上榆泉煤矿煤泥水多参数在线检测系统应用研究

针对上榆泉煤矿煤泥水性质参数人工检测周期长、误差大、滞后性严重的问题,采用多参数传感器和PLC,开发了煤泥水多参数在线检测系统。经过8 h稳定运行,平均结果显示该厂煤泥水参数为pH值为7.71、浓度9.45%、中值直径(D50)39.56μm、流量2 440 m3/h、水质硬度428.94 mg/L,与人工检测结果相一致。应用结果表明,该检测系统能够实现煤泥水多参数在线监测和数据分析功能,使煤泥水的处理更高效。     

矿井顶板水探测方法及富水区成因分析

某矿1303工作面为三采区首采工作面，顶板含水层主要为第四系含水层、K₁₀和K₈砂岩裂隙含水层。为了解顶板K₁₀和K₈砂岩裂隙含水层的富水性，在1303工作面运输巷布置了覆盖全工作面的10个钻场（46个顶板探放水钻孔）进行探测，结合地表钻孔的顶板覆岩规律以及抽水试验结果可知，K₁₀和K₈砂岩裂隙含水层属于弱富水含水层，在2号钻场和8号钻场周边局部存在局部富水区；局部富水区均与构造有关，2号钻场位于背斜翼部，8号钻场位于向斜轴部，而背斜翼部和向斜轴部易出现砂岩裂隙带，成为地下水的富集区。     

煤矿薄煤层定向钻孔水害探查技术研究

从保德煤矿水文地质条件出发，研究了矿区奥灰水突水危险性，计算了8号煤层底板安全隔水层厚度和突水系数。采用薄煤层定向钻探工艺对保德煤矿81312工作面胶带运输巷道和81313工作面辅助运输巷道底板以下岩层及10号煤层进行水害及隐伏构造探查，探明了8号煤层底板岩层地质构造及富水性，形成了区域薄煤层定向钻孔水害探查方法。研究表明，利用定向钻孔的一孔多用功能超前预抽10号煤层瓦斯，可为矿区安全开采提供保障。     

离子色谱-脉冲安培法测定饮料中的聚葡萄糖的含量

通过优化标准方法AOAC 2000.11和GB 5009.245-2016的样品前处理过程，简单、高效准确地分析饮料中添加的聚葡萄糖含量。方法 样品经离心超滤净化，去除基质干扰，采用色谱柱分离，离子色谱-脉冲安培检测器测定，外标法定量。结果 聚葡萄糖在0.20 ~ 2.00 g/L的浓度范围内呈良好线性关系 (R2 > 0.999)。方法检出限和定量限分别为0.01 和0.03 g/L，三个添加水平的平均加标回收率为90.0%~98.8%，相对标准偏差为0.63%~2.36%。同时考察了市场上10种不同品类的饮料，加标回收率为93.0%-105%。结论 本方法样品前处理简单、快速、广泛适用于饮料中添加的聚葡萄糖含量测定。     

基于改进CGANs的入侵检测方法研究

近年来,机器学习算法在入侵检测系统(IDS)中的应用获得越来越多的关注。然而,传统的机器学习算法更多的依赖于已知样本,因此需要尽可能多的数据样本来对模型进行训练。遗憾地是,随着越来越多未知攻击的出现,且用于训练的攻击样本具有不平衡性,传统的机器学习模型会遇到瓶颈。文章提出一种将改进后的条件生成对抗网络(CGANs)与深度神经网络(DNN)相结合的入侵检测模型(CGANs-DNN),通过解决样本不平衡性问题来提高检测模型对未知攻击类型或只有少数攻击样本类型的检测率。深度神经网络(DNN)具有表征数据潜在特征的能力,而经过改进后的条件CGANs,能够通过学习已知攻击样本潜在数据特征分布,来根据指定类型生成新的攻击样本。此外,与生成对抗网络(GANs)和变分自编码器(VAE)等无监督生成模型相比,CGANsDNN经过改进后加入梯度惩罚项,在训练的稳定性上有了很大地提升。通过NSL-KDD数据集对模型进行评估,与传统算法相比CGANs-DNN不仅在整体准确率、召回率和误报率等方面有更好的性能,而且对未知攻击和只有少数样本的攻击类型具有较高的检测率。     

视频侦查中人像智能分析应用及算法优化

人像智能分析指的是对视频或录像中的人像进行结构化和可视化分析,对目标人物进行性别、年龄、发型等特征的智能识别,这项技术在视频侦查中有极高的应用价值。人像识别早期的算法是通过人工提取特征,通过学习低级视觉特征来针对不同属性进行分类学习,这种基于传统方法的模型表现常常不尽如人意。在计算机视觉领域,通过海量图像数据学习的神经网络比传统方法有更丰富的信息量和特征可以被提取。文章尝试通过深度学习技术训练神经网络模型对行人进行检测和识别,对于衣着不同的行人进行智能识别,具有更好的鲁棒性,提升了视频人像识别的准确率,拓展了人工智能技术在身份识别领域的应用。     

Peptide–Peptide Co-Assembly: A Design Strategy for Functional Detection of C-peptide,A Biomarker of Diabetic Neuropathy

Diabetes-related neuropathy is a debilitating condition that may be averted if it can be detected early. One possible way this can be achieved at low cost is to utilise peptides to detect C-peptide, a biomarker of diabetic neuropathy. This depends on peptide-peptide co-assembly, which is currently in a nascent stage of intense study. Instead, we propose a bead-based triple-overlay combinatorial strategy that can preserve inter-residue information during the screening process for a suitable complementary peptide to co-assemble with C-peptide. The screening process commenced with a pentapeptide general library, which revealed histidine to be an essential residue. Further screening with seven tetrapeptide focused libraries led to a table of self-consistent peptide sequences that included tryptophan and lysine at high frequencies. Three complementary nonapeptides (9mer com-peptides), wpkkhfwgq (Trp-D), kwkkhfwgq (Lys-D), and KWKKHFWGQ (Lys-L) (as a negative control) were picked from this table for co-assembly studies with C-peptide. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) and circular dichroism (CD) spectroscopies were utilized to study inter-peptide interactions and changes in secondary structures respectively. ATR-FTIR studies showed that there is indeed inter-peptide interaction between C-peptide and the tryptophan residues of the 9mer com-peptides. CD studies of unaggregated and colloidal C-peptide with the 9mer com-peptides suggest that the extent of co-assembly of C-peptide with Trp-D is greatest, followed by Lys-D and Lys-L. These results are promising and indicate that the presented strategy is viable for designing and evaluating longer complementary peptides, as well as complementary peptides for co-assembly with other polypeptides of interest and importance. We discuss the possibility of designing complementary peptides to inhibit toxic amyloidosis with this approach.     

Modified Two-Phase Titration Methods to Quantify Surfactant Concentrations in Chemical-Enhanced Oil Recovery Applications

In enhanced oil recovery applications, surfactants are injected into reservoirs along with polymers and salts. The effluents eluted from lab experiments and field tests are analyzed by HPLC methods using an evaporative light scattering detector (ELSD) detector. When the surfactant concentrations are less than 100 ppm, HPLC methods are inaccurate. A novel two-phase titration method is developed where surfactant concentrations can be quantified using a calibration curve constructed with UV/vis absorption. This method can analyze surfactant concentrations 5–80 ppm where dilution eliminates any high-salinity interferences with the absorption measurements. The method is based on formation of a dye-surfactant complex and the light absorption of the complex has a linear correlation with the surfactant concentration. Anionic surfactant concentrations lower than 100 ppm can be accurately quantified using this method with methylene blue. The method was also developed for low concentrations (<50 ppm) of cationic surfactants using methyl orange and indigo carmine. The indigo carmine method can be used without the use of an organic phase. All methods are applicable at salinities up to 3 wt%. Both the methylene blue method and the methyl orange method can be used to detect zwitterionic surfactants. These methods can be used in the presence of polymers without any prior treatments.     

Metallic Nanoparticle-Enabled Sensing of a Drug-of-Abuse: An Attempt at Forensic Application

γ-Hydroxybutyric acid (GHB) functions as a depressant on the central nerve system and serves as a pharmaceutical agent in the treatment of narcolepsy and alcohol withdraw. In recent years, GHB has been misused as a recreational drug due to its ability to induce euphoric feelings. Moreover, it has gained increasing attention as a popular drug of abuse that is frequently related to drug-facilitated sexual assaults. At the moment, detection methods based on chromatography exhibit extraordinary sensitivity for GHB sensing. However, such techniques require complicated sample treatment prior to analysis. Optical sensors provide an alternative approach for rapid and simple analysis of GHB samples. Unfortunately, currently reported probes are mostly based on hydrogen bonding to recognize GHB, and this raises concerns about, for example, the lack of specificity. In this work, we report a bioinspired strategy for selective sensing of GHB. The method is based on specific enzyme recognition to allow highly selective detection of GHB with minimum interference, even in a complex sample matrix (e. g., simulated urine). In addition, the result can be obtained by either quantitative spectroscopy analysis or colorimetric change observed by the naked-eye, thus demonstrating its potential application in drug screening and forensic analysis.     

Process monitoring method based on correlation variable classification and vine copula

Chemical processes are becoming increasingly complicated, leading to an increase in process variables and more complex relationships among them. The vine copula has a significant advantage in portraying the dependence of high-dimensional variables. However, as the dimensions increase, the vine copula model incurs a high computational load; such pressure greatly reduces model efficiency. Relationships among variables in the industrial process are complex. Different variables may be strongly or weakly associated or even independent. This paper proposes a process monitoring method based on correlation variable classification and vine copula. The weighted correlation measure is first used to divide variables into a correlated subspace and weakly correlated subspace. Then, two vine structures, C-vine and D-vine, are applied to the correlated and weakly correlated subspaces, respectively. This method takes advantage of C-vine for correlated variables and the flexibility of D-vine for weakly correlated variables. Finally, comprehensive statistics are established based on different subspaces. Monitoring results of the numerical system and the Tennessee Eastman process demonstrate the effectiveness and validity of the proposed method.