自动化立体仓库消防给水设施设计探讨

立体仓库采用智能化出入库管理,集自动入库、智能化储存和自动化出库于一体,物品高度集中,火灾负荷集中的建筑。立体仓库的消防给水设施是其中重要的消防设施之一,本文主要分析立体仓库消防水系统的设计及参数的选择。     

Influence of cerium oxide additive and aloe vera biodiesel on a CI engine

Diesel is the main source of world transportation due to higher combustion efficiency, compliance, consistency and cost-economy. It is also a major contributor to the world prosperity since it is used extensively. Diesel engine’s emissions are the serious hazard to the world environment and it is measured to be the major causes of air pollution. The demand in biofuels for years created a scope for aloe vera into biodiesels. Aloe vera, having higher calorific value vnthan other plant sources used as biodiesels, enhanced us in making another alternative biodiesel, which has lesser emissions and better performance. In this research work, four biodiesel blends from aloe vera oil with cerium oxide additive are explored for their performance and emission characteristics. The results proved B30 (30% biodiesel, 68% diesel and 1% cerium oxide) gives good performance when compared to other blends.     

滇南兰坪南厂铅锌矿矿床成因及成矿作用

南厂铅锌矿位于兰坪—思茅中生代坳陷北端，构造格局复杂，成矿条件优越。通过对矿床进行野外地质调查，对成矿地质背景、矿床地质特征、成矿机制和矿床成因进行了分析，发现南厂铅锌矿体主要呈透镜状赋存于第三系云龙组（Eyb）及下白垩统景星组（K1j1）地层中，与围岩接触部位发育微弱褪色蚀变，云龙组下段（Eya）是矿体底板有效的“屏蔽层”；区内发育原生砂岩型矿石、灰岩型矿石和氧化矿石，沉积构造特征明显，局部具有热液、变质作用改造特征；矿区地层、构造是矿床形成的重要因素，断裂构造是主要的容矿、导矿构造，高盐度热卤水沿导矿构造循环至有利层位、构造部位富集成矿，综合分析认为南厂矿床属热卤水为主的多成因层控型铅锌矿床。     

腾冲市茨竹地铁多金属矿成矿地质特征及控矿因素

腾冲市茨竹地铁多金属矿现目前是一座正在开采的以磁铁矿为主、共（伴）生锡钨矿的小型矿山。为了增加矿山资源储量、延长矿山服务年限，通过对矿区的地质特征阐述和对矿区地层岩性、构造、岩体等控矿因素的分析研究，总结了矿区成矿规律并形成了矿区的成矿模式，为矿区深部探矿、外围找矿提供了一定的理论依据。     

基于大数据挖掘的用电行为分析

得益于智能电网的发展,电力大数据的价值与作用逐渐被挖掘。通过对电力大数据的深入挖掘与研究,可促进电网企业管理水平的进一步提升,为我国智能电网的发展提供助力。而依托于大数据挖掘的用户用电行为分析,可实现当前企业电网调控与需求侧的充分融合,为电力系统的稳定性运行提供保障。基于此,文章针对大数据挖掘的用电行为分析进行深入探析,以期为智能电网发展提供帮助。     

裴沟煤矿典型断裂构造带水化学变化特征

断裂运动是错综复杂的地质作用，物质组成的变化能够通过水质中阴阳离子含量直观表现。以裴沟煤矿近20年来的突（涌）水点化验数据为依据探究各含水层水化学普遍性质，发现阳离子含量和矿化度可以作为水源判别的重要指标，以典型构造浮山寨断层为对象研究断裂构造带水化学变化特征，结果显示距离断裂构造带越近，水中Ca²⁺+Mg²⁺含量减小、Na⁺+K⁺含量增大、矿化度降低，揭示断层两盘不同含水层间水掺和程度。     

Study of tritium transport characteristics in a transportable fluoride‐salt‐cooled high‐temperature reactor

Tritium management is one of the most critical issues that limit the development of fluoride‐salt‐cooled high‐temperature reactor (FHR); therefore, it is important to figure out the tritium transport characteristics in FHRs. In this paper, 3 works concerning about tritium in FHR are conducted: first, the tritium transport characteristics in the primary loop of FHRs are introduced, including tritium production and speciation, the absorption and desorption by graphite, dissolution and diffusion in molten salt, and permeation through structural materials. Second, the physical and mathematical models are established for tritium transport characteristic analysis in a transportable FHR (TFHR). The tritium transport characteristic analysis code (TAPAS) for TFHR is developed and benchmarked. The results prove the fidelity and accuracy of TAPAS. Finally, the tritium transport characteristics in the TFHR are analyzed systematically by TAPAS. Three conclusions are obtained: (1) tritium in the primary coolant loop is mainly in the form of T₂; (2) when TFHR operates at steady state, the permeation rate of T₂ can be regarded as a constant (9.03 × 10⁹ Bq ? EFPD^?1 ); and (3) ⁷Li enrichment and redox potential of molten salt have great influence on the tritium distribution. This work might provide contribution to the tritium control in FHRs.     

Human action recognition toward massive-scale sport sceneries based on deep multi-model feature fusion

In sport sceneries, automatically recognizing human actions is a useful technique that can be popularly applied in may domains, such as human body tracking and athlete behavior analysis Most state-of-the-art deep architectures have achieved competitive performance in recognizing human action. However, it is still a challenging task due to the unavoidable occlusion, camera angle changes, and varied human posture. In this paper, we propose a novel deep multimodal feature fusion algorithm for human action recognition. The key technique is a multi-model feature fusion scheme. More specifically, we fuse visual feature, skeleton posture, probability maps and audio signal into a hybrid feature, which is utilized to represent human action. Then these feature channels are optimally combined using a deep model, wherein the weights of multiple feature channels can be predicted intelligently. Finally, the optimally fused feature are fed into a multi-class SVM for conducting human action recognition. Extensive comparative results and parameter analysis have shown the effectiveness of our proposed method.