Synthesis and characterization of hydrogenated novel AlCrFeMnNiW high entropy alloy

A novel high entropy alloy (HEA) i.e. AlCrFeMnNiW is synthesized via high-energy planetary ball milling with an average crystallite size of 10.37 nm. The morphology study of hydrogenated and dehydrogenated HEA is carried out through Scanning Electron Microscope (SEM). The HEA is charged with hydrogen using inhouse Sievert's Apparatus which results to be maximum hydrogen storage capacity of 0.615 wt% at atmospheric pressure and temperature. The dehydrogenation of the sample is performed through thermogravimetry (TG) at different scanning rate. The crystalline structure (i.e. lattice parameters) and chemical composition of HEA is studied using X-Ray Diffraction (XRD) and Energy Dispersive X-Ray analysis (EDX) respectively. The unit cell volume of as-prepared alloy is estimated as 0.03131 nm³ whereas the average crystallite size as 10.37 nm. It is observed that the unit cell volume is increased by 0.67% and crystallite size decreased by 10.8% upon hydrogenation whereas it is then decreased by 0.2% and increased by 6.7% respectively upon dehydrogenation. Activation energy during hydrogen desorption is found to be −8.161 kJ/mol. The enthalpy and entropy of the mixing are estimated to be −2.645 kJ/mol and 1.793 R J/mol K.     

Predicting visual similarity between colour palettes

This work is concerned with the prediction of visual colour difference between pairs of palettes. In this study, the palettes contained five colours arranged in a horizontal row. A total of 95 pairs of palettes were rated for visual difference by 20 participants. The colour difference between the palettes was predicted using two algorithms, each based on one of six colour-difference formulae. The best performance (r² = 0.86 and STRESS = 16.9) was obtained using the minimum colour-difference algorithm (MICDM) using the CIEDE2000 equation with a lightness weighing of 2. There was some evidence that the order (or arrangement) of the colours in the palettes was a factor affecting the visual colour differences although the MICDM algorithm does not take order into account. Application of this algorithm is intended for digital design workflows where colour palettes are generated automatically using machine learning and for comparing palettes obtained from psychophysical studies to explore, for example, the effect of culture, age, or gender on colour associations.     

基于改进交叉熵算法的电力系统随机生产模拟

蒙特卡洛模拟的计算效率与系统的可靠性密切相关，在其用于高可靠性系统的随机模拟时存在计算效率偏低的问题。为此，提出了一种基于多层交叉熵与对偶变数抽样技术相结合的随机模拟算法。首先使用多层交叉熵构造零方差概率密度函数的近似函数，提高小概率失效事件的抽取概率；其次基于已构造的近似概率函数，采用对偶变数抽样法进行抽样，进一步提高抽样的收敛速度。以IEEE RTS修改系统为例进行了算例分析，算例结果验证了所提出的基于改进交叉熵的电力系统随机生产模拟算法的有效性。     

Influence of Bridgman solidification on microstructures and magnetic behaviors of a non-equiatomic FeCoNiAlSi high-entropy alloy

The non-equiatomic FeCoNiAlSi alloy is prepared by the Bridgman solidification (BS) technique at different withdrawal velocities (V = 30, 100, and 200 μm/s). Various characterization techniques have been used to study the microstructure and crystal orientation. The morphological evolutions accompanying the crystal growth of the alloy prepared at different withdrawal velocities are nearly the same, from equiaxed grains to columnar crystals. The transition of coercivity is closely related to the local microstructure, while the saturation magnetization changes little at different sites. The coercivity can be significantly reduced from the equiaxed grain area to the columnar crystal area when the applied magnetic field direction is parallel to the crystal growth direction, no matter what is the withdrawal velocity. In addition, the alloy possesses magnetic anisotropy when the applied magnetic field is in different directions.     

Accelerated exploration of multi-principal element alloys for structural applications

A strategy for accelerated discovery and exploration of multi-principal element alloys was developed and used to identify new alloys within a design window of desired microstructures and properties. As an example, the strategy was applied to analyze thousands of 3- 4-, 5- and 6-component alloys at equiatomic compositions of the alloying elements. Currently available thermodynamic databases were used to assess equilibrium phase diagrams for these alloys. The validity and reliability of the calculated phase diagrams were estimated based on the extent of experimental binary and ternary data used to build the respective thermodynamic databases. Alloys with specific characteristics, such as single-phase solid solution alloys with the use temperature above 1000 °C, were identified using an automated analysis of the calculated phase diagrams. The density, elastic moduli and costs of these alloys were estimated using the rule of mixtures of pure elements and were used as additional criteria for alloy selection. This approach allowed rapid, albeit preliminary, screening of many thousands of alloys and identification of promising candidate compositions, some of which are reported in this paper, for more time intensive experimental validations and assessments.     

控规"心结"之解

法定的控规在执行过程中屡遭调整,困扰着规划师.这是由于编制控规的"时间差"、"批零差"、"计划/市场契合"等问题导致.依据法理,创新编制控规的新机制,发挥政府主导,各利益相关者多方参与是解因之道.     

相邻雷达回波强度差异分析

文章选用2017-12-13的青浦站和南通站体扫数据和单站格点CAPPI数据,利用时空一致性匹配法和单站格点数据等距离线法对两站雷达回波强度差异情况进行了研究,并分析了两种算法可能存在的误差,结果表明:单站格点数据等距离线法要优于时空一致性匹配法,更适用于单部雷达体扫数据的相邻雷达回波差异对比;在雷达基数据正确的情况下,时空一致性匹配法误差主要来源于做不到真正的时空匹配一致,格点数据等距离线法误差主要来源于生成格点数据时插值算法不完善。     

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%.