Many practical systems are phased-mission (PMSs), where the mission consists of multiple, consecutive, and non-overlapping phases operation. An accurate reliability analysis a PMS must consider statistical dependence component states across phases, as well dynamics in system configurations, success criteria, behavior. This paper proposes new method based on multiple-valued decision diagrams (MDDs) for non-repairable binary-state PMS. Due to its multi-valued logic nature, MDD model has...

Sentiment classification is an interesting and crucial research topic in the field of natural language processing (NLP). Data-driven methods, including machine learning deep techniques, provide one direct effective solution to solve sentiment problem. However, performance declines when input includes review comments for multiple tasks. The most appropriate way constructing a model under multi-tasking circumstances remains questionable related field. In this study, aiming at problem, we...

Artificial intelligence (AI)-enhanced automated fault diagnosis (AFD) has become increasingly popular for chiller with promising classification performance. In practice, a sufficient number of samples are required by the AI methods in training phase. However, faulty generally much more difficult to be collected than normal samples. Data augmentation is introduced these scenarios enhance data set synthetic data. this study, variational autoencoder-based conditional Wasserstein GAN gradient...

The syrB gene is required for syringomycin production by Pseudomonas syringae pv. and full virulence during plant pathogenesis. Strain B3AR132 containing a syrB::lacZ fusion was used to detect transcriptional activation of the in minimal medium metabolites with signal activity. Among 34 phenolic compounds tested, arbutin, phenyl-beta-D-glucopyranoside, salicin were shown be strong inducers syrB, giving rise approximately 1,200 U beta-galactosidase activity at 100 microM; esculin helicin...

Many practical systems are phased-mission with multimode failures (MFPMSs) where the mission consists of multiple nonoverlapping phases operation, and system components may assume more than one failure mode. In MFPMSs, dependence arises among different modes same component, which makes reliability analysis MFPMSs difficult. This paper proposes a new analytical method based on multivalued decision diagrams (MDDs) for nonrepairable MFPMSs. MDDs have recently been applied to single-phase...

Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation irradiance, and stationarizing output integration between different grids. Considering randomness multiple dimension weather data, a hybrid deep learning model that combines gated recurrent unit (GRU) neural network an attention mechanism proposed forecasting changes four seasons. In first step, Inception ResNet...

The wide utilization of gas-fired generation and the rapid development power-to-gas technologies have led to intensified integration electricity gas systems. random failures components in either or system may a considerable impact on reliabilities both Therefore, it is necessary evaluate systems considering their integration. In this paper, novel reliability evaluation method for integrated electricity–gas (IEGSs) proposed. First, network equivalents are utilized represent models generating...

Ground surface settlement forecasting in the process of tunnel construction is one most important techniques towards sustainable city development and preventing serious damages, such as landscape collapse. It evident that modern artificial intelligence (AI) models, neural network, extreme learning machine, support vector regression, are capable providing reliable results for settlement. However, two limitations exist current techniques. First, data provided by company usually univariate...

An important aspect of the interaction Pseudomonas syringae pv with plant hosts is perception signal molecules that regulate expression genes, such as syrB, required for synthesis phytotoxin, syringomycin. In this study, leaves sweet cherry (Prunus avium L.) were analyzed to determine nature syrB-inducing activity associated tissues a susceptible host. Crude leaf extracts yielded high amounts total more than 12,000 units g-1 (fresh weight) based on activation syrB-lacZ fusion in strain...

We present a generalized analysis methodology for binary decision diagram-based fault tree of wide range phased-mission systems, with various mission requirements, and structure characteristics. This includes 1) four alternative variable ordering schemes: forward/backward phased dependent operations, concatenation; 2) strategy to choose an adequate scheme process new system instance depending on its phase configuration; 3) efficient generation evaluation algorithms diagrams adopting any...

The accurate prediction of photovoltaic (PV) power is essential for planning systems and constructing intelligent grids. However, this has become difficult due to the intermittency instability PV data. This paper introduces a deep learning framework based on 7.5 min-ahead 15 approaches predict short-term power. Specifically, we propose hybrid model singular spectrum analysis (SSA) bidirectional long memory (BiLSTM) networks with Bayesian optimization (BO) algorithm. To begin, SSA decomposes...

Recently, Z. Tang, and J. B. Dugan proposed a new algorithm (DEP-BDD) based on binary decision diagrams (BDD) for reliability analysis of phased-mission systems (PMS) with multimode failures. Although the variable ordering is very important from practical point view, it has not been treated directly. This paper develops four heuristics DEP-BDD two ordinary schemes, evaluates these schemes & hundreds randomly generated fault trees having different sizes structure properties. As synthesis...

kNN is a simple, but effective and powerful lazy learning algorithm. It has been now widely used in practice plays an important role pattern classification. However, how to choose optimal value of k still challenge, which straightforwardly affects the performance kNN. To alleviate this problem, paper we propose new algorithm under framework The primary characteristic our method that it adopts mutual nearest neighbors, rather than determine class labels unknown instances. advantage neighbors...

To improve the success probability of a mission execution, scheduled checkpointing is often implemented to save completed portions task so that system can resume execution effectively after its restoration whenever failure occurs. This paper considers repairable computing subject checkpointing. The intervals are deterministic, but be even or uneven. repair time fixed while time-to-failure follow any arbitrary type distributions. maximum number repairs specified by certain threshold value. A...

In the era of big data, multi-task learning has become one crucial technologies for sentiment analysis and classification. Most existing models are developed based on soft-sharing mechanism that less interference between different tasks than hard-sharing mechanism. However, there also fewer essential features model can extract with method, resulting in unsatisfactory classification performance. this paper, we propose a framework various fields. The is achieved by shared layer to build...

In binary decision diagram–based fault tree analysis, the size of diagram encoding trees heavily depends on chosen ordering. Heuristics are often used to obtain good orderings. The most important heuristics depth‐first leftmost (DFLM) and its variants weighting DFLM (WDFLM) repeated‐event‐priority (RDFLM). Although having been widely, their performance is still only vaguely understood, not much formal work has done. This article firstly identifies some basic requirements for a reliable...