Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Secure outsourced attribute-based signatures

Attribute-based signature (ABS) enables users to sign messages over attributes without revealing any information other than the fact that they have attested to the messages. However, heavy computational cost is required during signing in existing work of ABS, which grows linearly with the size of the predicate formula. As a result, this presents a significant challenge for resource-constrained devices (such as mobile devices or RFID tags) to perform such heavy computations independently. Aiming at tackling the challenge above, we first propose and formalize a new paradigm called Outsourced ABS, i.e., OABS, in which the computational overhead at user side is greatly reduced through outsourcing intensive computations to an untrusted signing-cloud service provider (S-CSP). Furthermore, we apply this novel paradigm to existing ABS schemes to reduce the complexity. As a result, we present two concrete OABS schemes: i) in the first OABS scheme, the number of exponentiations involving in signing is reduced from O(d) to O(1) (nearly three), where d is the upper bound of threshold value defined in the predicate; ii) our second scheme is built on Herranz et al.'s construction with constant-size signatures. The number of exponentiations in signing is reduced from O(d2) to O(d) and the communication overhead is O(1). Security analysis demonstrates that both OABS schemes are secure in terms of the unforgeability and attribute-signer privacy definitions specified in the proposed security model. Finally, to allow for high efficiency and flexibility, we discuss extensions of OABS and show how to achieve accountability as well.

Improved robust Kalman filtering for uncertain systems with missing measurements

In this paper, a novel robust finite-horizon Kalman filter is developed for discrete linear time-varying systems with missing measurements and normbounded parameter uncertainties. The missing measurements are modelled by a Bernoulli distributed sequence and the system parameter uncertainties are in the state and output matrices. A two stage recursive structure is considered for the Kalman filter and its parameters are determined guaranteeing that the covariances of the state estimation errorsare not more than the known upper bound. Finally, simulation results are presented to illustrate the outperformance of the proposed robust estimator compared with the previous results in the literature.

A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

Linear functional state bounding for perturbed time-delay systems and its application

This paper considers time-delay systems with bounded disturbances. We study a new problem of finding an upper bound of an absolute value function of any given linear functional of the state vector starting from the origin of the system. Based on the Lyapunov-Krasovskii method combining with the recent Wirtinger-based integral inequality that has just been proposed by Seuret & Gouaisbaut (2013. Wirtinger-based integral inequality: application to time-delay systems. Automatica, 49, 2860-2866), sufficient conditions for the existence of an upper bound of the function are derived. The obtained results are shown to be more effective than those adapted from the existing works on reachable set bounding. Furthermore, the obtained results are applied to refine existing ellipsoidal bounds of the reachable sets. The effectiveness of the obtained results is illustrated by two numerical examples.

Improving the quality of prediction intervals through optimal aggregation

Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting performance significantly drops in the presence of process uncertainties and disturbances. NN-based prediction intervals (PIs) offer an alternative solution to appropriately quantify uncertainties and disturbances associated with point forecasts. In this paper, an NN ensemble procedure is proposed to construct quality PIs. A recently developed lower-upper bound estimation method is applied to develop NN-based PIs. Then, constructed PIs from the NN ensemble members are combined using a weighted averaging mechanism. Simulated annealing and a genetic algorithm are used to optimally adjust the weights for the aggregation mechanism. The proposed method is examined for three different case studies. Simulation results reveal that the proposed method improves the average PI quality of individual NNs by 22%, 18%, and 78% for the first, second, and third case studies, respectively. The simulation study also demonstrates that a 3%-4% improvement in the quality of PIs can be achieved using the proposed method compared to the simple averaging aggregation method.

A new fuzzy-based combined prediction interval for wind power forecasting

This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.

Small cluster in cyber physical systems: network topology, interdependence and cascading failures

In cyber physical system (CPS), computational resources and physical resources are strongly correlated and mutually dependent. Cascading failures occur between coupled networks, cause the system more fragile than single network. Besides widely used metric giant component, we study small cluster (small component) in interdependent networks after cascading failures occur. We first introduce an overview on how small clusters distribute in various single networks. Then we propose a percolation theory based mathematical method to study how small clusters be affected by the interdependence between two coupled networks. We prove that the upper bounds exist for both the fraction and the number of operating small clusters. Without loss of generality, we apply both synthetic network and real network data in simulation to study small clusters under different interdependence models and network topologies. The extensive simulations highlight our findings: except the giant component, considerable proportion of small clusters exists, with the remaining part fragmenting to very tiny pieces or even massive isolated single vertex; no matter how the two networks are tightly coupled, an upper bound exists for the size of small clusters. We also discover that the interdependent small-world networks generally have the highest fractions of operating small clusters. Three attack strategies are compared: Inter Degree Priority Attack, Intra Degree Priority Attack and Random Attack. We observe that the fraction of functioning small clusters keeps stable and is independent from the attack strategies.

Investigation of seismic effects on disturbed rock slope stability assessments

For general stability analysis of rock slopes, rock mass strength and rock mass disturbance are definitely should be considered. In addition, the impact of earthquakes must be taken into account. In fact, the rock mass strength is very difficult to be assessed which causes the difficulty of analysing rock slope stability. Therefore, an empirical failure criterion, the Hoek-Brown failure criterion, has been proposed. It is one of the most widely accepted approaches to estimate rock mass strength. The rock mass disturbance is important and was found having significant influence on evaluating rock slope stability, especially for rock slope with poor quality rock mass. In the Hoek-Brown failure criterion, the disturbance factor can represent the level of the rock mass disturbance which would provide a reasonable basis for estimating rock mass strength. This research will not only discuss the slope factor of safety, but also consider the influence of the seismic force on rock slope stability assessment using pseudo-static method. In practice, only horizontal seismic coefficient is used. Various magnitudes of the disturbance factor and recommended blasting damage zone thickness are also taken into account. The blasting damage zone thickness considered ranges from 0.5 to 2.5 times of slope height. The research results have potential to be extended and then sets of comprehensive stability charts can be provided for the rock slope stability evaluations. They will be convenient tools for practising engineers. In this study, finite element upper bound and lower bound limit analysis methods are employed. Their applicability has been investigated in some previous studies. The differences between upper bound and lower bound solutions are less than ±10% which would provide reasonable and acceptable range for rock slope stability safety factor estimation.

Componentwise ultimate bounds for positive discrete time-delay systems perturbed by interval disturbances

This paper presents a method to derive componentwise ultimate upper bounds and componentwise ultimate lower bounds for linear positive systems with time-varying delays and bounded disturbances. The disturbance vector is assumed to vary within a known interval whose lower bound may be different from zero. We first derive a sufficient condition for the existence of componentwise ultimate bounds. This condition is given in terms of the spectral radius of the system matrices which is easy to check and allows us to compute directly both the smallest componentwise ultimate upper bound and the largest componentwise ultimate lower bound. Then, by using the comparison method, we extend the obtained result to a class of nonlinear time-delay systems which has linear positive bounds. Two numerical examples are given to illustrate the effectiveness of the obtained results.