深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。     

高密度聚乙烯/铅硼复合材料屏蔽性能和力学性能研究

研究了高密度聚乙烯/铅硼复合材料的屏蔽性能和力学性能，通过屏蔽仿真比较了密度及碳化硼（B4C）含量对屏蔽性能的影响，通过试验比较了B4C含量对屏蔽性能、弯曲强度及冲击强度的影响。仿真结果表明,随聚乙烯/铅硼复合材料密度升高，快中子屏蔽性能下降，热中子屏蔽性能和γ屏蔽性能提高；保持聚乙烯/铅硼复合材料密度不变，随B4C含量的提高，中子屏蔽性能提高而γ屏蔽性能下降;实验数据表明,随B4C含量的升高，高密度聚乙烯/铅硼材料的快中子屏蔽性能、热中子屏蔽性能升高，γ屏蔽系数下降，冲击强度和弯曲强度下降明显，屏蔽性能测试结果和仿真结果规律性相符；综合仿真结果和实验数据表明，含B4C 2 %左右的高密度聚乙烯/铅硼复合材料同时具有较好的屏蔽性能和力学性能。     

BIM技术在地下工程中的应用分析及展望

BIM技术以三维设计为基础,以数字信息为载体,将建筑信息贯穿于建筑的全生命周期中。然而由于地下工程的内部结构较为复杂,受地质条件影响较大,BIM技术在地下工程中的应用并不多见。叙述了BIM技术在隧道、地铁和矿井建设几种地下工程中应用的现状,并对几个典型案例进行分析,指出了BIM技术在地上工程和地下工程中应用的异同。最后,提出了目前BIM技术在地下工程中应用的问题以及对未来发展的建议。为推动BIM在我国地下工程中更广泛深入的应用提供重要参考价值。     

基于特征融合的中文简历解析方法研究

针对基于规则和统计的传统中文简历解析方法效率低、成本高、泛化能力差的缺点，提出一种基于特征融合的中文简历解析方法，即级联Word2Vec生成的词向量和用BLSTM（Bidirectional Long Short-Term Memory）建模字序列生成的词向量，然后再结合BLSTM和CRF（Conditional Random Fields）对中文简历进行解析（BLSTM-CRF）。为了提高中文简历解析的效率，级联包含字序列信息的词向量和用Word2Vec生成的词向量，融合成一个新的词向量表示；再由BLSTM强大的学习能力融合词的上下文信息，输出所有可能标签序列的分值给CRF层；再由CRF引入标签之间约束关系求解最优序列。利用梯度下降算法训练神经网络，使用预先训练的词向量和Dropout优化神经网络，最终完成对中文简历的解析工作。实验结果表明，所提的特征融合方法优于传统的简历解析方法。     

基于萤石溶解/表面转化的含钙矿物表面定向调控浮选技术及机理研究

研究了萤石在白钨矿和方解石表面溶解和转化的可能性, 发现萤石可以在特定的捕收剂体系下作为抑制剂实现含钙矿物的浮选分离。浮选实验结果表明, 萤石作为抑制剂, 在白钨矿和方解石的浮选中, 用量小、作用效果明显; 动电位检测结果证实了萤石能明显降低方解石的动电位, 但对白钨矿基本没有影响; XPS检测结果证明了萤石对方解石的作用主要在于对Ca-O键能的减弱和Ca-F基团的生成, 即在方解石表面生成CaF2膜; AFM检测则可直观观察到方解石表面经过萤石处理之后出现明显的起伏, 印证了离子的侵蚀与CaF₂膜的生成。     

A dense and compact laser cladding layer with solid metallurgical bonding significantly improved corrosion resistance of Mg alloy for engineering applications

Although Mg alloy attracts great attention for engineering applications because of high specific strength and low density, low corrosion resistance limits its extensive use. In this study, Mg–Al–Zn–Mn alloy was treated via a laser cladding process to generate a dense and compact laser cladding layer with solid metallurgical bonding on the substrate for improving corrosion resistance, effectively hindering the corrosion pervasion into Mg alloy. The corrosion current density declined from 103 μA/cm² for Mg alloy to 13 μA/cm² for the laser cladding layer in NaCl aqueous solution. Moreover, the laser cladding layer was slightly corroded in comparison with Mg alloy in NaCl aqueous solution. Besides, the microhardness of the cladding layer reached a mean value of 170.5 HV, 3.1 times of Mg alloy (56.8 HV) due to the in situ formation of hardening intermetallic phases. Wear resistance of laser cladding layer was also obviously improved. These results demonstrated that the laser cladding layer obviously enhanced anticorrosion property of Mg alloy for engineering applications.     

Formation of a protective layer against corrosion on Mg alloy via alkali pretreatment followed by vanillic acid treatment

Although Mg alloy possesses high specific strength, low density, and good biocompatibility, poor corrosion resistance hinders its further applications. In the present study, an innovative protective layer against corrosion was prepared on the AZ31 Mg alloy via alkali pretreatment followed by vanillic acid treatment. The alkali pretreatment supplied –OH for the AZ31 Mg alloy surface to react with vanillic acid. The vanillic acid treatment played a crucial role in enhancing the corrosion resistance due to the excellent ability to act as a barrier and retard aqueous solution penetration, which effectively isolated the underlying Mg alloy from the corrosive environment. The corrosion current density of alkali and vanillic acid-treated Mg alloy (AZ31V) almost showed two orders of magnitude lower values in comparison with that of the AZ31 Mg alloy, and the corrosion potential of AZ31V Mg alloy increased from −1.41 to −1.25 V. The immersion tests proved that there was no occurrence of severe corrosion. Hence, the alkali pretreatment and vanillic acid treatment may represent a promising method to improve the corrosion resistance of Mg alloy.     

能源互联网技术成熟度等级评估

能源互联网是实现未来可持续发展的重要建设内容之一。构建能源互联网需要众多关键技术的支撑,而技术处于不断地进化过程中,了解技术发展的现实水平,把握技术的未来需求,可以指导正确的发展路线和科学的战略决策的制定。本文旨在评估能源互联网关键技术成熟度。基于文献计量方法评估理念建立具有系统性和普适性的综合评估指标体系,并引入数据分析方法和变异系数法构建技术成熟度综合评估方法,为能源互联网的建设提供科学的指导。对能源互联网的云大物移创新应用关键技术进行算例分析,结果表明该方法能准确地判断技术的成熟度,定位技术性能。     

Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.