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.     

Machine fault diagnosis with small sample based on variational information constrained generative adversarial network

In actual engineering scenarios, limited fault data leads to insufficient model training and over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic models. To solve the problem, this paper proposes a variational information constrained generative adversarial network (VICGAN) for effective machine fault diagnosis. Firstly, by incorporating the encoder into the discriminator to map the deep features, an improved generative adversarial network with stronger data synthesis capability is established. Secondly, to promote the stable training of the model and guarantee better convergence, a variational information constraint technique is utilized, which constrains the input signals and deep features of the discriminator using the information bottleneck method. In addition, a representation matching module is added to impose restrictions on the generator, avoiding the mode collapse problem and boosting the sample diversity. Two rolling bearing datasets are utilized to verify the effectiveness and stability of the presented network, which demonstrates that the presented network has an admirable ability in processing fault diagnosis with few samples, and performs better than state-of-the-art approaches.     

The effects of washing a collagen sample prior to TEM examination

Transmission electron microscopy (TEM) is an important analysis technique to visualize (bio)macromolecules and their assemblies, including collagen fibers. Many protocols for TEM sample preparation of collagen involve one or more washing steps to remove excess salts from the dispersion that could hamper analysis when dried on a TEM grid. Such protocols are not standardized and washing times as well as washing solvents vary from procedure to procedure, with each research group typically having their own protocol. Here, we investigate the influence of washing with water, ethanol, but also methanol and 2-propanol, for both mineralized and unmineralized collagen samples via a protocol based on centrifugation. Washing with water maintains the hydrated collagen structure and the characteristic banding pattern can be clearly observed. Conversely, washing with ethanol results in dehydration of the fibrils, often leading to aggregation of the fibers and a less obvious banding pattern, already within 1 min of ethanol exposure. As we show, this process is fully reversible. Similar observations were made for methanol and propanol. Based on these results, a standardized washing protocol for collagenous samples is proposed.     

DCNN深度特征与交替方向乘子的相关滤波跟踪

针对采用相关滤波的判别式目标跟踪遇到的瓶颈问题：由于目标快速移动引起边界效应，使得相关滤波器在学习与更新过程中可能会引入错误，最终错误的累积将导致跟踪失败。在采集深度学习特征与样本相似性度量的基础上，提出一种引入交替方向乘子方法的改进相关滤波目标跟踪算法，选择DCNN深度特征有效地表征待跟踪目标的初始状态，通过在线分类过程中样本相似性比对与半监督学习，辅助解决相关滤波器在学习过程中存在的自学习问题。所提目标跟踪算法特别适合训练样本为持续获得的、同时存储空间较小的机器学习过程，提高目标在快速运动与部分遮挡等复杂情况下的跟踪成功率，针对VOT2016标准测试视频的实验表明：当目标面临快速运动时，对比CN、SAMF、STC算法，所提DA-CFT跟踪算法将跟踪成功率分别由60.4%～73.4%、67.2%～82.9%、80.9%～88.1%提升至85.6%～91.0%。     

Optimal data-based binning for histograms and histogram-based probability density models

Histograms are convenient non-parametric density estimators, which continue to be used ubiquitously. Summary quantities estimated from histogram-based probability density models depend on the choice of the number of bins. We introduce a straightforward data-based method of determining the optimal number of bins in a uniform bin-width histogram. By assigning a multinomial likelihood and a non-informative prior, we derive the posterior probability for the number of bins in a piecewise-constant density model given the data. In addition, we estimate the mean and standard deviations of the resulting bin heights, examine the effects of small sample sizes and digitized data, and demonstrate the application to multi-dimensional histograms.     

Understanding the determinants on household electricity consumption in Korea: OLS regression and quantile regression

The paper aims to investigate the determinants of household electricity consumption in Korea by using both the OLS regression and quantile regression. The results show that the effects of socio-demographic, dwelling, and electricity consumption characteristics on household electricity consumption may differ between two regressions and may differ across quantiles. We found that age group of household head, number of household, housing area, the number of household appliances, and refrigerator usage time were significant in all quantiles.     

于都黄麟-祁禄山地区土地质量地球化学评价

通过对于都县黄麟-祁禄山地区开展1:5万土地质量地球化学调查,在野外土壤样品采集和化验测试的基础上,系统地研究了影响土地质量的各项地球化学指标特征与控制因素,基本查明有益元素、重金属元素分布,划分出重点污染区和绿色土地发展区,并进行土地质量等级评价,为永久基本农田建设、土地资源开发与利用等方面提出建议。     

Influence of zinc and cadmium co-doping on optical and magnetic properties of cobalt ferrites

Zinc and cadmium based cobalt ferrites Zn_xCd_0.375-xCo_0.625Fe₂O₄ (where x = 0, 0.075, 0.125, 0.25) were successfully synthesized by a facile co-precipitation technique. Structural, optical and magnetic characteristics of the doped ferrites were systematically analyzed. X-ray Diffraction (XRD) pattern confirmed the formation of cubic spinel structure in all samples. Scanning electron microscopic analysis of surface morphology revealed cubic and spherical shaped ferrite particles. Fourier transform infrared (FTIR) spectroscopy confirmed the existence of metal oxygen (M − O) bonding in the prepared samples. Moreover, the prepared samples exhibited two frequency bands corresponding to phonon vibrational stretching in both octahedral and tetrahedral lattice positions. The optical properties were investigated in detail through photoluminescence (PL) spectroscopy and Raman spectroscopy. The PL spectrum confirmed the strong emission peaks in the ultraviolet to visible region of all the samples. Further, four active Raman modes, associated with cubic spinel structure are identified in all prepared samples. Finally, the magnetic characteristics are evaluated by using vibrating sample magnetometer (VSM) revealing ferrimagnetic and soft magnetic behavior of the samples. As the Zn and Cd co-doping in Co was increased, the H_c was decreased. The magnetic studies show the maximum H_c of 576 Oe for Cd doped cobalt ferrite, and maximum saturation magnetization (M_s) for Zn–Cd doped cobalt ferrite. It is envisaged that the newly prepared Zn–Cd co-doped cobalt ferrite would be appropriate for a number of important applications, for example, magnetic recording devices, sensors, actuators, high-density data storage devices, and biomedical equipments.     

基于Bootstrap方法的密封寿命可靠性评估

针对高可靠、长寿命密封件在小样本试验下的可靠性寿命评估需求,提出一种基于虚拟增广样本和Bootstrap方法的密封寿命小样本数据可靠性评估方法。并以轴用阶梯圈为例,进行寿命可靠性评估。首先通过虚拟增广法将轴用阶梯圈台架寿命试验数据样本数增广至10个,使得样本数满足Bootstrap方法的适用条件;然后再利用Bootstrap方法得到轴用阶梯圈的寿命可靠性评估结果。