控制爆破法处理卡钻事故

介绍了在包头市某工程实施管道穿越黄河施工中,采用爆破法处理卡钻的经验。针对深水环境条件及钻杆内径小不宜采用集团装药的条件,确定采用"小直径爆破筒,钻杆内部装药"的爆破方案,阐述了爆破设计及施工注意事项。可供类似工程参考。     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。     

The Young Scientists Network: How the European Federation for Medicinal Chemistry (EFMC) Became Young Again

The European Federation for Medicinal Chemistry (EFMC) created the Young Scientists Network (YSN) to support early-career medicinal chemists and chemical biologists. By doing this, it addressed the rapid changes taking place in the scientific community and in our society, such as the rise of social media, the evolution of the gender balance in the scientific population, and educational needs. Creating the YSN was also a way to ensure that the next generation of scientists would contribute to shaping EFMC's strategy, while recognizing and addressing their needs. The YSN was set up as a very dynamic concept, and has now developed to the point where its impact is evident. The activities it promotes complement EFMC's community support and scientific opportunities, rejuvenating the Federation and preparing it for the future. It also provides opportunities for many brilliant young scientists, who do not hesitate to invest time and energy in supporting our community and shaping their own future.     

基于Faster R-CNN的颜色导向火焰检测

基于深度特征的目标检测方法Faster R-CNN在火焰检测任务上存在检测效率低的问题,因此提出了基于颜色引导的抛锚策略。该策略设计火焰颜色模型来限制锚的生成,即利用火焰颜色约束锚的生成区域,从而减少了初始锚的数量,提升了计算效率。为了进一步提高网络的计算效率,将区域生成网络中的卷积层替换成掩膜卷积。为了验证所提方法的检测效果,采用BoWFire和Corsician数据集进行验证。实验结果表明,该方法实际检测速度相较于原Faster R-CNN提高了10.1%,BoWFire上该方法的火焰检测F值为0.87,Corsician上该方法的准确度可达99.33%。所提方法可以提高火焰检测的效率,并能够准确检测图像中的火焰。     

基于循环一致性对抗网络的室内火焰图像场景迁移

基于深度学习的视频火灾探测模型的训练依赖于大量的正负样本数据,即火灾视频和带有干扰的场景视频。由于很多室内场合禁止点火,导致该场景下的火灾视频样本不足。本文基于生成对抗网络,将其他相似场景下录制的火焰迁移到指定场景,以此增广限制性场合下的火灾视频数据。文中提出将火焰内核预先植入场景使之具备完整的内容信息,再通过添加烟雾和地面反射等风格信息,完成场景与火焰的融合。该方法克服了现有多模态图像转换方法在图像转换过程中因丢失信息而造成的背景失真问题。同时为减少数据采集工作量,采用循环一致性生成对抗网络以解除训练图像必须严格匹配的限制。实验表明,与现有多模态图像转换相比,本文方法可以保证场景中火焰形态的多样性,迁移后的场景具有较高的视觉真实性,所得结果的FID与LPIPS值最小,分别为119.6和0.134 2。     

电力系统自动化发展方向分析

计算机技术、控制技术及信息技术的发展,电力系统自动化面临着空前的变革,多媒体技术、智能控制技术将迅速进入电力系统自动化领域。     

基于特征选择和深度信念网络的文本情感分类算法

由于人类语言的复杂性，文本情感分类算法大多都存在因为冗余而造成的词汇量过大的问题。深度信念网络（DBN）通过学习输入语料中的有用信息以及它的几个隐藏层来解决这个问题。然而对于大型应用程序来说，DBN是一个耗时且计算代价昂贵的算法。针对这个问题，提出了一种半监督的情感分类算法，即基于特征选择和深度信念网络的文本情感分类算法（FSDBN）。首先使用特征选择方法（文档频率（DF）、信息增益（IG）、卡方统计（CHI）、互信息（MI））过滤掉一些不相关的特征从而使词汇表的复杂性降低；然后将特征选择的结果输入到DBN中，使得DBN的学习阶段更加高效。将所提算法应用到中文以及维吾尔语中，实验结果表明在酒店评论数据集上，FSDBN在准确率方面比DBN提高了1.6%，在训练时间上比DBN缩短一半。     

基于深度学习的文本自动摘要方案

针对自然语言处理（NLP）生成式自动摘要领域的语义理解不充分、摘要语句不通顺和摘要准确度不够高的问题，提出了一种新的生成式自动摘要解决方案，包括一种改进的词向量生成技术和一个生成式自动摘要模型。改进的词向量生成技术以Skip-Gram方法生成的词向量为基础，结合摘要的特点，引入词性、词频和逆文本频率三个词特征，有效地提高了词语的理解；而提出的Bi-MulRnn+生成式自动摘要模型以序列映射（seq2seq）与自编码器结构为基础，引入注意力机制、门控循环单元（GRU）结构、双向循环神经网络（BiRnn）、多层循环神经网络（MultiRnn）和集束搜索，提高了生成式摘要准确性与语句流畅度。基于大规模中文短文本摘要（LCSTS）数据集的实验结果表明，该方案能够有效地解决短文本生成式摘要问题，并在Rouge标准评价体系中表现良好，提高了摘要准确性与语句流畅度。     

The effect of alkali metal oxide on the properties of borosilicate fireproof glass: Structure,thermal properties,viscosity, chemical stability

The purpose of the current work was to research the effect of alkali metal oxide on the structure, thermal properties, viscosity and chemical stability in the glass system (R₂O–CaO–B₂O₃–SiO₂) systematically. Because the glass would emulsify when Li₂O was added to the glass batch, this article did not discuss Li₂O. The results showed that when the amount of Na₂O was less than 4 mol.%, there was a higher interconnectivity of borate and silicate sub-networks in glass, as more mixed Si–O–B bonds were present in glass. The glass samples exhibited excellent thermal properties and chemical stabilities. As the amount of Na₂O exceeded 4 mol.%, the interconnectivity of borate and silicate sub-networks was weakened. The thermal properties and chemical stabilities of the glass samples were reduced. The connectivity of the silicate sub-network was weakened slightly as the Na/K ratio varied, and the coefficient of thermal expansion (CTE) of the glass samples gradually increased, and the resistance to thermal shock (RTS) value gradually decreased. Moreover, the viscosity of the glass samples decreased with the ratio of Na/Si and Na/K increased.