塔河油田污水处理系统腐蚀治理对策

通过对塔河油田各污水处理系统腐蚀环境的系统研究,结合塔河油田及其它油田的腐蚀防治经验,在综合考虑各种方案的防腐效果和经济成本之后,设计了针对性的治理方案。研究成果有助于指导塔河油田污水处理系统的防腐工作,对其它类似油田的防腐工作也有一定的指导意义。     

A technique for studying interacting cracks of complex geometry in 2D

A method for studying brittle fracture in an infinite plate containing interacting cracks of complex shape under general loading conditions is developed and studied for accuracy and potential applications. This technique is based on superposition and dislocation theory and can be used to determine the full stress and displacement fields in a cracked body. In addition, stress intensity factors at both crack tips and wedges, created by crack kinking and branching, are calculated so that crack growth and initiation can be analyzed at these locations of possible crack propagation. Such information can then be used to study damage accumulation in structures containing a large number of interacting cracks.     

Joint tracking and classification based on aerodynamic model and radar cross section

We present interacting multiple model regularized particle filter for the X-band active surveillance radar to jointly track and classify air threaten targets. The actual aerodynamic equations for flight are used as motion model, and automatic target classification is made possible by the inclusion of radar cross section in the measurement vector. Thus, tracking and classification are closely coupled, giving full play to the advantages of joint tracking and classification. The proposed methodologies show good performance according to simulations.     

Failure analysis of corroded high-strength pipeline subject to hydrogen damage based on FEM and GA-BP neural network

The pipeline is a major approach to achieving large-scale hydrogen transportation. Hydrogen damage can deteriorate the material performance of the pipe steel, like ductility and plasticity reduction. Corrosion is dominating damage that impairs a pipeline's bearing capacity and structural reliability. However, previous research barely investigated the effect of hydrogen damage on failure behaviors, residual strength and interacting effect between adjacent corrosions of corroded high-strength pipelines transporting hydrogen. Besides, hardly any burst pressure model considers hydrogen damage. In this paper, several approaches, including the finite element method (FEM), regression analysis, the orthogonal test method, and the artificial neural network method, are applied to fill the gap. First, a series of finite element models with different geometric features and hydrogen damage is established to investigate the effects of hydrogen damage and corrosion on failure behaviors and residual strength. The results show that hydrogen damage can change the corroded pipeline's failure behaviors and reduce the residual strength. Second, based on the simulation results and regression analysis, a new burst model is developed to consider the hydrogen damage and improve the estimation accuracy. Third, based on the genetic algorithm (GA), a GA-BP neural network is established and trained for accurate and efficient residual strength estimation considering hydrogen damage. Furthermore, an orthogonal test is designed and performed to investigate the effects of critical parameters on the burst pressure of the corroded pipeline after hydrogen damage. The results indicate that hydrogen damage and corrosion length have similar contributions to the residual strength. Finally, the simulation results of pipelines with multiple corrosions show that hydrogen damage has a significant impact on the interacting effect between adjacent corrosions. The results obtained are valuable for further integrity management of steel pipelines carrying hydrogen.     

Incorporating multiple genomic features with the utilization of interacting domain patterns to improve the prediction of protein-protein interactions

Protein-protein interaction (PPI) networks play an outstanding role in the organization of life. Parallel to the growth of experimental techniques on determining PPIs, the emergence of computational methods has greatly accelerated the time needed for the identification of PPIs on a wide genomic scale. Although experimental approaches have limitations that can be complemented by the computational methods, the results from computational methods still suffer from high false positive rates which contribute to the lack of solid PPI information. Our study introduces the PPI-Filter; a computational framework aimed at improving PPI prediction results. It is a post-prediction process which involves filtration, using information based on three different genomic features; (i) gene ontology annotation (GOA), (ii) homologous interactions and (iii) protein families (PFAM) domain interactions. In the study, we incorporated a protein function prediction method, based on interacting domain patterns, the protein function predictor or PFP (), for the purpose of aiding the GOA. The goal is to improve the robustness of predicted PPI pairs by removing the false positive pairs and sustaining as much true positive pairs as possible, thus achieving a high confidence level of PPI datasets. The PPI-Filter has been proven to be applicable based on the satisfactory results obtained using signal-to-noise ratio (SNR) and strength measurements that were applied on different computational PPI prediction methods.     

Editorial

Abstract

With the increasing interest in autonomous shipping technology, to ensure ship’s safety it is vital to equip the ship with a navigational algorithm to continuously track the moving vessels in the area of interest and subsequently avoid any collisions. To address this problem, in this paper, an intelligent and robust tracking algorithm using the Kalman filter (KF) and Interacting Multiple Model (IMM) scheme has therefore been designed. The algorithm is able to provide accurate motion information of other ships in constrained environments using least navigational information. The capability of the proposed algorithm has been tested and verified in a number of computer-based simulations.     

环境智能下老龄用户方向性手势交互偏好研究

目的 方向性手势指用空间中肢体运动的方向来表示其含义的一类交互手势语言.考虑到老龄用户自身的认知习惯,需要研究该群体对面向环境智能交互的方向性手势在任务语义——运动方向映射上的偏好.方法 根据认知语言学基本理论,通过快速反应实验、用户自我评价和实景评价3个阶段,让老年被试评判从特定任务的词义联想到三维空间运动方向的程度,从而得到其对于方向性手势的偏好.结果 老龄用户认为某些任务和特定方向的关联性更显著,这些任务更适合用朝该方向划动的手势来表达;老龄用户对方向性手势的总体偏好与年轻群体差别不大.结论 通过判定偏好的基础分和差异显著性,可以得出一些任务——方向的语义关联度较高的手势,从而为智能环境下老年人群交互方式的研究提供有益的建议.     

一种具有自适应关联门的杂波中机动目标跟踪算法

针对杂波环境下的机动目标跟踪,该文提出一种基于自适应关联门的跟踪算法。该算法以传统交互多模型概率数据关联算法为基础,在关联门内无有效量测点迹时,假设目标在前一滤波时刻或是更早时刻以最大机动水平改变原运动模式,利用该假设条件下所获得的目标预测量测及当前真实预测量测,对用于确定关联门的新息协方差进行修正,使得关联门逐步适当扩大,以尽可能地包含目标真实量测点迹。仿真结果表明,自适应关联门跟踪算法能在不影响跟踪精度和算法运算量的情况下,有效降低机动目标的跟踪丢失概率。     

场景统计类红外图像非均匀性校正算法研究

场景统计类非均匀性校正算法对场景分布进行假定,获得探测单元接收红外能量的一、二阶矩,进而估计探测单元的响应参数,校正非均匀性。分析比较了现有的场景统计类非均匀性校正算法,并应用交互多模(IMM)算法校正非均匀性,实验结果表明以连续图像序列作为观测数据时,能够有效地校正非均匀性,扩展了卡尔曼滤波法的适用范围。对各种方法进行仿真,表明删算法具有较好的收敛特性。