Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This pape...Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.展开更多
Issues concerning spatial dependence among cross-sectional units in econometrics have received more and more attention,while in statistical modeling,rarely can the analysts have a priori knowledge of the dependency re...Issues concerning spatial dependence among cross-sectional units in econometrics have received more and more attention,while in statistical modeling,rarely can the analysts have a priori knowledge of the dependency relationship of the response variable with respect to independent variables.This paper proposes an automatic structure identification and variable selection procedure for semiparametric spatial autoregressive model,based on the generalized method of moments and the smooth-threshold estimating equations.The novel method is easily implemented without solving any convex optimization problems.Model identification consistency is theoretically established in the sense that the proposed method can automatically separate the linear and zero components from the varying ones with probability approaching to one.Detailed issues on computation and turning parameter selection are discussed.Some Monte Carlo simulations are conducted to demonstrate the finite sample performance of the proposed procedure.Two empirical applications on Boston housing price data and New York leukemia data are further considered.展开更多
The particles-induced soft errors are a major threat to the reliability of microprocessors. Even worse,multi-bits upsets（MBUs） are ever-increased due to the rapidly shrinking feature size of the IC on a chip. Severa...The particles-induced soft errors are a major threat to the reliability of microprocessors. Even worse,multi-bits upsets（MBUs） are ever-increased due to the rapidly shrinking feature size of the IC on a chip. Several architecture-level mechanisms have been proposed to protect microprocessors from soft errors, such as dual and triple modular redundancies（DMR and TMR）. However, most of them are inefficient to combat the growing multibits errors or cannot well balance the critical paths delay, area and power penalty. This paper proposes a novel architecture, self-recovery dual-pipeline（SRDP）, to effectively provide soft error detection and recovery with low cost for general RISC structures. We focus on the following three aspects. First, an advanced DMR pipeline is devised to detect soft error, especially MBU. Second, SEU/MBU errors can be located by enhancing self-checking logic into pipelines stage registers. Third, a recovery scheme is proposed with a recovery cost of 1 or 5 clock cycles.Our evaluation of a prototype implementation exhibits that the SRDP can successfully detect particle-induced soft errors up to 100% and recovery is nearly 95%, the other 5% will inter a specific trap.展开更多
文摘Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.
基金supported by the Natural Science Foundation of Hunan Province(Grant 2022JJ30368)the National Natural Science Foundation of China(Grants 11801168,11801169,12071124)the Discovery Grants(RG/PIN261567-2013)from National Science and Engineering Council of Canada.
文摘Issues concerning spatial dependence among cross-sectional units in econometrics have received more and more attention,while in statistical modeling,rarely can the analysts have a priori knowledge of the dependency relationship of the response variable with respect to independent variables.This paper proposes an automatic structure identification and variable selection procedure for semiparametric spatial autoregressive model,based on the generalized method of moments and the smooth-threshold estimating equations.The novel method is easily implemented without solving any convex optimization problems.Model identification consistency is theoretically established in the sense that the proposed method can automatically separate the linear and zero components from the varying ones with probability approaching to one.Detailed issues on computation and turning parameter selection are discussed.Some Monte Carlo simulations are conducted to demonstrate the finite sample performance of the proposed procedure.Two empirical applications on Boston housing price data and New York leukemia data are further considered.
文摘The particles-induced soft errors are a major threat to the reliability of microprocessors. Even worse,multi-bits upsets（MBUs） are ever-increased due to the rapidly shrinking feature size of the IC on a chip. Several architecture-level mechanisms have been proposed to protect microprocessors from soft errors, such as dual and triple modular redundancies（DMR and TMR）. However, most of them are inefficient to combat the growing multibits errors or cannot well balance the critical paths delay, area and power penalty. This paper proposes a novel architecture, self-recovery dual-pipeline（SRDP）, to effectively provide soft error detection and recovery with low cost for general RISC structures. We focus on the following three aspects. First, an advanced DMR pipeline is devised to detect soft error, especially MBU. Second, SEU/MBU errors can be located by enhancing self-checking logic into pipelines stage registers. Third, a recovery scheme is proposed with a recovery cost of 1 or 5 clock cycles.Our evaluation of a prototype implementation exhibits that the SRDP can successfully detect particle-induced soft errors up to 100% and recovery is nearly 95%, the other 5% will inter a specific trap.
