Global distribution of grid connected electrical energy storage systems

This article gives an overview of grid connected electrical energy storage systems worldwide, based on public available data. Technologies considered in this study are pumped hydroelectric energy storage (PHES), compressed air energy storage (CAES), sodium-sulfur batteries (NaS), lead-acid batteries, redox-flow batteries, nickel-cadmium batteries (NiCd) and lithium-ion batteries. As the research indicates, the worldwide installed capacity of grid connected electrical energy storage systems is approximately 154 GW. This corresponds to a share of 5.5 % of the worldwide installed generation capacity. Furthermore, the article gives an overview of the historical development of installed and used storage systems worldwide. Subsequently, the focus is on each considered technology concerning the current storage size, number of plants and location.In summary it can be stated, PHES is the most commonly used technology worldwide, whereas electrochemical technologies are increasingly gaining in importance. Regarding the distribution of grid connected storage systems reveals the share of installed storage capacity is in Europe and Eastern Asia twice as high as in North America

Comparison of Bernoulli and Gaussian HMMs using a vertical repositioning technique for off-line handwriting recognition

—In this paper a vertical repositioning method based on the center of gravity is investigated for handwriting recognition systems and evaluated on databases containing Arabic and French handwriting. Experiments show that vertical distortion in images has a large impact on the performance of HMM based handwriting recognition systems. Recently good results were obtained with Bernoulli HMMs (BHMMs) using a preprocessing with vertical repositioning of binarized images. In order to isolate the effect of the preprocessing from the BHMM model, experiments were conducted with Gaussian HMMs and the LSTM-RNN tandem HMM approach with relative improvements of 33% WER on the Arabic and up to 62% on the French database.Doetsch, P.; Hamdani, M.; Ney, H.; Giménez Pastor, A.; Andrés Ferrer, J.; Juan Císcar, A. (2012). Comparison of Bernoulli and Gaussian HMMs using a vertical repositioning technique for off-line handwriting recognition. En 2012 International Conference on Frontiers in Handwriting Recognition ICFHR 2012. Institute of Electrical and Electronics Engineers (IEEE). 3-7. doi:10.1109/ICFHR.2012.194S3

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Alignment models for recurrent neural networks

Modern recognition systems for speech and handwriting make use of neural networks to convert the acoustic signal or handwritten image into text. Neural networks hereby learn the required parameters from transcribed data in a training phase. In the beginning only feed-forward neural networks were used, which had to be initialized with the alignment of observations and labels of a previously trained Gaussian hidden Markov model for good performance. More recently, recurrent neural network architectures have been shown to outperform their non-recurrent counterparts, with Long Short-Term Memories being the most prominent example. Recurrent neural networks can model the temporal nature of the data directly, and thus are able to dynamically change the alignment to better fit the model. In this thesis, we will investigate applications and training techniques of recurrent neural network architectures for speech and handwriting recognition. As part of this thesis we developed a neural network toolkit for hardware accelerated training and recognition of speech and handwriting systems. The software allows to train recurrent neural network architectures as well as traditional feed-forward neural networks and is capable of processing very large amounts of data on multiple computing devices. After training, he models can be loaded into the the RWTH Aachen speech recognition toolkit for recognition. Our experiments show that recurrent models outperform feed-forward structures in terms of recognition error and we demonstrate their effectiveness in various experiments on handwriting recognition. Further contributions were made by developing techniques to improve the training performance through optimized data ordering. With our toolkit we then evaluate neural network based methods for handwriting recognition. Our focus is hereby on recurrent topologies that operate on images either in a one-dimensional or two-dimensional fashion, and we investigate various system architectures and implementation techniques. We examine the effectiveness of our proposed solutions on prominent handwriting recognition corpora and compare our systems to other groups in a competitive setting. In the final part of this thesis we investigate the effects of handling the alignment problem within recurrent neural networks. We describe overfitting problems of conventional alignment approaches and study properties of the connectionist temporal classification error criterion. Furthermore, we investigate methods that do not make use of external alignment computations, and instead only rely on a special composition of two recurrent neural networks that is able transcribe input observations into output symbols directly. Motivated by these results, we develop direct hidden Markov models as a novel inverted alignment method, which is able to overcome some of the limitations we noticed, and we evaluate our method on speech and handwriting recognition tasks

Improvements in RWTH’s system for off-line handwriting recognition

Abstract—In this paper we describe a novel HMM-based system for off-line handwriting recognition. We adapt successful techniques from the domains of large vocabulary speech recognition and image object recognition: moment-based image normalization, writer adaptation, discriminative feature extraction and training, and open-vocabulary recognition. We evaluate those methods and examine their cumulative effect on the recognition performance. The final system outperforms current state-of-theart approaches on two standard evaluation corpora for English and French handwriting. I