Application of the probability-based covering algorithm model in text classification

The probability-based covering algorithm(PBCA) is a new algorithm based on probability distribution. It decides, by voting, the class of the tested samples on the border of the coverage area, based on the probability of training samples. When using the original covering algorithm(CA), many tested samples that are located on the border of the coverage cannot be classified by the spherical neighborhood gained. The network structure of PBCA is a mixed structure composed of both a feed-forward network and a feedback network. By using this method of adding some heterogeneous samples and enlarging the coverage radius,it is possible to decrease the number of rejected samples and improve the rate of recognition accuracy. Relevant computer experiments indicate that the algorithm improves the study precision and achieves reasonably good results in text classification.

Sequence optimization for multiple asteroids rendezvous via cluster analysis and probability-based beam search

It is of great significance to visit multiple asteroids in a space mission.In this paper,the multiple asteroids mission optimization is implemented using cluster analysis and probability-based beam search.Clustering is performed to select the first asteroid to visit.Four cluster algorithms are investigated and affinity propagation is selected.Then four beam search algorithms that are deterministic beam search and three probability-based beam search variants,probabilistic beam search,ant-colony beam search,and evolving beam search,are applied to search for the rendezvous sequence.Deterministic beam search as a heuristic tree search algorithm is widely applied in multitarget sequence optimization,but it has an obvious drawback of the conflict between the number of pruned nodes and the possibility of finding optimal solutions,which can be improved by probability-based beam search.Among three probability-based beam search,the ant-colony beam search has a learning mechanism,and evolving beam search is constructed based on ant-colony beam search and has an evolutionary mechanism.Results show that the introduction of randomness can improve beam search,and beam search variants with the learning and evolutionary mechanism have an excellent performance.

Towards multiple hazard resilient bridges:a methodology for modeling frequent and infrequent time-varying loads Part I,Comprehensive reliability and partial failure probabilities

The current AASHTO load and resistance factor design （LRFD） guidelines are formulated based on bridge reliability, which interprets traditional design safety factors into more rigorously deduced factors based on the theory of probability. This is a major advancement in bridge design specifications. However, LRFD is only calibrated for dead and live loads. In cases when extreme loads are significant, they need to be individually assessed. Combining regular loads with extreme loads has been a major challenge, mainly because the extreme loads are time variables and cannot be directly combined with time invariant loads to formulate the probability of structural failure. To overcome these difficulties, this paper suggests a methodology of comprehensive reliability, by introducing the concept of partial failure probability to separate the loads so that each individual load combination under a certain condition can be approximated as time invariant. Based on these conditions, the extreme loads （also referred to as multiple hazard or MH loads） can be broken down into single effects. In Part II of this paper, a further breakdown of these conditional occurrence probabilities into pure conditions is discussed by using a live truck and earthquake loads on a bridge as an example. There are three major steps in establishing load factors from MH load distributions： （1） formulate the failure probabilities; （2） normalize various load distributions; and （3） establish design limit state equations. This paper describes the formulation of the failure probabilities of single and combined loads.

Towards multiple hazard resilient bridges:a methodology for modeling frequent and infrequent time-varying loads Part Ⅱ,Examples for live and earthquake load effects

The current AASHTO load and resistance factor design （LRFD） guidelines are formulated based on bridge reliability, which interprets traditional design safety factors into more rigorously deduced factors based on the theory of probability. This is a major advancement in bridge design specifications. However, LRFD is only calibrated for dead and live loads. In cases when extreme loads are significant, they need to be individually assessed. Combining regular loads with extreme loads has been a major challenge, mainly because the extreme loads are time variable and cannot be directly combined with time invariant loads to formulate the probability of structural failure.To overcome these difficulties, this paper suggests a methodology of comprehensive reliability, by introducing the concept of partial failure probability to separate the loads so that each individual load combination under a certain condition can be approximated a,; time invariant. Based on these conditions, the extreme loads （also referred to as multiple hazard or MH loads） can be broken down into single effects. In this paper, a further breakdown of these conditional occurrence probabilities into pure conditions is discussed by using a live truck and earthquake loads on a bridge as an example.

Multi-Mode Guided Waves Based Reference-Free Damage Diagnostic Imaging in Plates

Probability-based diagnostic imaging(PDI)is one of the most well-known damage identification methods using guided waves.It is usually applied to diagnose damage in plates.The previous studies were dependent on the certain damage index(DI)which is always calculated from the guided wave signals.In conventional methods,DI is simply defined by comparing the real-time data with the baseline data as reference.However,the baseline signal is easily affected by varying environmental conditions of structures.In this paper,a reference-free diagnostic imaging method is developed to avoid the influence of environmental factors,such as temperature and load conditions.The DI is defined based on the mode conversion of multi-mode guided waves with realtime signals without baseline signals.To improve the accuracy of diagnosis,two terms are included in the reference-free DI.One is called energy DI,which is defined based on the feature of signal energy.The other is called correlation DI and is defined based on the correlation coefficient.Then the PDI algorithm can be carried out instantaneously according to the reference-free DI.The real-time signals which are used to calculate DI are collected by the piezoelectric lead zirconate titanate(PZT)transducers placed on both sides of a plate.The numerical simulations by the finite element(FE)method on aluminum plates with PZT arrays are performed to validate the effectiveness of the reference-free damage diagnostic imaging.The approach is validated by two different arrays:a circle network and a square network.The results of diagnostic imaging are demonstrated and discussed in this paper.Furthermore,the advantage of reference-free DI is investigated by comparing the accuracy of defined reference-free DI and energy DI.

Near-ground trajectory planning for UAVs via multi-resolution hybrid voxel-surfel map

This paper is concerned with trajectory planning problems for UAVs operating near ground.Most existing studies focus on solving the problem of collision-free trajectory planning between pre-defined path points,but ignore the need of navigation method for UAVs working on specific operating surfaces in near-ground space.In this paper,a novel near-ground trajectory planning framework is proposed,where the hybrid voxel-surfel map is developed to model the environment with special attention to the uneven operating surface.To improve the frequency of updates,a probability-based surfel fusion method and a resolution adaptive adjustment method based on the fusion result are proposed in this paper.By using possibility information in the map,a path search method is established to generate the initial trajectory.The trajectory is then further optimized based on map gradient information to generate a final trajectory that tracks the specified operating surface according to the task requirements.Compared with existing methods,the multi-resolution hybrid voxel-surfel map proposed in this paper has advantages in terms of operating efficiency.A series of experiments in simulated and real scenarios validate the effectiveness of the proposed trajectory planning framework.