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However, KPCR is constructed using Ordinary Least Squares OLS for estimating its coefficient regression.

It is well known that the main disadvantage of the OLS method is its sensitivity to outliers.

Outliers have a large influence the prediction values because squaring residuals magnifies the effect of the outliers.

Therefore, KPCR is not suitable technique when observation data involve outliers.

Under this circumstance, we proposed several nonlinear robust techniques using the hybridization of KPCR, Î±-regression quantile, LMS, LTS, R-estimator and genetic algorithms GA to overcome the effects of outliers on regression models.

We use KPCR to construct nonlinearity and å®çæŽ»äŸã«ãããç¢ºçã²ãŒã Î±-regression quantile, LMS, LTS and R-estimators to perform the linear robust regressions in the feature space, while GA is used to estimate the regression coefficients of those robust regressions.

Our experiments showed that the proposed methods gave better results compared to the existing techniques based on linear robust regressions and KPCR.

Then we discuss the axiomatic properties of the model and their implications by making comparison with the models presented by several other related studies.

In most previous studies, the system was assumed to deteriorate in accordance with a stationary state transition law.

However, systems can also deteriorate with age.

In such cases, the transition state probabilities of the system should be non-stationary for different ages of the systems.

This talk will focus on the condition monitoring maintenance for an aging system of which the deterioration undergoes as a non-stationary Markov process.

The optimal decision policy is investigated, and the structural properties of the resulting optimal expected cost function are obtained.

These structural properties establish the existence of an optimal control limit policy with respect to both the system's deterioration and age under some assumptions.

Furthermore, the monotonic property of control limits is also obtained.

If the optimal decision policy can be limited into the set of control limit policies, https://jackpot-promocode-list.site/1/204.html tremendous amount of calculation time required to find the optimal decision policy would be reduced.

Furthermore, the monotonicity of control limits can reduce the computational efforts substantially by simplifying the algorithm and reducing the computation errors.

Kishor Trivedi Duke University, USA Survivability is critical attribute of modern computer and communication systems.

This talk addresses the current åŠç²Ÿã®ãã¬ã¹ã¢ããã²ãŒã ç¡æãªã³ã©ã€ã³ãã¬ã€ status of quantification of survivability.

First we carefully define survivability and contrast it with traditional measures such as reliability, availability https://jackpot-promocode-list.site/1/122.html performability.

We then discuss probabilistic models for the quantification of survivability based on our chosen definition.

Next, two case ããã5ã³ã³ãã¥ãŒã¿ã²ãŒã ç¡æ are presented to illustrate our approach.

One case study is about the quantitative evaluation of several survivable architectures for the telephone access network.

Hierarchical models are developed to derive various survivability measures.

Numerical results are provided to show how a comprehensive understanding of the system behavior after failure can be achieved through such models.

The second case study deals with the survivability quantification of communication networks.

Hiroyuki Okamura Hiroshima University, Japan This talk discusses Krylov subspace approximation for transient solutions of continuous-time Markov chains CTMCs.

The CTMC is a powerful method to evaluate quantitative system performance based on state-based stochastic models.

In general, the transient solution of CTMC can be represented article source the matrix exponential function.

However, it is computationally difficult to solve the matrix exponential function in the case of a large CTMC.

Saad 1992 presented Krylov subspace approximation for the matrix exponential function.

This is one of the most promising methods to compute the transient solution of large-sized CTMCs.

In the paper, we propose the modified Krylov subspace approximation for transient solutions of CTMCs by using the stationary distribution.

Concretely, this paper reveals the relationship between the uniformization for CTMCs and Krylov subspace approximation mathematically.

According to the relationship, the modified Krylov subspace approximation is proposed by the idea behind the modified https://jackpot-promocode-list.site/1/201.html />Fumio Ohi Nagoya Institute of Technology, Japan A well known model of a binary state system assumes state spaces to be binary as {0,1}, where 0 and 1 respectively mean failure and normal states.

We, however, may frequently observe cases in which components and systems can take intermediate states between total failure and perfectly functioning states, which require us to develop a theory of multi-state systems and stochastic evaluation methods.

Recently many researchers have studied this case and proposed some effective methods for stochastic evaluation of systems, but many of them assume the totally ordered state spaces.

In this presentation, showing a definition of multi-state systems for the case of partially ordered state spaces, we summarize some methods for stochastic evaluation of the system at a time slice or in a steady state as the inclusion and exclusion method, Boolean method, stochastic bounds by minimal and maximal state vectors, stochastic bounds by series and parallel decomposition of the multi-state system and stochastic bounds via modular decomposition.

Zuo University of Alberta, Canada This talk addresses key challenges in assurance of the reliability of systems in continuous operation utilizing condition monitoring data.

Key issues include quantification of the health status of system in operation and the relationship between health indicators and the predicted remaining useful life.

The covered research aspects include fault detection, fault assessment, fault diagnosis, deterioration trend prediction, reliability assessment, maintenance optimization models and methods, and decision-making tools for inspection, maintenance, and operation.

Yun Pusan National University, Korea In this talk, I introduce simulation-based Reliability and Article source optimization problems for multi-unit systems.

System operational availability and life cycle cost are considered as optimization criteria.

Meta-heuristics and heuristic techniques are used to find the near optimal solutions in the optimization problems.

Three optimization topics are discussed; Firstly, I explain a reliability read more maintainability optimization problem for a searching system and want to determine the optimal value of MTBF Mean Time between FailuresMTTR Mean Time to Repair and ALDT Administrative and Logistics Delay Time of all units that minimize the life cycle cost and satisfy the target system availability.

Secondly, I talk about a preventive maintenance problem for KTX Korean Train eXpress because system availability can be improved through effective preventive maintenance.

The objective is to determine the preventive maintenance intervals of units in the system optimally.

Finally, I introduce an inspection optimization problem for one-shot systems with two types of units where Type 1 units are failed at random times and Type 2 units are degraded with time.

The interval availability and life cycle cost are used as optimization criteria and the optimal inspection interval is obtained for a one-shot system with given replacement times of Type 2 units.

Next, an inspection scheduling problem is studied for one-shot systems under PCçšã®æé«ã®3Dã²ãŒã constraint of maintenance resources.

Wenbin Wang University of Science and Technology Beijing, China Remaining useful life RUL estimation is regarded as one å®çæŽ»äŸã«ãããç¢ºçã²ãŒã the most central components in prognostics and health management PHM.

Accurate RUL estimation can enable failure prevention in a more controllable manner in that effective maintenance can be executed in appropriate time to correct impending faults.

In this talk we consider the problem of estimating the RUL from observed degradation data for a general system.

A degradation path-dependent approach for RUL estimation is presented through the combination of Bayesian å®çæŽ»äŸã«ãããç¢ºçã²ãŒã and expectation maximization EM algorithm.

The use of both Bayesian updating and EM algorithm to update the model parameters and RUL distribution at the time obtaining a newly observed data is a novel contribution of this research, which makes å®çæŽ»äŸã«ãããç¢ºçã²ãŒã estimated RUL depend on the observed degradation data history.

As two specific cases, a linear degradation model and an exponential-based degradation model are considered to illustrate the implementation of our presented approach.

A major contribution under click to see more two special cases is that å®çæŽ»äŸã«ãããç¢ºçã²ãŒã approach can obtain an exact and closed-form RUL distribution respectively, and the moment of the obtained RUL distribution from our presented approach exists.

This contrasts sharply with the approximated results obtained in the literature for the å®çæŽ»äŸã«ãããç¢ºçã²ãŒã cases.

To our knowledge, the RUL estimation approach learn more here in this talk for the two special cases is the only one that can provide an exact and closed-form RUL distribution utilizing the monitoring history.

Finally, numerical examples for RUL estimation and a practical case study for condition-based replacement decision making with comparison to a previously reported approach are provided to substantiate the superiority of the proposed model.

Tadashi Dohi Hiroshima University, Japan The empirical software engineering is becoming much popular and is useful to quantify the software development process via the measurement-based approach.

Especially, an effective utilization of software metrics measured in the development process plays a central role to evaluate the engineering aspect of software development.

On the other hand, the software reliability engineering aims at evaluating the quantitative software product reliability, which is defined as the probability that the software failure does not occur in the operational phase, but has just focused on the curve fitting of the cumulative number of software faults from the software fault count data.

In other words, the software reliability engineering community has often missed to utilize the software metrics effectively during the å®çæŽ»äŸã«ãããç¢ºçã²ãŒã three decades.

In this talk, I summarize the metrics-based software reliability modeling framework and give a significant approach to bridge between the software metrics data and the software reliability assessment.

The fundamental idea is to apply non-trivial regression-based models to represent the software failure rate.

Throughout illustrative examples with actual software development project data, I show that the metrics-based software reliability assessment technique outperforms the existing software fault count methods.

I also refer to the reliability assessment for incremental software development, arising in the well-known agile software development paradigm.

This talk will discuss some of the research activities on techniques read more methodologies for building and monitoring dependable software systems conducted within the Queen's Reliable Software Technology QRST research group.

Current optical networks are already capable of transporting 100 channels on a single fiber, where each channel can carry 40 Gbps.

Since companies, government agencies, and the military are dependent on receiving uninterrupted, reliable service, instantaneous service restoration in the event of link or node failures has become critically important.

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However, KPCR is constructed using Ordinary Least Squares OLS for å®çæŽ»äŸã«ãããç¢ºçã²ãŒã its coefficient regression.

It is well known that the å®çæŽ»äŸã«ãããç¢ºçã²ãŒã disadvantage of the OLS method is its sensitivity to outliers.

Therefore, KPCR is not suitable technique when observation data involve outliers.

Under this circumstance, we proposed several nonlinear robust techniques using the hybridization of KPCR, Î±-regression quantile, LMS, LTS, R-estimator and genetic algorithms GA to overcome the effects of outliers on regression models.

We use KPCR to construct nonlinearity and employ Î±-regression quantile, LMS, LTS and R-estimators to perform the linear robust regressions in the feature space, while GA is used to estimate the regression coefficients of those robust regressions.

Our read article showed that the proposed methods gave better results compared to the congratulate, ã¬ãã«ã¢ãã2ã²ãŒã certainly techniques based on linear robust regressions and KPCR.

Then å®çæŽ»äŸã«ãããç¢ºçã²ãŒã discuss the axiomatic properties of the model and their implications by making comparison with the models presented by several other related studies.

In most previous studies, the system was assumed to deteriorate in accordance with a stationary state transition law.

However, systems can also deteriorate with age.

In such cases, the transition state probabilities of the system should be non-stationary for different ages of the systems.

This talk will focus on the condition monitoring maintenance for an aging system of which the deterioration undergoes as a non-stationary Markov process.

The optimal decision policy is investigated, and the structural properties of the resulting optimal expected cost function are obtained.

These structural properties establish the existence of an optimal control limit policy with respect to both the system's deterioration and age under some assumptions.

Furthermore, the monotonic property of control limits is also obtained.

If the optimal decision policy can be limited into the set of control limit policies, the tremendous amount of calculation time required to find the optimal decision policy would be reduced.

Furthermore, the monotonicity of control limits can reduce the computational efforts substantially by simplifying the algorithm and reducing the computation errors.

Kishor Trivedi Duke University, USA Survivability is critical attribute of modern computer and communication systems.

This talk addresses the current research status of quantification of survivability.

First we carefully define survivability and contrast it with traditional measures such as reliability, availability and performability.

We then discuss probabilistic models for the quantification of survivability based on our chosen definition.

Next, two case studies are presented to illustrate our approach.

One case study is about the quantitative evaluation of several survivable are ã«ãžããã¯ã€ã€ã«ãã¡ãã·ã§ã³ think for the telephone access network.

Hierarchical models are developed to derive various survivability measures.

Numerical results are provided to show how a comprehensive understanding of the system behavior after failure can be achieved through such models.

The second case study deals with the survivability quantification of communication networks.

Hiroyuki Okamura Hiroshima University, Japan This talk discusses Krylov subspace approximation for transient solutions of continuous-time Markov chains CTMCs.

The CTMC is a powerful method to evaluate å®çæŽ»äŸã«ãããç¢ºçã²ãŒã system performance based on state-based stochastic models.

In general, the transient solution of CTMC can be represented by the matrix exponential function.

However, it is computationally difficult to solve the matrix exponential function in the case of a large CTMC.

Saad 1992 presented Krylov subspace approximation for the matrix exponential function.

This is one of the most promising methods to compute the transient solution of large-sized CTMCs.

In the paper, we propose the modified Krylov subspace approximation for transient solutions of CTMCs by using the stationary distribution.

Concretely, this paper reveals the relationship between the uniformization for CTMCs and Krylov subspace approximation mathematically.

According to the relationship, the modified Krylov subspace approximation is proposed by the idea behind the modified uniformization.

Fumio Ohi Nagoya Institute of Technology, Japan A well known model of a binary state system assumes state spaces to be binary as {0,1}, where 0 and 1 respectively mean failure and normal states.

We, however, may frequently observe cases in which components and systems can take intermediate states between total failure and perfectly functioning states, which require us to develop a theory of multi-state systems and stochastic evaluation methods.

Recently many researchers have studied this case and proposed some effective methods for stochastic evaluation of systems, but many of them assume the totally ordered state spaces.

In this presentation, showing a definition of multi-state systems for the case of partially ordered state spaces, we summarize some methods for stochastic evaluation of the system at a time slice or in a steady state as the inclusion and exclusion method, Boolean method, stochastic bounds by minimal and maximal state vectors, stochastic bounds by series and parallel decomposition of the multi-state system and stochastic bounds via modular decomposition.

Zuo University of Alberta, Canada This talk addresses key challenges in assurance of the reliability of systems in continuous operation utilizing condition monitoring data.

Key issues include quantification of the health status of system in operation and the relationship between health indicators and the predicted remaining useful life.

The covered research aspects include fault detection, fault assessment, fault diagnosis, deterioration trend prediction, reliability assessment, maintenance optimization models and methods, and decision-making tools for inspection, maintenance, and operation.

Yun Pusan National University, Korea In this talk, I introduce simulation-based Reliability and Maintenance optimization problems for https://jackpot-promocode-list.site/1/156.html systems.

System operational availability and life cycle cost are considered as optimization interesting ã¹ããããã·ã³ã®ãšã©ãŒã³ãŒã think />Meta-heuristics and heuristic techniques are used to find the near optimal solutions in the optimization problems.

Three optimization topics are discussed; Firstly, I explain a reliability and maintainability optimization problem for a searching system and want to determine the optimal value of MTBF Mean Time between FailuresMTTR Mean Time to Repair and ALDT Administrative and Logistics Delay Time of all units that minimize the life cycle cost and satisfy the target system availability.

Secondly, I talk about a preventive maintenance problem for KTX Korean Train eXpress because system availability can be improved through effective preventive maintenance.

The objective is to determine the preventive maintenance intervals of units in the system optimally.

Finally, I introduce an inspection optimization problem for one-shot systems with two types of units where Type 1 units are failed at random times and Type 2 units are degraded with time.

The interval availability and life cycle cost are used as optimization criteria and the optimal inspection interval is obtained for a one-shot system with given replacement times of Type 2 units.

Next, an inspection scheduling problem is studied for one-shot systems under the constraint of maintenance resources.

Wenbin Wang University of Science and Technology Beijing, China Remaining useful life RUL estimation is regarded as one of the most central components in prognostics and health management PHM.

Accurate RUL estimation can enable failure prevention in a more controllable manner in that effective maintenance can be executed in appropriate time to correct impending faults.

In this talk we consider the problem of estimating the RUL from observed degradation data for a general system.

A degradation path-dependent approach for RUL estimation is presented through the combination of Bayesian updating and expectation maximization EM algorithm.

The use of both Bayesian updating and EM algorithm to update the model parameters and RUL distribution at the time obtaining a newly observed data is a novel contribution of this research, which makes the estimated RUL depend on the observed degradation data history.

As two specific cases, a linear degradation model and an exponential-based degradation model are considered to illustrate the implementation of ã«ãžããµã€ã³ã¢ããããŒãã¹ presented approach.

A major contribution under these two special cases is that our approach can obtain an exact and closed-form RUL distribution respectively, click here the moment of the obtained RUL distribution from our presented approach exists.

This contrasts sharply with the approximated results obtained in the literature for the same cases.

To our knowledge, the RUL estimation approach presented in this talk for the two special cases is the only one that can provide an exact and closed-form RUL distribution utilizing the monitoring history.

Finally, numerical examples for RUL estimation and a practical case study for condition-based replacement decision making with comparison to a previously reported approach are provided to substantiate the superiority of the proposed model.

Tadashi Dohi Hiroshima University, Japan The empirical software engineering is becoming much popular and is useful to quantify the software development process via the measurement-based approach.

Especially, an effective utilization of software metrics measured in the development process plays a central role to evaluate the engineering aspect of software development.

On the other hand, the software reliability engineering aims at evaluating the quantitative software product reliability, which is defined as the probability that the software failure does not occur in the operational phase, but has just focused on the curve fitting of the cumulative number of software faults from the software fault count data.

In other words, the software reliability engineering community has often missed to utilize the software metrics effectively during the last three decades.

In this talk, I summarize the metrics-based software reliability modeling framework and give a significant approach to bridge between the software metrics data and the software reliability assessment.

The fundamental idea is to apply non-trivial regression-based models to represent the software failure rate.

Throughout illustrative examples with actual software development project data, I show that the metrics-based software reliability assessment technique outperforms the existing software fault count methods.

I also refer to the reliability assessment for incremental software development, arising in the well-known agile software development paradigm.

This talk will discuss some of the research activities on techniques and methodologies for building and monitoring dependable software systems conducted within the Queen's Reliable Software Technology QRST research group.

Current optical networks are already capable of transporting go here channels on a single fiber, where each channel can carry 40 Gbps.

Since companies, government agencies, and the military are dependent on receiving uninterrupted, reliable service, instantaneous service restoration in the event of link or node failures has become critically important.

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However, KPCR is constructed using Ordinary Least Squares OLS for estimating its coefficient regression.

It is well known that the main disadvantage of the OLS method is its sensitivity to outliers.

Outliers have a large influence the prediction values because squaring residuals magnifies the effect of the outliers.

Therefore, KPCR is not suitable technique when observation data involve outliers.

Under this circumstance, we proposed several nonlinear robust techniques using the hybridization of KPCR, Î±-regression quantile, LMS, LTS, R-estimator and genetic algorithms GA to overcome the effects of outliers on regression models.

We use KPCR to construct nonlinearity and employ Î±-regression quantile, LMS, LTS and R-estimators to perform the linear robust regressions in the feature space, while GA is used to estimate the regression coefficients of those robust regressions.

Our experiments showed that the proposed methods gave better results compared to the existing techniques based on linear robust regressions and KPCR.

Then we discuss the axiomatic properties of the model and their implications by making comparison with the models presented by several other related studies.

In most previous studies, the system was assumed to deteriorate in accordance with a stationary state transition law.

However, systems can also deteriorate with age.

In such cases, the transition state probabilities of the system should be non-stationary for different ages of the systems.

This talk will focus on the condition å®çæŽ»äŸã«ãããç¢ºçã²ãŒã maintenance for an aging system of which the deterioration undergoes as a non-stationary Markov process.

The optimal decision policy is investigated, and the structural properties of the resulting optimal expected cost function are obtained.

These structural properties establish the existence of an optimal control limit policy with respect to both the system's deterioration and age under some assumptions.

Furthermore, the monotonic property of control limits is also obtained.

If the optimal decision policy can be limited into the set of control limit policies, the tremendous amount of calculation time required to find the optimal decision policy would be reduced.

Furthermore, the monotonicity of control limits can reduce the computational efforts substantially by simplifying the algorithm and reducing the computation errors.

Kishor Trivedi Duke University, USA Survivability is critical attribute of modern computer and communication systems.

This talk addresses the current research status of quantification of survivability.

First we carefully define survivability and contrast it with traditional measures such as reliability, availability and performability.

We then discuss probabilistic models for the quantification of survivability based on our chosen definition.

Next, two case studies are presented to illustrate our approach.

One case study is about the quantitative evaluation of several survivable architectures for the telephone access network.

Hierarchical models are developed to derive various survivability measures.

Numerical results are provided to å®çæŽ»äŸã«ãããç¢ºçã²ãŒã how a comprehensive understanding of the system behavior after failure can be achieved through such models.

The second case study deals with the survivability quantification of communication networks.

Hiroyuki Okamura Hiroshima University, Japan This talk discusses Krylov subspace approximation for transient solutions of continuous-time Markov chains CTMCs.

The CTMC is a powerful method to evaluate quantitative system performance based on state-based stochastic models.

In general, the transient solution of CTMC can be represented by the matrix exponential function.

However, it is computationally difficult to solve the matrix exponential function in the case of a large CTMC.

Saad message æºåž¯é»è©±çšbejeweledãã©ãã·ã¥ã²ãŒã ã®ããŠã³ããŒã congratulate presented Krylov subspace approximation for the matrix exponential function.

This is one of the most promising methods to compute the transient solution of large-sized CTMCs.

In the paper, we propose the modified Krylov subspace approximation for transient solutions of CTMCs by using the stationary distribution.

Concretely, this paper reveals the relationship between the uniformization for CTMCs and Krylov subspace approximation mathematically.

According to the relationship, the modified Krylov subspace approximation is proposed by the idea behind the modified uniformization.

Fumio Ohi Nagoya Institute of Technology, Japan A well known model of a binary state system assumes state spaces to be binary as {0,1}, where 0 and 1 respectively mean failure and normal states.

We, however, may frequently observe cases in which components and systems can take intermediate states between total failure and perfectly functioning states, which require us to develop a theory of multi-state systems and stochastic evaluation methods.

Recently many researchers have studied this case and proposed some effective methods for stochastic evaluation of systems, but many of them assume the totally ordered state spaces.

In this presentation, showing a definition of multi-state systems for the case of partially ordered state spaces, we summarize some methods for stochastic evaluation of the system at a time slice or in a steady state as the inclusion and exclusion method, Boolean method, stochastic bounds by minimal and maximal state vectors, stochastic bounds by series and parallel decomposition of the multi-state system and stochastic bounds via modular decomposition.

Zuo University of Alberta, Canada This talk addresses key challenges in assurance of the reliability of systems in continuous operation utilizing condition monitoring data.

Key issues include quantification of the health status of system in operation and check this out relationship between health ããžãã¯ã¹ããã and the predicted remaining useful life.

The covered research aspects include fault detection, fault assessment, ãªã¬ãŽã³ãã¬ã€ã«ã²ãŒã ã®ããŠã³ããŒã diagnosis, deterioration trend prediction, reliability assessment, maintenance optimization models and methods, and decision-making tools for inspection, maintenance, and operation.

Yun Pusan National University, Korea In this talk, I introduce simulation-based Reliability and Maintenance optimization problems for å®çæŽ»äŸã«ãããç¢ºçã²ãŒã systems.

System operational availability and life cycle cost are considered as optimization criteria.

Meta-heuristics and heuristic techniques are used to find the near optimal solutions in the optimization problems.

Three optimization topics are discussed; Firstly, I explain a reliability and maintainability optimization problem for a searching system and want to determine the optimal value of MTBF Mean Time between FailuresMTTR Mean Time to æž©å®€ã²ãŒã and ALDT Administrative and Logistics Delay Time of all units that minimize the life cycle cost and satisfy the target system availability.

Secondly, I talk about a preventive maintenance problem for KTX Korean Train eXpress because system availability can be improved through effective preventive maintenance.

The objective is to determine the preventive maintenance intervals of units in the system optimally.

Finally, I introduce an inspection optimization problem for one-shot systems with two types of units where Type 1 units are failed at random times and Type 2 units are degraded with time.

The interval availability and life cycle cost are used as optimization criteria and the optimal inspection interval is obtained for a one-shot system with given replacement times of Type 2 units.

Next, an inspection scheduling problem is studied for one-shot systems under the constraint of maintenance resources.

Wenbin Wang University of Science and Technology Beijing, China Remaining useful life RUL njã§ã«ãžããã£ãŒã©ãŒã«ãªãæ¹æ³ is regarded as one of the most central components in prognostics and health management PHM.

Accurate RUL estimation can enable failure prevention in a more controllable manner in that effective maintenance can be executed in appropriate time to correct impending faults.

In this talk we consider the problem of estimating the RUL from observed degradation data for a general system.

A degradation path-dependent approach for RUL estimation is presented through the combination of Bayesian updating and expectation maximization EM algorithm.

The use of both Bayesian updating and EM algorithm to update the model parameters and RUL distribution at the time obtaining a newly observed data is a novel contribution of this research, which makes the estimated RUL depend on the observed degradation data history.

As two specific cases, a linear degradation model and an exponential-based degradation model are considered to illustrate the implementation of our presented approach.

A major contribution under these two special cases is that our approach can obtain an exact and closed-form RUL distribution respectively, and the moment of the obtained RUL distribution from our presented approach exists.

This contrasts sharply with the approximated results obtained in the literature for the same cases.

To our knowledge, the RUL estimation approach presented in this talk for the two special cases is the only one that can provide an exact and closed-form RUL distribution utilizing the monitoring history.

Tadashi Dohi Hiroshima University, Japan The empirical software engineering is becoming much popular and is useful to quantify the software development process via the measurement-based approach.

Especially, an effective utilization of software metrics measured in the development process plays a central role to evaluate the engineering aspect of software development.

On the other hand, the software reliability engineering aims at evaluating the quantitative software product reliability, which is defined as the probability that the software failure does not occur in the operational phase, but has just focused on the curve fitting of the cumulative number of software article source from the software fault count data.

In other words, the software reliability engineering community has often missed to utilize the software metrics effectively during the last three decades.

In this talk, I summarize the metrics-based software reliability modeling framework and give a significant approach to bridge between the software metrics data and the software reliability assessment.

The fundamental idea is to apply non-trivial regression-based models to represent the software failure rate.

Throughout illustrative examples with actual software development project data, I show that the metrics-based software reliability assessment technique outperforms the existing software fault count methods.

I also refer to the reliability assessment for incremental software development, arising in the well-known agile software development æãããæ®ºäººãã¹ããªãŒã²ãŒã ãªã³ã©ã€ã³ />This talk will discuss some of the research activities on techniques and methodologies for building and monitoring å®çæŽ»äŸã«ãããç¢ºçã²ãŒã software systems conducted within the Queen's Reliable Software Technology QRST research group.

Current optical networks are already capable of transporting 100 channels on a single fiber, where each channel can carry 40 Gbps.

Since companies, government agencies, and the military are dependent on receiving uninterrupted, reliable service, instantaneous service restoration in the event of link or node failures has become critically important.