20MnCr5圆棒锯切困难原因分析

某厂在锯切20MnCr5圆棒过程中锯条容易断裂，采用光学显微镜(OM)和扫描电子显微镜(SEM)等观察分析手段，对锯条容易断裂的原因进行分析。分析结果表明：锯条断裂的原因为加工20MnCr5过程中粘刀崩齿。针对此类加工缺陷，提出了相应的改进措施。     

Understanding the asymmetric perceptions of smartphone security from security feature perspective: A comparative study

Smartphones are being used and relied on by people more than ever before. The open connectivity brings with it great convenience and leads to a variety of risks that cannot be overlooked. Smartphone vendors, security policy designers, and security application providers have put a variety of practical efforts to secure smartphones, and researchers have conducted extensive research on threat sources, security techniques, and user security behaviors. Regrettably, smartphone users do not pay enough attention to mobile security, making many efforts futile. This study identifies this gap between technology affordance and user requirements, and attempts to investigate the asymmetric perceptions toward security features between developers and users, between users and users, as well as between different security features. These asymmetric perceptions include perceptions of quality, perceptions of importance, and perceptions of satisfaction. After scoping the range of smartphone security features, this study conducts an improved Kano-based method and exhaustively analyzes the 245 collected samples using correspondence analysis and importance satisfaction analysis. The 14 security features of the smartphone are divided into four Kano quality types and the perceived quality differences between developers and users are compared. Correspondence analysis is utilized to capture the relationship between the perceived importance of security features across different groups of respondents, and results of importance-satisfaction analysis provide the basis for the developmental path and resource reallocation strategy of security features. This article offers new insights for researchers as well as practitioners of smartphone security.     

Reference guided image super-resolution via efficient dense warping and adaptive fusion

Due to the limited improvement of single-image based super-resolution (SR) methods in recent years, the reference based image SR (RefSR) methods, which super-resolve the low-resolution (LR) input with the guidance of similar high-resolution (HR) reference images are emerging. There are two main challenges in RefSR, i.e. reference image warping and exploring the guidance information from the warped references. For reference warping, we propose an efficient dense warping method to deal with large displacements, which is much faster than traditional patch (or texture) matching strategy. For the SR process, since different reference images complement each other, and have different similarities with the LR image, we further propose a similarity based feature fusion strategy to take advantage of the most similar reference regions. The SR process is realized by an encoder–decoder network and trained with pixel-level reconstruction loss, degradation loss and feature-level perceptual loss. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art SR methods in both subjective and objective measurements.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

基于音频特征的工程车辆工况识别研究

高效率地使用工程车辆是工程项目管理中节约成本的有效方法，无人监管环境下工程车辆的工况识别，是实现工程车辆高效率使用的有效手段。目前以GPS等技术为核心的车辆智能管理系统未对工程车辆进行工况识别，提出一种基于GRU循环神经网络的工程车辆工况识别方法，通过对工程车辆在不同工况下产生的音频信号进行分析，从中提取Mel倒谱系数作为主要特征，构建GRU循环神经网络模型进行训练和识别。实验结果表明，该方法可以实现对工程车辆工况的有效识别。     

A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis

Single-cell RNA-sequencing (scRNA-seq) is a rapidly increasing research area in biomedical signal processing. However, the high complexity of single-cell data makes efficient and accurate analysis difficult. To improve the performance of single-cell RNA data processing, two single-cell features calculation method and corresponding dual-input neural network structures are proposed. In this feature extraction and fusion scheme, the features at the cluster level are extracted by hierarchical clustering and differential gene analysis, and the features at the cell level are extracted by the calculation of gene frequency and cross cell frequency. Our experiments on COVID-19 data demonstrate that the combined use of these two feature achieves great results and high robustness for classification tasks.     

空间曲线的特征识别与高质量非均匀采样

为避免传统均匀采样方法因忽视曲线重要特征而生成不理想的采样结果,获得给定数量且由特征点和辅助点组成的采样点序列,提出基于特征识别的高质量空间曲线非均匀采样方法.首先使用抛物线插值法得到曲线上所有曲率极大值点和挠率极大值点的近似位置,经筛选后产生特征点,以更好地抓住空间曲线的轮廓特征.然后定义基于弧长、曲率和挠率加权组合的特征函数,并以此自适应地选取曲线上的辅助点.与3种主流采样方法比较的实验结果表明,该方法能够获得更高质量的采样结果且具有更好的实用性,从而进一步改善空间曲线的B样条拟合效果.     

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.     

基于SVD和SSA-VMD降噪的轴承故障特征提取

针对强噪声背景下轴承故障特征提取困难的问题，提出一种基于奇异值分解和参数优化变分模态分解联合降噪的轴承故障特征提取方法(SSVMD)：首先，对原始信号进行奇异值分解(Singular Value Decomposition，SVD)处理，运用奇异值差分谱法选取有效奇异值并将原始信号重构得到初步降噪信号；其次，为防止故障信息丢失，将残余信号进行麻雀算法(Sparrow Search Algorithm，SSA)优化的变分模态分解(Variational Mode Decomposition，VMD）算法处理，得到最佳的模态个数K和惩罚参数α，选取峭度值最大、包络熵最小的IMF分量与初步降噪信号叠加得到最终降噪信号，并对信号进行包络分析；最后，通过仿真和试验数据分析得出，该方法能在信噪比很低的情况下降低噪声含量并提取轴承故障特征，为设备的状态监测和故障诊断提供理论依据。     

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis. For Random Search and Grid Search models, extensive comparison findings are produced utilizing retrieval, F1 score, and precision measurements. The models are evaluated using the metrics and kappa statistics that illustrate the three models’ comparability. The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification. Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.