Volt-VAR control for distribution networks with high penetration of DGs: An overview

Continuously improving the ability to accept distributed renewable energies is the trend of future grid development, and a large number of papers have been published in recent years to study the problem of Volt-VAR control (VVC) for distribution networks with high penetration of distributed generations. This paper summarizes the relevant modeling and solution methods for VVC problems, mainly including VVC based on multiple time scales, hierarchical partitioning, multi-stage and network reconstruction, in conjunction with the operational characteristics of distribution networks containing distributed renewable energies; meanwhile, it analyzes the advantages and disadvantages of traditional optimization methods, heuristic intelligent algorithms and random variable processing methods used to solve VVC problems, and then introduces the application of model-free deep reinforcement learning as a latest decision method in VVC of distribution networks. Most of the models and methods compiled in this article are from the research results of the last three years and have some reference value.     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.     

Learning parities in the mistake-bound model

We study the problem of learning parity functions that depend on at most k variables (k-parities) attribute-efficiently in the mistake-bound model. We design a simple, deterministic, polynomial-time algorithm for learning k-parities with mistake bound . This is the first polynomial-time algorithm to learn ω(1)-parities in the mistake-bound model with mistake bound o(n).Using the standard conversion techniques from the mistake-bound model to the PAC model, our algorithm can also be used for learning k-parities in the PAC model. In particular, this implies a slight improvement over the results of Klivans and Servedio (2004) [1] for learning k-parities in the PAC model.We also show that the time algorithm from Klivans and Servedio (2004) [1] that PAC-learns k-parities with sample complexity can be extended to the mistake-bound model.     

Action categorization with modified hidden conditional random field

In this paper, we present a method for action categorization with a modified hidden conditional random field (HCRF). Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. We formulate a modified HCRF (mHCRF) to have a guaranteed global optimum in the modelling of the temporal action dependencies after the HMM pathing stage. Experimental results on action categorization using this model are compared favorably against several existing model-based methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF.     

基于集成学习的规范化LDA人脸识别

针对人脸识别问题中经常面临的“小样本”问题,在规范化的LDA算法的基础上加以改进,并结合集成学习的方法,利用Adaboost算法,在每一次的迭代过程中引进一个加权函数对难以分离的样本增加权重。增加分类器之间的差异度,从而提高样本在新的特征空间中的可分离性,将识别率提高至98.5%。通过ORL数据库的大量实验表明,该算法比传统算法有更好的性能。     

A quantitative multimodal discourse analysis of teaching and learning in a web-conferencing environment – The efficacy of student-centred learning designs

This paper presents a quantitative approach to multimodal discourse analysis for analyzing online collaborative learning. The coding framework draws together the fields of systemic functional linguistics and Activity Theory to analyze interactions between collaborative-, content- and technology-related discourse. The approach is used to examine how the task subject matter, the activity design, and the choice of interface affected interaction and collaboration for a computing course conducted in a web-conferencing environment. The analysis revealed the critical impact of activity design on the amount and type of discourse that transpired. Student-centred designs resulted in over six times more student discourse as compared to teacher-centred designs and created a learning environment where students took greater ownership over the tasks and contributed more to the content-based discussion. The paper also incorporates a rationale for the approach to coding and a reflection on its efficacy for discourse analysis in technology-based learning environments.     

Linking employees’ e-learning system use to their overall job outcomes: An empirical study based on the IS success model

e-Learning systems are increasingly being adopted by modern enterprises, because of their cost-down effect. However, the extant literature provides little insight into their beneficial consequences. It is expected that a transfer of acquired knowledge, skills and/or work attitudes to jobs will occur through e-learning systems use. This makes e-learning systems a useful tool for improving job outcomes, although this is not well documented. The IS success model, which captures both the human and technological elements of information systems, provides a theoretical basis for linking system use to system task. The current study then examines the relation between e-learning systems use and overall job outcomes based on the model. The data collected from one hundred and ninety-three e-learning system users were analyzed with partial least square (PLS). The results indicate that e-learning systems are perceived as useful and satisfying by employees, and employees’ e-learning systems use is significantly associated with overall job outcomes. In practice, these results provide a basis for establishing a link between an organization’s investment in e-learning and human capital management. They also lend empirical support to the IS success model.     

Change detection in desktop virtual environments: An eye-tracking study

It is common knowledge that attention is important for learning. We need to utilize attention in order to learn something efficiently and effectively. Similarly, we may also need to acquire familiarity with (i.e., learn) our surroundings in order to utilize our attention. In this study, learning is defined as a product of one’s exposure to natural visual stimuli. Using a virtual model of a natural scene, we investigate both attention and its relationship to learning, according to this definition. Specifically, our focus is the effect of environment familiarity on gaze direction. Our findings reveal that the factor of familiarity with one’s surroundings in virtual reality environments exerts a significant influence on peoples’ ability to detect a variety of specific changes that occur within scenes under their observation.     

谈智能科学多媒体辅助教学设计

在多媒体辅助教学中,教学设计直接影响教学效果。认知心理学的发展,尤其是R.E.Mayer提出的多媒体教学理论给我们提供了一个研究教学设计规则的依据。描述同多媒体教学相关的认知心理学的几个基本理论,在此基础上提出一些关于提高多媒体辅助教学效果的教学设计方法。     

Investigating factors that influence social presence and learning outcomes in distance higher education

There are many factors that influence distance learning especially in higher education where collaborative and communicative discourse is necessary for pursuing knowledge. Social presence, among other factors, is an important concept to be facilitated, developed and sustained in distance higher education as it promotes and supports discourse based learning. This study examines the relationship among demographic and other variables, social presence and learning satisfaction. Results showed demographic variables, such as gender, online learning experience and work status were not significant factors in terms of influencing on either social presence or learning satisfaction. While media integration and instructor’s quality teaching were significant predictors of both social presence and learning satisfaction, interactivity among participants was a predictor of social presence but not of learning satisfaction. Along with the study findings, some implications were discussed for online learning practitioners in higher education setting.