Faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, comp...
详细信息
Faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.
In order to describe the behavior and the performance of the photovoltaic modules we can found in the literature different equivalent circuit models such us: single diode model (SDM), double diode model (DDM), three d...
详细信息
In order to describe the behavior and the performance of the photovoltaic modules we can found in the literature different equivalent circuit models such us: single diode model (SDM), double diode model (DDM), three diode model (TDM). Almost of the authors obtained that the SDM is the best solution to describe the electrical characterization of the PV modules. The PV parameters can be extracted by three common approaches: Analytical based on the derivation of mathematical equations, Numeric or iterative usually use non-linear optimization techniques, among all the technique the Newton Raphson is widely used. The last one is meta-heuristic approach characterized by their global search point and their soft computing algorithms. The aim of this study is to explore and discuss the behavior of the single-diode model, which require five parameters are estimated using Particle Swarm Optimization approach. The performance of this method is evaluated using the experimental values of R.T.C France PV cell at irradiation G = 1000 W/m2 and at temperature T = 33 degrees C. The obtained results are also compared with results of the others computing approach. After this study, we can conclude that the proposed PSO method has the best performance because the RMSE obtained using this approach is around the 1.73.10-4 A and low than those obtained through all compared algorithms.
In order to describe the behavior and the performance of the photovoltaic modules we can found in the literature different equivalent circuit models such us: single diode model (SDM), double diode model (DDM), three d...
详细信息
In order to describe the behavior and the performance of the photovoltaic modules we can found in the literature different equivalent circuit models such us: single diode model (SDM), double diode model (DDM), three diode model (TDM). Almost of the authors obtained that the SDM is the best solution to describe the electrical characterization of the PV modules. The PV parameters can be extracted by three common approaches: Analytical based on the derivation of mathematical equations, Numeric or iterative usually use non-linear optimization techniques, among all the technique the Newton Raphson is widely used. The last one is meta-heuristic approach characterized by their global search point and their soft computing algorithms. The aim of this study is to explore and discuss the behavior of the single-diode model, which require five parameters are estimated using Particle Swarm Optimization approach. The performance of this method is evaluated using the experimental values of R.T.C France PV cell at irradiation G = 1000 W/m 2 and at temperature T = 33 °C. The obtained results are also compared with results of the others computing approach. After this study, we can conclude that the proposed PSO method has the best performance because the RMSE obtained using this approach is around the 1.73.10 −4 A and low than those obtained through all compared algorithms.
There are some NP-hard problems in the prediction of RNA structures. Prediction of RNA folding structure in RNA nucleotide sequence remains an unsolved challenge. We investigate the computing algorithm in RNA folding ...
详细信息
There are some NP-hard problems in the prediction of RNA structures. Prediction of RNA folding structure in RNA nucleotide sequence remains an unsolved challenge. We investigate the computing algorithm in RNA folding structural prediction based on extended structure and basin hopping graph, it is a computing mode of basin hopping graph in RNA folding structural prediction including pseudoknots. This study presents the predicting algorithm based on extended structure, it also proposes an improved computing algorithm based on barrier tree and basin hopping graph, which are the attractive approaches in RNA folding structural prediction. Many experiments have been implemented in Rfam14.1 database and PseudoBase database, the experimental results show that our two algorithms are efficient and accurate than the other existing algorithms.
We derive formulas for the differential item functioning (DIF) measures that two routinely used DIF statistics are designed to estimate. The DIF measures that match on observed scores are compared to DIF measures base...
详细信息
The ultimate objective of the problem solving system is to provide an interrelated framework for prospective users to facilitate their work, such as biological and biomedieal knowledge retrieval, discovery, eapture. I...
详细信息
ISBN:
(纸本)9781479979356
The ultimate objective of the problem solving system is to provide an interrelated framework for prospective users to facilitate their work, such as biological and biomedieal knowledge retrieval, discovery, eapture. In addition to this objeetive, there is demand for secure and practical computing algorithms which address the challenge to safely outsource data processing onto remote computing resources. This allows users to confidently outsource computation over sensitive information from the security level of the remote delegate. In this paper, we present the computing algorithms which preserve privacy of users and confidentiality of service providers in the cloud environment.
This short article shows an unified approach to representing and computing the cumulative distribution function for noncentral t, F, and chi(2). Unlike the existing algorithms, which involve different expansion and/or...
详细信息
This short article shows an unified approach to representing and computing the cumulative distribution function for noncentral t, F, and chi(2). Unlike the existing algorithms, which involve different expansion and/or recurrence, the new approach consistently represents all the three noncentral cumulative distribution functions as the integral of the normal cumulative distribution function and chi(2) density function.
Suppose independent observations X-i, i = 1,..., n are observed from a mixture model f(x;Q) equivalent to integral f (x;lambda) d Q (lambda), where lambda is a scalar and Q(lambda) is a nondegenerate distribution with...
详细信息
Suppose independent observations X-i, i = 1,..., n are observed from a mixture model f(x;Q) equivalent to integral f (x;lambda) d Q (lambda), where lambda is a scalar and Q(lambda) is a nondegenerate distribution with an unspecified form. We consider to estimate Q(lambda) by nonparametric maximum likelihood (NPML) method under two scenarios: (1) the likelihood is penalized by a functional g(Q);and (2)Q is under a constraint g(Q) = g(0). We propose a simple and reliable algorithm termed VDM/ECM for Q-estimation when the likelihood is penalized by a linear functional. We show this algorithm can be applied to a more general situation where the penalty is not linear, but a function of linear functionals by a linearization procedure. The constrained NPMLE can be found by penalizing the quadratic distance vertical bar g(Q) - g(0)vertical bar(2) under a large penalty factor gamma > 0 using this algorithm. The algorithm is illustrated with two real data sets. (c) 2006 Elsevier B.V. All rights reserved.
Protein phosphorylation is a key post-translational modification that governs biological processes. Despite the fact that a number of analytical strategies have been exploited for the characterization of protein phosp...
详细信息
Protein phosphorylation is a key post-translational modification that governs biological processes. Despite the fact that a number of analytical strategies have been exploited for the characterization of protein phosphorylation, the identification of protein phosphorylation sites is still challenging. We proposed here an alternative approach to mine phosphopeptide signals generated from a mixture of proteins when liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis is involved. The approach combined dephosphorylation reaction, accurate mass measurements from a quadrupole/time-of-flight mass spectrometer, and a computing algorithm to differentiate possible phosphopeptide signals obtained from the LC-MS analyses by taking advantage of the mass shift generated by alkaline phosphatase treatment. The retention times and m/z values of these selected LC-MS signals were used to facilitate subsequent LC-MS/MS experiments for phosphorylation site determination. Unlike commonly used neutral loss scan experiments for phosphopeptide detection, this strategy may not bias against tyrosine-phosphorylated peptides. We have demonstrated the applicability of this strategy to sequence more, in comparison with conventional data-dependent LC-MS/MS experiments, phosphopeptides in a mixture of alpha- and beta-caseins. The analytical scheme was applied to characterize the nasopharyngeal carcinoma (NPC) cellular phosphoproteome and yielded 221 distinct phosphorylation sites. Our data presented in this paper demonstrated the merits of computation in mining phosphopeptide signals from a complex mass spectrometric data set. Keywords: protein phosphorylation center dot mass spectrometry center dot computing algorithm center dot alkaline phosphatase center dot mass shift center dot phosphoproteome
暂无评论