Multi-variate Weibull model for predicting system-reliability, from testing results of the components

Modern products are complicated and standards of reliability are higher than ever before. Therefore, prediction of system reliability is essential, especially as the time available for developing products has decreased. Most of the previous studies assumed that the causes of failure are random. That is, the failure rates of components follow exponential distributions. To reflect various types of failure such as initial, random, or wear-out failure, we developed a novel procedure for estimating system reliability based on the Weibull distribution. We analyzed the effect of component selection on the types of system failure. Finally, we verified the proposed method by analyzing warranty data on actual Korean products.

Study of a hull form optimization system based on a Gaussian process regression algorithm and an adaptive sampling strategy, Part I: Single-objective optimization

First-Trimester Use of Selective Serotonin-Reuptake Inhibitors and the Risk of Birth Defects Original Article, N Engl J Med 2007;356:2675-2683. In the (page 2675), the second sentence of Results should have read, “Analyses of the associations between individual SSRIs and specific defects showed significant associations between the use of sertraline and omphalocele (odds ratio, 5.7; 95% CI, 1.6 to 20.7; 3 exposed subjects) and between the use of paroxetine and right ventricular outflow tract obstruction defects (odds ratio, 3.3; 95% CI, 1.3 to 8.8; 6 exposed subjects),” rather than “. . . and omphalocele (odds ratio, 5.7; 95% CI, 1.6 to 20.7; 3 exposed subjects) and septal defects (odds ratio, 2.0; 95% CI, 1.2 to 4.0; 13 exposed subjects) and between the use of paroxetine . . . .” In the third paragraph of the “Assessment of Previous Hypotheses” subsection of Results (page 2678), the second sentence should have ended, “. . . ranged from 0.5 to 3.3, and one risk estimate had lower bounds that exceeded 1.0: paroxetine in relation to right ventricular . . . ,” rather than “. . . 3.3, and two risk estimates had lower bounds that exceeded 1.0: sertraline in relation to septal defects (odds ratio, 2.0; 95% CI, 1.2 to 4.0, based on 13 exposed subjects) and paroxetine in relation to right ventricular . . . .” In Table 2 (page 2679), the 95% confidence interval given for septal defects in the “Sertraline” column should have been “(1.0–4.0),” rather than “(1.2–4.0).” In the second paragraph of the Discussion, the first parenthetical in the second sentence (page 2680) should have read, “(odds ratio, 2.0; lower confidence bound, 1.0),” rather than “(odds ratio, 2.0).” In the first sentence of the third paragraph (page 2680), the term “selected cardiac defects” should have been “selected defects.” In the fifth paragraph, the final sentence (page 2681) should have ended, “. . . neither of which was common to both drugs,” rather than “. . . none of which were common . . . .” The article is correct at NEJM.org.

This paper presents a probabilistic-based fault-model approach to the generation of optimized single distributions of weights for random built-in self-test. Many techniques use multiple sets of weights to obtain an important reduction in the test length. However, these strategies consume large memory areas to store the different distributions. In order to obtain a highly optimized set of weights, which reduces area overhead, a global optimization procedure is used to minimize a testability cost function that projects random fault coverage. Important reduction in test length, using only a highly optimized single distribution of weights, is reported. >

This paper focuses on analysis and prediction of system reliability by using methods for achieving data-driven, customer focused reliability and availability assessment. The Markov models can be used to create system level architecture-based models that allow assessment of reliability and available parameter estimation on a system level. Analysis of finer granularity provides more insights on the root cause of problems from the customer view and future work also addressed how this can be made practical and useful.

Nowadays, products are complicated and standards of reliability are higher than ever before. In contrast, the time available for developing products has gradually decreased. In this context, assessment of the system reliability of products is an active field of study. In this paper, an experimental design was used to investigate the effect of the failure type of components on the whole system’s failure type. For this, a general procedure for estimating system reliability based on the Weibull distribution was adopted.

Today, numerous reliability prediction methods exist for electronic systems. A detailed literature survey is conducted to investigate the various techniques/models to ensure electronic components/system's reliability. Based on such a method's merits and limitations, we recommend using the hybrid model to predict an electronic system's reliability prediction in the distinct development phase of the product life cycle to get higher accuracy.

There is little published data on the performance of hand-portable polymerase chain reaction (PCR) systems that can be used by first responders to determine if a suspicious powder contains a potential biothreat agent. We evaluated 5 commercially available hand-portable PCR instruments for detection of Bacillus anthracis. We used a cost-effective, statistically based test plan to evaluate systems at performance levels ranging from 0.85-0.95 lower confidence bound (LCB) of the probability of detection (POD) at confidence levels of 80% to 95%. We assessed specificity using purified genomic DNA from 13 B. anthracis strains and 18 Bacillus near neighbors, potential interference with 22 suspicious powders that are commonly encountered in the field by first responders during suspected biothreat incidents, and the potential for PCR inhibition when B. anthracis spores were spiked into these powders. Our results indicate that 3 of the 5 systems achieved 0.95 LCB of the probability of detection with 95% confidence levels at test concentrations of 2,000 genome equivalents/mL (GE/mL), which is comparable to 2,000 spores/mL. This is more than sufficient sensitivity for screening visible suspicious powders. These systems exhibited no false-positive results or PCR inhibition with common suspicious powders and reliably detected B. anthracis spores spiked into these powders, though some issues with assay controls were observed. Our testing approach enables efficient performance testing using a statistically rigorous and cost-effective test plan to generate performance data that allow users to make informed decisions regarding the purchase and use of field biodetection equipment.

Patients with curatively resected colorectal cancer hepatic metastases often harbor occult metastatic disease and are at high risk of experiencing recurrence. This patient cohort is ideally suited to test novel therapies such as immunotherapy. We treated patients-post-hepatic resection-with anti-idiotype monoclonal antibody vaccines to the tumor-associated antigens carcinoembryonic antigen (CeaVac) and human milk fat globule (TriAb), both of which are co-expressed in more than 90% of colorectal cancer patients. Vaccinations commenced 6-12 weeks post-hepatic resection and consisted of four biweekly treatments of 2 mg CeaVac and TriAb, then monthly treatments for 2 years, then on every other month for 3 years. The primary endpoint was to investigate the proportion of patients recurrence-free at 2 years, and the objective of the study was to demonstrate that at least 58% would be recurrence-free at this time to consider the regimen worthy of further study. Between July 2001 and October 2004, 56 patients were accrued; 52 patients with margin-negative resection were eligible for analysis. Hepatic lobectomy was performed in 56% of patients with a median of one metastasis (range 1-3). Of the 52 eligible patients, 49 were evaluable for the primary end point. Median follow-up was 3.1 years. The proportion of patients recurrence-free at 2 years was 39%, with a lower confidence bound (LCB) of 0.29. Median recurrence-free survival was 16 months. The 2-year overall survival was 94% (95% CI, 0.81, 0.98). Only 10% of patients had documented grade-3 adverse events. Anti-idiotype monoclonal antibody vaccine therapy with CeaVac and TriAb as an adjuvant to curative resection of colorectal cancer hepatic metastases is well tolerated but did not improve 2-year recurrence-free survival when compared with the expected value of 40% reported for hepatic resection alone.

Gaussian processes (Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems. These optimization problems are nonconvex and global optimization is desired. However, previous literature observed computational burdens limiting deterministic global optimization to Gaussian processes trained on few data points. We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and-bound solver branches only on the free variables and McCormick relaxations are propagated through explicit Gaussian process models. The approach also leads to significantly smaller and computationally cheaper subproblems for lower and upper bounding. To further accelerate convergence, we derive envelopes of common covariance functions for GPs and tight relaxations of acquisition functions used in Bayesian optimization including expected improvement, probability of improvement, and lower confidence bound. In total, we reduce computational time by orders of magnitude compared to state-of-the-art methods, thus overcoming previous computational burdens. We demonstrate the performance and scaling of the proposed method and apply it to Bayesian optimization with global optimization of the acquisition function and chance-constrained programming. The Gaussian process models, acquisition functions, and training scripts are available open-source within the “MeLOn—MachineLearning Models for Optimization” toolbox (https://git.rwth-aachen.de/avt.svt/public/MeLOn).

Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we presents a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimations.

The end user often needs to define extremely complex interpretation tasks and to require the analysis results to be quantitatively proven for each pixel without the test data. To this end, this paper extends the ideas underlying the model-based unsupervised classification method previously proposed by us (Koltunov and Ben-Dor 2001). Consistently with that method, the quality of assigning a pixel to a cluster is defined as the lower confidence bound (l.c.b.) of the corresponding posterior probability estimate. We propose to compute the l.c.b.s in an approximate way using the Fisher information matrix instead of the bootstrap scheme suggested previously, leading to an l.c.b.-estimation procedure that is faster by a factor of hundreds to thousands, while being reasonably accurate. The issue of selecting the number of clusters is considered in accordance with the quantitative requirements for the level of detail and the reliability of the thematic interpretation. Specifically, the l.c.b.s form a novel selection criterion that allows the most detailed landscape descriptions to be provided with at least a pre-specified value of confidence. This implies detection of highly overlapping clusters, leading to very detailed segmentations. Consequently, numerous thematic classification and object detection problems can be solved, based on single clustering and assuming that thematic classes are unions of components. Then the thematic classification accuracy can be computed in a well-founded manner for each separate pixel, using the obtained covariance matrices of the posterior probability estimators of component membership. The procedures for thematic mapping and object detection are described. The accuracy of the l.c.b. estimation and stability of the new criterion in choosing the number of clusters are illustrated on simulated datasets. The hyperspectral data analysis experiment performed demonstrates part of the developments described in this paper. Several issues that are relevant for remote sensing data interpretation are addressed constructively. In particular, we draft the novel algorithms that use model-based cluster analysis for detection and recognition of remotely sensed objects based on prior information on their size and shape. In addition, we introduce a generalized approach to unsupervised feature extraction from data acquired by a plurality of sensors of different physical nature. A new data model generalizing the traditional Gaussian mixture model is also presented.

A competitive variable-fidelity surrogate-assisted CMA-ES algorithm using data mining techniques

PurposeThe aim of this paper is to consider the effect of gauge measurement capability on the multivariate process capability index (MCp).Design/methodology/approachWith respect to measurement capability, the paper investigates the statistical properties of the estimated MCp and considers the effect of gauge measurement capability on the lower confidence bound, hypothesis testing, critical value and power of testing for MCp at the mentioned state.FindingsThe results show that gauge measurement capabilities will notably change the results of estimating and testing the process capability index.Originality/valueThe research would help quality experts to determine whether their processes meet the required capability, and to make more reliable decisions.

The pursuit of chiral nanographenes with robust chiral stability and good chiroptical responses is of great interest for material-based applications. However, the most reported preparation processes involve intricate synthetic pathways and harsh conditions, resulting in readily epimerization due to their low epimerization barriers. In this study, we present a streamlined one-pot electrochemical synthesis for a novel double oxaza[7]dehydrohelicene, characterized by a substantial epimerization barrier (33.8 kcal mol−1) and notable chiroptical responses (|glum| = 1.5 × 10−3). To optimize the electrochemical conditions efficiently, we applied a Bayesian optimization (BO) approach with expected improvement (EI) or lower confidence bound (LCB) as an acquisition function, aiming to maximize exploration and exploitation while minimizing the number of experiments needed to identify global maxima. Additionally, the structural and optical features of this molecule have been studied using X-ray crystallographic analysis, and the absorption and emission behaviors were rationalized based on DFT calculations.