薄煤层中垂直裂缝地震响应特征模拟分析

通过定义一个简单的薄煤层模型,在不断增加煤层中垂直裂缝数量的条件下,利用基于分裂节点的有限元数值方法模拟了含有不同条数裂隙煤层的地震反射波场;通过与不含裂缝、各向同性薄煤层反射的多波波场对比分析发现:裂缝的存在会对煤层的反射产生影响,但裂缝密度低时影响微弱,裂缝密度增加到一定程度后会出现类似调谐作用的反射能量干涉加强;裂缝密度进一步升高,对煤层反射的影响主要反映在高频成分的增加,对振幅影响较弱,说明煤层的反射强度主要受薄层的干涉作用影响较大;从理论地震波场分析的角度证明了薄煤层含裂缝的不可忽视性及反演的可能性。     

Influences of chitosan on freeze–thaw stability of Arenga pinnata starch

This study aims to investigate chitosan (CS) with five different molecular weight (Mw) on freeze–thaw stability of Arenga pinnata starch (APS) gel subjected to five freeze–thaw cycles (FTC). The syneresis of APS gels was reduced by adding CS and the APS gel with high Mw CS had lower syneresis duo to a higher water holding capacity (P < 0.05). The addition of CS significantly decreased the hardness and molecular ordered structure of APS gel. In addition, CS could improve the microstructural stability. The results suggested that CS could effectively improve the freeze–thaw stability of APS gel, and CS with higher Mw might have more practical utility to improve stability of APS gel.     

基于星座图对射频指纹识别方法的研究

针对星座图的射频(RF)指纹识别方案中,低信噪比环境下识别准确率低的问题,提出一种基于欧式距离与幅度距离的二维识别算法来进行RF指纹识别。该方案通过对星座图进行优化处理,可从优化后的星座图中提取识别性能更好的RF指纹,再通过二维识别算法来提高识别准确率。仿真结果表明:与仅用欧式距离作为判断依据的方法相比,所提出的二维识别算法的识别准确率最高可提升8%,在设备容量为50组的情况下识别准确率为77.8%,并且从优化后的星座图中所提取的RF指纹具有更好的唯一性和稳定性。     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

一种振动矢量测量及调整的方法

针对消失模生产中振实设备的振动过程不满足工艺要求的情况下会产生的问题,详细阐述了基于消失模振实、紧实过程的测量方法和评价方法,通过使用该种方法可以得出振动矢量和振幅,进而对振实紧实设备和振动紧实工艺进行调整和优化,杜绝干砂振动紧实过程中的各种问题,保证铸件质量。可以普遍应用在干砂振实设备研发、校检、定期维护等各个过程,可以有效提升设备的工艺符合性,减少模样及铸件不合格品的产生。     

Evolution of elastic behavior of alite paste at early hydration stages

Elastic behavior is an important aspect of fresh cement-based materials. In this paper, the elasticity development of fresh pure alite paste within 5 hours was monitored in situ by small amplitude oscillation shear. Isothermal microcalorimetry, X-ray diffraction, scanning electronic microscopy, ¹H nuclear magnetic resonance, zeta potential measurements were used to investigate the underlying mechanisms. It was found that the evolution of storage modulus can be characterized by three different stages. The first stage stems from the compression of electrical double layer and the slight increase of solid volume fraction; the second stage is only controlled by the colloidal interaction; and the third stage is associated to the significant growth of solid volume fraction, the enhancement of short-range attraction and the rigidification of calcium silicate hydrate (CSH) network. Moreover, the relationship between the formation of CSH and the growth of elasticity was also quantified.     

基于多要素视角的矿产资源节约集约利用水平评估

根据2016~2018年度对浙江省内53座典型露天开采矿山的跟踪调研,分析矿产资源节约集约利用水平现状,经多批次的专家咨询、研讨,确定了矿产资源节约集约利用水平评价指标项和评价标准值计算方法,结合专家打分方式确定各项指标权重,构建了评价指标体系。通过对选取的典型矿山试点评估,探讨评价指标体系的科学合理性。结果显示,由评价体系得出的评估值与矿山企业的开发利用实际情况较为吻合,能够客观的反映出矿山企业的资源节约集约利用水平。以此可建立矿产资源节约集约利用水平评价机制,定期开展系统性评价工作,为建立矿政管理考核和监管提供依据。     

A Minimalist Single-Layer Metasurface for Arbitrary and Full Control of Vector Vortex Beams

Vector vortex beams (VVBs) possess ubiquitous applications from particle trapping to quantum information. Recently, the bulky optical devices for generating VVBs have been miniaturized by using metasurfaces. Nevertheless, it is quite challenging for the metasurface-generated VVBs to possess arbitrary polarization and phase distributions. More critical is that the VVBs' annular intensity profiles demonstrated hitherto are dependent on topological charges and are hence not perfect, posing difficulties in spatially shared co-propagation of multiple vortex beams. Here, a single-layer metasurface to address all those aforementioned challenges in one go is proposed, which consists of two identical crystal-silicon nanoblocks with varying positions and rotation angles (i.e., four geometric parameters throughout). Those four geometric parameters are found to be adequate for independent and arbitrary control of the amplitude, phase, and polarization of light. Perfect VVBs with arbitrary polarization and phase distributions are successfully generated, and the constant intensity profiles independent of their topological charges and polarization orders are demonstrated. The proposed strategy casts a distinct perception that a minimalist design of just one single-layer metasurface can empower such robust and versatile control of VVBs. That provides promising opportunities for generating more complex vortex field for advanced applications in structural light, optical micromanipulation, and data communication.