“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Acknowledgements: We would like to thank the following for contributions and suggestions: Bill Baxter, Brian Templeton, Christian Rishoj, Christian Schroppel Douglas L. Theobald, Esben Hoegh-Rasmussen, Glynne Casteel, Jan Larsen, Jun Bin Gao, Jurgen Struckmeier, Kamil Dedecius, Korbinian Strimmer, Lars Christiansen, Lars Kai Hansen, Leland Wilkinson, Liguo He, Loic Thibaut, Miguel Barao, Ole Winther, Pavel Sakov, Stephan Hattinger, Vasile Sima, Vincent Rabaud, Zhaoshui He. We would also like thank The Oticon Foundation for funding our PhD studies.

The Matrix Cookbook

The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity, and significant improvement in users’ perceived quality of service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency, and QoS are expected to be the panacea of most of the current cellular networks’ problems. In this survey, we make an exhaustive review of wireless evolution toward 5G networks. We first discuss the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in-depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms, and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE, and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests, and simulation experiments. Finally, we point out major existing research issues and identify possible future research directions.

Next Generation 5G Wireless Networks: A Comprehensive Survey

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF) This includes NMFs various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD) NMF/NTF and their extensions are increasingly used as tools in signal and image processing, and data analysis, having garnered interest due to their capability to provide new insights and relevant information about the complex latent relationships in experimental data sets It is suggested that NMF can provide meaningful components with physical interpretations; for example, in bioinformatics, NMF and its extensions have been successfully applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining As such, the authors focus on the algorithms that are most useful in practice, looking at the fastest, most robust, and suitable for large-scale models Key features: Acts as a single source reference guide to NMF, collating information that is widely dispersed in current literature, including the authors own recently developed techniques in the subject area Uses generalized cost functions such as Bregman, Alpha and Beta divergences, to present practical implementations of several types of robust algorithms, in particular Multiplicative, Alternating Least Squares, Projected Gradient and Quasi Newton algorithms Provides a comparative analysis of the different methods in order to identify approximation error and complexity Includes pseudo codes and optimized MATLAB source codes for almost all algorithms presented in the book The increasing interest in nonnegative matrix and tensor factorizations, as well as decompositions and sparse representation of data, will ensure that this book is essential reading for engineers, scientists, researchers, industry practitioners and graduate students across signal and image processing; neuroscience; data mining and data analysis; computer science; bioinformatics; speech processing; biomedical engineering; and multimedia

Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and grid, which provides powerful demand response, balancing the electricity demand and supply in smart grid Mobility is the key feature of EVs, which is also a significant challenge for V2G systems In order to model the EV mobility in V2G systems, we propose a complex networking modeling for V2G mobile energy network Each district has a V2G system EVs travel among different districts The EV fleets transport energy and impact the V2G systems of districts The theory of complex network synchronization is employed to analyze the dynamics of the mobile energy network Numerical results show the energy transportation of EV fleets may achieve synchronous stability of demand level of different districts, balancing the demand response in the mobile energy network

Dynamic demand balance in vehicle-to-grid mobile energy networks

The objective of this paper is to propose a local electricity and carbon trading method for interconnected multi-energy microgrids. A local electricity market and a local carbon market are established, allowing microgrids to trade electricity and carbon allowance within the microgrid network. Specifically, excessive electricity and carbon allowance of a microgrid can be shared with other microgrids that require them. A local electricity trading problem and a local carbon trading problem are formulated for multi-energy microgrids using the Nash bargaining theory. Each Nash bargaining problem can be decomposed into two subproblems, including an energy/carbon scheduling problem and a payment bargaining problem. By solving the subproblems of the Nash bargaining problems, the traded amounts of electricity/carbon allowance between microgrids and the corresponding payments will be determined. In addition, to enable secure information interactions and trading payments, we introduce an electricity blockchain and a carbon blockchain to record the trading data for microgrids. The novelty of the usage of the blockchain technology lies in using a notary mechanism-based cross-chain interaction method to achieve value transfer between blockchains. The simulation results show that the proposed local electricity and carbon trading method has great performance in lowering total payments and carbon emissions for microgrids.

/pdf/a-local-electricity-and-carbon-trading-method-for-multi-27qikthg.pdf

A Local Electricity and Carbon Trading Method for Multi-Energy Microgrids Considering Cross-Chain Interaction

Based on sparse representation, this paper discusses convolutive BSS of sparse sources and presents a FIR convolutive BSS algorithm that works in the frequency domain. This algorithm does not require that source signals be i.i.d or stationary, but require that source signals be sufficiently sparse in frequency domain. Furthermore, our algorithm can overcome permutation problem of frequency convolutive BSS method. For short-order FIR convolution, simulation shows good performance of our algorithm.

FIR convolutive BSS based on sparse representation

Large curvature aspheric optical elements are widely used in visual system. But its morphological detection is very difficult because its accuracy requirement is very high. When we use the self-developed multi-beam angle sensor (MBAS) to detect large curvature aspheric optical elements, the accuracy will be reduced due to spot distortion. Therefore, we propose a scheme combining distorted spot correction neural network (DSCNet) and gaussian fitting method to improve the detection accuracy of distorted spot center. We develop a spot discrimination method to determine spot region in multi-spot images. The spot discrimination threshold is obtained by the quantitative distribution of pixels in the connected domain. We design a DSCNet, which corrects the distorted spot to Gaussian spot, to extract the central information of distorted spot images by multiple pooling. The experimental results demonstrate that the DSCNet can effectively correct the distorted spot, and the spot center can be extracted to sub-pixel level, which improves the measurement accuracy of the MBAS. The standard deviations of plano-convex lenses with curvature radii of 500 mm, 700 mm and 1000 mm measured with the proposed method are respectively 0.0112 um, 0.0086 um and 0.0074 um.

Distortion spot correction and center location base on deep neural network and MBAS in measuring large curvature aspheric optical element.

https://robots.iopscience.iop.org/article/10.1088/1361-6501/ac0b6e/pdf

Shengli Xie

Papers

Dynamic demand balance in vehicle-to-grid mobile energy networks

A Local Electricity and Carbon Trading Method for Multi-Energy Microgrids Considering Cross-Chain Interaction

FIR convolutive BSS based on sparse representation

Distortion spot correction and center location base on deep neural network and MBAS in measuring large curvature aspheric optical element.

Phase-sensitive optical coherence tomography for non-contact monitoring photocuring process