CL－1型缓蚀剂的研制

为了阻缓高含水原油对集输管线的腐蚀，研制了ＣＬ－１型输油管线缓蚀剂。这种缓蚀剂是以两种咪唑啉衍生物与联氨为主要成分的复配药剂，运用于油水共在体系．本文介绍了该剂的室内性能评价和现场试验结果。     

Utilizing an Animal Model to Identify Brain Neurodegeneration-Related Biomarkers in Aging

Glycine N-methyltransferase (GNMT) regulates S-adenosylmethionine (SAMe), a methyl donor in methylation. Over-expressed SAMe may cause neurogenic capacity reduction and memory impairment. GNMT knockout mice (GNMT-KO) was applied as an experimental model to evaluate its effect on neurons. In this study, proteins from brain tissues were studied using proteomic approaches, Haemotoxylin and Eosin staining, immunohistochemistry, Western blotting, and ingenuity pathway analysis. The expression of Receptor-interacting protein 1(RIPK1) and Caspase 3 were up-regulated and activity-dependent neuroprotective protein (ADNP) was down-regulated in GNMT-KO mice regardless of the age. Besides, proteins related to neuropathology, such as excitatory amino acid transporter 2, calcium/calmodulin-dependent protein kinase type II subunit alpha, and Cu-Zn superoxide dismutase were found only in the group of aged wild-type mice; 4-aminobutyrate amino transferase, limbic system-associated membrane protein, sodium- and chloride-dependent GABA transporter 3 and ProSAAS were found only in the group of young GNMT-KO mice and are related to function of neurons; serum albumin and Rho GDP dissociation inhibitor 1 were found only in the group of aged GNMT-KO mice and are connected to neurodegenerative disorders. With proteomic analyses, a pathway involving Gonadotropin-releasing hormone (GnRH) signal was found to be associated with aging. The GnRH pathway could provide additional information on the mechanism of aging and non-aging related neurodegeneration, and these protein markers may be served in developing future therapeutic treatments to ameliorate aging and prevent diseases.     

基于互相关定位法的干涉仪分析与仿真

首次提出用时域互相关法处理Sagnac/Mach-Zehnder分布式光纤传感系统的干涉输出信号, 对扰动进行检测和定位。首先对Sagnac/Mach-Zehnder分布式光纤传感系统进行了扰动检测和定位的理论分析, 推导出扰动引起的干涉输出信号的表达式, 利用Bessel函数展开, 确定了该系统的两路干涉输出满足时域互相关的特性。在此基础上进一步分析了两路干涉信号的表达式, 提出基频定位法和微分定位法两种时域互相关方法处理干涉信号, 确定扰动位置。对两种扰动定位方法进行了Matlab仿真, 并通过实验在多个扰动位置多次验证了两种方法的正确性。     

Theoretical Investigation of the Viscous Damping Coefficient of Hydraulic Actuators

The viscous damping coefficient(VDC) of hydraulic actuators is crucial for system modeling,control and dynamic characteristic analysis.Currently,the researches on hydraulic actuators focus on behavior assessment,promotion of control performance and efficiency.However,the estimation of the VDC is difficult due to a lack of study.Firstly,using two types of hydraulic cylinders,behaviors of the VDC are experimentally examined with velocities and pressure variations.For the tested plunger type hydraulic cylinder,the exponential model B=αυ~(-β),(α0,β0)or B=α_1e~(-β_1υ)+α_2e~(-β_2υ)(α_1,α_20,β_1,β_20),fits the relation between the VDC and velocities for a given pressure of chamber with high precision.The magnitude of the VDC decreases almost linearly under certain velocities when increasing the chamber pressure from 0.6 MPa to 6.0 MPa.Furthermore,the effects of the chamber pressures on the VDC of piston and plunge type hydraulic cylinders are different due to different sealing types.In order to investigate the VDC of a plunger type hydraulic actuator drastically,a steady-state numerical model has been developed to describe the mechanism incorporating tandem seal lubrication,back-up ring related friction behaviors and shear stress of fluid.It is shown that the simulated results of VDC agree with the measured results with a good accuracy.The proposed method provides an instruction to predict the VDC in system modeling and analysis.     

基于动力学与控制统一模型的蛇形机器人速度跟踪控制方法研究

对带有被动轮的蛇形机器人进行速度跟踪控制时,利用传统的动力学建模方法得到的动力学方程复杂且不利于控制器的设计. 本文基于微分几何的方法将带有被动轮的蛇形机器人动力学投影到速度分布空间中, 得到了动力学与控制统一模型, 更有利于速度跟踪控制器的设计. 考虑到蛇形机器人在进行速度跟踪时容易出现奇异位形, 提出增加头部扰动速度的方法. 基于头部扰动速度和统一模型, 提出避免奇异位形的速度跟踪控制方法, 最后通过逆向动力学得到控制力矩. 文中对速度跟踪控制进行了数值仿真和实验验证. 仿真和实验结果表明, 提出的速度跟踪控制方法能够跟踪想要方向的速度, 并且在跟踪过程中可以有效地避免奇异位形.     

TV图像插值的双方向扩散改进算法

提出了一种双方向扩散图像插值方法,有效地减小了插值图像的边缘宽度,从而获得清晰而光滑的图像边缘.这种双方向扩散在图像边缘斜坡较亮一侧进行前向扩散,而在边缘斜坡较暗一侧进行后向扩散.同时,它能根据图像边缘的特征自适应地调整前向、后向扩散强度,从而避免了在插值的图像中产生虚的纹理或边缘.数值实验结果显示,该方法既能很好地减小插值图像的边缘宽度,又不会产生其他人工虚像.     

Efficient and provably secure password-based group key agreement protocol

This paper considers the issue on authenticated group key agreement protocol among n users broadcasting communication over an insecure public network. Many authenticated group Diffie-Hellman key agreement protocols have been proposed to meet the challenges. However, existing protocols are either limited by the use of public key infrastructure or by their scalability, requiring O(n) rounds. To overcome these disadvantages, we propose an efficient password-based group key agreement protocol resistant to the dictionary attacks by adding password-authentication services to a non-authenticated multi-party key agreement protocol proposed by Horng. The proposed protocol is very efficient since it only requires constant rounds to agree upon a session key, and each user broadcasts a constant number of messages and only requires four exponentiations. Under the Decisional Diffie-Hellman assumption, we will show the proposed protocol is provably secure in both the ideal-cipher model and the random-oracle model.     

Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm

This paper proposes a TSK-type recurrent neuro fuzzy system (TRNFS) and hybrid algorithm- GA_BPPSO to develop a direct adaptive control scheme for stable path tracking of mobile robots. The TRNFS is a modified model of the recurrent fuzzy neural network (RFNN) to obtain generalization and fast convergence. The TRNFS is designed using hybridization of genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO), called GA_BPPSO. For the tracking control of mobile robot, two TRNFSs are designed to generate the control inputs by direct adaptive control scheme and hybrid algorithm GA_BPPSO. Through simulation results, we demonstrate the effectiveness of our proposed controller.     

基于样本个体差异性的深度神经网络训练方法

深度神经网络目前在许多任务中的表现已经达到甚至超越了人类的水平,但是其泛化能力和人类相比还是相去甚远.如何提高网络的泛化性,一直是重要的研究方向之一.围绕这个方向开展的大量卓有成效的研究,从扩展增强训练数据、通过正则化抑制模型复杂度、优化训练策略等角度,提出了很多行之有效的方法.这些方法对于训练数据集来说都是某种全局性质的策略,每一个样本数据都会被平等的对待.但是,每一个样本数据由于其携带的信息量、噪声等的不同,在训练过程中,对模型的拟合性能和泛化性能的影响也应该是有差异性的.针对是否一些样本在反复的迭代训练中更倾向于使得模型过度拟合,如何找到这些样本,是否可以通过对不同的样本采用差异化的抗过拟合策略使得模型获得更好的泛化性能等问题,提出了一种依据样本数据的差异性来训练深度神经网络的方法,首先使用预训练模型对每一个训练样本进行评估,判断每个样本对该模型的拟合效果;然后依据评估结果将训练集分为易使得模型过拟合的样本和普通的样本两个子集;最后,再使用两个子集的数据对模型进行交替训练,过程中对易使得模型过拟合的子集采用更强有力的抗过拟合策略.通过在不同的数据集上对多种深度模型进行的一系列实验...