Selecting the Best Routing Traffic for Packets in LAN via Machine Learning to Achieve the Best Strategy

The application of machine learning touches all activities of human behavior such as computer network and routing packets in LAN. In the field of our research here, emphasis was placed on extracting weights that would affect the speed of the network's response and finding the best path, such as the number of nodes in the path and the congestion on each path, in addition to the cache used for each node. Therefore, the use of these elements in building the neural network is worthy, as is the exploitation of the feed forwarding and the backpropagation in the neural network in order to reach the best prediction for the best path. The goal of the proposed neural network is to minimize the network time delay within the optimization of the packet paths being addressed in this study. The shortest path is considered as the key issue in routing algorithm that can be carried out with real time of path computations. Exploiting the gaps in previous studies, which are represented in the lack of training of the system and the inaccurate prediction as a result of not taking into consideration the hidden layers' feedback, leads to great performance. This study aims to suggest an efficient algorithm that could help in selecting the shortest path to improve the existing methods using weights derived from packet ID and to change neural network iteration simultaneously. In this study, the design of the efficient neural network of appropriate output is discussed in detail including the principles of the network. The findings of the study revealed that exploiting the power of computational system to demonstrate computer simulation is really effective. It is also shown that the system achieved good results when training the neural network system to get 2.4% time delay with 5 nodes in local LAN. Besides, the results showed that the major features of the proposed model will be able to run in real time and are also adaptive to change with path topology.

Efficient fastest-path computations for road maps

In the age of real-time online traffic information and GPS-enabled devices, fastest-path computations between two points in a road network modeled as a directed graph, where each directed edge is weighted by a “travel time” value, are becoming a standard feature of many navigation-related applications. To support this, very efficient computation of these paths in very large road networks is critical. Fastest paths may be computed as minimal-cost paths in a weighted directed graph, but traditional minimal-cost path algorithms based on variants of the classical Dijkstra algorithm do not scale well, as in the worst case they may traverse the entire graph. A common improvement, which can dramatically reduce the number of graph vertices traversed, is the A* algorithm, which requires a good heuristic lower bound on the minimal cost. We introduce a simple, but very effective, heuristic function based on a small number of values assigned to each graph vertex. The values are based on graph separators and are computed efficiently in a preprocessing stage. We present experimental results demonstrating that our heuristic provides estimates of the minimal cost superior to those of other heuristics. Our experiments show that when used in the A* algorithm, this heuristic can reduce the number of vertices traversed by an order of magnitude compared to other heuristics.

Pyrocko - A Versatile Software Framework for Seismology

<p>Pyrocko is an open source seismology toolbox and library, written in the Python programming language. It can be utilized flexibly for a variety of geophysical tasks, like seismological data processing and analysis, modelling of waveforms, InSAR or GPS displacement data, or for seismic source characterization. At its core, Pyrocko is a  library  and  framework  providing  building  blocks  for researchers  and  students  wishing  to  develop  their  own applications. Pyrocko contains a few standalone applications for everyday seismological practice. These include the Snuffler program, an extensible seismogram browser and workbench, the Cake tool, providing travel-time and ray-path computations for 1D layered earthmodels, Fomosto, a tool to manage pre-calculated Green’s function stores, Jackseis, a command-line tool for common waveform archive data manipulations, Colosseo, a tool to create synthetic earthquake scenarios, serving waveforms and static displacements, and new, Sparrow, a 3D geophysical data visualization tool. This poster gives a glimpse of Pyrocko’s features, for more examples and tutorials visit https://pyrocko.org/.</p>

The Effect of Modifying a CFD-AB Approach on Fish Passage through a Model Hydraulic Dam

The threat of invasive bigheaded carp swimming into the upper reaches of the Mississippi River (USA) demands new and effective approaches to block these species. To explore how navigational Lock and Dams (LDs) on the Mississippi River could be used to deter the upstream migration of invasive fish species, computer modelling that combined computational fluid dynamics (CFD) and agent-based (AB) fish passage model (CFD-AB model) could be used to hypothetically quantify the passage of bigheaded carp (Hypophthalmichthys spp.) through LDs. Agent-based fish (AB-fish) are always located on a node of the CFD mesh and move by selecting the neighboring node that minimizes fatigue. A possible limitation of this approach is that the AB-fish movement exhibits a dependence upon the CFD mesh. The proposed modified approach allows the AB-fish to occupy any point in the computational domain and to continually (within the size of the time step) update their minimum fatigue path. Computations in a simplified channel/dam structure show that the modified CFD-AB results are smoother swimming trajectories and increased estimates of fish passage when compared to the original CFD-AB model.

The FastMap Algorithm for Shortest Path Computations

We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. The Euclidean distance between any two nodes in this space approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed with an A* search using the Euclidean distances as heuristic. Our preprocessing algorithm, called FastMap, is inspired by the data-mining algorithm of the same name and runs in near-linear time. Hence, FastMap is orders of magnitude faster than competing approaches that produce a Euclidean embedding using Semidefinite Programming. FastMap also produces admissible and consistent heuristics and therefore guarantees the generation of shortest paths. Moreover, FastMap applies to general undirected graphs for which many traditional heuristics, such as the Manhattan Distance heuristic, are not well defined. Empirically, we demonstrate that A* search using the FastMap heuristic is competitive with A* search using other state-of-the-art heuristics, such as the Differential heuristic.

Fully Coupled Through-Flow Method for Industrial Gas Turbine Analysis

A fully coupled method for calculation of the entire flow in single- and twin-shaft industrial gas turbines is described. It is based on individual through-flow methods for axial compressors and air-cooled gas turbines developed by the authors that are coupled using simple combustion and cooling flow models connecting compressor and turbine flow paths. The through-flow computation for the analysis of cooled axial multistage turbines is fed by air from the compressor bleeds, which are part of the through-flow model of the compressor. The through-flow methods are based on a stream function approach and a finite element solution procedure. They include high-fidelity loss and deviation models with improved correlations. Advanced radial mixing and endwall boundary layer models are applied to simulate 3D flow effects. For air-cooled turbine analysis, various types of cooling air injection were adopted: film cooling, trailing edge injection and disc/endwall coolant flow. Compressor and turbine flow path computations were extensively validated individually and previously published by the authors. The coupled method was applied to operation analysis and performance prediction of a newly developed industrial gas turbine in single- and twin-shaft configurations. In the latter case, the matching point of the compressor and high-pressure turbine has to be determined iteratively as a function of the compressor speed line, firing temperature, cooling and bleed-off characteristics, which may be important for strong part-load behavior. This process is explained in the paper. Predicted gas turbine operation points are compared with experimental test data. It is demonstrated that the new method presented is an essential tool for overall gas turbine design and matching of the gas turbine components based on test rig experience. In addition, it is useful for diagnosis and supports the root-cause analysis of misbehaving field engines.