塑料牙刷柄的傅立叶变换红外光谱分析

本文采用傅立叶变换光谱法对13种不同品牌的白色牙刷柄塑料进行了红外光谱分析,实验结果表明,依据红外光谱数据的异同,能够对不同品牌的牙刷柄样品进行鉴别。     

A novel wavelet transform based voltage sag/swell detection algorithm

This paper proposes a novel sag/swell detection algorithm based on wavelet transform (WT) operating even in the presence of flicker and harmonics in source voltage. The developed algorithm is the hybrid of Daubechies wavelets of order 2 (db2) and order 8 (db8) to detect voltage sag/swell with and without positive/negative phase jumps. The hybrid detection algorithm can detect the start and end times of voltage sag/swell with and without phase jumps within 0.5 ms and 1.15 ms, respectively. The performance of the proposed voltage sag/swell detection method is compared with the results of dq-transformation, Fast Fourier Transform (FFT) and Enhanced Phase Locked Loop (EPLL) based voltage sag/swell detection methods. The good robustness and faster processing time to detect balanced and unbalanced voltage sag/swell are provided using proposed method. With the proposed hybrid detection algorithm consisting of db2 and db8 wavelet functions, a robust sag/swell detection is achieved which can give precise and quick response. The performance of proposed hybrid algorithm is validated and confirmed through simulation studies using the PSCAD/EMTDC analysis program.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.     

基于超混沌和Slant变换的鲁棒水印算法

为进一步提高水印算法的安全性和抗攻击能力,结合超混沌和Slant变换的优点,提出一种基于超混沌和Slant变换的鲁棒水印算法。首先,在隐藏水印之前采用超混沌系统对水印图像进行加密,提高了待隐藏图像的安全性;其次,将原始载体图像划分成大小为8×8的不重叠分块并对各个分块分别进行Slant变换;最后,将加密后的水印图像隐藏到Slant变换后的直流系数和中频系数中。大量的仿真结果表明,本文算法具有较好的隐蔽性,对JPEG压缩、噪声、滤波、缩放、对比度调整等攻击具有较好的抗攻击能力。与相似算法相比,本文算法具有更好的鲁棒性能。     

基于深度信念网络的列车故障音频诊断方法

铁路在交通运输行业有着举足轻重的地位，一旦列车发生故障将会导致严重的生命财产损失。由于列车发生故障的概率相对较低，因此难以捕获列车的故障样本。针对上述问题，提出了一种无监督学习的列车故障识别方法，通过检测列车音频信号来识别列车故障。该方法基于深度信念网络(DBN)，利用小波包分解提取检测信号的特征向量并将其作为DBN的输入，待网络充分训练后，由训练好的DBN识别当前列车的运行状况。现场监测实验结果表明，该方法能够在无监督的条件下有效识别列车故障，保障了列车的运行安全。     

TG-FTIR and Py-GC/MS study of the pyrolysis mechanism and composition of volatiles from flash pyrolysis of PVC

To facilitate the reuse and recycling of polyvinyl chloride (PVC) to achieve sustainable development and new industrialization, the composition and mechanism of formation of volatiles during the flash pyrolysis of PVC were studied by thermogravimetry-Fourier transform infrared (TG-FTIR) and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). TG and derivative thermogravimetry (DTG) analyses indicated two main degradation stages during flash pyrolysis of PVC, namely dehydrochlorination of PVC and decomposition of dechlorinated-PVC. Simultaneously, the FTIR results revealed that the main functional groups in the pyrolysis process were H–Cl, -C-Cl, C–H, CH, and aromatic groups. The relative content of main volatiles was determined by Py-GC/MS, and decreased in the following order: aromatics > alkenes > hydrogen chloride (HCl) > chlorinated hydrocarbons. Specifically, the relative content of aromatics was as high as 76.790–81.809%, while that of HCl was in the range of 3.016–3.096%. The carbon number distribution and the relative content of main products obtained from the flash pyrolysis of PVC at different final temperatures were also analysed. According to the experimental results, the mechanism of formation of the main volatiles based on free-radical reactions was deduced in detail. Therefore, this study provides further details for deepening the understanding of the PVC pyrolysis process.     

Polyaniline sensitized Pt@TiO2 for visible-light-driven H2 generation

Polyaniline is a typical conducting polymer with high migration electron rate, good stability, eco-friendly properties, and high absorption coefficients for visible light. In the present study, polyaniline decorated Pt@TiO₂ for visible light-driven H₂ generation is reported for the first time. The above-mentioned nanocomposite is prepared through a simple oxidative-polymerization and characterized by infrared spectroscopy, transmission electron microscopy, X–ray diffraction, thermogravimetric analysis, and ultraviolet–visible diffuse reflectance spectra. Polyaniline modification improves the absorption of the nanocomposite in visible light region via a photosensitization effect similar to dye–sensitization but does not influence the crystal structure and size of Pt@TiO₂. The polyaniline modified Pt@TiO₂ exhibits a remarkable visible light activity (61.8 μmol h⁻¹ g⁻¹) and good stability for H₂ generation (with an average apparent quantum yield of 10.1%) with thioglycolic acid as an electron donor. This work provides new insights into using conducting polymers, including polyaniline, as a sensitizer to modify Pt@TiO₂ for visible-light hydrogen generation.