Two-level optimal output feedback stabilizer design

The paper addresses the design of two-level power system stabilizers using an optimal reduced order model whose state variables are torque angles and speeds. The reduced order model retains their physical meaning and is used to design a two-level linear feedback controller that takes into account the realities and constraints of electrical power systems. The two-level control strategy is used, and a global control signal is generated from the output variables to minimize the effect of interactions. The effectiveness of this controller is evaluated and a multimachine system is given as an example to illustrate the advantages of the proposed method. Responses of the system with a two-level scheme and an optimal reduced order scheme are included for comparative analysis.     

Analysis and visualisation with host-based software – The features of STNAnaVist™

This article is based on experiences with data and text mining to gain information for strategic business decisions, using host-based analysis and visualisation (A/V), primarily in the field of patents. The potential advantages of host-based A/V are pointed out and the features of the first such A/V software, STN^®AnaVist™, are described in detail. Areas covered include the user interfaces, initial set of documents for A/V, data mining, text mining, reporting, and suggestions for further development.     

Syllables as processing units in handwriting production.

This research focused on the syllable as a processing unit in handwriting. Participants wrote, in uppercase letters, words that had been visually presented. The interletter intervals provide information on the timing of motor production. In Experiment 1, French participants wrote words that shared the initial letters but had different syllable boundaries. In Experiment 2, French- and Spanish-speaking participants wrote cognates and pseudowords with a letter sequence that was always intrasyllabic in French and intersyllabic in Spanish. In Experiment 3, French-Spanish bilinguals wrote the cognates and pseudowords with the same type of sequences. In the 3 experiments, the critical interletter intervals were longer between syllables than within syllables, indicating that word syllable structure constrains motor production both in French and Spanish. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using information-systems constructs to study online- and telephone-banking technologies

The research literature has seen a number of studies aimed at understanding customer attitudes towards banking technologies, and thereby providing implications for developmental planning and marketing. However, even though banking technologies have a significant amount of information systems (IS) component to them, there is a paucity of research that considers them from the IS perspective. In this paper, we fill this gap by considering three constructs, namely usefulness, ease of use and usage, that are all rooted in the IS literature. We study user’s perceptions of these constructs, as well as the relationships among them, for both online and telephone banking.     

基于B/S网上教学资源管理平台的分析与设计

随着信息化技术的不断发展,网上教学资源管理平台已经成为目前高校教学中非常重要的工具,尤其是在当前信息技术和计算机网络技术飞速发展的时代,高校开始充分利用现有网络资源来进行教学资源管理。区别于传统的C/S模式,B/S开发模式使得高校内部用户通过校园网或者互联网就可访问本平台。本文就基于B/S模式的网上教学资源管理平台进行详细分析。     

通用在线考试系统的设计与实现

目前的大多数考试系统都局限于某个学科或者专业,而忽略其通用性的研究,通过对通用在线考试系统进行需求分析,了解系统的功能需求,设计出系统功能模块例如学生考试、题库管理、考卷评分和考试分析。给出关键模块的实现细节,并实现一个完善的通用在线考试系统。     

Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services

As the keystones of the personalized manufacturing, the Industrial Internet of Things (IIoT) consolidated with 3D printing pave the path for the era of Industry 4.0 and smart manufacturing. By resembling the age of craft manufacturing, Industry 4.0 expedites the alteration from mass production to mass customization. When distributed 3D printers (3DPs) are shared and collaborated in the IIoT, a promising dynamic, globalized, economical, and time-effective manufacturing environment for customized products will appear. However, the optimum allocation and scheduling of the personalized 3D printing tasks (3DPTs) in the IIoT in a manner that respects the customized attributes submitted for each model while satisfying not only the real-time requirements but also the workload balancing between the distributed 3DPs is an inevitable research challenge that needs further in-depth investigations. Therefore, to address this issue, this paper proposes a real-time green-aware multi-task scheduling architecture for personalized 3DPTs in the IIoT. The proposed architecture is divided into two interconnected folds, namely, allocation and scheduling. A robust online allocation algorithm is proposed to generate the optimal allocation for the 3DPTs. This allocation algorithm takes into consideration meeting precisely the customized user-defined attributes for each submitted 3DPT in the IIoT as well as balancing the workload between the distributed 3DPs simultaneously with improving their energy efficiency. Moreover, meeting the predefined deadline for each submitted 3DPT is among the main objectives of the proposed architecture. Consequently, an adaptive real-time multi-task priority-based scheduling (ARMPS) algorithm has been developed. The built ARMPS algorithm respects both the dynamicity and the real-time requirements of the submitted 3DPTs. A set of performance evaluation tests has been performed to thoroughly investigate the robustness of the proposed algorithm. Simulation results proved the robustness and scalability of the proposed architecture that surpasses its counterpart state-of-the-art architectures, especially in high-load environments.     

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.     

Learning experiences from digital laboratory safety training

We present a digital laboratory safety training platform called AALTOLAB which we have developed to address the need for more flexible and engaging ways to teach laboratory safety for chemical engineers. AALTOLAB consists of an interactive, web-based 360° virtual laboratory, integrated with a Moodle-based digital exam with automatic assessment. The modular platform offers different modules from general comprehensive laboratory training to more specialised modules. Students and staff can freely navigate in the laboratory rooms at any time and interact with different action points including interactive videos, slideshows, and minigames. Our technical solution allows teachers even with limited technological skills to easily modify the training materials in real time. The relationship of students’ learning experiences and level of learning is also discussed. We believe that our safety training concept facilitates deep learning and students’ engagement, thus improving safety culture in our laboratories.     

在线煤灰分检测技术在中国的应用

介绍了应用双能量γ射线透射法测量煤灰分的技术原理及特点;清华大学工程物理系采用该法研制的在线煤灰分仪应用在选煤厂可以指导生产,保证产品质量,提高选煤效率,还可用于介耗<2kg/t的重介质选煤厂及自动配煤系统,为企业创造了显著的经济效益。