PHASE TRANSFORMATION AND BIOMECHANICAL COMPATIBILITY OF CALCIUM-PHOSPHATE BIOCERAMICS

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Coconut Fiber Pyrolysis: Bio-Oil Characterization for Potential Application as an Alternative Energy Source and Production of Bio-Degradable Plastics

The current energy crisis could be alleviated by enhancing energy generation using the abundant biomass waste resources. Agricultural and forest wastes are the leading organic waste streams that can be transformed into useful alternative energy resources. Pyrolysis is one of the technologies for converting biomass into more valuable products, such as bio-oil, bio-char, and syngas. This work investigated the production of bio-oil through batch pyrolysis technology. A fixed bed pyrolyzer was designed and fabricated for bio-oil production. The major components of the system include a fixed bed reactor, a condenser, and a bio-oil collector. The reactor was heated using a cylindrical biomass external heater. The pyrolysis process was carried out in a reactor at a pressure of 1atm and a varying operating temperature of 150˚C, 250˚C, 350˚C to 450˚C for 120 minutes. The mass of 1kg of coconut fiber was used with particle sizes between 2.36 mm - 4.75 mm. The results show that the higher the temperature, the more volume of bio-oil produced, with the highest yield being 39.2%, at 450˚C with a heating rate of 10˚C/min. The Fourier transformation Infrared (FTIR) Spectroscopy analysis was used to analyze the bio-oil components. The obtained bio-oil has a pH of 2.4, a density of 1019.385 kg/m3, and a calorific value of 17.5 MJ/kg. The analysis also showed the presence of high-oxygenated compounds;carboxylic acids, phenols, alcohols, and branched oxygenated hydrocarbons as the main compounds present in the bio-oil. The results inferred that the liquid product could be bestowed as an alternative resource for polycarbonate material production.

Spherical Near-Field - Far-Field Transformation for Quasi-Planar Antennas from Irregularly Spaced Data

An effective near-field - far-field (NF - FF) transformation with spherical scanning for quasi-planar antennas from irregularly spaced data is developed in this paper. Two efficient approaches for evaluating the regularly spaced spherical samples from the nonuniformly distributed ones are proposed and numerically compared. Both the approaches rely on a nonredundant sampling representation of the voltage measured by the probe, based on an oblate ellipsoidal modelling of the antenna under test. The former employs the singular value decomposition method to reconstruct the NF data at the points fixed by the nonredundant sampling representation and can be applied when the irregularly acquired samples lie on nonuniform parallels. The latter is based on an iterative technique and can be used also when such a hypothesis does not hold, but requires the existence of a biunique correspondence between the uniform and nonuniform samples, associ- ating at each uniform sampling point the nearest irregular one. Once the regularly spaced spherical samples have been recovered, the NF data needed by a probe compensated NF - FF transformation with spherical scanning are efficiently evaluated by using an optimal sampling interpolation algorithm. It is so possible to accurately compensate known posi- tioning errors in the NF - FF transformation with spherical scanning for quasi-planar antennas. Some numerical tests assessing the accuracy and the robustness of the proposed approaches are reported.

Biotechnological Transformation of Lignocellulosic Biomass in to Industrial Products: An Overview

Lignocellulose—a major component of biomass available on earth is a renewable and abundantly available with great potential for bioconversion to value-added bio-products. The review aims at physio-chemical features of lignocellulosic biomass and composition of different lignocellulosic materials. This work is an overview about the conversion of lignocellulosic biomass into bio-energy products such as bio-ethanol, 1-butanol, bio-methane, bio-hydrogen, organic acids including citric acid, succinic acid and lactic acid, microbial polysaccharides, single cell protein and xylitol. The biotechnological aspect of bio-transformation of lignocelluloses research and its future prospects are also discussed.

基于集成GCN-Transformer网络的ENSO预测模型

厄尔尼诺-南方涛动(El Niño-Southern Oscillation, ENSO)是热带太平洋海表面温度发生异常的现象,会导致冰雹、洪水、台风等极端天气的出现,因此对ENSO的准确预测意义重大。本文设计了集成GCN-Transformer(GCNTR)模型,首先运用Transformer网络的全局特征聚焦能力对数据特征进行编码,然后结合GCN提取图数据特征的能力,最后引入特征融合门控机制将经过编码的特征和GCN提取的特征进行融合,实现ENSO的精确预测。结果表明,GCNTR模型实现了对ENSO提前20个月的预测,比ENSOTR多了3个月,比Transformer多了5个月,并且模型绝大部分的预测精度优于其他模型。与现有的方法相比,GCNTR模型能够实现对ENSO更好的预测。

基于Transformer复杂运动辨识的机动星凸形扩展目标跟踪方法

针对复杂的机动扩展目标跟踪问题,利用Transformer网络设计了一种有效的星凸不规则形状机动扩展目标跟踪方法。首先,该文研究利用alpha-shape算法建立了星凸形状的变化模型,实现了静态场景下的星凸形扩展目标的形状估计。然后,通过对目标状态转移矩阵进行重新设计,结合Transformer网络对机动扩展目标运动状态转移矩阵进行实时估计,实现了对复杂机动目标运动过程的精准跟踪。进一步地,将估计得到的形状轮廓与运动状态进行融合,最终实现了对星凸形机动扩展目标的实时跟踪。最后,通过构造复杂的机动扩展目标跟踪场景,利用多重性能指标测试算法对形状和运动状态的综合估计性能,验证了算法的有效性。

面向高光谱医学图像分类的空-谱自注意力Transformer

高光谱成像技术的飞速发展给非侵入式医学成像带来新的契机,但高光谱医学图像具有高维度、高冗余以及“图谱合一”的特点,亟需针对上述特点设计智能诊断算法。近年来,Transformer已经在高光谱医学图像处理领域得到广泛应用。然而,不同仪器设备、不同采集操作所获得的高光谱医学图像差异较大,这给现有Transformer诊断模型的实际应用带来了巨大挑战。针对上述问题,本文提出了一种空-谱自注意力Transformer(S3AT),自适应挖掘像素与像素间、波段与波段间的内蕴联系,并在分类阶段融合多个视野下的预测结果。首先,在Transformer编码器中,设计一种空-谱自注意力机制,获取不同视野下高光谱图像上的关键空间信息和重要波段,并将不同视野下所获得的空-谱自注意力进行融合。其次,在模型分类阶段,将不同视野下的预测结果根据可学习权重进行加权融合,对图像进行综合预测。在In-vivo Human Brain和BloodCell HSI两个数据集上,本文算法总体分类精度分别达到82.25%和91.74%。实验结果表明,所提出的算法有效改善高光谱医学图像分类性能。

基于光谱-空间联合Transformer模型的黄河三角洲湿地高光谱影像分类

黄河三角洲湿地地物类型在光谱曲线上差异较小,且在空间上分布零散,呈破碎化特性。现有的分类方法受限于局部感受野难以捕捉到图像的长距离依赖关系,导致在黄河三角洲湿地高光谱影像中分类精度不理想,针对此问题,本文提出了一种光谱-空间联合Transformer模型。光谱和空间支路分别以光谱向量和空间邻域为输入,基于自注意力机制提取全局光谱和空间特征,在两个支路引入多阶特征交互层,实现浅层边缘信息和深层语义信息的融合,最后采用自适应相加的方式将两路特征融合,送入分类器实现最终分类。本文在黄河三角洲湿地高分五号GF-5和CHRIS两幅高光谱影像上验证方法的有效性,实验结果表明,该方法显著提高了湿地分类的精度,在选用3%的样本训练条件下总体精度分别达到了90.90%和94.17%,优于其他分类方法。研究结果可实现黄河三角洲湿地地物类型的高精度分类,为湿地的保护提供技术支持。

宽卷积局部特征扩展的Transformer网络故障诊断模型

视觉Transformer网络的高精度诊断性能依赖于充分的训练数据,利用卷积网络在提取局部特征上的优势,构造能同时描述故障局部和全局特征的提取层,提高诊断模型的抗噪声干扰能力。首先,引入卷积网络模块将原始振动信号转换为Transformer网络可以直接接收的特征向量,提取故障局部特征,并通过增加卷积网络的感受野。然后,结合Transformer网络多头自注意力机制生成的全局信息,构建能同时描述故障局部和全局特征的特征向量。最后,在Transformer网络的预测层,利用高效通道注意力机制对特征向量的贡献度进行自动筛选。在西储大学(CWRU)轴承数据集上的故障诊断结果表明,在信噪比-4 dB的噪声干扰下,改进后的Transformer网络轴承故障诊断模型的准确率达90.21%,与原始Transformer模型相比,准确率提高了13.2%,在噪声环境下表现出优异的诊断性能。

基于Transformer网络和多任务学习的园区综合能源系统电-热短期负荷预测方法

负荷预测是综合能源系统(integrated energy system,IES)能量管理和优化调度的基础,其预测精度直接关系到系统的整体运行性能。提出了一种基于Transformer网络和多任务学习的园区综合能源系统电-热短期负荷预测模型。首先对Transformer网络和多任务学习结构的基本原理进行了介绍;然后通过基于随机森林的特征选择步骤提取反映负荷特性和变化规律的典型指标,构建多任务学习输入特征,基于Transformer网络构建多任务学习权值共享层,并通过全连接层输出多能负荷的预测值;最后通过实际园区微能源系统的数据验证所提方法和算法的有效性,结果表明本文所提模型可以充分学习电-热耦合特征,提高负荷预测的精度。

Modal identification based on Hilbert-Huang Transform of structural response with S VD preprocessing

In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.

高光谱图像去噪的稀疏空谱Transformer模型

现阶段Transformer模型的应用提升了高光谱图像去噪的性能,但原始Transformer模型对图像空间-光谱耦合关联性的利用仍存在不足;对空间特征的处理存在过于平滑,容易丢失小尺度结构的现象;同时在光谱维度上也过于关注全部通道特征,缺乏对不同光谱波段间差异性的利用;为了应对这些问题,本文提出了一种新的稀疏空谱Transformer模型,提升了对空谱耦合关联性的利用。在空间维度,引入局部增强模块增强空间特征细节,应对过平滑问题;同时在光谱维度上提出了Top-k稀疏自注意力机制,自适应选择前K个最相关的光谱通道特征进行特征交互,从而能够有效捕获空谱特征。最终通过稀疏空谱Transformer的层级残差连接实现高光谱图像的去噪。在ICVL数据集上分别对高斯噪声和复杂噪声进行去噪处理,峰值信噪比分别达到40.56 dB和40.19 dB,证明了本文提出的稀疏空谱Transformer模型优越的性能。

Treatment of Dyes Wastewater by a New Kind of Bio - Fluid Bed

A new kind of bio-fluid bed used to treat dyes wastewater is described in detail due to its several special features,such as high removal efficiency,simple struc-ture,shock load resistance,etc.By means of analyzing the experiment data,the results show that the dye wastewater’s organic matter is removed greatly after be-ing treated by this new kind of bio-fluid bed.On the other hand,the removal efficiency of chromaticity of

IN SITU OBSERVATION ON FORMATION OF BASKET DURING BAINITIC TRANSFORMATION

By means of riM-190 hot-stage microscopy,the in situ observation of α-β transformation in 12Cr2MoWVTiB steel has been carried out.The sequence of the growth of ferritic needles composing bainitic basket has also been determined.According to the crystallographic analy- sis a multislip system transform model concerning the formation of basket has been proposed.

基于三维特征和Transformer的数字化古籍文档图像矫正

古籍文档图像矫正是古籍文档数字化中的一个关键环节,对提高古籍数字化质量具有重要的现实意义。针对古籍中普遍存在的氧化弯曲、粘连折叠、装订方式特殊等原因导致的形变复杂、矫正难度大的问题,本文提出了一种基于深度学习和三维特征信息提取的古籍文档图像矫正方法。首先使用U-Net形式的编码器-解码器提取古籍文档图像的三维特征,然后基于Transformer模型对得到的三维特征图进行后向映射,最后使用双线性插值得到矫正后的图像。为了验证所提出方法的有效性,在两个自制测试集上分别进行实验。实验结果表明,该方法在局部失真(Local Distortion,LD)概率上,相较于DewarpNet模型降低了2.61%~6.58%。实验证明所提出的方法能有效完成古籍文档图像的矫正任务,提升古籍数字化质量。

Effect of two-step solid solution on microstructure andδphase precipitation of Inconel 718 alloy

Inconel 718 is the most popular nickel-based superalloy and is extensively used in aerospace,automotive,and energy indus-tries owing to its extraordinary thermomechanical properties.The effects of different two-step solid solution treatments on microstructure andδphase precipitation of Inconel 718 alloy were studied,and the transformation mechanism fromγ″metastable phase toδphase was clarified.The precipitates were statistically analyzed by X-ray diffractometry.The results show that theδphase content firstly increased,and then decreased with the temperature of the second-step solid solution.The changes in microstructure andδphase were studied by scanning electron microscopy and transmission electron microscopy.An intragranularδphase formed in Inconel 718 alloy at the second-[100]_(δ)[011]γ step solid solution temperature of 925℃,and its orientation relationship withγmatrix was determined as//and(010)_(δ)//(111)γ.Furthermore,the Vickers hardness of different heat treatment samples was measured,and the sample treated by second-step solid solution at 1010℃ reached the maximum hardness of HV 446.84.

考虑多变量相关性改进的风电场Transformer中长期预测模型

挖掘风电场多变量相关性对提高中长期的预测精度具有积极影响。针对Transformer模型在捕获多变量间相关性方面的不足,提出考虑多变量相关性的多变量中长期预测模型。首先,采用多变量独立嵌入(MIE)对风电场多变量进行独立建模;然后,使用二维概率稀疏注意力(TPSA)提取时间和变量间的特征信息;最后,通过多层式编码器-解码器(MED)将多尺度的特征信息聚合,一次性输出预测结果。算例分析表明,所提模型与LSTM模型、Transformer模型、Informer模型相比,均方误差在各预测时长分别降低了42.58%~66.83%,32.58%~53.49%,14.38%~30.92%,并通过消融实验验证和分析了所提改进的有效性。

基于Transformer模型的中文文本生成方法研究

文章研究了基于Transformer模型的中文文本生成方法,重点探讨了Transformer模型的编码器-解码器结构及其工作原理。在详细分析了编码器和解码器的工作机制后,文章利用Hugging Face Transformers开源模型进行了中文文本生成实验。结果表明,该方法在自制数据集上取得了良好的效果,其准确率、精确率和召回率分别达到92.5%、91.8%和90.6%。该研究不仅拓展了中文自然语言处理的理论基础,还为实际应用提供了高效的技术支持。

矮牵牛转基因体系的建立及转BIO和bio基因研究初报

BIO ORGANS(BIO)是百脉根中调控器官形态及大小的基因。首先改良了矮牵牛愈伤诱导和再生体系,并进一步利用根癌农杆菌介导法定向将BIO及其突变基因bio导入矮牵牛中进行功能研究。结果证实含0.1mg/L6-BA的MS培养基有利于矮牵牛组培苗的增殖,添加了1.0mg/L 6-BA和0.1mg/L NAA的MS培养基有利于愈伤以及不定芽的诱导。对转基因植株目的基因的PCR检测结果显示,BIO及其突变基因bio被成功转入了矮牵牛中。表型观察结果显示:转BIO及其突变基因bio的植株叶片边缘均呈现不规则形态,部分叶片缺刻,出现由一个叶片向两个叶片分裂的趋势。其中转BIO基因植株部分叶片面积减小,叶片变窄或几乎只剩主叶脉。该研究证实BIO基因对保持叶片器官形态起到关键的作用,从而为进一步探索BIO基因的调控机制提供了实验依据。

维药小茴香抗肝纤维化作用及对TGF-β/smad信号转导通路的影响

目的探讨小茴香提取物抗肝纤维化的作用及其对TGF-β/smad信号转导通路和肝星状细胞活化的影响。方法选择44只SD大鼠给予40%四氯化碳(CCl4)橄榄油溶液4 ml/(kg·体重)皮下注射5周制备肝纤维化模型,成模后随机分成治疗组和模型对照组,同时取6只正常大鼠做空白对照。治疗组给予小茴香提取物灌胃,模型对照组给予生理盐水灌胃,4周末处死大鼠检测血清ALT、AST、HA、LN,肝石蜡切片行Masson染色和抗α平滑肌动蛋白(α-SMA)、TGF-β受体Ⅰ型(TGF-βRⅠ)、TGF-β1免疫组织化学染色,反转录实时荧光定量PCR分析肝组织TGF-β1、smad2 mRNA相对内参基因甘油醛-3-磷酸脱氢酶(GAPDH)的表达量。结果小茴香提取物治疗组血清ALT、AST、HA水平,肝组织内胶原纤维含量、α-SMA、TGF-βRⅠ、TGF-β1表达以及TGF-β1、smad2 mRNA相对表达量均明显低于模型对照组。结论小茴香提取物可通过抑制TGF-β/smad信号转导通路抑制肝星状细胞活化,从而减轻大鼠肝纤维化。