压力加工机械载荷与轴温的监测

分析了压力加工载荷和轴温监测的基本要求，介绍了载荷和轴温测量的原理与技术，说明了监测方案的设计及实施方法。     

金属蜂窝负载Pd催化甲烷部分氧化制合成气

采用浸渍法制备负载金属Pd(其中金属Pd含量0.6g/L)活性组分的金属蜂窝载体催化剂,应用于甲烷部分氧化制合成气。实验表明,焙烧温度为950℃的催化剂较600℃的催化剂甲烷转化率、CO和H2的选择性更高,焙烧温度为950℃的催化剂在反应温度800℃,GHSV=1×105ml/g·h条件下,甲烷转化率为91%,CO和H2的选择性分别达到90%和89%。用SEM与XPS对催化剂进行表征,结果表明,950℃焙烧催化剂并没有出现明显的烧结现象,且可能由于更多零价Pd的出现,使得该催化剂的性能更好。由于金属蜂窝载体优良的导热性,对950℃焙烧温度制备的金属蜂窝催化剂,催化剂床层的飞温点温度小于880℃,可以解决其它类型催化剂在甲烷部分氧化反应中出现的飞温问题,具有一定的工业化意义。     

Hydraulic erosion of concrete by a submerged jet

The hydraulic erosion of concrete is often found in civil and marine engineering construction. The present study explores the effects of several erosion parameters on the material loss of a concrete specimen subject to the hydraulic flow produced by a submerged jet. Such an investigation has rarely been reported in the literature. The concrete specimen has a typical compressive strength of 35 MPa, and the experimental parameters include the exposure time, incidence angle, standoff distance, and the hydraulic jet pressure. The impinging velocity is estimated based on the distance considered in the round jet model. The regression analysis shows the relationship between each parameter and the material loss. One finds that the material loss is proportional to the exposure time and the hydraulic pressure. The maximum erosion lies at an incidence angle of around 30° to 45° and is affected by the fourth-order polynomial of impinging velocity. The result of the present study provides a reference to engineering practice where concrete erosion is a concern.     

Adaptive niche quantum-inspired immune clonal algorithm

The adaptive niche quantum-inspired immune clonal algorithm (ANQICA) is proposed by combining the quantum coding, immune clone and niche mechanism together to solve the multi-modal function optimization more effectively and make the function converge to as many as possible extreme value points. The quantum coding can better explore the solution space, the niche mechanism ensures the algorithm to converge to multi-extremum and the adaptive mechanism is introduced according to the characteristics of each procedure of the algorithm to improve the effect of the algorithm. Example analysis shows that the ANQICA is better in exploration and convergence. Therefore, the ANQICA can be used to solve the problem of multi-modal function optimization effectively.     

盖下坝水电站坝址区岩溶发育情况初步分析

盖下坝水电站工程为高山峡谷型电站,坝址区及附近岩石为碳酸盐岩,属中等坚硬易溶岩.岩溶发育是影响本工程库水外渗的重大工程地质问题之一.从施工开挖揭示的各种溶蚀现象中分析岩溶发育特征及其规律,为防渗设计提供依据.     

一种面向中小企业PDM系统的设计与实现

根据中小企业在信息化建设中对于产品数据管理的实际需求,针对某一具体行业设计了一个适合中小企业的产品数据管理系统。分析系统的开发平台和采用基于．Net框架下采用B／S模式的系统结构,阐述系统具有的图档管理、产品结构和配置管理、项目管理和安全管理等主要功能。应用结果表明,该系统能有效地提高企业对产品数据管理的效率。     

Java程序设计课程的案例设计

解释了"项目驱动、案例教学、理论实践一体化"教学方法的基本思路,给出了Java程序设计课程的案例设计.     

溯源维特鲁威人的本义

维特鲁威建筑论的核心是“建筑人体摹仿论”。遵从技艺创制的摹仿论,他构建了一个抽象的理想人体,以之作为建筑创制所摹仿的原本。以希腊化时代的“知识统一论”来溯源理想人体的本义,人的形象寓意人神同形的理性灵魂、人体比例的本质属性在于完美数及其可推演的关系、四肢展开的圆方外形指涉宇宙秩序。而体现建筑本质属性的均衡比例,也因摹仿而具有与理想人体“同一” 的属性。以此为基础,并依照时代的科学论,维特鲁威创建了古典科学意义上的建筑学。     

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively reason over temporal knowledge graphs and predict events happening in the future. To address this problem, some recent works learn to infer future events based on historical event-based temporal knowledge graphs. However, these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously. This paper proposes a new graph representation learning model, namely Recurrent Event Graph ATtention Network (RE-GAT), based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently. More specifically, our RE-GAT uses an attention-based historical events embedding module to encode past events, and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp. A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations. We evaluate our proposed method on four benchmark datasets. Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various baselines, which proves that our method can more accurately predict what events are going to happen.     

基于均值漂移聚类的开关柜局部放电异常检测

针对开关柜现场带电检测数据,提出一种基于多维特征量的均值漂移聚类算法,对开关柜的局部放电进行异常识别。采用局部放电检测数据的离散度、平均距离百分比、集中度和最大波动率指标全面量化开关柜局部放电的程度,并构建多维特征数据库;通过自动搜索偏移量的均值漂移聚类算法对开关柜的状态进行划分,并通过所给定的开关柜簇标签隶属度函数判定是否为异常点,由此实现开关柜的绝缘状态异常检测。对现场带电检测实际数据进行实例分析,验证该方法的可行性,为开关柜的绝缘状态异常识别提供一定的理论依据。