Study on General Performance of Jack-up Under Elevated Condition

Method of checking for jack-up elevated performance including leg structure strength, fixation system or jacking system beating capacity, pre-load requirements, spud can beating capacity and overturning stability is suggested in this paper. As an example, a jack-up with truss legs is analyzed by finite element analysis method. This paper may be helpful to the rig owners, operators and designers.

Multidisciplinary Design Optimization of A Human Occupied Vehicle Based on Bi-Level Integrated System Collaborative Optimization

The design of Human Occupied Vehicle （HOV） is a typical multidisciplinary problem, but heavily dependent on the experience of naval architects at present engineering design. In order to relieve the experience dependence and improve the design, a new Multidisciplinary Design Optimization （MDO） method ＂Bi-Level Integrated System Collaborative Optimization （BLISCO）＂ is applied to the conceptual design of an HOV, which consists of hull module, resistance module, energy module, structure module, weight module, and the stability module. This design problem is defined by 21 design variables and 23 constraints, and its objective is to maximize the ratio of payload to weight. The results show that the general performance of the HOV can be greatly improved by BLISCO.

Stopping time of a one-dimensional bounded quantum walk

The stopping time of a one-dimensional bounded classical random walk（RW） is defined as the number of steps taken by a random walker to arrive at a fixed boundary for the first time.A quantum walk（QW） is a non-trivial generalization of RW,and has attracted a great deal of interest from researchers working in quantum physics and quantum information.In this paper,we develop a method to calculate the stopping time for a one-dimensional QW.Using our method,we further compare the properties of stopping time for QW and RW.We find that the mean value of the stopping time is the same for both of these problems.However,for short times,the probability for a walker performing a QW to arrive at the boundary is larger than that for a RW.This means that,although the mean stopping time of a quantum and classical walker are the same,the quantum walker has a greater probability of arriving at the boundary earlier than the classical walker.

Investigating the influence of segmentation in estimating safety performance functions for roadway sections

Safety performance functions（SPFs） are crucial to science-based road safety management.Success in developing and applying SPFs, apart data quality and availability, depends fundamentally on two key factors： the validity of the statistical inferences for the available data and on how well the data can be organized into distinct homogeneous entities. The latter aspect plays a key role in the identification and treatment of road sections or corridors with problems related to safety. Indeed, the segmentation of a road network could be especially critical in the development of SPFs that could be used in safety management for roadway types, such as motorways（freeways in North America）, which have a large number of variables that could result in very short segments if these are desired to be homogeneous. This consequence, from an analytical point of view, can be a problem when the location of crashes is not precise and when there is an overabundance of segments with zero crashes. Lengthening the segments for developing and applying SPFs can mitigate this problem, but at a sacrifice of homogeneity. This paper seeks to address this dilemma by investigating four approaches for segmentation for motorways, using sample data from Italy. The best results were obtained for the segmentation based on two curves and two tangents within a segment and with fixed length segments. The segmentation characterized by a constant value of all original variables inside each segment was the poorest approach by all measures.

Hybrid Optimization-Based GRU Neural Network for Software Reliability Prediction

Aiming at the problems of low prediction accuracy and weak generalization ability of current reliability prediction models,this paper proposes a hybrid multi-layer heterogeneous particle swarm optimization algorithm(HMHPSO)that can simultaneously optimize the structure and parameters of the GRU neural network.It first introduced a multi-layer heteromass particle swarm optimization(MHPSO)algorithm,which sets the population topology as a hierarchical structure and introduces the concept of attractors,so as to improve the update formula of particle speed,and enhance the information interaction ability between particles,increase the diversity of the groups,thereby improving the optimization ability of the algorithm.Then the HMHPSO used the quantum particle swarm optimization(QPSO)algorithm to determine the structure of the GRU,that is,the number of hidden nodes.Experimental results show that the algorithm can generate GRU neural networks with high generalization performance and low architecture complexity,and has better prediction accuracy in software reliability prediction.