Generalized Distributed Multicell Architecture:Group Cell

In order to make full use of advanced technologies for future mobile communications systems such as Space Time Code (STC), Joint Transmission (JT) and Multiple Input Multiple Output (MIMO), and to meet the requirements of high-bit-rate multimedia services, new network topologies should be studied. Generalized distributed multicell architecture can take full advantage of multi-antenna technologies and solve the problem of frequent handover caused by higher carrier frequencies. Group handover, the handover policy based on the architecture, can eliminate the cell edge effect. Furthermore, by applying the concept of group handover to 3G mobile communication systems, the Fast Cell Group Selection (FCGS) scheme can effectively improve the data rate for cell edge users.

An Approach to Modelling and Analysing Reliability of Breeze/ADL-based Software Architecture

Breeze/architecture description language（ADL）, is an eX tensible markup language（XML） based architecture description language which is used to model software systems at the architecture level. Though Breeze/ADL provides an appropriate basis for architecture modelling, it can neither analyse nor evaluate the architecture reliability. In this paper, we propose a Breeze/ADL based strategy which, by combining generalized stochastic Petri net（GSPN） and tools for reliability analysis, supports architecture reliability modelling and evaluation. This work expands the idea in three directions： Firstly, we give a Breeze/ADL reliability model in which we add error attributes to Breeze/ADL error model for capturing architecture error information, and at the same time perform the system error state transition through the Breeze/ADL production. Secondly, we present how to map a Breeze/ADL reliability model to a GSPN model, which in turn can be used for reliability analysis. The other task is to develop a Breeze/ADL reliability analysis modelling tool–EXGSPN（Breeze/ADL reliability analysis modelling tool）, and combine it with platform independent petri net editor 2（PIPE2） to carry out a reliability assessment.Abstract： Breeze/architecture description language （ADL）, is an eXtensible markup language （XML） based architecture description language which is used to model software systems at the architecture level. Though Breeze/ADL provides an appropriate basis for architecture modelling, it can neither analyse nor evaluate the architecture reliability. In this paper, we propose a Breeze/ADL based strategy which, by combining generalized stochastic Petri net （GSPN） and tools for reliability analysis, supports architecture reliability modelling and evaluation. This work expands the idea in three directions： Firstly, we give a Breeze/ADL reliability model in which we add error attributes to Breeze/ADL error model for capturing architecture error information, and at the same time perform the system error state transition through the Breeze/ADL production. Secondly, we present how to map a Breeze/ADL reliability model to a GSPN model, which in turn can be used for reliability analysis. The other task is to develop a Breeze/ADL reliability analysis modelling tool-EXGSPN （Breeze/ADL reliability analysis modelling tool）, and combine it with platform independent petri net editor 2 （PIPE2） to carry out a reliability assessment.

Deep Learning in Power Systems Research:A Review

With the rapid growth of power systems measurements in terms of size and complexity,discovering statistical patterns for a large variety of real-world applications such as renewable energy prediction,demand response,energy disaggregation,and state estimation is considered a crucial challenge.In recent years,deep learning has emerged as a novel class of machine learning algorithms that represents power systems data via a large hypothesis space that leads to the state-of-the-art performance compared to most recent data-driven algorithms.This study explores the theoretical advantages of deep representation learning in power systems research.We review deep learning methodologies presented and applied in a wide range of supervised,unsupervised,and semi-supervised applications as well as reinforcement learning tasks.We discuss various settings of problems solved by discriminative deep models including stacked autoencoders and convolutional neural networks as well as generative deep architectures such as deep belief networks and variational autoencoders.The theoretical and experimental analysis of deep neural networks in this study motivates longterm research on optimizing this cutting-edge class of models to achieve significant improvements in the future power systems research.