混合动力车用VRLA蓄电池负极的失效机理及碳添加剂的作用（2）

综述了常规阀控式铅酸（VRLA）蓄电池在混合动力车应用中,硫酸铅不断积累的特点及产生的机理。结合失效机理,论述了各类碳材料加入负极后,对电池性能改善作用方面的最新研发进展。不同种类的碳添加剂对抑制硫酸盐积累具有各自不同的作用,其复合添加剂的协同效应更能有效抑制负极硫酸铅的不断积累和延长电池循环寿命,是更有前途的添加剂。     

金属磁记忆技术用于去应力退火评估的可行性研究

本文基于磁致伸缩效应的新型金属磁记忆无损检测技术,利用金属磁记忆信号与应力大小严格的对应关系,对金属磁记忆检测技术用于去应力退火进行评估,进行可行性研究.通过研究分析发现,金属磁记忆信号与45钢的去应力退火程度有一致的单调性,通过检测金属磁记忆信号磁场的强度,可实现对去应力退火工艺的评估.     

次氯酸钠消毒液在宽扎河净水处理中的应用

非洲安哥拉的罗安达水泥有限公司5000t／d生产线的水处理混凝剂采用了硫酸铝和氯化钠,但效果不佳。加碱可提高水处理效果,但设施复杂、运行成本高。经研究采用投加次氯酸钠消毒液办法,既经济且效果又好,使用后水质完全符合生活饮用水国家标准。但使用中应注意投加方法、投加量的掌控,且次氯酸钠消毒液不可久储,最好应现场配制。     

HGK助剂对波特丝织物吸附热力学性能的研究

研究了HGK助剂在波特丝织物上的吸附机理。探究了HGK助剂吸附波特丝织物的吸附热力学性能、吸附牢度,并评价助剂吸附量对后期染色性能的影响。研究了不同助剂用量和不同碱剂用量对吸附效果的影响。结果表明：HGK助剂在波特丝织物上的吸附属于朗缪尔（Langmuir）型吸附。随着碱用量的增大,吸附量、吸附牢度随之增大。采用HGK助剂改性波特丝织物,在织物上引入正电荷,能显著提高阴离子活性染料的染色性能。     

Mo对NbCr2/ Nb合金组织及性能影响

采用机械合金化+热压工艺制备了NbCr_2/Nb-XMo (X=0,2.5,5.0,7.5,10, at%)合金,研究了合金元素Mo对NbCr_2/Nb合金组织及性能的影响。结果表明:合金元素Mo主要存在于Nb基体中,对合金的物相不产生明显影响,合金仍由Nb固溶体和NbCr_2组成;Mo的添加使得NbCr_2/Nb的相界面处应力增加,导致NbCr_2颗粒中的层错/孪晶的密度增加,并促进了Nb基体中位错的运动,从而使得NbCr_2/Nb合金在保持高强度的同时,具有良好的塑性和韧性。     

基于蚂蚁分散费洛蒙的RFID消防疏散系统

以蚂蚁分散费洛蒙行为分析理论为基础,利用行动装置结合被动式无线射频辨识系统(RFID)构建基于RFID的消防疏散系统。介绍此系统的4项功能,分别为室内虚拟实境定位系统、信息交换系统、灾难信息储存及分析系统。该系统使当事人能在处于黑暗又无法使用通信设备等恶劣环境中通过相互间的信息共享而有序逃生。     

Energy distribution and effective components analysis of 2n sequence pseudo-random signal

In order to extract usable harmonics from real 2ⁿ sequence pseudo-random data, a technical method is proposed. An equation for predicting the average amplitude of the main frequencies is proposed to guide the choice of signal type for different exploration tasks. By the threshold of the amplitude of the transmitted signal, a set of candidate frequencies are first selected. Then, by operating a spectrum envelope method at these candidate frequencies on received data, effective components in data are extracted. A frequency density calculation method is proposed based on a logical number summation method, to reasonably characterize the frequency density in different frequency bands. By applying this method to real data in Sichuan, China, with signal Type 13, 75 effective components are extracted, including both main frequencies and harmonics. The result suggests that the number of effective frequencies in the 2ⁿ sequence pseudo-random signal can be increased by extracting usable harmonics, without any additional fieldwork.     

Image deraining with Adversarial Residual Refinement Network

Prior image deraining works mainly have two problems: (1) they do not generalize well to various datasets; (2) too much detail information is lost in the heavy rain area of the rain image. To overcome these two problems, we propose a new two-stage Adversarial Residual Refinement Network (ARRN) to deal with heavy rain images. Specifically, for the first problem, we first introduce a new implicit rain model to model a rain image as a composition of a background image and a residual image. Based on the proposed implicit model, we then propose the ARRN which consists of an image decomposition stage and an image refinement stage. For the second problem, a new attention Wasserstein Generative Adversarial Networks (WGAN) loss in the refinement stage is introduced to force the network to focus on refining heavily degraded areas. Comprehensive experiments demonstrate the effectiveness of the proposed approach.     

Evolution of Nanocrystalline TiBCN Structure with Temperature and Mechanism

Evolution of the TiB0.71C3.32N0.79 quaternary nanocomposite structure at 600,700,800,900,and 1000℃ is investigated by high-resolution transmission electron microscopy,X-ray diffraction,X-ray photoelectron spectrometry,and micro-hardness indentation.The nc-Ti(C,N)nanocrystallites exhibit the(200)preferential orientation and the amorphous carbon(a-C)phase gradually transforms into the crystallite graphite phase as the temperature is increased.At 1000℃,the nc-Ti(C,N)nanocrystallites increase to a size of 13 nm but the microhardness diminishes to 18-19 GPa.The corresponding mechanism is discussed.     

Tracking topology structure adaptively with deep neural networks

Object tracking still remains challenging in computer vision because of the severe object variation, e.g., deformation, occlusion, and rotation. To handle the object variation and achieve robust object tracking performance, we propose a novel relationship-based tracking algorithm using neural networks in this paper. Compared with existing approaches in the literature, our method assumes the targeted object to be consisted of several parts and considers the evolution of the topology structure among these parts. After training a candidate neural network for predicting the probable areas each part may locate at in the successive frame, we then design a novel collaboration neural network to determine the precise area each part will locate at with account for the topology structure among these individual parts, which is learned from their historical physical locations during online tracking process. Experimental results show that the proposed method outperforms state-of-the-art trackers on a benchmark dataset, yielding the significant improvements in accuracy on high-distorted sequences.