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To date, a number of parameter identification methods have been developed for the purpose of structural health monitoring and vibration control. Among them, the extended Kalman filter (EKF) series methods are attractive in view of the efficient unbiased estimation in recursive manner. However, most of these methods are performed on the premise that the parameters are time-invariant and/or the loadings are known. To circumvent the aforementioned limitations, an online EKF with unknown input (OEKF-UI) approach is proposed in this paper for the identification of time-varying parameters and the unknown excitation. A revised observation equation is obtained with the aid of projection matrix. To capture the changes of structural parameters in real-time, an online tracking matrix (OTM) associated with the time-varying parameters is introduced and determined via an optimization procedure. Then, based on the principle of EKF, the recursive solution of structural states including the time-variant parameters can be analytically derived. Finally, using the estimated structural states, the unknown inputs are identified by means of least-squares estimation (LSE) at the same time-step. The effectiveness of the proposed approach is validated via linear and nonlinear numerical examples with the consideration of parameters being varied abruptly.

Online identification of time-variant parameters without knowledge of external loads is an important but challenging task for structural health monitoring and vibration control. In this study, a two-stage approach, named extended Kalman filter with forgetting factor matrix under unknown inputs (EKF-FFM-UI), is proposed for simultaneously identifying the time-variant parameters and external loads. In stage 1, an extended Kalman filter under unknown inputs (EKF-UI) approach previously proposed by the authors is employed for estimating the structural states and unknown loads. This EKF-UI approach is solely suitable for time-invariant system identification. Therefore, the aim of stage 2 is to improve this approach for the purpose of possessing tracking capability. In this stage, the acceleration responses are first reconstructed by using the differential equation of motion and employed for improving the accuracy of estimated structural states. A forgetting factor matrix is introduced into the priori estimation error covariance matrix to track time-varying parameters. The square errors between the measurements and the corresponding estimates are defined as an index and used for detecting the damage time instant. Then, a covariance resetting technique is employed to assure that such changes in structural parameters can be efficiently captured. A shear-type building structure without/with magneto-rheological (MR) dampers and a fixed beam structure are used as numerical examples for validating the effectiveness of the proposed approach. Experimental tests on a six-story building model are also conducted. Results show the time-varying parameters and unknown inputs can be simultaneously identified with acceptable accuracy.

The development of graphene oxide/rare earth complexes luminescent materials is limited due to the strong quenching ability of graphene oxide. However, the hybrid materials with pyromellitic acid (PMA) as ligand not only have better performance, but also have stronger luminescence intensity. Herein we tested the effect of the carboxylic acid structure of PMA on the luminescence intensity of graphene oxide/rare earth complexes hybrid materials. Phthalic acid (PA), Isophthalic acid (i-PA) and Terephthalic acid (p-PA) are selected as test ligand, representing the o-position, meta-position and para-position effects of PMA. By comparing the luminescence intensity, we found that o-position effect is helpful for the ligand to sensitize rare earth ions, and meta-position and para-position effects are helpful for the graphene oxide sheets (GOSs) to improve the luminescence intensity of hybrid materials and inhibit the quenching effect of the GOSs. Furthermore, we speculate on the relationship between the distance of π-π stacking of GOSs and rare earth complexes and luminescence intensity of hybrid materials. This work provides an important reference for the design of graphene oxide/rare-earth aromatic carboxylic acid hybrid luminescent materials.

It has been proved that the usage of multi-type observations including global and local information for structural health monitoring (SHM) outperforms that of solo-type measurement. Kalman filter (KF) is a simple but powerful tool for the online state estimation. However, external force is required for the classic KF. Moreover, direct implementation of KF in time domain may be time consuming especially for large structures with many degrees-of-freedoms (DOFs) involved. From these points of view, the idea of modal Kalman filter (MKF) is introduced. An MKF-based approach is then proposed for joint estimation of multi-scale responses and unknown loadings with data fusion of multi-type observations. By using a projection matrix and the selected several modes, a modified observation equation in modal domain is derived. Limited multi-type measurements are fused together for online estimating multi-scale responses of structure at its critical locations. The unknown excitation is simultaneously identified by least squares estimation (LSE). The drift problem of the real time estimation of structural responses and unknown loads is avoided. The effectiveness of the proposed approach is numerically demonstrated via several examples. Results show that the proposed approach is capable of satisfactorily estimating the unmeasured multi-scale responses and identifying the unknown loadings.

With the advancement of technology and science, the power system is getting more intelligent and flexible, and the way people use electric energy in their daily lives is changing. Monitoring the condition of electrical energy loads, particularly in the early detection of aberrant loads and behaviors, is critical for power grid maintenance and power theft detection. In this paper, we combine the widely used deep learning model Transformer with the clustering approach K-means to estimate power consumption over time and detect anomalies. The Transformer model is used to forecast the following hour’s power usage, and the K-means clustering method is utilized to optimize the prediction results, finally, the anomalies is detected by comparing the predicted value and the test value. On real hourly electric energy consumption data, we test the proposed model, and the results show that our method outperforms the most commonly used LSTM time series model.

Background An emergency response to a medical situation is generally considered to be a risk decision-making problem. When an emergency event occurs, it makes sense to take into account more than one decision maker’s opinions and psychological behaviors. The existing research tends to ignore these multidimensional aspects. To fill this literature gap, we propose a multi-attribute model. Methods The model is based on cumulative prospect theory (CPT), considering multiple experts’ psychological factors. By not assuming full rationality, we extend existing models to allow multiple experts’ risk preferences to be incorporated into the decision-making process in the case of an emergency. Then, traditional CPT is extended by allowing for multiple attributes. In addition, rather than using crisp data, interval values are adopted to tackle the usual uncertainties in reality. Results The multi-attribute CPT based model proposed can deal with the selection of potential emergency alternatives. The model incorporates interval values to allow more uncertainty and the comparative studies show that the optimal solution changes under different scenarios. Conclusions Our illustrative example and comparative study show that considering multiple experts and multiple attributes is more reasonable, especially in complicated situations under an emergency. In addition, decision-makers’ risk preferences highly affect the selection outcomes, highlighting their importance in the medical decision-making process. Our proposed model can be applied to similar fields with appropriate modifications.

With the number of alternative systems increasing, the system portfolio selection problem for large-scale complex systems is an non-deterministic polynomial (NP)-hard problem. The time cost of the classification selection algorithm used for the portfolio selection is intolerable; thus, improving the algorithm is necessary. In this paper, first, the weapon system portfolio selection (WSPS) model is categorized into two types: single objective and multiobjective; the optimization difficulties are analyzed; and the feasible solution space reduction strategy is given. Second, a portfolio selection optimization algorithm based on the difference evolution technique for order preference by similarity to ideal solution (DE-TOPSIS) is proposed where the weapon system weighting method TOPSIS is integrated with the DE algorithm. Finally, considering different weapon system scales, the advantages of the proposed algorithm are illustrated by comparing it with two other algorithms in a single-target case and two other algorithms in a multiobjective case. The results indicate that the DE algorithm always has better performance with regard to optimal solution quality, convergence speed, and algorithm stability.

Heteroatom doping and three-dimensional (3D) porous structures are critical to the performance of graphene supercapacitor electrode materials. However, the conventional methods, such as chemical vapor deposition (CVD) and hydrothermal synthesis, require tedious processes and harsh conditions. In this work, we describe a very simple and efficient approach to fabricate a 3D nitrogen/sulfur-co-doped porous graphene hydrogel (3DNS-GH). It is found that thiourea can reduce graphene oxide (GO) obtaining 3DNS-GHs at low temperature and atmospheric pressure by adjusting the pH value, because the reducibility of thiourea significantly increases in acidic or alkaline media. Furthermore, the amount of nitrogen and sulfur doping and nano-pore structure varies with the pH value of solution. The 3DNS-GH exhibits uniform pore structure and can be directly used as electrode materials without additive binder. The binder-free electrode based on the optimum operating conditions (3DNS-GH-12) exhibit a high specific capacitance of 259.2 F g−1 and retain 96.0% of its initial capacitance after 10,000 cycles in 2 M KOH solution at 10 A g−1. Additionally, the symmetrical supercapacitor assembled by the 3DNS-GH-12 showed an impressive energy density of 6.08 W h kg−1 and a high power density of 7.51 kW kg−1. Therefore, these materials demonstrate excellent performance, indicating this route possess potential applied value in the production of supercapacitor electrode materials.

Strategic resource allocation into decision-making model plays a valuable role for the defender in mitigating damage and improving efficiency in military environments. In this paper, we develop a defensive resource allocation model based on cumulative prospect theory (CPT), which considers terrorists’ psychological factors of decision-making in reality. More specifically, we extend existing models in the presence of multiple attributes and terrorists’ deviations from rationality using a multi-attribute cumulative prospect theory. In addition, interval values are used to cope with uncertainties regarding gain and loss. Comparative studies are also carried out to demonstrate the differences among minmax, Nash eguilibrium (NE), and traditional probability risk analysis (PRA) strategies. Results show that the defender’s optimal defensive resource allocation will change along with terrorists’ behaviors and the proposed model makes more sense compared with other traditional resource allocation strategies.

Coke formation is an obstacle in using hydrocarbons as the coolant in hypersonic flight vehicles. In this paper, effective inhibition of coke deposition was realized by the addition of wall catalytic steam reforming, and the corresponding mechanism was revealed. The anticoking tests were evaluated during the thermal cracking and catalytic steam reforming processes of an endothermic hydrocarbon fuel under 3.0 MPa and outlet temperature from 600 to 680 °C. The amount and properties of coke deposited in the thermal cracking with and without steam reforming were investigated on the basis of their temperature-programmed oxidation profiles and scanning electron microscopy. The results show that the mass percentages of filamentous and amorphous cokes deposited during thermal cracking without steam reforming are 20.32 and 79.68%, respectively. The amount of coke deposited in a bare reactor is nearly twice that deposited in a reforming catalyst-coated reactor, and the coke formation rate in the former case is 8 times that in the latter case. The absence of filamentous deposits during catalytic steam reforming is ascribed to the catalyst layer on the inner surface, which prevents contact between the hydrocarbon fuel and active metal sites. Filamentous coke formation is therefore totally inhibited. Moreover, catalytic steam reforming also inhibits amorphous coke deposition. Analyses of the gaseous products and residual liquids from thermal cracking of jet fuel show that the monocyclic and polycyclic aromatic hydrocarbon contents decrease significantly under catalytic steam reforming. The large amount of hydrogen generated from the wall catalytic steam reforming reaction suppresses dehydrogenation, Diels–Alder, and condensation reactions; therefore, coke deposition decreases.

In this paper, a facile and highly efficient ternary composite ZnS/BiOBr/Graphene oxide (ZnS/BiOBr/GO) with zinc vacancies was successfully synthesized by solvothermal method. The photocatalysts showed an efficient photocatalytic performance for organic pollutant under visible light irradiation. The photocatalysts were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), UV–visible diffuse reflectance spectrophotometer (DRS), and photoluminescence spectroscopy (PL) . The experimental results indicated that the 100 mg ZnS and 6 mg GO co-doped BiOBr (100ZBG-6) shows the highest photocatalytic activity, achieving 86.6% degradation efficiency for Rhodamine B (RhB) under visible light irradiation, which was 28.7 and 6.1 times than that of pure ZnS and BiOBr. It is indicated that the ZnS/BiOBr/GO composite has the great potential application in the degradation organic pollutant. This article describes the enhancement of light absorption and photocatalytic activity by synthesizing zinc vacancies of ZnS and introducing GO modified BiOBr.

The problem of integrated project portfolio selection and scheduling (PPSS) is among the most important and highly pursed subjects in project management. In this study, a mathematical model and algorithm are designed specifically to assist decision makers decide which projects are to be chosen and when these projects are to be undertaken. More specifically, the PPSS problem is first formulated as a nonlinear multi-objective model with simultaneous consideration of benefit and risk factors. Due to the complexity and uncertainty involved in most real life situations, fuzzy numbers are incorporated into the model, which can provide decision makers with more flexibility. Then, an inverse modeling based multi-objective evolutionary algorithm using a Gaussian Process is presented to obtain the Pareto set. Finally, an illustrative example is used to demonstrate the high efficacy of the foregoing approach, which can provide decision makers with valuable insights into the PPSS process. The proposed algorithm is found to be more effective compared with two other popular algorithms.