The normal approach to digital control is to sample periodically in time. Using an analog of integration theory we can call this Riemann sampling. Lebesgue sampling or event based sampling is an alternative to Riemann sampling. It means that signals are sampled only when measurements pass certain limits. In this paper it is shown that Lebesgue sampling gives better performance for some simple systems.

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Comparison of Riemann and Lebesque sampling for first order stochastic systems

Distributed particle filters (PFs) have received extensive attention because of its excellent performance. Generally, it needs to transmit particles to multiple processing units (PUs) in order to improve the performance. However, how to balance communication costs and computation costs in particle routing is still an open problem. This paper presents a multi-objective discrete particle swarm optimization (PSO) algorithm to solve the problem. In the algorithm, the particle routing problem in distributed PFs is modeled as a multi-objective constrained optimization model for the first time. Following that, an improved hybrid discrete multi-objective PSO is proposed. Two new operators designed based on the problem’s characteristics, that is, a local search strategy based on molecular force and a constraint processing mechanism with greedy search, are developed to improve the performance of the proposed algorithm. By comparing with three commonly used methods on classical particle filters problems, experimental results show that the proposed algorithm is a highly competitive approach, and it can provide multiple high-quality Pareto optimal solutions for decision-makers to meet their different needs. 

A multi-objective discrete particle swarm optimization method for particle routing in distributed particle filters

Review on Environmental Aspects in Smart City Concept: Water, Waste, Air Pollution and Transportation Smart Applications using IoT Techniques

Parallel pipelines are widely used to transport energy resources. Leakages usually can occur in pipelines due to aging, corrosion, metal failure, etc. When an accident happened, not only the energy company would take the financial loss, but also it would cause pollution and safety issues to the local environment. Therefore, an efficient way to identify leakages in parallel pipelines is necessary to be proposed. In this study, ANSYS was used to simulate different leak scenarios in parallel pipelines. Fluid and pipe parameters were used to simulate different leak scenarios. In each leak scenario, there were different pressure drops along the leak pipeline based on leak locations and different flow rates. After determining there is more than a leak in pipelines, the relationship among pressure drops, leak locations, and flow rates can be used to build a mathematical model for detecting leaks. During the pipeline operations, the pressure drops were affected by leak locations and flow rates. Therefore, applying flow parameters in real leak scenarios to the mathematical model that is built from the parameters in the reality will identify the leak locations. In addition, lab experiments were applied to verify the validity of the simulations. The deviations between the experiments and simulations are less than 4%. The pressure drops through the leak pipe in the experiments and simulation vary from 1,955 to 2,898 Pa and 1,992 to 2,803 Pa, respectively. This research investigated a method to identify two-point leakages in parallel pipelines based on flow parameter analysis. 

Identifying two-point leakages in parallel pipelines based on flow parameter analysis

In order to solve the problem of accurately and robustly recovering fish trajectories from videos, the author proposes a physical habitat assessment model for fishery resources based on particle filter algorithm. The idea of “group tracking” was proposed, which divides all targets into several groups based on their mutual distance and processes different groups independently and in parallel, avoiding the extremely high dimensionality caused by estimating all zebrafish parameters simultaneously. The parameters of all individuals in the same group are estimated together, and it handles occlusion by modeling the interaction and occlusion of objects in the same group. The experimental results show that from frame 16 to frame 21, two fish suddenly turn around, especially the fish marked in red; From frame 34 to frame 42, these two fish interact for the first time, but do not intersect; From frame 49 to frame 58, these two fish interact and pass through each other for the second time. The algorithm proposed by the author, GCT, and contrast algorithm CT, can effectively handle the protruding and directional motion of fish. Conclusion: The algorithm is accurate and reliable. 

Research and application of fishery resource assessment based on particle filter algorithm physical habitat model

This paper proposes an improved model based pipeline leak detection and localization method based on compressed sensing (CS) and event-triggered (ET) particle filter (ET-PF). First, the state space model of the pipeline system is established based on the characteristic line method. Then, the CS method is used to preprocess the sensor signals to recover the potentially lost leak information which is caused by the low sampling frequency of the industrial pipeline sensors, and an event based beetle antennae search (BAS) particle filter (BAS-PF) is proposed to improve the accuracy and efficiency of the pipeline state estimation. Finally, a pipeline leak detection and localization method is developed based on the proposed signal processing, and state estimation algorithms, as well as a pipeline partition strategy. Experiment results show that the proposed method can accurately detect and locate the leak of the pipeline system with a localization error of about 1.4%.

An improved pipeline leak detection and localization method based on compressed sensing and event-triggered particle filter

As an important energy source, lithium-ion batteries have vast applications in daily production and life. Therefore, the life prediction of such batteries is of great significance, which can help us maintain the efficacy and reliability of the system powered by lithium-ion batteries. In this paper, a hybrid battery model and an improved particle filter (PF) are developed to better estimate the remaining useful life (RUL) of lithium-ion battery. Firstly, a hybrid model is constructed as the degradation model of the battery. The developed hybrid model is a combination of the empirical battery model and neural network model such that it could characterize both the internal mechanism of the battery and the change of external environment in a more accurate way. Then, the beetle antennae search (BAS) based PF (BAS-PF) is utilized to compute the battery parameters online based on the proposed hybrid model. Compared with the existing PF based method, BAS-PF can well handle the problem of diversity loss and improve the estimation accuracy. Finally, the battery data set published by NASA is used as the experiment object. Experiments show that the proposed hybrid model can well represent the internal and external changes of the battery, and the BAS-PF based method has a great improvement in the estimation accuracy. Those results illustrate that the developed technique could achieve an improved life prediction for lithium-ion batteries.

Ruoxia Li

Papers

An improved pipeline leak detection and localization method based on compressed sensing and event-triggered particle filter

A novel RUL prediction method for Lithium-ion Batteries via hybrid modeling and BAS based particle filter