• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.918-927
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• 2018

Based on the actual operating environment of transformer, the aging tests of nitrile butadiene rubber (NBR) were conducted systematically under four conditions: in air, in transform oil, under compression in air and under compression in transform oil to studythe effect of high temperature, transform oil and compression stress simultaneously on the thermal aging behaviors of nitrile butadiene rubber and predict the lifetime. The effects of liquid media and compression stress simultaneously on the thermal aging behaviors of nitrile butadiene rubber were studied by using characterization methods such as IR spectrosc-opy, thermogravimetric measurements, Differential Scanning Calorimetry (DSC) measurements and mechanical property measurements. The changes in physical properties during the aging process were analyzed and compared. Different aging conditions yielded materials with different properties. Aging at $70^{\circ}C$ under compression stress in oil, the change in elongation at break was lower than that aging in oil, but larger than that aging under compression in air. The compression set or elongation at break as evaluation indexes, 50% as critical value, the lifetime of NBR at $25^{\circ}C$ was predicted and compared. When aging under compression in oil, the prediction lifetime was lower than in air and under compression in air, and in oil. It was clear that when predicting the service lifetime of NBR in oil sealing application, compression and media liquid should be involved simultaneously. Under compression in oil, compression set as the evaluation index, the prediction lifetime of NBR was shorter than that of elongation at break as the evaluation index. For the life prediction of NBR, we should take into account of the performance trends of NBR under actual operating conditions to select the appropriate evaluation index.

• Journal of Communications and Networks
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• 제15권4호
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• pp.338-351
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• 2013

Multiple-input multiple-output (MIMO) communication may provide high spectral efficiency through the deployment of a very large number of antenna elements at the base stations. The gains from massive MIMO communication come from the use of multi-user MIMO on the uplink and downlink, but with a large excess of antennas at the base station compared to the number of served users. Initial work on massive MIMO did not fully address several practical issues associated with its deployment. This paper considers the impact of channel aging on the performance of massive MIMO systems. The effects of channel variation are characterized as a function of different system parameters assuming a simple model for the channel time variations at the transmitter. Channel prediction is proposed to overcome channel aging effects. The analytical results on aging show how capacity is lost due to time variation in the channel. Numerical results in a multicell network show that massive MIMO works even with some channel variation and that channel prediction could partially overcome channel aging effects.

• 한국공간구조학회논문집
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• 제24권3호
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• pp.43-51
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• 2024

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson's ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

• 콘크리트학회논문집
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• 제12권6호
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• pp.35-42
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• 2000

In paper I, the relationships between compressive strength and splitting tensile strength or modulus of elasticity were proposed. In this paper, new prediction model is investigated from estimating splitting tensile strength and modulus of elasticity with curing temperature and aging without compressive strength. New prediction model is based on the model which was proposed to predict compressive strength, and splitting tensile strength and modulus of elasticity calculated by this model are compared with experimental values of paper I. To evaluate in-situ applicability of the model, strength and modulus of elasticity tested with variable temperatures are estimated by the prediction model. The prediction model reasonably estimates the strength and the modulus of elasticity of type I and V cement concretes tested in paper I and experimental results with variable temperature tested in this paper.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Journal of Mechanical Science and Technology
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• 제19권6호
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• pp.1229-1242
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• 2005

When natural rubber is used for a long period of time, it becomes aged; it usually becomes hardened and loses its damping capability. This aging process affects not only the material property but also the (fatigue) life of natural rubber. In this paper the aging effects on the material property and the fatigue life were experimentally investigated. In addition, several fatigue life prediction equations for natural rubber were proposed. In order to investigate the aging effects on the material property, the load-stretch ratio curves were plotted from the results of the tensile test, the compression test and the simple shear test for virgin and heat-aged rubber specimens. Rubber specimens were heat-aged in an oven at a temperature ranging from $50^{\circ}C$ to $90^{\circ}C$ for a period ranging from 2 days to 16 days. In order to investigate the aging effects on the fatigue life, fatigue tests were conducted for differently heat-aged hourglass-shaped and simple shear specimens. Moreover, finite element simulations were conducted for the specimens to calculate physical quantities occurring in the specimens such as the maximum value of the effective stress, the strain energy density, the first invariant of the Cauchy-Green deformation tensor and the maximum principal nominal strain. Then, four fatigue life prediction equations based on one of the physical quantities could be obtained by fitting the equations to the test data. Finally, the fatigue life of a rubber bush used in an automobile was predicted by using the prediction equations, and it was compared with the test data of the bush to evaluate the reliability of those equations.

• 한국자동차공학회논문집
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• 제10권1호
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• pp.45-50
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• 2002

Use of a phenomenological model, developed far prediction of catalytic deactivation, is demonstrated in comparing harshness of different driving cycles that are currently used to rapidly age catalytic converters on engine test benches. The model shows that seemingly equivalent driving cycles cause the catalytic converters to reach significantly different levels of deactivation. The comparison of the model prediction with the limited vehicle data seems encouraging despite the simplicity of the model at the current stage of its infancy.

• 한국염색가공학회지
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• 제29권3호
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• pp.155-161
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• 2017

In CPB(Cold-Pad-Batch) dyeing, the rubber of the padder roll is influenced by the heat, chemical and mechanical influences and thus aging of the padder roll rubber occurs. This study presents an accelerated thermal aging test of the CPB padder roll rubber with strong alkali conditions. Using Arrhenius formula of the various property values for the various aging temperatures($80^{\circ}C$, $90^{\circ}C$, $100^{\circ}C$) of the padder roll, the accelerated life predictions could be calculated. The threshold value of the property was set at different values. The hardness was set at the point where 5% degradation occurs based on the actual use conditions, and the tensile strength was set at the point where 50% degradation occurs based on the general life prediction standards. From the results of the different physical properties at differing temperatures, the Arrhenius plot could be obtained. Through the usage of the Arrhenius Equation, significant duration expectation could be predicted, and the chemical aging behavior of the CPB padder roll could be found at the arbitrary and actual temperatures.

• Asian-Australasian Journal of Animal Sciences
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• 제24권5호
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• pp.603-609
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• 2011

Telomeres are special structures at the ends of eukaryotic chromosomes. Vertebrate telomeres consist of tandem repeats of conserved TTAGGG sequence and associated proteins. Birds are interesting models for molecular studies on aging and cellular senescence because of their slow aging rates and longer life spans for their body size. In this longitudinal study, we explored the possibility of using telomeres as an age-marker to predict age in Single Comb White Leghorn layer chickens. We quantified the relative amount of telomeric DNA in isolated peripheral blood lymphocytes by the Quantitative Fluorescence in situ Hybridization technique on interphase nuclei (IQ FISH) using telomere-specific DNA probes. We found that the amount of telomeric DNA (ATD) reduced significantly with an increase in chronological age of the chicken. Especially, the telomere shortening rates are greatly increased in growing individuals compared to laying and old-aged individuals. Therefore, using the ATD values obtained by IQ FISH we established the possibility of age prediction in chickens based on the telomere theory of aging. By regression analysis of the ATD values at each age interval, we formulated an equation to predict the age of chickens. In conclusion, the telomeric DNA values by IQ FISH analyses can be used as an effective age-marker in predicting the chronological age of chickens. The study has implications in the breeding and population genetics of poultry, especially the reproductive potential.