scholarly journals Effect of redundancy on the mean time to failure of wireless sensor networks

2007 ◽  
Vol 19 (8) ◽  
pp. 1119-1128 ◽  
Author(s):  
Anh Phan Speer ◽  
Ing-Ray Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Time To Failure ◽  
Mean Time To Failure ◽  
The Mean ◽  
Mean Time

On classes of life distributions based on the mean time to failure function

2021 ◽  
Vol 58 (2) ◽  
pp. 289-313
Author(s):  
Ruhul Ali Khan ◽  
Dhrubasish Bhattacharyya ◽  
Murari Mitra
Keyword(s):  
Damage Model ◽  
Replacement Policy ◽  
Time To Failure ◽  
Mean Time To Failure ◽  
Moment Convergence ◽  
Life Distributions ◽  
Cumulative Damage Model ◽  
The Mean ◽  
Age Replacement ◽  
Mean Time

AbstractThe performance and effectiveness of an age replacement policy can be assessed by its mean time to failure (MTTF) function. We develop shock model theory in different scenarios for classes of life distributions based on the MTTF function where the probabilities $\bar{P}_k$ of surviving the first k shocks are assumed to have discrete DMTTF, IMTTF and IDMTTF properties. The cumulative damage model of A-Hameed and Proschan [1] is studied in this context and analogous results are established. Weak convergence and moment convergence issues within the IDMTTF class of life distributions are explored. The preservation of the IDMTTF property under some basic reliability operations is also investigated. Finally we show that the intersection of IDMRL and IDMTTF classes contains the BFR family and establish results outlining the positions of various non-monotonic ageing classes in the hierarchy.

Điều trị tăng liều Imatinib trên bệnh nhân u mô đệm đường tiêu hoá giai đoạn muộn tiến triển sau điều trị bước 1 liều chuẩn

2020 ◽  
Author(s):  
Kien Do Hung
Keyword(s):  
Primary Tumor ◽  
Standard Dose ◽  
Performance Status ◽  
Gastric Tumor ◽  
High Dose ◽  
Time To Failure ◽  
Gastrointestinal Stromal Tumours ◽  
Mean Time To Failure ◽  
The Mean ◽  
Mean Time

Objective: Evaluating the result of high-dose imatinib for metastatic gastrointestinal stromal tumours after failure standard-dose first line. Patients and method: Restrospective analysis of 46 patients with metastatic gastrointestinal stromal tumours after failure standard-dose imatinib treated with high-dose imatinib at K hospital from 1/2015 đến 10/2019. Results: Median age was 54.6±9.5, male was 58.7%. The common primary tumor was gastric tumor. The mean time to failure of imatinib standard-dose 400mg/day was 38.2±5.3 months. Liver lesions were the most common lesions progressed after imatinib standard-dose failure (71.7%), primary tumor progressed was 39.1%. There was no patient who had complete response with treatment, the proportion of partial response accounted for 21.7% and stable disease was 45.7%. The clinical benefit rate was 67.4%. The sex-female, primary gastric tumor, good ECOG performance status, neutrophils, hemoglobine and albumin before treatment were the significant prognostic factors affecting the treatment response, p <0.05. The mean time to failure was 22.5 ± 3.4 (months), (min: 2.0; max: 58.0), median was 11.0 months. Conclusion: Treatment of high-dose imatinib after failure standard-dose 400mg/day showed the efficacy and good tolerance in metastatic GISTs.

scholarly journals On the estimation of the mean time to failure by simulation

2017 ◽  
Author(s):  
Peter W. Glynn ◽  
Marvin K. Nakayama ◽  
Bruno Tuffin
Keyword(s):  
Time To Failure ◽  
Mean Time To Failure ◽  
The Mean ◽  
Mean Time

scholarly journals Diffusion Strategies for Distributed Kalman Filter with Dynamic Topologies in Virtualized Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Shujie Yang ◽  
Tao Huang ◽  
Jianfeng Guan ◽  
Yongping Xiong ◽  
Mu Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Kalman Filter ◽  
Sensor Networks ◽  
Network Virtualization ◽  
Physical Layer ◽  
Wireless Sensor ◽  
Kalman Filter Algorithm ◽  
The Mean ◽  
Improved Performance ◽  
Filter Algorithms

Network virtualization has become pervasive and is used in many applications. Through the combination of network virtualization and wireless sensor networks, it can greatly improve the multiple applications of traditional wireless sensor networks. However, because of the dynamic reconfiguration of topologies in the physical layer of virtualized sensor networks (VSNs), it requires a mechanism to guarantee the accuracy of estimate values by sensors. In this paper, we focus on the distributed Kalman filter algorithm with dynamic topologies to support this requirement. As one strategy of distributed Kalman filter algorithms, diffusion Kalman filter algorithm has a better performance on the state estimation. However, the existing diffusion Kalman filter algorithms all focus on the fixed topologies. Considering the dynamic topologies in the physical layer of VSNs mentioned above, we present a diffusion Kalman filter algorithm with dynamic topologies (DKFdt). Then, we emphatically derive the theoretical expressions of the mean and mean-square performance. From the expressions, the feasibility of the algorithm is verified. Finally, simulations confirm that the proposed algorithm achieves a greatly improved performance as compared with a noncooperative manner.

On estimating the mean time to failure with unknown censoring

1989 ◽  
Vol 38 (3) ◽  
pp. 343-347 ◽  
Author(s):  
R. Shanmugam ◽  
D.O. Richards
Keyword(s):  
Time To Failure ◽  
Mean Time To Failure ◽  
The Mean ◽  
Mean Time

RELIABILITY PROPERTIES OF MEAN TIME TO FAILURE IN AGE REPLACEMENT MODELS

2010 ◽  
Vol 17 (01) ◽  
pp. 15-26 ◽  
Author(s):  
G. ASHA ◽  
N. UNNIKRISHNAN NAIR
Keyword(s):  
Residual Life ◽  
Stochastic Orders ◽  
Mean Residual Life ◽  
Time To Failure ◽  
Mean Time To Failure ◽  
Replacement Model ◽  
The Mean ◽  
Age Replacement ◽  
The Relationship ◽  
Mean Time

In this article some properties of the mean time to failure in an age replacement model is presented by examining the relationship it has with hazard (reversed hazard) rate and mean (reversed mean) residual life functions. An ordering based on mean time to failure is used to examine its implications with other stochastic orders.

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