Estimation of elastic constant of rocks using an ANFIS approach

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.     

Accurate determination of the mean relative humidity in controlled climates at high humidities (new method)

The measurement of the relative humidity (RH) at high levels to the desired accuracy is still a problem, especially for levels above 90% RH. The reasons for this are:

• i The accuracy of the standard apparatus is lower at high RH than at moderate and low RH;
• ii The need for accurate measurement of the RH in moisture controlled rooms is greater at high RH than at lower RH, because for moisture sensitive products, such as fibreboard boxes and certain horticultural products, the influence of a minor deviation in RH on the properties of these products is considerably greater at high RH than at moderate and low RH.

     

一种无线链状网络路由算法的研究与应用

文章基于货运列车状态监控的无线传感器网络应用,针对网络节点能量有限以及链状网络节点负载不均衡的问题进行分析和研究,提出一种适合该应用的新型路由算法,该算法基于网络节点可通过功率控制来调整通信距离这一前提,为每个传感器节点选择合适的数据传输路径,以达到整个网络节省能耗,负载均衡的目的。仿真结果验证了该算法有效地平衡了网络负载,节省了网络能量,提高了网络生命周期。     

可靠性技术在继电器控制中的应用

本文以可靠性技术,针对继电器在使用时主要失效模式触点失效故障进行分析,从触点材料、控制负载、抑制触点电弧,并对触点在常用环境下不合理使用等问题一并进行了较详细的阐述。     

多元QoS约束下电力通信无线资源分配的边缘代理调度方法

为满足不同配电通信业务的服务质量要求,BS需要在时变网络条件下实时优化无线资源。提出一种基于级联深度网络的接入网无线资源边缘代理调度方法,将时频资源和发射功率分配给延迟耐受度不同的业务。核心网采用网元功能与专用硬件设备解耦的软切片方法,在保证负荷在规定时长内可靠切除的同时,提高了核心网服务器对不同业务的时空间复用能力。仿真结果表明,多业务并列运行时RTU与协控子站间的接入时延与TD-LTE专网相比降低40.76%,相邻第一第二信道间的功率泄露比均高于45 dB,满足毫秒级负荷切除业务分路整组动作时延和通信可靠性的要求。     

基于隐枚举法的快速分级切负荷优化方法

目前稳控装置广泛采用的穷举法已经很难满足快速、精准切负荷的要求。为了高效地求解切负荷优化问题,为紧急决策争取更多时间,提出了基于隐枚举法的快速分级切负荷优化方法。该方法协调低频和低压切负荷动作轮次,建立切负荷优化模型,通过制定合适的搜索策略和剪枝策略,仅在可行解范围内进行寻优,大大提高了计算最优解的效率。切负荷算例结果表明同等条件下该方法可以节约至少50%以上的计算时间,实现了稳控装置的快速切负荷,保证了紧急情况下电网的安全稳定运行。     

智能化磁控谐振高压试验系统研究

目前电网智能化水平的提升对高压试验的智能化和试验效率也有了更高的需求,为解决现有的高压试验电源在高压交流绝缘试验中存在的不足,提升高压试验的效率,提出了一种智能化磁控谐振高压试验系统。该试验系统集升压、补偿和调谐于一体,具有升压调节平滑连续,结构紧凑等优势,可用于工频耐压试验。阐述了该系统的结构组成与基本原理,并搭建了该试验系统仿真模型,通过研究空载和负载运行特性验证了该系统原理的正确性。     

Improved Controlled Release and Brain Penetration of the Small Molecule S14 Using PLGA Nanoparticles

Phosphodiesterase 7 (PDE7) is an enzyme responsible for the degradation of cyclic adenosine monophosphate (cAMP), an important cellular messenger. PDE7’s role in neurotransmission, expression profile in the brain and the druggability of other phosphodiesterases have motivated the search for potent inhibitors to treat neurodegenerative and inflammatory diseases. Different heterocyclic compounds have been described over the years; among them, phenyl-2-thioxo-(1H)-quinazolin-4-one, called S14, has shown very promising results in different in vitro and in vivo studies. Recently, polymeric nanoparticles have been used as new formulations to target specific organs and produce controlled release of certain drugs. In this work, we describe poly(lactic-co-glycolic acid) (PLGA)-based polymeric nanoparticles loaded with S14. Their preparation, optimization, characterization and in vivo drug release profile are here presented as an effort to improve pharmacokinetic properties of this interesting PDE7 inhibitor.     

电厂宽负荷选择性催化还原脱硝技术改造

选择性催化还原（SCR）脱硝技术是我国电厂常用的脱硝技术。该技术运行中对烟气温度的要求比较严格,需要控制好SCR脱硝系统的入口烟温。针对这一问题,从影响SCR脱硝效率的关键因素及常见的宽负荷脱硝技术改进方案入手进行了分析。通过宽负荷脱硝技术改造,明显提高了SCR脱硝系统的入口烟温,进而提高了脱硝效率,减少了氨逃逸率,为电力行业脱硝技术发展提供了实际参考。     

基于门控循环单元与误差修正的短期负荷预测

针对负荷预测模型迭代训练过程中存在误差积累的问题,提出结合叠式双向门控循环单元（SBiGRU）、完整自适应噪声集成经验模态分解（CEEMDAN）和误差修正的组合预测模型. 建立SBiGRU模型学习在气温、日期类型影响下负荷序列的时序特征,误差特征体现在SBiGRU模型预测产生的误差序列中;使用CEEMDAN算法将误差序列分解为数个本征模态函数（IMF）分量与趋势分量,对每项分量再次建立SBiGRU模型进行学习与预测,并对各分量的预测值进行序列重构,得到误差的预测结果;对预测结果进行求和以修正误差. 模型评估结果表明,组合模型的预测准确精度为98.86%,与SBiGRU、BiRNN、支持向量回归等方法相比,该模型具有更好的精度.