• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.1945-1947
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• 2005

This study considers find out best accelerated life testing and lifetime prediction of electrolytic capacitors. We proved about relation between failure and deterioration mechanism from last thesis. Beside we performed test that temperature and voltage press higher than allowance specification. Failure distribution acquired from those test. And wiebull function and Minitab program applied to accelerated constant and lifetime by means of calculation. At the result, goodness of fit affect to weibull function and acceleration factor therefore fitting is important factor in reliability testing.

• KIEE International Transactions on Electrophysics and Applications
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• v.3C no.1
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• pp.1-4
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• 2003

This paper presents an attempt to develop probabilistic multistress life models to evaluate the lifetime characteristics of epoxy-encapsulated magnet wire with heavy build polyurethane enamel. A set of accelerated life tests were conducted over a wide range of pulsating voltages, temperatures, and frequencies. Samples of fine gauge twisted pairs of the encapsulated magnet wire were tested us-ing a pulse endurance dielectric test system. An electrical-thermal lifetime function was combined with the Weibull distribution of lifetimes. The parameters of the combined Weibull-electrical-thermal model were estimated using maximum likelihood estimation. Likewise, a generalized electrical-thermal-frequency life model was also developed. The parameters of this new model were estimated using multiple linear regression technique. It was found in this paper that lifetime estimates of the two proposed probabilistic multistress life models are good enough. This suggests the suitability of using the general electrical-thermal-frequency model to estimate the lifetime of the encapsulated magnet wire over a wide range of voltages, temperatures and pulsating frequencies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1715-1724
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• 2013

The maximum lifetime data aggregation problem is to maximize the network lifetime as minimizing the transmission energy of all deployed nodes in wireless sensor networks. In this paper, we propose a simulated annealing algorithm to solve efficiently the maximum lifetime data aggregation problem on the basis of meta-heuristic approach in wireless sensor networks. In order to make a search more efficient, we propose a novel neighborhood generating method and a repair function of the proposed algorithm. We compare the performance of the proposed algorithm with other existing algorithms through some experiments in terms of the network lifetime and algorithm computation time. Experimental results show that the proposed algorithm is efficient for the maximum lifetime data aggregation problem in wireless sensor networks.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.147-161
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• 2016

This paper introduces the four parameter new generalized inverse Weibull distribution and investigates the potential usefulness of this model with application to reliability data from engineering studies. The new extended model has upside-down hazard rate function and provides an alternative to existing lifetime distributions. Various structural properties of the new distribution are derived that include explicit expressions for the moments, moment generating function, quantile function and the moments of order statistics. The estimation of model parameters are performed by the method of maximum likelihood and evaluate the performance of maximum likelihood estimation using simulation.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2747-2757
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• 2018

We consider the MLE (maximum likelihood estimate) and Bayesian estimates of three-parameter bathtub-shaped lifetime distribution based on the progressive type II censoring with binomial removal. Jung, Chung (2018) proposed the three-parameter bathtub-shaped distribution which is the extension of the two-parameter bathtub-shaped distribution given by Zhang (2004). Jung, Chung (2018) investigated its properties and estimations. The maximum likelihood estimates are computed using Newton-Raphson algorithm. Also, Bayesian estimates are obtained under the balanced loss function using MCMC (Markov chain Monte Carlo) method. In particular, BSEL (balanced squared error loss) function is considered as a special form of balanced loss function given by Zellner (1994). For comparing theirs MLEs with the corresponding Bayes estimates, some simulations are performed. It shows that Bayes estimates is better than MLEs in terms of risks. Finally, concluding remarks are mentioned.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1661-1669
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• 2010

In this paper, I propose an Energy efficient Clustering based on Genetic Algorithm(ECGA) which reduces energy consumption by distributing energy overload to cluster group head and cluster head in order to lengthen the lifetime of sensor network. ECGA algorithm calculates the values like estimated energy cost summary, average and standard deviation of residual quantity of sensor node and applies them to fitness function. By using the fitness function, we can obtain the optimum condition of cluster group and cluster. I demonstrated that ECGA algorithm reduces the energy consumption and lengthens the lifetime of network compared with the previous clustering method by stimulation.

• Safety and Health at Work
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• v.12 no.2
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• pp.174-183
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• 2021

Background: Toluene diisocyanate (TDI) is a highly reactive chemical that causes sensitization and has also been associated with increased lung cancer. A risk assessment was conducted based on occupational epidemiologic estimates for several health outcomes. Methods: Exposure and outcome details were extracted from published studies and a NIOSH Health Hazard Evaluation for new onset asthma, pulmonary function measurements, symptom prevalence, and mortality from lung cancer and respiratory disease. Summary exposure-response estimates were calculated taking into account relative precision and possible survivor selection effects. Attributable incidence of sensitization was estimated as were annual proportional losses of pulmonary function. Excess lifetime risks and benchmark doses were calculated. Results: Respiratory outcomes exhibited strong survivor bias. Asthma/sensitization exposure response decreased with increasing facility-average TDI air concentration as did TDI-associated pulmonary impairment. In a mortality cohort where mean employment duration was less than 1 year, survivor bias pre-empted estimation of lung cancer and respiratory disease exposure response. Conclusion: Controlling for survivor bias and assuming a linear dose-response with facility-average TDI concentrations, excess lifetime risks exceeding one per thousand occurred at about 2 ppt TDI for sensitization and respiratory impairment. Under alternate assumptions regarding stationary and cumulative effects, one per thousand excess risks were estimated at TDI concentrations of 10 - 30 ppt. The unexplained reported excess mortality from lung cancer and other lung diseases, if attributable to TDI or associated emissions, could represent a lifetime risk comparable to that of sensitization.

• Journal of Communications and Networks
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• v.18 no.2
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• pp.227-237
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• 2016

Data collection is a key function for wireless sensor networks. There has been numerous data collection scheduling algorithms, but they fail to consider the deep and complex relationship among network lifetime, sink aggregated information and sample cycle for wireless sensor networks. This paper gives the upper bound on the sample period under the given network topology. An optimal schedule algorithm focusing on aggregated information named OSFAI is proposed. In the schedule algorithm, the nodes in hotspots would hold on transmission and accumulate their data before sending them to sink at once. This could realize the dual goals of improving the network lifetime and increasing the amount of information aggregated to sink. We formulate the optimization problem as to achieve trade-off among sample cycle, sink aggregated information and network lifetime by controlling the sample cycle. The results of simulation on the random generated wireless sensor networks show that when choosing the optimized sample cycle, the sink aggregated information quantity can be increased by 30.5%, and the network lifetime can be increased by 27.78%.

• Communications for Statistical Applications and Methods
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• v.23 no.5
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• pp.363-383
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• 2016

The Weibull family of lifetime distributions play a fundamental role in reliability engineering and life testing problems. This paper investigates the potential usefulness of transmuted new generalized Weibull (TNGW) distribution for modeling lifetime data. This distribution is an important competitive model that contains twenty-three lifetime distributions as special cases. We can obtain the TNGW distribution using the quadratic rank transmutation map (QRTM) technique. We derive the analytical shapes of the density and hazard functions for graphical illustrations. In addition, we explore some mathematical properties of the TNGW model including expressions for the quantile function, moments, entropies, mean deviation, Bonferroni and Lorenz curves and the moments of order statistics. The method of maximum likelihood is used to estimate the model parameters. Finally the applicability of the TNGW model is presented using nicotine in cigarettes data for illustration.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10A
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• pp.1023-1033
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• 2007

This study considers a joint congestion control, routing and power control for energy-constrained wireless networks. A mathematical model is introduced which includes maximization of network utility, maximization of network lifetime, and trade-off between network utility and network lifetime. The framework would maximize the overall throughput of the network where the overall throughput depends on the data flow rates which in turn is dependent on the link capacities. The link capacity on the other hand is a function of transmit power levels and link Signal-to-Interference-plus-Noise-Ratio (SINR) which makes the power allocation problem inherently difficult to solve. Using dual decomposition techniques, subgradient method, and logarithmic transformations, a joint algorithm for rate and power allocation problems was formulated. Numerical examples for each optimization problem were also provided.