Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.Comment: 9 pages, 2 figure

Data augmentation using generative adversarial networks for electrical insulator anomaly detection

Master of ScienceDepartment of Computer ScienceWilliam H. HsuElectricity has been an essential part of our life. Insulators, which are widely used for electricity transmission, are prone to be damaged and need constant maintenance. Traditionally, the inspection job is time-consuming and dangerous as workers would have to climb up the electricity tower. Deep learning has offered a safe and quick way to inspections. About 3000 insulators images are taken from different angles using a drone. Due to great difference in number of good and damaged insulator, directly training a classifier on the imbalanced data lead to low recall value on the damaged insulators. Generative adversarial networks (GANs) were introduced as a novel way to augment data. However, traditional GANs are either incapable of generating high quality images or fail to generate minority class images when minority class examples are far less. In this study, a novel GAN model, Balancing and Progressive GANs (BPGANs), was proposed for effectively making use of all classes information and generating high quality minority images at the same time. Results show that PGANs, StyleGANs, and BPGANs were able to generate high-resolution images and improve classification performance. PGANs achieved the better results than BPGANs. This may be because BPGANs only provides 2 additional latent codes since it is a binary classification, having little effect on generating desired images. BPGANs seemed to have difficulties generating class-specific images, which might be because that the classification loss is too little compared to the source loss and optimization was more focused to optimize the source loss. This indicates that learning representations of data progressively from low resolution to high resolution is an effective approach, however, embedding class label information in the fashion of AC-GANs and BGANs might not be appropriate for augmenting binary class data sets

Current State of Heating and Cooling Markets in United Kingdom

The RES-H Policy project: The project "Policy development for improving RES-H/C penetration in European Member States (RES-H Policy)" aims at assisting Member State governments in pre-paring for the implementation of the forthcoming Directive on Renewables as far as aspects related to renewable heating and cooling (RES-H/C) are concerned. Member States are supported in setting up national sector specific 2020/2030 RES-H/C targets. Moreover the project initiates participatory National Policy Processes in which selected policy options to support RES-H/C are qualitatively and quantitatively assessed. Based on this assessment the project develops tailor made policy options and recommenda-tions as to how to best design a support framework for increased RES-H/C penetration in national heating and cooling markets. The target countries/regions of the project comprise Austria, Greece, Lithuania, The Netherlands, Poland and UK – countries that represent a variety in regard of the framework conditions for RES-H/C. On the European level the projects assesses op-tions for coordinating and harmonising national policy approaches. This results in common design criteria for a general EU framework for RES-H/The report represents a key output of the RES-H Policy project, an EU FP7 funded research project aiming to assist EU Member States in selecting support options for increased renewable heat. The body of literature in this field is very narrow, particularly given the much less advanced state of renewable heat policy and the significance of heat in developed countries, where it accounts for 40-50% of national energy use. A revised version has been submitted to Energy Policy journal.A report prepared as part of the IEE project "Policy development for improving RES-H/C penetration in European Member States (RES-H Policy)"The purpose of this report is to present an overall picture of the situation in the heating and cooling sectors of the United Kingdom. The report summarizes the policy and regulatory framework of the UK heating and cooling markets and gives the available statistics on the penetration rate of the different RES-H/C technologies, as well as the RES potentials for heating and cooling purposes.European Commission through the IEE programm

Limits of bifractional Brownian noises

Let $B^{H,K}=(B^{H,K}_{t}, t\geq 0)$ be a bifractional Brownian motion with two parameters $H\in (0,1)$ and $K\in(0,1]$. The main result of this paper is that the increment process generated by the bifractional Brownian motion $(B^{H,K}_{h+t} -B^{H,K}_{h}, t\geq 0)$ converges when $h\to \infty$ to $(2^{(1-K)/{2}}B^{HK}_{t}, t\geq 0)$, where $(B^{HK}_{t}, t\geq 0)$ is the fractional Brownian motion with Hurst index $HK$. We also study the behavior of the noise associated to the bifractional Brownian motion and limit theorems to $B^{H,K}$