大红山铜矿岩体结构调查分析

大量工程实践已证明岩体结构是控制岩体稳定性的重要影响因素。为此，以大红山铜矿为研究背景，在选定有代表性调查地段的基础上，引入详细观测线法对其岩体结构进行了详细调查与统计。分析结果表明：调查区域内巷道岩体节理裂隙比较密集和发育，主要发育有倾向N53E、走向N37W、倾角53°，倾向W26S、走向N36W、倾角68°；倾向E1S、走向NIE、倾角89°的三组优势结构面，其中优势结构面第二组和第三组构成不稳定块体，并且断层及软弱夹层比较发育，加剧了巷道的破碎程度，对巷道围岩的稳定性有控制作用。最后提出了在巷道施工中密切注意围岩稳定性变化，避免工程事故的发生的建议。     

高炉冷却技术的开发与应用

通过对中小型高型炉寿命的分析，结合河北省冶金设计院高炉设计的实践经验，总结了高炉冷却设备寿命短的原因，并提出冷却设备在材质和结构上的改进措施。     

石油天然气长输管线用钢新进展

本文介绍了１９９０年以来国外在石油天然气长输管线用钢方面的最新成果，以获得较低的强度和韧性匹配为目的经轧后加速冷却工艺后的钢的强度的预测和控制；具有优良抗氢致裂纹的含硼、微碳、低硫、Ｎｂ－Ｔｉ微合金化、Ｘ６５－Ｘ８０级管线钢的开发，满足于特殊性能要求的ＴｉＯ处理技术及Ｘ８０级ＴｉＯ细化晶粒钢、低硬度和低屈强比钢，以及复合板加工耐腐蚀钢管的生产。     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

视觉测量中多光学靶点亮度自适应控制

提出了一种面向大视场高精度视觉测量的多红外目标靶点（红外LED）亮度自适应控制方法。以微控制器PIC16F873A和LED驱动芯片MAX6964作为核心器件,设计了可满足大空间范围内靶点亮度控制需求的多目标靶点控制驱动器。为了使LED在近距测量时的调节精度和远近距离测量时的发光亮度都能满足要求,根据MAX6964输出特点设计了PWM信号输出电路,并采用推挽电路驱动LED。实验证明,目标靶点在CCD相机上成像光斑稳定一致。     

Prediction of scouring around an arch-shaped bed sill using Neuro-Fuzzy model

In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used to model local scouring depth and pattern scouring around concave and convex arch shaped circular bed sills. The experimental part of this research study includes seven sets of laboratory test cases which were performed in an experimental flume under different flow conditions. A data set consists of 2754 data points of scouring depth were collected to use in the ANFIS model. The ratio of arch diameter, D, to flume width, W, is used as a non dimensional variables in all test cases. The results from ANFIS model were compared with the results of ANN model obtained by Homayoon et al. [24] and previously presented models. The results indicated that for D/W equal to 1 and 1.2, the ANFIS models produced a good performance for convex and concave bed sills. As a result, the ANFIS models can be used as an alternative to ANN for estimation of scour depth and scour pattern around a concave bed sill installed with a bridge pier.     

基于前景理论的风-蓄联合系统中长期电量分解模型的研究

为解决风-蓄联合系统同时参与中长期市场与日前市场的组合交易问题,提出一种考虑发电商心理特征的方法,并给出基于前景理论的风-蓄联合系统中长期电量分解模型。该模型通过构建发电商收益的价值函数衡量发电商对收益偏差的感受,运用概率权重函数描述发电商对不同场景下中长期电量分配方案的偏好,建立以前景效用值最大化为目标的风-蓄联合系统中长期电量分解模型。算例分析验证了所建模型的合理性和有效性,为发电商制定合理可行的盈利目标,提升发电企业在电力市场环境下的盈利水平提供理论依据和参考。     

城市社区尺度降雨径流快速模拟——以福州市一排水小区为例

随着城市化进程的不断加快,我国城市正面临着越来越严峻的洪涝问题。本文在社区尺度上构建雨洪管理模型（SWMM）,使用遗传算法率定SWMM模型参数;在对研究区降雨分析的基础上,采用模糊识别法筛选出最具代表性的两种雨型;基于不同的降雨情景与SWMM模拟值组成的数据集,建立长短期记忆神经网络（LSTM）模型模拟研究区降雨径流关系,并使用不同工况评估了LSTM模型效果。结果表明,LSTM模型对降雨径流的模拟与SWMM模型基本吻合,而其对洪峰流量的拟合略有偏差。在较小降雨下,LSTM模型模拟洪峰流量较SWMM输出结果偏小;在较大降雨下,模拟结果偏大;在中等降雨时,模拟效果最好。此外,50个隐含层单元的拟合效果更好,但同时更多的隐含层单元对洪峰流量拟合效果更好。