Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications

Mapping biomass availability to decrease the dependency on fossil fuels

To decrease the dependency on fossil fuels, more renewable energy sources need to be explored. Over the last years, the consumption of biomass has risen steadily and it has become a major source for re-growing energy. Besides the most common sources of biomass (forests, agriculture etc.) there are smaller supplies available in mostly unused areas like hedges, vegetation along streets, railways, rivers and field margins. However, these sources are not mapped and in order to obtain their potential for usage as a renewable energy, a method to quickly assess their spatial distribution and their volume is needed. We use a range of data sets including satellite imagery, GIS and elevation data to evaluate these parameters. With the upcoming Sentinel missions, our satellite data is chosen to match the spatial resolution of Sentinel-2 (10-20m) as well as its spectral characteristics. To obtain sub-pixel information from the satellite data, we use a spectral unmixing approach. Additional GIS data is provided by the German Digital Landscape Model (ATKIS Base-DLM). To estimate the height (and derive the volume) of the vegetation, we use LIDAR data to produce a digital surface model. These data sets allow us to map the extent of previously unused biomass sources. This map can then be used as a starting point for further analyses about the feasibility of the biomass extraction and their usage as a renewable energy source.BMWi/DLR/50EE1333BMWi/DLR/50EE1334BMWi/DLR/50EE133

Energy management of grid connected hybrid solar/wind/battery system using golden eagle optimization with incremental conductance

Renewable Energy Sources (RES) are currently being used on a much larger scale to support and satisfy the higher energy demands caused by industrialization and population growth. Due to this rise in the number of consumers of power systems and the unpredictable nature of the electric load, the vast power demand proves to be a tough challenge for electric utilities and system operators. So, power demands have occurred over many periods and become a threat to the system's functionality. Therefore, an effective Energy Management System (EMS) name called Golden Eagle Optimization with Incremental Conductance (GEO-INC) is proposed to meet the load demand. Three different systems, namely: RES Photovoltaic (PV) module, wind turbine, and battery create an effective EMS. The proposed method extracts more power from the PV panel and effectively controls the switching between the wind turbine and the battery storage system. The proposed method achieves 1.98 % distortion from the results, which is less than the existing methods.The authors would like to thank the Spanish Ministerio de Ciencia, Innovación y Universidades (MICINN)-Agencia Estatal de Investigación (AEI) and the European Regional Development Funds (ERDF), by grant PGC2018-098946-B-I00 funded by MCIN/ AEI/10.13039/501100011033/ and by ERDF ERDF A way of making EuropePeer ReviewedPostprint (published version

Nature-Inspired Algorithms in Optimization: Introduction, Hybridization and Insights

Many problems in science and engineering are optimization problems, which may require sophisticated optimization techniques to solve. Nature-inspired algorithms are a class of metaheuristic algorithms for optimization, and some algorithms or variants are often developed by hybridization. Benchmarking is also important in evaluating the performance of optimization algorithms. This chapter focuses on the overview of optimization, nature-inspired algorithms and the role of hybridization. We will also highlight some issues with hybridization of algorithms.Comment: 15 pages, 4 figure

Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.Comment: to appear in ACM Computing Survey

Nature-inspired optimization algorithms: challenges and open problems

Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research

Improvement of active distribution systems with high penetration capacities of shunt reactive compensators and distributed generators using Bald Eagle Search

This work proposes an intelligent allocation of distributed generation (DG) units and shunt reactive compensators (SRC) with high penetration capacities into distribution systems for power loss mitigation using the Bald Eagle Search (BES) optimization algorithm. The intelligent allocation causes a reduction in voltage variations and enhances the voltage stability of the systems. The SRC units include shunt capacitors (SC), Static Var Compensators (SVC), and Distribution Static Compensators (DSTATCOM), which are determined according to their capacities. The optimization study includes the 33-bus and the 118-bus distribution systems as medium to large systems. Performance parameters, including the reactive power loss, Total Voltage Deviation (TVD), and Stability Index (SI), besides the power loss, are recorded for each optimization case study. When the BES algorithm optimizes 1, 2, and 3 DG units operating at optimal power factor (OPF) into the 33-bus systems, percentage reductions of power loss reach 67.84%, 86.49%, and 94.44%, respectively. Reductions of 28.26%, 34.47%, 35.24%, and 35.44% are achieved in power loss while optimizing 1, 3, 5, and 7 SRC units. With a combination of DG/SRC units, the power loss reductions achieve 72.30%, 93.89%, and 97.49%, optimizing 1, 3, and 5 pairs of them. Similar reductions are achieved for the rest of the performance parameters. With high penetration of compensators into the 118-bus system, the percentage reductions of power loss are 29.14%, 73.27%, 83.72%, 90.14%, and 93.41% for optimal allocations of 1, 3, 5, 7, and 9 DG units operating at OPF. The reduction reaches 11.15%, 39.08% with 1 and 21 devices when optimizing the SRC. When DG SRC units are optimized together, power loss turns out to be 32.83%, 73.31%, 83.32%, 88.52%, and 91.29% with 1, 3, 5, 7, and 9 pairs of them. The approach leads to an enhanced voltage profile near an acceptable range of bus voltages, reduces the voltage fluctuation substantially, and enhances the system stability. The study also ensures the BES algorithm’s capability to solve these nonlinear optimization problems with high decision-variable numbers

Estimation of biomass potential based on classification and height information

On the way to make energy supply independent from fossil resources more and more renewable energy sources have to be explored. Biomass has become an important energy resource during the last years and the consumption is rising steadily. Common sources of biomass are agricultural production and forestry but the production of these sources is stagnating due to limited space. To explore new sources of biomass like in the field of landscape conservation the location and available amount of biomass is unknown. Normally, there are no reliable data sources to give information about the objects of interest such as hedges, vegetation along streets, railways and rivers, field margins and ruderal sites. There is a great demand for an inventory of these biomass sources which could be answered by applying remote sensing technology. As biomass objects considered here are sometimes only a few meters wide, spectral unmixing is applied to separate different material mixtures reflected in one image pixel. The spectral images are assumed to have a spatial resolution of 5-20m with multispectral or hyperspectral band configurations. Combining the identified material part fractions with height information and GIS data afterwards will give estimates about the location of biomass objects. The method is applied to test data of a Sentinel-2 simulation and the results are evaluated visually.Federal Ministry of Economics and Technology (BMWi)DLR/50EE1212DLR/50EE1213DLR/50EE121