CONDITIONAL FAULT DIAGNOSABILITY OF DUAL-CUBES

The growing size of the multiprocessor system increases its vulnerability to component failures. It is crucial to locate and replace the faulty processors to maintain a system's high reliability. The fault diagnosis is the process of identifying faulty processors in a system through testing. This paper shows that the largest connected component of the survival graph contains almost all of the remaining vertices in the dual-cube DCn when the number of faulty vertices is up to twice or three times of the traditional connectivity. Based on this fault resiliency, this paper determines that the conditional diagnosability of DCn (n ≥ 3) under the comparison model is 3n − 2, which is about three times of the traditional diagnosability.

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Two-Round Diagnosability Measures for Multiprocessor Systems

Complexity ◽

10.1155/2020/9535818 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Jiarong Liang ◽

Qian Zhang ◽

Changzhen Li

Keyword(s):

Fault Diagnosis ◽

Time Complexity ◽

Multiprocessor System ◽

Multiprocessor Systems ◽

Pmc Model ◽

Diagnosis Algorithm ◽

Conditional Diagnosability ◽

Novel Concept ◽

Measure Index

In a multiprocessor system, as a key measure index for evaluating its reliability, diagnosability has attracted lots of attentions. Traditional diagnosability and conditional diagnosability have already been widely discussed. However, the existing diagnosability measures are not sufficiently comprehensive to address a large number of faulty nodes in a system. This article introduces a novel concept of diagnosability, called two-round diagnosability, which means that all faulty nodes can be identified by at most a one-round replacement (repairing the faulty nodes). The characterization of two-round t-diagnosable systems is provided; moreover, several important properties are also presented. Based on the abovementioned theories, for the n-dimensional hypercube Qn, we show that its two-round diagnosability is n2+n/2, which is n+1/2 times its classic diagnosability. Furthermore, a fault diagnosis algorithm is proposed to identify each node in the system under the PMC model. For Qn, we prove that the proposed algorithm is the time complexity of On2n.

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Diagnosability and Diagnostic Algorithm for Pancake Graph under the Comparison Model

Journal of Interconnection Networks ◽

10.1142/s021926591550005x ◽

2015 ◽

Vol 15 (01n02) ◽

pp. 1550005

Author(s):

WENJUN LIU ◽

CHENG-KUAN LIN

Keyword(s):

Fault Diagnosis ◽

Interconnection Networks ◽

Linear Time ◽

Hamiltonian Path ◽

Diagnostic Algorithm ◽

Efficient Algorithms ◽

Comparison Model ◽

Diagnosis Model

Fault diagnosis is important for the reliability of interconnection networks. This paper addresses the fault diagnosis of n-dimensional pancake graph Pn under the comparison diagnosis model. By the concept of local diagnosability, we first prove that the diagnosabitly of Pn is n − 1, and it has strong local diagnosability property even if there are n − 3 faulty edges. Furthermore, we present efficient algorithms to locate extended star and Hamiltonian path structures in Pn, respectively. According to the works of Li et al. and Lai, the extended star and Hamiltonian path structures can be used to identify all faulty vertices in linear time, provided the number of faulty vertices is no more than n − 1.

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Adaptive Diagnosis of Hamiltonian Networks under the Comparison Model

Parallel Processing Letters ◽

10.1142/s0129626421500158 ◽

2021 ◽

pp. 2150015

Author(s):

Wenjun Liu ◽

Wenjun Li

Keyword(s):

Fault Diagnosis ◽

Upper Bound ◽

Multiprocessor Systems ◽

Major Goal ◽

Comparison Model ◽

A Value ◽

Pmc Model ◽

Diagnosis Algorithm ◽

Test Outcomes

Adaptive diagnosis is an approach in which tests can be scheduled dynamically during the diagnosis process based on the previous test outcomes. Naturally, reducing the number of test rounds as well as the total number of tests is a major goal of an efficient adaptive diagnosis algorithm. The adaptive diagnosis of multiprocessor systems under the PMC model has been widely investigated, while adaptive diagnosis using comparison model has been independently discussed only for three networks, including hypercube, torus, and completely connected networks. In addition, adaptive diagnosis of general Hamiltonian networks is more meaningful than that of special graph. In this paper, the problem of adaptive fault diagnosis in Hamiltonian networks under the comparison model is explored. First, we propose an adaptive diagnostic scheme which takes five to six test rounds. Second, we derive a dynamic upper bound of the number of fault nodes instead of setting a value like normal. Finally, we present an algorithm such that at least one sequence obtained from cycle partition can be picked out and all nodes in this sequence can be identified based on the previous upper bound.

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Diagnosability of Bubble-Sort Star Graphs with Missing Edges

Journal of Interconnection Networks ◽

10.1142/s0219265919500026 ◽

2019 ◽

Vol 19 (02) ◽

pp. 1950002 ◽

Cited By ~ 2

Author(s):

SHIYING WANG ◽

YINGYING WANG

Keyword(s):

Multiprocessor System ◽

Star Graph ◽

Star Graphs ◽

Comparison Model ◽

Model Following ◽

Strong Property

The diagnosability of a multiprocessor system plays an important role. The bubble-sort star graph BSn has many good properties. In this paper, we study the diagnosis on BSn under the comparison model. Following the concept of the local diagnosability, the strong local diagnosability property is discussed. This property describes the equivalence of the local diagnosability of a node and its degree. We prove that BSn (n ≥ 5) has this property, and it keeps this strong property even if there exist (2n − 5) missing edges in it, and the result is optimal with respect to the number of missing edges.

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The g-Good-Neighbor Diagnosability of Bubble-Sort Graphs under Preparata, Metze, and Chien’s (PMC) Model and Maeng and Malek’s (MM)* Model

Information ◽

10.3390/info10010021 ◽

2019 ◽

Vol 10 (1) ◽

pp. 21

Author(s):

Shiying Wang ◽

Zhenhua Wang

Keyword(s):

Fault Diagnosis ◽

Interconnection Networks ◽

Multiprocessor System ◽

Topology Structure ◽

Free Node ◽

Pmc Model ◽

Good Neighbor

Diagnosability of a multiprocessor system is an important topic of study. A measure for fault diagnosis of the system restrains that every fault-free node has at least g fault-free neighbor vertices, which is called the g-good-neighbor diagnosability of the system. As a famous topology structure of interconnection networks, the n-dimensional bubble-sort graph B n has many good properties. In this paper, we prove that (1) the 1-good-neighbor diagnosability of B n is 2 n − 3 under Preparata, Metze, and Chien’s (PMC) model for n ≥ 4 and Maeng and Malek’s (MM) ∗ model for n ≥ 5 ; (2) the 2-good-neighbor diagnosability of B n is 4 n − 9 under the PMC model and the MM ∗ model for n ≥ 4 ; (3) the 3-good-neighbor diagnosability of B n is 8 n − 25 under the PMC model and the MM ∗ model for n ≥ 7 .

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Caracterización de propiedades físico-mecánicas de rocas ígneas utilizadas en obras de infraestructura

Revista ALCONPAT ◽

10.21041/ra.v3i2.49 ◽

2013 ◽

Vol 3 (2) ◽

pp. 129-139 ◽

Cited By ~ 1

Author(s):

M. Navarrete ◽

W. Martínez Molina ◽

E. M. Alonso-Guzmán ◽

C. Lara-Gómez ◽

J. A. Bedolla-Arroyo ◽

...

Keyword(s):

Mechanical Properties ◽

High Reliability ◽

Physical And Mechanical Properties ◽

Point Load ◽

Load Test ◽

Economical Method ◽

Stone Materials ◽

Properties Of Materials ◽

Para Determinar ◽

Almost All

RESUMENLos materiales pétreos se emplean actualmente en casi todos los procesos edificatorios y debido a esto se les realizan diferentes pruebas, pudiendo ser tanto físicas como mecánicas; en éstas, el ensayo de carga puntual normado por ASTM D 5731, describe el empleo del equipo Point Load para determinar la resistencia mecánica en rocas no labradas, con tamaño aproximado de 4 pulgadas de diámetro, determinándose características mecánicas con alta confiabilidad y facilidad; es método económico porque el equipo se puede llevar al sitio o (banco) cantera, no siendo necesario labrar los especímenes. Si además se realizan pruebas físicas como humedad actual, absorción, densidad y gravedad específica, estos resultados ayudan a conocer mejor el comportamiento mecánico de los materiales pétreos, pudiéndose lograr beneficios económicos reflejados en la elaboración del concreto, tanto hidráulico como asfáltico. En este trabajo también se comparan las propiedades físicas y mecánicas de los bancos de materiales de la región de Morelia, Michoacán, México.Palabras Clave: Materiales pétreos; Point Load; muestra no labrada.ABSTRACTStone materials are currently used in almost all building processes and because of this they perform different tests can be both physical and mechanical , in these, the load test regulated by ASTM D 5731 , describes the use of the instrument Point Load to determine the mechanical strength unhewn rocks , with approximate size of 4 inches in diameter , determining mechanical properties with high reliability and ease , it is economical method because the team can take the site or ( bank) quarry , not necessary till the specimens. If further tests are physical and current humidity, absorption, density, specific gravity, these results help to better understand the mechanical behavior of the stone, being able to achieve an economy, as in the preparation of concrete, hydraulically and asphalt. This paper also compares the physical and mechanical properties of materials banks in the region of Morelia, Michoacan, MexicoKeywords: stone materials; Point Load; sample worked.

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FAULT DIAGNOSIS OF AN INDUSTRIAL MACHINE THROUGH SENSOR FUSION

International Journal of Information Acquisition ◽

10.1142/s0219878908001521 ◽

2008 ◽

Vol 05 (02) ◽

pp. 93-110 ◽

Cited By ~ 8

Author(s):

HAOXIANG LANG ◽

CLARENCE W. DE SILVA

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Sensor Fusion ◽

High Reliability ◽

Ccd Camera ◽

Fault Detection And Diagnosis ◽

Frequency Spectra ◽

Feature Vectors ◽

Cutting Machine ◽

Neuro Fuzzy

In this paper, a four layer neuro-fuzzy architecture of multi-sensor fusion is developed for a fault diagnosis system which is applied to an industrial fish cutting machine. An important characteristic of the fault diagnosis approach developed in this paper is to make an accurate decision of the machine condition by fusing information acquired from three types of sensors: Accelerometer, microphone and charge-coupled device (CCD) camera. Feature vectors for vibration and sound signals from their fast Fourier transform (FFT) frequency spectra are defined and extracted from the acquired information. A feature-based vision method is applied for object tracking in the machine, to detect and track the fish moving on the conveyor. A four-layer neural network including a fuzzy hidden layer is developed in the paper to analyze and diagnose existing faults. Feature vectors of vibration, sound and vision are provided as inputs to the neuro-fuzzy network for fault detection and diagnosis. By proper training of the neural network using data samples for typical faults, six crucial faults in the fish cutting machine are detected with high reliability and robustness. On this basis, not only the condition of the machine can be determined for possible retuning and maintenance, but also alarms to warn about impending faults may be generated during the machine operation.

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Reliability and Performance Analysis of Architecture-Based Software Implementing Restarts and Retries Subject to Correlated Component Failures

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194015500266 ◽

2015 ◽

Vol 25 (08) ◽

pp. 1307-1334 ◽

Cited By ~ 1

Author(s):

Xiao-Dan Li ◽

Yong-Feng Yin ◽

Lance Fiondella

Keyword(s):

Software Reliability ◽

High Reliability ◽

Experimental Studies ◽

Software Systems ◽

Fault Recovery ◽

Recovery Mechanism ◽

Software Models ◽

And Performance ◽

Component Failures ◽

The Impact

High reliability and performance are essential attributes of software systems designed for critical real-time applications. To improve the reliability and performance of software, many systems incorporate some form of fault recovery mechanism. However, contemporary models of software reliability and performance rarely consider these fault recovery mechanisms. Another notable shortcoming of many software models is that they make the simplifying assumption that component failures are statistically independent, which disagrees with several experimental studies that have shown that the failures of software components can exhibit correlation. This paper presents an architecture-based model of software reliability and performance that explicitly considers a two-stage fault recovery mechanism implementing component restarts and application-level retries. The application architecture is characterized by a Discrete Time Markov Chain (DTMC) to represent the dynamic branching behavior of control between the components of the application. Correlations between the component failures are computed with an efficient numerical algorithm for a multivariate Bernoulli (MVB) distribution. We illustrate the utility of the model through a case study of an embedded software application. The results suggest that the model can be used to quantify the impact of software fault recovery and correlated component failures on application reliability and performance.

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