Control System of Battery Storage to Eliminate the Power Variation According to the Electricity Prediction

Rapid variations of the output power fromphotovoltaic power plants can have some significant sideeffects on the quality of electricity, such as the voltagevariation, switching of tap changers, etc. In the other case,these variations also make the difference between theprediction and real electricity production. Generally, thegoal of the accumulation of electricity is to charge thestorage element in surplus of electricity and discharge whenthe energy is insufficient. In this paper, the accumulation ofelectricity in a photovoltaic power plant is not used for thispurpose, but to charge and discharge the storag

Characterizing Configurations of critical points through LBP Extended Abstract

In this abstract we extend ideas and results submitted to [3] in which a new codification of Local Binary Patterns (LBP) is given using combinatorial maps and a method for obtaining a representative LBP image is developed based on merging regions and Minimum Contrast Algorithm. The LBP code characterizes the topological category (max, min, slope, saddle) of the 2D gray level landscape around the center region. We extend the result studying how to merge non-singular slopes with one of its neighbors and how to extend the results to nonwell formed images/maps. Some ideas related to robust LBP and isolines are also given in last section

Structurally correct image segmentation using local binary patterns and the combinatorial pyramid

Zsfassung in dt. SpracheIn dieser Diplomarbeit präsentieren wir einen neuen Bildsegmentierungsalgorithmus, der auf den Local Binary Patterns und der Kombinatorischen Pyramide beruht. Existierende Algorithmen zur Bildsegmentierung, die auf den Local Binary Patterns basieren, nutzen statistische Methoden in Form von Histogrammen, um texturierte Regionen zu beschreiben und zu vergleichen, und ein Bild in homogene Regionen aufzuteilen. Die Neuheit unserer Methode liegt darin, dass wir die statistische Beschreibung mittels Histogrammen auslassen und eine Segmentierung direkt auf der lokalen Struktur des Bildes durchführen, und gleichzeitig die strukturelle Korrektheit sowie Topologie des Bildes beibehalten. In dieser Diplomarbeit definieren wir fünf topologische Klassen, die auf den Local Binary Patterns von Regionen basieren und invariant bezüglich der Anzahl und Verschiebung der Bits sind, nämlich locale Minima, Slopes, singuläre Slopes, Sattelpunkte, und lokale Maxima. Mithilfe dieser Klassen und des dualen Graphen ist es möglich redundante strukturelle Information zu identifizieren und zu entfernen. Diese Methode vereinfacht den Bildgraphen und erlaubt es verbundene Regionen zu verschmelzen ohne strukturelle Fehler zu erzeugen. Wir vergleichen unseren Algorithmus mit fünf anderen Algorithmen mittels den Global Consistency Error und Probabilistic Rand Index Fehlermetriken. Einer dieser fünf Algorithmen ist eine Vorversion unseres vorgeschlagenen Algorithmus, der aber keine strukturellen Beschränkungen beachtet. Die restlichen vier Algorithmen basieren auf internem- und externem Kontrast, minimalen Spannbäumen, Mean-Shift, und Superpixel Verfahren. Die Evaluation zeigt, dass der vorgeschlagene Algorithmus vergleichbar gute Ergebnisse in der Global Consistency Error Fehlermetrik zeigt, und alle anderen fünf Algorithmen in Bezug zum Probability Rand Index schlägt. Dieses Verhalten legt nahe, dass eine feinere Segmentierung in Regionen passiert, wo es Hinweise auf mehrere Ebenen der Granularität der menschlichen Segmentierung gibt, und der Algorithmus deshalb eine Anwendung in der Bildkompression finden kann.In this thesis we present a new image segmentation algorithm which is based on Local Binary Patterns and the Combinatorial Pyramid. Current Local Binary Pattern-based segmentation algorithms utilize statistical approaches in form of a histogram to describe and compare textured regions, and to subdivide an image into homogeneous regions. The novelty of our approach is that we omit the usage of histograms and perform a segmentation based directly on the local structure of the image, while at the same time preserving structural correctness and image topology. In our work we define five topological classes that are based on the Local Binary Patterns of regions and are invariant to the number and shifting of bits, namely local minima, slopes, singular slopes, saddles, and local maxima. Using these classes in combination with the dual graph we are able to identify and remove redundant structural information. This approach simplifies the image graph and enables a merging of connected regions without introducing structural errors. We compare our algorithm to five other algorithms using the Global Consistency Error and Probabilistic Rand Index error metrics. One of these algorithms is a pre-version of our proposed algorithm which does not take structural constraints into consideration, and the remaining four algorithms are existing algorithms based on internal- and external contrast, Minimum Spanning Trees, Mean-Shift, and superpixel approaches. The evaluation shows, that the proposed algorithm indicates comparably good results with the Global Consistency Error metric, and it beats all of the five algorithms in terms of a high Probability Rand Index score. This segmentation behavior suggests, that a refinement of segmentations takes place at regions where there is evidence of multiple levels of granularity of segmentations performed by human subjects, and thus an application in image compression can be found.10

Topology-based image segmentation using LBP pyramids

In this paper, we present a new image segmentation algorithmwhich is based on local binary patterns (LBPs) and the combinatorial pyramid and which preserves structural correctness and image topology. For this purpose, we define a codification of LBPs using graph pyramids. Since the LBP code characterizes the topological category (local max, min, slope, saddle) of the gray level landscape around the center region, we use it to obtain a “minimal” image representation in terms of the topological characterization of a given 2D grayscale image. Based on this idea, we further describe our hierarchical texture aware image segmentation algorithm and compare its segmentation output and the “minimal” image representation.Ministerio de Economía y Competitividad MTM2015-67072-

The Changes of Peripheral Nerve Microstructure After Surgical Manipulation – Experiment on Rat Model

The aim of our study was to describe histopathology of the peripheral nerve after its circular release followed by embedding in different environs. We operated on 18 male rats divided into 3 groups. In the first group right femoral nerve was surgically released. In the second group the nerve was enveloped by the subcutaneous fat flap. In the third one the nerve was wrapped up by the skeletal muscle. Six weeks later the animals were killed by exsanguination. The femoral nerve, in the first group, did not show any pathological changes. In the second group 3 animals appeared normal or nearly normal, nevertheless in 3 of them perineural fibrosis and axonal degeneration were observed. Histological reaction in the third group disclosed dispersed axonal injury. Our experiments using rat model imitate situation in humans. The results obtained will help us in making meaningful decision when performing peripheral nerve injury

Additional file 1 of Mild behavioral impairment in early Alzheimer’s disease and its association with APOE and BDNF risk genetic polymorphisms

Additional file 1: Table S1. The association between MBI total score and APOE and BDNF polymorphism groups. Table S2.1. The association between MBI domain scores and APOE polymorphism groups. Table S2.2. The association between MBI domain scores and BDNF polymorphism groups. Table S2.3. The association between MBI domain scores and APOE and BDNF polymorphism groups. Table S3. The association between GDS-15 and APOE and BDNF polymorphism groups. Table S4. The association between BAI and APOE and BDNF polymorphism groups