with large fault sets in large multiprocessor systems. Somani and Peleg proposed t/k-diagnosable system to diagnose more faults than t by allowing upper bounded few number of units to be diagnosed incorrectly. In this paper, we propose adaptive hypercube diagnosis algorithm using t/k-diagnosable system. When the number of faults exceeds t, we allow k faults to be diagnosed incorrectly. Simulation shows that the performance of the proposed algorithm is better than Feng's HADA algorithm. We propose new algorithm to reduce test rounds by analyzing the syndrome of RGC-ring obtained in the first step of HADA/IHADA method. The proposed algorithm also gives similar performance compared to HYP-DIAG algorithm.