General Prediction Method Research Articles

In-situ fatigue life prognosis for composite laminates based on stiffness degradation

In this paper, a real-time composite fatigue life prognosis framework is proposed. The proposed methodology combines Bayesian inference, piezoelectric sensor measurements, and a mechanical stiffness degradation model for in-situ fatigue life prediction. First, the composites stiffness degradation is introduced to account for the composites fatigue damage accumulation under cyclic loadings and a new growth rate-based stiffness degradation model is developed. Following this, the general Bayesian updating-based fatigue life prediction method is discussed. Several sources of uncertainties and the developed stiffness degradation model are included in the prognosis framework. Next, an in-situ composites fatigue testing with piezoelectric sensors is designed and performed to collected sensor signal and the global stiffness data. Signal processing techniques are implemented to extract damage diagnosis features. The detected stiffness degradation is integrated in the Bayesian inference framework for the remaining useful life (RUL) prediction. Prognosis performance on experimental data is validated using prognostics metric. Finally, some conclusions and future work are drawn based on the proposed study.

경제적 투자효과의 예측 정확도 향상을 위한 실질할인율 분석

투자에 의해 기대되는 경제적 효과는 실질할인율의 자승으로 매년 나누어서 현재가치로 전환된다. 따라서 실질할인율이 경제성 분석결과에 미치는 영향은 다른 요인들보다 크다. 실질할인율을 예측하는 기존의 일반적인 방법은 과거 특정기간의 평균값을 적용하는 것이다. 본 연구에서는 실질할인율의 예측 정확도를 향상시키기 위한 방법을 제안하였다. 먼저 실질할인율을 구성하는 기업대출 이자율과 소비자 물가지수에 영향을 미치는 경제변수들을 도출하였다. 기업대출 이자율에 영향을 주는 변수들로는 콜 금리와 환율, 소비자 물가지수에 영향을 주는 경제변수는 생산자 물가지수를 선정하였다. 다음으로 실질할인율과 선정된 변수들과의 영향관계를 검정하였다. 영향관계가 존재하는 것으로 분석되었다. 마지막으로 관련된 경제 변수들을 기반으로 2008년부터 2010년까지의 실질할인율을 예측하였다. 예측 결과의 정확도는 실측값과 평균값의 결과와 비교되었다. 실측값이 적용된 실질할인율은 -1.58%였으며, 예측 값은 -0.22%, 평균값은 6.06%으로 분석되었다. 본 연구에서 제안한 방법은 금융위기와 같은 특수 상황을 고려하지 않은 것이지만, 평균값보다 예측 정확도가 크게 우수한 것으로 분석되었다. The expected economic effect by investment was divided by square of real discount rate annually for change to present value. Thus, the impact of real discount rate on economic analysis is larger than other factors. The existing general method for prediction of real discount rate is application of average data during past certain period. This study proposed prediction method of real discount rate for accuracy improvement. First, the economic variables which impact on interest rate of business loan and consumer price of real discount rate were determined. The variables which impact on interest rate of business loan were selected to call rate and exchange rate. The variable which impact on consumer price index was selected to producer price index. Next, the effect relation was analyzed between real discount rate and selected variables. The significant effect relation were analyzed to exit. Lastly, the real discount rate was predicted from 2008 to 2010 based on related economic variables. The accuracy of prediction result was compared with actual data and average data. The real discount rate based on actual data, predicted data, and average data were analyzed to -1.58%, -0.22%, and 6.06%, respectively. Though the proposed method in this study was not considered special condition such as financial crisis, the prediction accuracy was much higher than result based on average data.

Application of Grey Least Square Support Vector Machine in New Equipment Materiel Demand Prediction

Due to the short investment time of the new equipment, the materiel consumption and maintenance data is not much. As a result, its demand prediction belongs to the prediction of small sample data. Since general demand prediction methods are difficult to predict the materiel demand of new equipment, an applicable and efficient prediction method should be explored to solve the problem. Therefore, combining grey prediction theory and least square support vector machine and operating accumulative generation on the original data sequence to extract its deep law characteristic, the new equipment materiel demand prediction model based on Grey Least Square Support Vector Machine (GLSSVM) was established, and the model's parameters was optimized by SIWPSO. Finally an example was set using Neural Network, traditional LSVSM and GLSSVM to predict the materiel demand of new equipment X to verify the accuracy and effectiveness of GLSSVM. The result shows that the prediction precision of GLSSVM is superior to the other two methods.

A General Current Ripple Prediction Method for the Multiphase Voltage Source Converter

Current ripple is generated by pulse width modulation (PWM) switching in multiphase voltage source converters (VSCs). This letter introduces a general and fast current ripple prediction method for multiphase VSCs with arbitrary phase numbers. An equivalent converter-load model is derived for the n -phase converter system. By combining the common-mode voltage of both converter terminal and load, the equivalent circuit for each phase can be modeled. The voltage dropping on the ac inductor can be calculated for the 2 n + 2 zones in each switching cycle based on the equivalent circuit for each phase. Then the current ripple can be reconstructed based on the linear di/dt model in each zone. Simulation examples of five- and six-phase converters prove that the current prediction method is accurate. With this real-time prediction method, the current ripple can be controlled in application. An application example of five-phase variable switching frequency PWM is introduced to control the peak current ripple and reduce the switching losses.

Identity obfuscation in graphs through the information theoretic lens

Analyzing the structure of social networks is of interest in a wide range of disciplines. Unfortunately, sharing social-network datasets is often restrained by privacy considerations. One way to address the privacy concern is to anonymize the data before publishing. Randomly adding or deleting edges from the social graph is one of the anonymization approaches that have been proposed in the literature. Recent studies have quantified the level of anonymity that is obtained by random perturbation by means of a posteriori belief probabilities and, by conducting experiments on small datasets, arrived at the conclusion that random perturbation cannot achieve meaningful levels of anonymity without deteriorating the graph properties.We offer a new information-theoretic perspective on the question of anonymizing a social network by means of random edge additions and deletions. We make an essential distinction between image and preimage anonymity and propose a more accurate quantification, based on entropy, of the anonymity level that is provided by the perturbed network. We explain why the entropy-based quantification, which is global, is more adequate than the previously used local quantification that was based on a posteriori belief probabilities. We also prove that the anonymity level as quantified by means of entropy is always greater than or equal to the one based on a posteriori belief probabilities. In addition, we introduce and explore the method of random sparsification, which randomly removes edges, without adding new ones.Extensive experimentation on several very large datasets shows that randomization techniques for identity obfuscation are back in the game, as they may achieve meaningful levels of anonymity while still preserving properties of the original graph.As the methods we study add and remove edges, it is natural to ask whether an adversary might use the disclosed perturbed graph structure to reconstruct, even partially, the original graph. We thus study the resilience of obfuscation by random sparsification to adversarial attacks that are based on link prediction. Given a general link prediction method, with a predefined level of prediction accuracy, we show how to quantify the level of anonymity that is guaranteed by the obfuscation. We empirically prove that even for very accurate link prediction methods, the level of anonymity guaranteed remains very close to the one before the attack.Finally, we show how the randomization method may be applied in a distributed setting, where the network data is distributed among several non-trusting sites, and explain why randomization is far more suitable for such settings than other existing approaches.

Fast Model Predictive Control Method for Large-Scale Structural Dynamic Systems: Computational Aspects

A fast and accurate model predictive control method is presented for dynamic systems representing large-scale structures. The fast model predictive control formulation is based on highly efficient computations of the state transition matrix, that is, the matrix exponential, using an improved precise integration method. The enhanced efficiency for model predictive control is achieved by exploiting the sparse structure of the matrix exponential at each discrete time step. Accuracy is maintained using the precise integration method. Compared with the general model predictive control method, the reduced central processing unit (CPU) time required by the fast model predictive control scheme can result in a shorter control update interval and a lower online computational burden. Therefore, the proposed method is more efficient for large-scale structural dynamic systems.

Subsemble: an ensemble method for combining subset-specific algorithm fits

Ensemble methods using the same underlying algorithm trained on different subsets of observations have recently received increased attention as practical prediction tools for massive data sets. We propose Subsemble: a general subset ensemble prediction method, which can be used for small, moderate, or large data sets. Subsemble partitions the full data set into subsets of observations, fits a specified underlying algorithm on each subset, and uses a clever form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. We give an oracle result that provides a theoretical performance guarantee for Subsemble. Through simulations, we demonstrate that Subsemble can be a beneficial tool for small- to moderate-sized data sets, and often has better prediction performance than the underlying algorithm fit just once on the full data set. We also describe how to include Subsemble as a candidate in a SuperLearner library, providing a practical way to evaluate the performance of Subsemble relative to the underlying algorithm fit just once on the full data set.

VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses

BackgroundSelection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed “VIRsiRNApred” a support vector machine (SVM) based method for predicting the efficacy of viral siRNA.MethodsIn the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T1380) and validation (V345) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development.ResultsDuring 10-fold cross validation on T1380 using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V345 independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree.ConclusionVIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/.

Fundamental Issues of Critical Heat Flux Phenomena During Flow Boiling in Microscale-Channels and Nucleate Pool Boiling in Confined Spaces

This article presents a comprehensive literature review on the fundamental issues of critical heat flux (CHF) during flow boiling and nucleate pool boiling in microscale channels and confined spaces. First, distinction between macro- and micro-scale channels is discussed. Then the CHF mechanisms are discussed. Next, experimental and theoretical studies of subcooled flow boiling CHF in microscale channels together with the prediction methods are reviewed and analyzed. Following this, experimental and theoretical studies on saturated flow boiling CHF together with the prediction methods are summarized and discussed. Furthermore, experimental and theoretical studies on nucleate pool boiling CHF in confined spaces together with the relevant prediction methods are reviewed as well. So far, limited studies on CHF microscale channels and confined spaces are available in the literature. There are numerous discrepancies in the existing studies on CHF results, mechanisms, and prediction methods. Furthermore, there are no generalized prediction methods for CHF in microscale channels and confined spaces. According to this review, future research needs for the experiments, mechanisms, and prediction methods of CHF phenomena in microscale channels and confined spaces have been addressed.

The structure design and prediction of noise reduction coefficients of dual layered nonwoven absorbers

Predicting the acoustical behavior of noise control elements made of fibrous materials, especially the ones comprised by two types of nonwoven materials with different structural parameters, is a relevant topic in large spaces such as gymnasiums, cinemas, shopping malls, airports and stations. In this paper, we propose a more general prediction method for the noise reduction coefficient (NRC) of the dual layered nonwoven absorber by employing general regression neural network (GRNN). In the experiment section, fifty dual layered nonwoven absorbers are specifically designed by five different melt blown polypropylene and hydroentangled E-glass fiber nonwoven materials. Four structural parameters including thickness, area density, porosity, and pore size of each layer are measured, which are used as the inputs of GRNN. The sound absorption coefficients of each absorber are measured with SW477 impedance tube from 80 to 6300 Hz. The sound absorption average at 250, 500, 1000 and 2000 Hz is used to represent the NRC, which is also considered as an output of GRNN at prediction stage. Finally, the prediction framework is carried out after the desired training set selection and spread parameter optimization of GRNN. The prediction results of 10 test samples show that the prediction method proposed in this paper is reliable and efficient.

Field-Failure Predictions Based on Failure-Time Data With Dynamic Covariate Information

Modern technological developments, such as smart chips, sensors, and wireless networks, have changed many data-collection processes. For example, there are more and more products being produced with automatic data-collecting devices that track how and under which environments the products are being used. Although there is a tremendous amount of dynamic data being collected, there has been little research on using such data to provide more accurate reliability information for products and systems. Motivated by a warranty-prediction application, this article focuses on using failure-time data with dynamic covariate information to make field-failure predictions. We provide a general method for prediction using failure-time data with dynamic covariate information. The dynamic covariate information is incorporated into the failure-time distribution through a cumulative exposure model. We develop a procedure to predict field-failure returns up to a specified future time. This procedure accounts for unit-to-unit variability in the covariate process. We also define a metric to quantify the improvements in prediction accuracy obtained by using dynamic information. We conduct simulations to study the effect of different sources of covariate process variability on predictions. We also provide some discussion of future opportunities for using dynamic data. This article has supplementary material online.

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

Critical heat flux in microscale channels and confined spaces: A review on experimental studies and prediction methods

This paper presents a comprehensive literature review on critical heat flux (CHF) of flow boiling and pool boiling in micrscale channels and confined spaces. First, distinction between macro- and micro-scale channels is discussed. Then, the critical heat flux mechanisms are discussed. Next, experimental and theoretical studies of subcooled flow boiling CHF in microscale channels together with the prediction methods are reviewed. Following this, experimental and theoretical studies on saturated flow boiling CHF together with the prediction methods are summarized. Furthermore, experimental and theoretical studies on nucleate pool boiling CHF in confined spaces together with the prediction methods are briefly reviewed. So far, limited studies on CHF in microscale channels and confined spaces are available in the literature and there are numerous discrepancies in the existing studies on CHF results and mechanisms. There are no generalized prediction methods for CHF in microscale channels and confined spaces. According to this review, future research needs have been identified, including the frontier research of nanofluids CHF in microscale channels and confined spaces.

The structural performance of axially loaded CFST columns under various loading conditions

Concrete filled steel tube (CFST) structures have been used widely in high-rise buildings and bridges due to the efficiency of structurally favourable interaction between the steel tube and the concrete core. In the current design codes only one loading condition in the column members is considered, i.e., the load is applied on the steel tube and concrete core at the same time. However, in engineering practice the tube structures may be subjected to various loading conditions such as loading on the concrete core only, preloading on the steel tube skeleton before filling of concrete core, and so on. In this research, a series of comparative experiments were carried out to study the structural performance of concrete filled circular steel tube columns subject to four concentric loading schemes. Then, a generalized prediction method is developed to evaluate the ultimate load capacity of CFST columns subject to various loading conditions. It is shown that the predictions by the proposed method agree well with test results.

AVPpred: collection and prediction of highly effective antiviral peptides

In the battle against viruses, antiviral peptides (AVPs) had demonstrated the immense potential. Presently, more than 15 peptide-based drugs are in various stages of clinical trials. Emerging and re-emerging viruses further emphasize the efforts to accelerate antiviral drug discovery efforts. Despite, huge importance of the field, no dedicated AVP resource is available. In the present study, we have collected 1245 peptides which were experimentally checked for antiviral activity targeting important human viruses like influenza, HIV, HCV and SARS, etc. After removing redundant peptides, 1056 peptides were divided into 951 training and 105 validation data sets. We have exploited various peptides sequence features, i.e. motifs and alignment followed by amino acid composition and physicochemical properties during 5-fold cross validation using Support Vector Machine. Physiochemical properties-based model achieved maximum 85% accuracy and 0.70 Matthew’s Correlation Coefficient (MCC). Performance of this model on the experimental validation data set showed 86% accuracy and 0.71 MCC which is far better than the general antimicrobial peptides prediction methods. Therefore, AVPpred—the first web server for predicting the highly effective AVPs would certainly be helpful to researchers working on peptide-based antiviral development. The web server is freely available at http://crdd.osdd.net/servers/avppred.

Inelastic Bending Capacity of Cold-Formed Steel Members

AbstractThe objective of this paper is to provide and verify a general design method for prediction of inelastic bending capacity in cold-formed steel members potentially subject to local, distortional, and/or lateral-torsional buckling modes. An extensive experimental database of tested cold-formed steel beams is collected and indicates that inelastic reserve in the bending capacity of thin-walled cold-formed steel members is more common than typically assumed. Elementary mechanics for inelastic reserve are reviewed and simplified expressions provided for connecting the strain demand to the inelastic bending capacity in the range between the yield moment and the fully plastic moment. The strain capacity that can be sustained in inelastic local and inelastic distortional buckling is investigated through existing experiments coupled with nonlinear finite-element (FE) analysis. The nonlinear FE models provide a comprehensive means to investigate the relationship between cross-section slenderness, normalized...

Bayesian serial revision method for RLLC cluster systems failure prediction

Failure prediction plays an important role for many tasks such as optimal resource management in large-scale system. However, accurately failure number prediction of repairable large-scale long-running computing (RLLC) is a challenge because of the reparability and large-scale. To address the challenge, a general Bayesian serial revision prediction method based on Bootstrap approach and moving average approach is put forward, which can make an accurately prediction for the failure number. To demonstrate the performance gains of our method, extensive experiments on the data of Los Alamos National Laboratory (LANL) cluster is implemented, which is a typical RLLC system. And experimental results show that the prediction accuracy of our method is 80.2%, and it is a greatly improvement with 4% compared with some typical methods. Finally, the managerial implications of the models are discussed.

Acceleration method for propagation prediction

This letter focuses on an acceleration method using a child node construction to completely enhance the wave propagation prediction time in linking between parent and child nodes.The child node construction, corresponding and linking with an actual parent node, is called an interobject visibility set (IVS) and is a source-independent (i.e., transmitter or image) structure. Also, child node construction is independent of database information and is used for linking dynamically to a higher-order parent node. After linking, the entire ray path construction is completed from the receiver to transmitter, and the received signal level is estimated using backward ray tracing. Finally, we demonstrate that the computational time needed for ray tracing prediction less than in a general ray tracing prediction method, and the prediction results using the ray tracing model are in good agreement with measurements at the millimeter wave band in terms of accuracy. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:982–984, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25907

EEBoost: A General Method for Prediction and Variable Selection Based on Estimating Equations

The modern statistical literature is replete with methods for performing variable selection and prediction in standard regression problems. However, simple models may misspecify or fail to capture important aspects of the data generating process such as missingness, correlation, and over/underdispersion. In this article we describe EEBoost, a strategy for variable selection and prediction which can be applied in high-dimensional settings where inference for low-dimensional parameters would typically be based on estimating equations. The method is simple, flexible, and easily implemented using existing software. The EEBoost algorithm is obtained as a modification of the standard boosting (or functional gradient descent) technique. We show that EEBoost is closely related to a class of L1 constrained projected likelihood ratio minimizations, and therefore produces similar variable selection paths to penalized methods without the need to apply constrained optimization algorithms. The flexibility of EEBoost is illustrated by applying it to simulated examples with correlated outcomes and time-to-event data with missing covariates. In both cases, EEBoost outperforms variable selection methods which do not account for the relevant data characteristics. Furthermore, it is shown to be substantially faster to compute than competing methods based on penalized estimating equations. We also apply a version of EEBoost based on the Buckley–James estimating equations to data from an HIV treatment trial, where the aim is to identify mutations which confer resistance to antiretroviral medications. Proofs of the main results appear in the Supplemental Materials (available online).