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The feasibility of a novel surface-anchored bifunctional photocatalytic nanofiltration membrane for simultaneously degrading and separating target antibiotics in sewage effluent in a continuous flow-through process was verified.

The event-triggered consensus control for second-order multi-agent systems subject to actuator saturation and input time delay, is investigated in this paper. Based on the designed triggering function, a distributed event-triggered control strategy is presented to drive the system to achieve consensus. Communication energy can be saved as the agents send their state information only at infrequent event instants, the continuous communication among agents is not necessary. Lyapunov-Krasovskii functional is used together with linear matrix inequality technique to analyze the stability of the closed-loop error system. The results show that agents achieve exponentially consensus under the proposed controller. Furthermore, the bounds of solution are obtained by establishing the differential equation associated with the first delay interval. The initial domain is estimated by optimizing the linear matrix inequalities. Finally, simulation examples are presented to illustrate the effectiveness of the proposed controller.

In recent years, multi-sensor fusion has been paid a great attention and is widely applied to simultaneous localization and mapping (SLAM), e.g. Visual-Inertial Odometry (VIO). However, in the most of previous works the observation noise of sensor is assumed to be Gaussian with invariant constant mean and covariance, which is not the case actually. In this paper, a variational Bayesian adaptive algorithm is proposed to improve the performance of visual-inertial odometry, where covariance of observation noise is modeled as inverse-Wishart distribution with harmonic mean so that observation noise is non-Gaussian. Besides, unscented transformation is utilized to handle the nonlinear system problem. The proposed algorithm is applied to the framework of stereo multi-state constraint Kalman filter system (S-MSCKF), which is a state-of-the-art VIO system with low computational cost and good robustness. Extensive experiments are performed on simulation data and open source datasets. The results demonstrate that our proposed algorithm is more robust and accurate compared with S-MSCKF.

In the estimation of distributed sensor networks, process noise and measurement noise may have outliers which have heavy-tailed characteristics. To solve this problem, this paper proposes a distributed consensus estimating method for sensor networks based on Student-t distribution. In the state space model, both process noise and measurement noise are modeled as Student-t distributions with heavy-tailed characteristics. First, for the assumption that the process noise and measurement noise have the same degree of freedom parameters, an exact distributed consensus Student-t filtering algorithm is derived. In practical applications, this assumption is often not true, and due to the increasing degrees of freedom, the method will quickly converge to the traditional distributed consensus Kalman filter. Therefore, it is necessary to relax the assumption of the same degree of freedom and keep the degree of freedom unchanged within a certain range. Based on this, an approximate distributed consensus Student-t filter algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm.

The deinking of mixed office waste paper by cellulase, M/Las (modification of laccase aspartic acid system), and C-M/Las (cellulase synergistic modification of laccase aspartic acid system) was investigated. The fiber morphology parameters, hydrogen bond patterns, cellulose crystallinity, and fiber microstructure were observed via fiber quality analyzer (FQA), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The results showed that the effective residual ink concentration of enzyme deinked pulp decreased compared with blank pulp (pulp without enzymatic treatment). The deinking efficiencies of C-M/Las, cellulase, and M/Las were 18.17%, 14.01%, and 12.31%, respectively. FQA analysis indicated that the fiber length and curl index decreased, while the fiber width slightly increased. FTIR analysis revealed that after cellulase, M/Las, and C-M/Las treatment, the content of intermolecular hydrogen bonds increased by 14.65%, 13.37%, and 19.80%, respectively. This indicates that there is a synergistic effect between cellulase and M/Las. XRD showed that the cellulose crystallinity decreased after enzymatic deinking. SEM micrographs of fibers treated with enzymes revealed that the fiber surface became rough and more fibrils appeared. The water retention value was increased after enzymatic deinking.

This paper considers the problem of maneuvering target tracking for distributed sensor networks. Consensus-based multiple model (MM) filters are effective methods to deal with this problem. However, the model set of these filters is fixed. When the scene is complicated and the model set for MM methods is large, these methods may become inefficient and performance may decrease while the variable structure multiple model (VSMM) method can handle it well. The VSMM version of consensus filter based on the expected mode augmentation (EMA) is presented in this paper to track maneuvering target for distributed sensor networks. The distributed consensus filter for VSMM is presented first and it runs parallel consensus on both the mode probability and the mode-matched probability density function (PDF). Then the EMA algorithm is used to determine the model set utilized at each time step. Simulation results demonstrate the superiority of the proposed method.

The paper studies the consensus of event-triggered for second-order multi-agent systems (MASs) with or without input time delay. Based on the designed triggering function, a distributed event-triggered control strategy is presented to drive the system to achieve consensus. For each agent, controller update only needs the neighbor’s information communication and its own error value, thus continuous communication among agents is not necessary. Communication energy can be saved as the agents send their state information only at infrequent event instants. To demonstrate the asymptotical stability of the closed-loop error system, S-procedure approach is used by combing Lyapunov stability theories with linear matrix inequality (LMI) technique. Two sufficient conditions are derived by solving LMIs for achieving second-order consensus under the cases of with or without input time delay, respectively.Numerical examples are presented to verify the effectiveness of the proposed controllers.

Integrated energy systems (IESs) provide a synergistic supply of electricity and heat, which offers a new approach for district heating. However, the operational risk of IESs increases continuously as the extent of renewable energy source (RES) penetration continues to increase. This condition negatively impacts the benefits of IESs to the participants in the integrated energy market (IEM) and increases the costs of the system. This paper addresses these issues by proposing a two-stage stochastic programming model that considers RES penetration for IESs operated independently of electricity grids (i.e., islanded IESs). Then, a two-stage clearing model of the IEM is established, and a market settlement process is designed. The simulation results demonstrate that the two-stage clearing model of the IEM can effectively alleviate the operational risk associated with high RES penetration by allowing market participants to make timely adjustments corresponding to their interests according to changes in RES generation.

The establishment and development of an integrated energy market (IEM) contributes to the equitable distribution of electrical and thermal energy production resources. However, the application of conventional locational marginal price theory generally fails to promote the declaration of truthful marginal costs by market participants in the process of clearing and settlement of the IEM, which detracts from market fairness and may reduce market efficiency. Simultaneously, the continuous expansion in the scale of renewable energy sources (RESs) threatens the safe and stable operation of electrical power systems. Accordingly, the present study seeks to improve the efficiency of the IEM under large-scale RES penetration, and promote the truthful declarations of market participants by applying a Vickrey-Clarke-Groves (VCG) auction scheme to the IEM, and establishes a two-stage IEM model that promotes compatibility between the incentives of market participants to enhance market fairness. The present study also addresses the imbalance between market revenue and expenditure typically produced by the VCG auction scheme by designing an ex-post payment redistribution mechanism to ensure the equitable cost recovery of all market participants. Simulation results demonstrate that the application of the proposed VCG auction system to the IEM ensures maximum efficiency, cost recovery, and incentive compatibility as dominant strategies, and helps to integrate large-scale RES penetration with the IEM.

A new labeled multi-Bernoulli (LMB) filter is proposed for multi-target tracking with joint heavy-tailed noises of the state and measurement. In contrast to the Gaussian assumption, the proposed method models both the process and measurement noises as multivariate Student-t distributions to handle the heavy-tailed noises. A closed form recursion of the LMB filter to propagate the parameters of Student-t mixture components is derived based on the multi-target Student-t models. Some approximations are applied to make the filter available in practice. The gating technique is also developed for the proposed method. Furthermore, a strategy of managing the number of Student-t components is introduced here to ensure the efficiency of the proposed method. Simulations with joint heavy-tailed noises of the state and measurement are performed to assess the proposed filter, and results demonstrate effectiveness of the new LMB filter.

The coverage control problem of mobile directional sensor networks (DSNs) via a steam function approach, is investigated in the paper. A novel coverage strategy is proposed to coordinate the sensors to cover the designated area. Then, based on the requirement of the proposed coverage network topology, a potential-based sensor placement algorithm is further developed, the superposition of stream function and dipole flow function are applied to achieve formation coverage control and obstacle avoidance. It is shown that the proposed potential-based sensor placement algorithm is convergent based on stability theory. Furthermore, when the mobile sensors reach the intended positions, a rotation strategy is given, the 1-coverage of sensors is realized. Finally, some simulation results demonstrate the effectiveness of the proposed algorithms.

Consensus filtering is an effective method for distributed state estimation of distributed sensor networks and the assumption of white measurement noise is widely used. However, when the measurement noise is colored, the traditional consensus filter cannot work well. In this paper, we first propose a consensus-based distributed filter for colored measurement noise by augmenting the state to include the colored measurement noise. To improve the efficiency of the filter, only local colored measurement noise is integrated into the augmented state for each local filter. Furthermore, another consensus-based distributed filter based on measurement differencing scheme is developed to eliminate the ill-conditioned computations of the augmented state approach. In addition, this method does not need to augment the state and thus has lower dimension than the augmented state filter. Simulation results demonstrate the superiority of the proposed methods.

In multi-target tracking (MTT), the imprecise model for sensor characteristics might result in poor performance. The Variational Bayesian labeled multi-Bernoulli (VB-LMB) filter based on Gamma distribution can handle this problem. However, the predictive likelihood of the existing VB-LMB filter is simply treated as a Gaussian, which is inaccurate. In this paper, a VB-LMB filter with inverse Wishart distribution is presented to perform MTT under the unknown sensor characteristics. The measurement noise covariance is modeled as an inverse Wishart (IW) distribution. This distribution has potential to deal with the full noise covariance matrix compared with the Gamma distribution. Since the state and the measurement noise covariance are coupled, the updated equation can be solved by variational Bayesian (VB) method. The predictive likelihood is calculated via minimizing the Kullback-Leibler divergence by the VB lower bound. A MTT scenario is used to evaluate the proposed method. Simulation results show that our approach has better performance than the existing VB-LMB filter with the Gamma distribution.

The event-based leader-following consensus problem for multi-agent systems with input saturation is stud­ied in this paper. The control input update is event-driven, and the triggering function is linear, under the given trigger function and trigger condition. At first, a sufficient condition of the leader-following consensus for multi-agent systems without input saturation is presented. Furthermore, under the constraint of input saturation and communication time delay, the leader-following consensus control is achieved through designing control protocol and choosing suitable pa­rameters of control input. Lyapunov function in combination with S-procedure method is adopted to prove the consensus stability for multi-agent systems with input saturation and communication time delay. Moreover, the Zeno-behavior is excluded for the triggering time sequence. Finally, simulations are present to illustrate the effectiveness of the proposed consensus control protocols.

A great deal of interest has been paid to computation problem of Dezert-Smarandache theory (DSmT). But there are still problems on complex analysis and frequently search. The computational measurement of DSmT is presented in which the computation is generated in the search for focal elements, the combination of focal elements and basic belief assignment, the expression of focal elements. A new DSmT computational optimization approach is presented to solve the problems. The proposed approach optimizes the original evidence and combination of focal elements. The original evidence is reduced to keep the effective focal elements. And the focal element relationship is integrated into evidence code to realize self-adaption for combination of focal elements. Numerical results are provided to validate our approach.

The implementation of new course standard of mathematics is one of the significant changes of our educational business. Through our analysis of the present mathematics education to discuss the new idea of mathematics education under the new curriculum standard, the new ways of mathematics classroom teaching under the new curriculum standard are given in the paper.

The paper has put forward an idea about the teaching reformation to course of probability and statistics in agriculture and forestry universities. We have paid attention to the theory contact with practice. We also present a new teaching method that the idea of mathematical modeling is applied in the teaching process of application cases to reform teaching methods and seek new teaching modes. Students' ability on applying and innovation will be trained by strengthening the practice of students.

The implementation of new course standard of mathematics is one of the significant changes of our educational business. Through our analysis of the present mathematics education to discuss the new idea of mathematics education under the new curriculum standard, the new ways of mathematics classroom teaching under the new curriculum standard are given in the paper.

It is practical to apply queuing theory in service system. Aimed at hospitalized queuing problem of the ophthalmology hospital, by analyzing the hospital statistics data, this text built sick beds’ proportion allotment model based on queueing system with multi-server method and evaluation index. And solved the patient visiting’s beds allotment problem, made beds most utilization and make using of the maximum resource rate. Carried on an examination and evaluation to the model, the paper gets four suitable models of hospital beds arrangement, admission waiting time, hospital beds’ distribution ratio and hospital beds’ distribution ratio. It also serves as reference for similar projects.

The interpolation fitting is an important method in the numerical analysis, and it has applied in the practical life widely. As for the questions proposed in the mathematics model, to resolve the construction of roads in the mountains with the least cost, this paper used local optimization theory, under the specific circumstances of the mountain set up control points, made the topographic map method into the network, established new network of roads, bridges tunnels and gave different weights, By minimizing the cost instead using the shortest path problem in fitting with interpolation and solved problem using Dijkstra algorithm, finally reached the optimal route and the minimum cost.

To overcome the shortage of the conventional Back-propagation (BP) Network, the BP network is trained by using one step secant (OSS) algorithm. According to the Yangtze River water statistics reported from 1995 to 2004, the BP neural network model for water quality evaluation was established to predict the consequence of water pollution in the next ten years. The result shows: (1) With no effective measures, the Yangtze River water pollution will be on drastic rise in ten years; (2) This model can predict development tendency in ten years and its result is reasonable and also proves that it has strong generalization ability. It is a very valid model of estimating non-linear problem. (3) BP neural network based on OSS algorithm possesses the advantage of high accuracy and high speed for convergence.