煤炭地下气化过程中温度场及其传热特征研究进展

煤炭地下气化(UCG)技术作为一种环境友好的采煤方法,可被用来开发深部煤层与矿井遗留的煤炭资源。当地下气化时,随着气化时间的推移,煤炭在煤层内部"燃烧"逐渐形成气化炉,炉内温度最高可达1 200℃。从UCG过程中的放热反应出发,综述了热量来源、温度场的变化及其传热特征等,总结了研究温度场的重要手段。分析认为,UCG是一个复杂的温热动态变化过程,在此过程中氧化还原反应、吸热和放热的可逆反应同时进行,导致了气化炉的温度场及其传热机制的复杂性。研究气化过程传热机制的方法主要有物理模拟、试验监测、理论计算和数值模拟4种,各自具有不同的适应性。其中,物理模拟的可操控性强,但是难以解决地层接触关系导致的接触热阻误差;试验监测能真实反映温度场等的变化情况,但是实际操作的经济性差;理论计算通过对气化过程的拆分,从理论角度定性定量了UCG的温度场特征,但理论计算难以考虑到温度渗流等对温度的影响;数值模拟借助计算机进行温度场的多场耦合,综合考虑了渗流、变形、温度等对传热的影响,但多场的物理耦合计算难度较大,且煤层及岩石的热物理性质随温度变化会产生较大的差异,导致数值建模的计算难度增加,制约了数值模拟方法的...     

Development of silicon wafer packaging technology for deep UV LED

This paper reports a deep‐ultraviolet LED (deep‐UV‐LED) package based on silicon MEMS process technology (Si‐PKG). The package consists of a cavity formed by silicon crystalline anisotropic etching, through‐silicon vias (TSVs) filled with electroplated Cu, bonding metals made of electroplated Ni/AuSn and a quartz lid for hermetic sealing. A deep‐UV LED die is directly mounted in the Si‐PKG by AuSn eutectic bonding without a submount. It has advantages in terms of size, heat dissipation, light utilization efficiency, productivity and cost over conventional AlN ceramic packages. We confirmed a light output of 30 mW and effective reflection on Si (111) cavity slopes in the Si‐PKG. Based on simulation, further improvement of the optical output is expected by optimizing DUV‐LED die mount condition.     

泥鳅多糖的研究进展

近年来随着泥鳅养殖及产业化的快速发展, 泥鳅资源的开发和高值化利用已成为研究热点。泥鳅是一种含有丰富优质蛋白、脂肪酸以及微量元素的营养健康食品, 具备多种保健功能和药用价值, 其中泥鳅多糖是从泥鳅中提取得到的一种游离中性多糖, 具有耐缺氧、抗肿瘤、抗氧化、调节免疫、降血糖、抗炎等功能。本文主要综述了泥鳅多糖的制备方法、分离提纯方法、结构表征、生物活性、精深加工等方面的研究进展,旨在为泥鳅多糖的高值化利用提供一些理论基础, 为泥鳅产业链的快速发展提供新的发展方向。     

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Side-channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side-channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side-channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short-term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45-nm library.     

Soft Matter Technology at KIT: Chemical Perspective from Nanoarchitectures to Microstructures

Bioinspiration has emerged as an important design principle in the rapidly growing field of materials science and especially its subarea, soft matter science. For example, biological cells form hierarchically organized tissues that not only are optimized and designed for durability, but also have to adapt to their external environment, undergo self‐repair, and perform many highly complex functions. Being able to create artificial soft materials that mimic those highly complex functions will enable future materials applications. Herein, soft matter technologies that are used to realize bioinspired material structures are described, and potential pathways to integrate these into a comprehensive soft matter research environment are addressed. Solutions become available because soft matter technologies are benefitting from the synergies between organic synthesis, polymer chemistry, and materials science.     

Misconceptions of human factors concepts

Abstract

Like many scientific topics, Human Factors, and Ergonomics concepts are susceptible to being misunderstood by people unfamiliar with the subject matter. Most of the time these misunderstandings are harmless, like when a safety poster within a work setting encourages employees to 'overcome complacency'. This misunderstanding of complacency suggests it is a motivational aspect of human behaviour correctable with encouragement, whereas the human factors approach to overcoming complacency would be to evaluate how task design could diminish the destructive consequences of unexpected changes within a routine setting. No harm comes from the message within the safety poster, other than some wasted ink and paper, but misconceptions among particular audiences can eventually result in dire consequences for the human operator. This paper presents recent evidence that the concepts are being misapplied by casual consumers of human factors, particularly in the aftermath of accidents within complex systems, in ways detrimental to the core mission of improving the well-being of the human operator. Later, because this special issue presents new ways to demonstrate value via return on investment, practical efforts we can take to overcome such misconceptions are suggested.     

退火炉内罩中气体流动及钢卷传热的数值模拟

摘要：轧制力预报一直是热连轧过程控制模型的核心，浅层神经网络对复杂函数的表示能力有限，而深度学习模型通过学习一种深层非线性网络结构，实现复杂函数逼近。利用深度学习框架TensorFlow，构建了一种深度前馈神经网络轧制力模型，采用BP算法计算网络损失函数的梯度，运用融入Mini batch策略的Adam优化算法进行参数寻优，采用Early stopping、参数惩罚和Dropout正则化策略提高模型的泛化能力。基于上述建模策略，针对宝钢1880热连轧精轧机组的大量轧制历史数据进行了建模实验，对比分析了4种不同结构的前馈网络预测精度。结果表明，相比于传统SIMS轧制力模型，深度神经网络可实现轧制力的高精度预测，针对所有机架的预测精度平均提升21.11%。     

Performances of SDCM and DCM walls under deep excavation in soft clay: Field tests and 3D simulations

This study presents the results of full-scale tests and three-dimensional finite element analyses of deep cement mixing (DCM) and stiffened deep cement mixing (SDCM) columns under lateral loads and DCM and SDCM walls under deep excavation in soft clay. The DCM walls used in this study comprised one, two and three rows of DCM columns, whereas the SDCM walls consisted of only one row of DCM columns with steel H-beams inserted in either all DCM columns or in alternating DCM columns. The measured and simulated results are presented in terms of profiles of lateral displacement, settlement and bending moment.     

加权信息熵与增强局部二值模式结合的人脸识别

针对人脸识别因光照、姿态、表情、遮挡及噪声等多种因素的影响而导致的识别率不高的问题，提出一种加权信息熵（IEw）与自适应阈值环形局部二值模式（ATRLBP）算子相结合的人脸识别方法（IE （w） ATR-LBP）。首先，从原始人脸图像分块提取信息熵，得到每个子块的IEw；然后，利用ATRLBP算子分别对每个人脸子块提取特征从而得到概率直方图；最后，将各个块的IEw与概率直方图相乘，再串联成为原始人脸图像最后的特征直方图，并利用支持向量机（SVM）对人脸进行识别。在AR人脸库的表情、光照、遮挡A和遮挡B四个数据集上，IE （w） ATR-LBP方法分别取得了98.37%、94.17%、98.20%和99.34%的识别率。在ORL人脸库上，IE （w） ATR-LBP方法的最大识别率为99.85%；而且在ORL人脸库5次不同训练样本的实验中，与无噪声时相比，加入高斯和椒盐噪声后的平均识别率分别下降了14.04和2.95个百分点。实验结果表明，IE （w） ATR-LBP方法能够有效提高人脸在受光照、姿态、遮挡等影响时的识别率，尤其是存在表情变化及脉冲类噪声干扰时的识别率。