Should I scan my face? The influence of perceived value and trust on Chinese users’ intention to use facial recognition payment

While China has seen the widespread adoption of facial recognition payment, concerns over the potential risks impede the further growth of user acceptance. Drawing on the perspectives of perceived value and trust, we developed a research model to explore how the Chinese users’ trade-off between perceived benefits (perceived convenience and perceived novelty) and perceived risks (perceived privacy risk and perceived financial risk) influences their usage intention. Based on survey data from 1,200 Chinese mobile payment users, this study uses structural equation modeling to examine the hypotheses. Our results show that perceived convenience positively influences perceived value, while privacy risk and financial risk negatively influence perceived value and trust. Although perceived novelty has no significant effect on perceived value and trust, perceived value positively influences trust, and both of them have a positive effect on the intention to use. Furthermore, our moderation analysis indicates that the influence of perceived value on the intention to use is stronger among users who consider facial information to be more sensitive. The theoretical implications for research as well as the practical implications for practitioners are also discussed.     

基于曲线拟合的毫米波雷达安装角度校准方法

毫米波雷达的安装角度校准是雷达正常使用并与摄像头进行数据融合的重要前提,雷达安装角度偏 转过大会导致雷达数据与摄像头数据融合失败,影响高级驾驶辅助系统(Advanced Driving Assistance System,ADAS) 的正常使用。文中提出一种基于曲线拟合的毫米波雷达安装角度校准方法,当车辆在道路上行驶时辅以车辆输入 的车速和偏航角信息,通过2000 个静止点进行曲线拟合得到雷达需要补偿的角度。相较于选择有护栏的道路进行 绕行和在标定场地部署角反射器进行安装角度校准的方法,这种方法适用的场景种类更多并且校准时间从15 分钟 以上缩短为5 分钟以下。经过实验验证,在花费更短时间完成校准后,校准精度与其它自校准方法相同为±5°。     

Information diffusion-based spatio-temporal risk analysis of grassland fire disaster in northern China

This study presents a Geographical Information Systems (GIS) and information diffusion-based methodology for spatio-temporal risk analysis of grassland fire disaster to livestock production in the grassland area of the northern China. We employed information matrix to analyze and to quantify fuzzy relationship between the number of annual severe grassland fire disasters and annual burned area. We also evaluated the consequences of grassland fire disaster between 1991 and 2006 based on historical data from 12 northern China provinces. The results show that the probabilities of annual grassland fire disasters and annual damage rates on different levels increase gradually from southwest to northeast across the northern China. The annual burned area can be predicted effectively using the number of annual severe grassland fire disasters. The result shows reliability as tested by two-tailed Pearson correlation coefficient. This study contributes a reference in decision making for prevention of grassland fire disaster and for stockbreeding sustainable development planning. The fuzzy relationship could provide information to make compensation plan for the disaster affected area.     

Usability principles and best practices for the user interface design of complex 3D architectural design and engineering tools

This study proposes usability principles for the user interfaces (UI) design of complex 3D parametric architectural design and engineering tools. Numerous usability principles have been developed for generic desktop or web applications. The authors tried to apply existing usability principles as guidelines for evaluating complex 3D design and engineering applications. However, the principles were too generic and high-level to be useful as design or evaluation guidelines. The authors, all with more than 10 or 30 years of experience with various CAD systems, selected and reviewed 10 state-of-the-art 3D parametric design and engineering applications and captured what they thought were best practices, as screenshots and videos. The collected best practices were reviewed through a series of discussion sessions. During the discussion sessions, UI design principles underlying the collected best practices were characterized in the line of existing UI principles. Based on the best practices and the derived common UI principles, a new set of refined and detailed UI principles were proposed for improving and evaluating 3D parametric engineering design tools in the future.     

Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

Sanitary sewer systems are designed to collect and transport sanitary wastewater and stormwater. Pipe inspection is important in identifying both the type and location of pipe defects to maintain the normal sewer operations. Closed-circuit television (CCTV) has been commonly utilized for sewer pipe inspection. Currently, interpretation of the CCTV images is mostly conducted manually to identify the defect type and location, which is time-consuming, labor-intensive and inaccurate. Conventional computer vision techniques are explored for automated interpretation of CCTV images, but such process requires large amount of image pre-processing and the design of complex feature extractor for certain cases. In this study, an automated approach is developed for detecting sewer pipe defects based on a deep learning technique namely faster region-based convolutional neural network (faster R-CNN). The detection model is trained using 3000 images collected from CCTV inspection videos of sewer pipes. After training, the model is evaluated in terms of detection accuracy and computation cost using mean average precision (mAP), missing rate, detection speed and training time. The proposed approach is demonstrated to be applicable for detecting sewer pipe defects accurately with high accuracy and fast speed. In addition, a new model is constructed and several hyper-parameters are adjusted to study the influential factors of the proposed approach. The experiment results demonstrate that dataset size, initialization network type and training mode, and network hyper-parameters have influence on model performance. Specifically, the increase of dataset size and convolutional layers can improve the model accuracy. The adjustment of hyper-parameters such as filter dimensions or stride values contributes to higher detection accuracy, achieving an mAP of 83%. The study lays the foundation for applying deep learning techniques in sewer pipe defect detection as well as addressing similar issues for construction and facility management.     

Improved dielectric energy storage performance of Na0.5Bi0.5TiO3-based lead-free relaxation ferroelectric ceramics achieved by domain structural regulation and enhanced densification

There is still a problem of low energy storage density in dielectric capacitors which is a core component of power systems. For the improvement of the energy storage density, the linear dielectric material CaTiO₃ (CT) was introduced in Na_0.5Bi_0.5TiO₃ (NBT) ceramics in this paper. By modifying the A site, a new relaxor ferroelectric ceramic was successfully synthesized and attained a recoverable density (W_rec) of 2.34 J/cm³ at x = 0.18. Moreover, the preparation process was optimized in this paper. Through the viscous polymer process (VPP) route, the energy density (W_A) of 82NBT-18CT_VPP ceramic further reaches 6.45 J/cm³ at 340 kV/cm, with efficiency (η) up to 75% and a W_rec of 4.82 J/cm³. At the same time, the change of W_rec is small at temperature (30–150 °C) and frequency (1 Hz–300 Hz), which demonstrates its excellent stability. The discharge power density reaches about 180 MW/cm³ and the discharge time is 0.117 μs, which indicates its excellent pulse discharge performance. The results show that 82NBT-18CT lead-free relaxation ferroelectric material is expected to become ideal for high-energy storage applications.     

Degradation-oriented real-time power control of fuel cell hybrid vehicles under uncertain driving conditions

This work provides a real-time power allocation algorithm to address uncertain actual driving situations for fuel cell hybrid vehicles. To predict the vehicle speed under nondeterministic driving conditions, a fusion prediction model is developed based on the advantages of the Markov chain and neural network. The optimal power splitting decision in each receding horizon is then solved using the Pontryagin's minimum principle (PMP) method, considering fuel consumption, State of Charge (SOC), and performance degradation. A degradation model of electrochemical active surface area (ECSA) based on Pt catalyst dissolution was developed. Then the effect of the energy management algorithm on fuel cell degradation was evaluated using the degradation model. Compared with the two conventional real-time power splitting strategies, the approach suggested in this research can better reduce the fuel consumption and maintain the stability of battery SOC with a lower fluctuation while taking into account the degradation of the fuel cell.     

U-系统与 V-系统的理论及应用综述

传统的 Fourier 级数在逼近间断信号时因 Gibbs 现象的干扰,会产生比较大的误差。针对此问 题,国内学者齐东旭教授带领的课题组提出了非连续正交函数系的研究课题,其中 U-系统和 V-系统是两类典 型的非连续完备正交函数系。从数学理论上来说,U-系统和 V-系统分别是对著名的 Walsh 函数和 Haar 函数由 分段常数向分段 k 次多项式进行推广的结果,其最重要的特点是函数系中既有光滑函数又有各个层次的间断函 数。因此,U,V-系统可以处理连续和间断并存的信息,在一定程度上弥补了 Fourier 分析和连续小波的缺憾。 本文从理论与应用 2 个方面对 U,V-系统进行了综述。在理论方面,首先介绍了单变量 U-系统与 V-系统各自 的构造方法,其次介绍三角域上 U,V-系统的构造方法,最后介绍 U,V-系统的主要性质。在应用方面,介绍 了若干具有代表性的应用案例。     

Adaptive consensus model with multiplicative linguistic preferences based on fuzzy information granulation

An adaptive consensus model based on fuzzy information granulation (fuzzy IG) is presented for group consensus decision-making problems with multiplicative linguistic preference relations (MLPRs). Firstly, a granular representation of linguistic terms is concerned with the triangular fuzzy formation of a family of information granules over given Analytical Hierarchy Process (AHP) numerical scales. On this basis, the individual consistency and group consensus measure indices using fuzzy granulation technique are constructed, respectively. Then, the optimal cut-off points of fuzzy information granules are obtained by establishing a multi-objective optimization model together with a multi-objective particle swarm optimization (MOPSO) algorithm. A novel group consensus decision-making approach where consensus reaching process (CRP) is achieved by adaptively adjusting individual preferences through the optimization of the cut-off points is proposed. After conflict elimination, the obtained group preference gives the ranking of the alternatives. Finally, a real emergency decision-making case for liquid ammonia leak is given to illustrate the application steps of the proposed method and comparative analysis with the existing GDM methods. Comparative results demonstrate that the proposed method has some advantages in aspects of avoiding information loss or distortion and improving consensus performance.