有色噪声激发下结构随机振动离散分析方法

本文以线性结构体系在白噪声激发下随机振动离散分析方法为基础,对线性结构体系在有色噪声激发下的随机振动离散分析方法进行了研究。给出均值、均方响应的离散递推式.并为了能在计算机上进行实际运算,给出了实用化计算公式,最后用该方法对一结构进行了实际计算,分析了计算结果。     

随机振动离散分析方法的实用化处理

通过作者前文[2]的讨论,我们得到了随机振动离散分析方法的一个具有无条件稳定性和高精确度的递推公式—β递推格式。但由于在这个递推格式中存在一个增广矩阵求逆的问题,这就给它在实际应用中带来了一定的困难。本文通过等效变换,成功地克服了这一困难,避免了这个增广矩阵求逆,并完全应用了原动力体系质量阵、阻尼阵和刚度阵的对称、带状等特点,大大减少了计算所需的内存和工作量,从而使该方法很实用。     

电动缸式钢包长水口机械手的技术改造

介绍了宝钢5号连铸机钢包长水口机械手的设备构成及技术性能,阐述设备长时间运行所存在的问题,针对问题提出了设备改进的措施。     

氧化锰矿直接浸出制备硫酸锰的几个影响因素

根据云南建水锰矿一年多来的生产实践，就氧化锰矿直接浸出制备硫酸锰工业生产的几个影响因素，进行了探讨并提出了提高浸出回收率的措施。     

多波束条带测深中的声线跟踪技术

海洋环境里声速随着盐度、温度和深度以及其他因素的变化而变化，导致声波并不沿直线传播，利用声波到达海底某区域的往返时间和声线离换能器的出射角计算该区域的海底深度时，如果采用固定声速，则必然带来误差。为提高测深精度，有必要进行声速补偿，为此，研究一种在深度方向上采用声线跟踪技术进行补偿以提高海底深度测量精度的方法，叙述了射线跟踪的基本原理，进行了计算机仿真，并利用海试数据进行了相应的处理。结果表明，依据声速的变化进行声线跟踪可以大大提高测深的精度。     

A general stochastic clustering method for automatic cluster discovery

Finding clusters in data is a challenging problem. Given a dataset, we usually do not know the number of natural clusters hidden in the dataset. The problem is exacerbated when there is little or no additional information except the data itself. This paper proposes a general stochastic clustering method that is a simplification of nature-inspired ant-based clustering approach. It begins with a basic solution and then performs stochastic search to incrementally improve the solution until the underlying clusters emerge, resulting in automatic cluster discovery in datasets. This method differs from several recent methods in that it does not require users to input the number of clusters and it makes no explicit assumption about the underlying distribution of a dataset. Our experimental results show that the proposed method performs better than several existing methods in terms of clustering accuracy and efficiency in majority of the datasets used in this study. Our theoretical analysis shows that the proposed method has linear time and space complexities, and our empirical study shows that it can accurately and efficiently discover clusters in large datasets in which many existing methods fail to run.     

Ultralow feeding gas flow guiding growth of large-scale horizontally aligned single-walled carbon nanotube arrays

On the basis of the rational analysis about the fluidic property of the system, an ultralow gas flow chemical vapor deposition (CVD) strategy was designed to prepare large-scale horizontally aligned ultralong single-walled carbon nanotube (SWNT) arrays. SWNT arrays could be well obtained under extremely low feeding flow of 1.5 sccm in a 1 in. quartz tube reactor. It was confirmed that the tubes grew floatingly and could cross microtrenches or climb over micro-obstacles in ultraslow gas flow. SWNTs arrays also could be formed no matter the substrate was placed vertically or upside down. The growth mechanism was discussed. Both the buoyancy effect induced by gas temperature/density difference and gas flow stability played dominant roles. More attractively, simultaneous batch-scale preparation of SWNT arrays was realized by the ultralow gas flow strategy. This new strategy turns to be more abstemious, efficient, promising, and flexible compared with the high gas flow rate fast-heating CVD processes.     

Truly Concomitant and Independently Expressed Short‐ and Long‐Term Plasticity in a Bi2O2Se‐Based Three‐Terminal Memristor

Concomitance of diverse synaptic plasticity across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive neuromorphic systems, devices that are capable of emulating short‐term (STP) and long‐term plasticity (LTP) concomitantly are essential. In existing memristors, however, STP and LTP can only be induced selectively because of the inability to be decoupled using different loci and mechanisms. In this work, the first demonstration of truly concomitant STP and LTP is reported in a three‐terminal memristor that uses independent physical phenomena to represent each form of plasticity. The emerging layered material Bi₂O₂Se is used for memristors for the first time, opening up the prospects for ultrathin, high‐speed, and low‐power neuromorphic devices. The concerted action of STP and LTP allows full‐range modulation of the transient synaptic efficacy, from depression to facilitation, by stimulus frequency or intensity, providing a versatile device platform for neuromorphic function implementation. A heuristic recurrent neural circuitry model is developed to simulate the intricate “sleep–wake cycle autoregulation” process, in which the concomitance of STP and LTP is posited as a key factor in enabling this neural homeostasis. This work sheds new light on the development of generic memristor platforms for highly dynamic neuromorphic computing.     

Architecture engineering of hierarchically porous chitosan/vacuum-stripped graphene scaffold as bioanode for high performance microbial fuel cell

The bioanode is the defining feature of microbial fuel cell (MFC) technology and often limits its performance. In the current work, we report the engineering of a novel hierarchically porous architecture as an efficient bioanode, consisting of biocompatible chitosan and vacuum-stripped graphene (CHI/VSG). With the hierarchical pores and unique VSG, an optimized bioanode delivered a remarkable maximum power density of 1530 mW m(-2) in a mediator-less MFC, 78 times higher than a carbon cloth anode.