Measuring the likelihood of VR visual fatigue through ocular biomechanics

Immersion in virtual reality is still linked to symptoms of visual fatigue such as eye strain, dizziness, and overall discomfort. Studies have investigated visual fatigue through pre- and post-immersion tests of the visual function. In this work, we extend on our previous study and derive a visual fatigue likelihood metric using biomechanical analysis. Previously, we have investigated the effect of VR on the vergence system during immersion. The proposed visual fatigue metric exhibited a significant correlation to vergence angle variability which was previously linked to vergence accommodation conflict in VR. We also discuss subjective feedback and its relationship with the proposed visual fatigue metric.     

New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

Cross-source Data Error Detection Approach Based on Federated Learning

With the emergence and accumulation of massive data, data governance has become an important manner to improve data quality and maximize data value. Specifically, data error detection is a crucial step to improve data quality, which has attracted wide attention from both industry and academia. At present, various detection methods tailored for a single data source have been proposed. However, in many real-world scenarios, data are not centrally stored or managed. Data from different sources but highly correlated can be employed to improve the accuracy of error detection. Unfortunately, due to privacy/security issues, cross-source data are often not allowed to be integrated centrally. To this end, this paper proposes FeLeDetect, a cross-source data error detection method based on federated learning, so as to improve the error detection accuracy by using cross-source data information on the premise of data privacy. First, a Graph-based Error Detection Model, namely GEDM, is presented to capture sufficient data features from each data source. On this basis, the paper then designs a federated co-training algorithm, namely FCTA, to collaboratively train GEDM by using different cross-source data without privacy leakage of data. Furthermore, the paper designs a series of optimization methods to reduce communication costs during federated learning and manual labeling efforts. Finally, extensive experiments on three real-world datasets demonstrate that (1) GEDM achieves an average improvement of 10.3% and 25.2% in terms of the $F1$ score in the local and centralized scenarios, respectively, outperforming all the five existing state-of-the-art methods for error detection; (2) the F1 score of the error detection by FeLeDetect is 23.2% on average higher than that by GEDM in the local scenario.     

变速永磁同步发电机系统稳压控制（连载之五）直接电压控制在发电机系统中的应用

在所研究的具有强弱磁能力变速永磁同步发电机的270V直流电能系统中,应用PWM整流器,提出直接电压控制技术,设计了直流母线电压PI控制器,并结合有源阻尼技术,在Matlab/Simulink仿真及测试平台中加以实现。最后,对本系列连载所研究的各类控制算法的性能进行对比研究。     

水利工程项目施工精细化管理的实施

<正>1精细化管理及其内涵精细化管理是源于发达国家的一种管理理念,是社会分工的精细化以及服务质量的精细化对现代管理的必然要求。现代管理认为,管理有三个层次,一是规范化,二是精细化,三是个性化。它是建立在常规管理的基础上,并将常规管理引向深入的关键一步。精细化管理就是要求把每一项工作都抓细、量化,有利于落实到行动中。"精"就是切中要点,抓住运营管理中的关键环节;"细"就是管理标准的具体量化、考核、督促和执行。精细化管理的核心在于实行刚性的制度,规范人     

正弦波伏安法检测尿酸的研究

以玻碳电极为工作电极,利用正弦波伏安法在磷酸缓冲液中选择性检测尿酸.通过傅立叶变换比较了纯的磷酸缓冲液、尿酸和抗坏血酸的正弦波伏安法频域响应,进而发现在第5次谐波处选择性检测尿酸的可能.相比于传统的尿酸检测技术,该技术具有较高的信噪比,操作简单且无需对样品进行预处理.分析了玻碳电极表面抗坏血酸和尿酸的线性扫描响应,在不同扫描速率下两者的伏安图变化趋势相同.在裸玻碳电极表面,利用正弦波伏安法检测尿酸的检测限为1.0μmol/L,在250倍浓度的抗坏血酸共存时仍能利用第5次谐波选择性检测出尿酸.     

面向关系型数据与知识图谱的数据集成技术综述

目前,各个国家和地区均已将大数据视为重要的战略资源.然而,大数据时代普遍存在数据流通困难、数据监管不足等问题,致使数据孤岛现象严重,数据质量低下,数据要素潜能难以释放.这驱使研究人员探索数据集成技术,以打破数据壁垒、实现信息共享、提升数据质量,进而激活数据要素潜能.关系型数据和知识图谱作为两种至关重要的数据组织与存储形式,在现实生活中应用广泛.为此,聚焦关系型数据和知识图谱,归纳总结并分析实体解析、数据融合、数据清洗3方面的数据集成关键技术,最后展望未来研究方向与趋势.     

水泥回转窑一维数学模型研究进展及趋势

全面系统地综述了国内外水泥回转窑一维数学模型,重点介绍了传热模型、料床运动模型、煤粉燃烧模型和熟料矿物形成模型。应用MATLAB编程、求解,展示并分析了不同模型及关键参数对模拟结果的影响。在此基础上,展望了回转窑一维数学模型的发展趋势。     

UPLC-ESI-MS/MS for determining trans- and cis-vitamin K1 in infant formulas: method and applications

A simple and sensitive method of determining vitamin K₁ isomers (cis-and trans-forms) was developed for routine monitoring of supplemented infant formulas. The proposed method involves the applications of the lipase hydrolysis and the liquid-to-liquid extraction. After the hydrolysis and extraction procedures, the vitamin K₁-enriched extract was directly injected into the ultra-performance liquid chromatography–tandem mass spectrometry system (UPLC-MS/MS). The components were detected using electrospray ionization (ESI) in positive-ion and quantified by multiple-reaction monitoring (MRM) mode. trans-vitamin K₁ was separated from the biologically inactive cis-isomer through a C₃₀ column (4.6?×?150?mm, 3?μm), and vitamin K₁-d₇ was used as an internal standard. Calibration curves were linear over the range of 9.3–464.75?ng?ml^?1 for trans-vitamin K₁ (r ²?>?0.999) and 1.71–85.25?ng?ml^?1 for cis-vitamin K₁ (r ²?>?0.999). The limit of detection for trans- and cis-vitamin K₁ was 0.011?μg 100?g^?1 and 0.01?μg 100?g^?1. The limit of quantification was 0.037?μg 100?g^?1 and 0.031?μg 100?g^?1, respectively. The intra- and inter-batch variations (RSD%) were less than 5?%. The proposed method was applied to determine vitamin K₁ isomers in milk-based, soy-based and rice-based infant formulas, and the cis-vitamin K₁ isomer contributes to 7.05–17.21?% of the total vitamin K₁ in certain infant formulas.     

A wireless multi-channel implantable neural recording microsystem based on optimized analog TDM-FDM combination

Crosstalk noise (CT) is a limiting factor to increase the number of channels in analog Time-Division-Multiplexing (TDM)-based Wireless Neural Recording microsystems (WNRs). This paper proposes a novel approach to mitigate and decrease the effect of the CT by combining TDM with Frequency-Division-Multiplexing (FDM). In particular, we evaluate some possible configurations of the TDM-FDM combination and present a system that has less CT than other configurations. A 12-channel WNR based on the proposed system is designed in both system and circuit-level. In this system, channels are first divided into three 4-channel groups and after multiplexing in time domain, they are combined together with FDM method. While the group containing the marker pulse is located in the base-band, the second and third group are shifted to the frequency domain by employing quadrature modulation. The circuit-level of the system is designed and simulated by using 0.18 μm CMOS technology. The designed circuit consumes a power of 1.4 mW at a supply voltage of 1.8 V. The performance of the proposed system is also compared with simple TDM-based WNR. Simulations shows that in the proposed system the CT is considerably decreased.