A New Metaheuristic Bat-Inspired Algorithm

Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.Comment: 10 pages, 2 figure

Improvements and critique on Sugeno's and Yasukawa's qualitative modeling

Investigates Sugeno's and Yasukawa's (1993) qualitative fuzzy modeling approach. We propose some easily implementable solutions for the unclear details of the original paper, such as trapezoid approximation of membership functions, rule creation from sample data points, and selection of important variables. We further suggest an improved parameter identification algorithm to be applied instead of the original one. These details are crucial concerning the method's performance as it is shown in a comparative analysis and helps to improve the accuracy of the built-up model. Finally, we propose a possible further rule base reduction which can be applied successfully in certain cases. This improvement reduces the time requirement of the method by up to 16% in our experiments

CNN-based multi-scale hierarchical land use classification for the verification of geospatial databases

Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object catalogue. The object catalogues often have a hierarchical structure, with the level of detail of the semantic information depending on the hierarchy level. In this paper, we extend our prior work for the CNN (Convolutional Neural Network)-based prediction of land use for database objects at multiple semantic levels corresponding to different levels of a hierarchical class catalogue. The main goal is the improvement of the classification accuracy for small database objects, which we observed to be one of the largest problems of the existing method. In order to classify large objects using a CNN of a fixed input size, they are split into tiles that are classified independently before fusing the results to a joint prediction for the object. In this procedure, small objects will only be represented by a single patch, which might even be dominated by the background. To overcome this problem, a multi-scale approach for the classification of small objects is proposed in this paper. Using this approach, such objects are represented by multiple patches at different scales that are presented to the CNN for classification, and the classification results are combined. The new strategy is applied in combination with the earlier tiling-based approach. This method based on an ensemble of the two approaches is tested in two sites located in Germany and improves the classification performance up to +1.8% in overall accuracy and +3.2% in terms of mean F1 score

Study of surface damage in silicon by irradiation with focused rubidium ions using a cold-atom ion source

Cold-atom ion sources have been developed and commercialized as alternative sources for focused ion beams (FIBs). So far, applications and related research have not been widely reported. In this paper, a prototype rubidium FIB is used to study the irradiation damage of 8.5 keV beam energy Rb + ions on silicon to examine the suitability of rubidium for nanomachining applications. Transmission electron microscopy combined with energy dispersive x-ray spectroscopy is applied to silicon samples irradiated by different doses of rubidium ions. The experimental results show a duplex damage layer consisting of an outer layer of oxidation without Rb and an inner layer containing Rb mostly at the interface to the underlying Si substrate. The steady-state damage layer is measured to be 23.2(±0.3)  nm thick with a rubidium staining level of 7(±1) atomic percentage

Operator Manifold Approach to Geometry and Particle Physics

The question that guides our discussion is how did the geometry and particles come into being. The present theory reveals primordial deeper structures underlying fundamental concepts of contemporary physics. We begin with a drastic revision of a role of local internal symmetries in physical concept of curved geometry. A standard gauge principle of local internal symmetries is generalized. The gravitation gauge group is proposed, which is generated by hidden local internal symmetries. Last two parts address to the question of physical origin of geometry and basic concepts of particle physics such as the fields of quarks with the spins and various quantum numbers, internal symmetries and so forth; also four basic principles of Relativity, Quantum, Gauge and Color Confinement, which are, as it was proven, all derivative and come into being simultaneously. The most promising aspect of our approach so far is the fact that many of the important anticipated properties, basic concepts and principles of particle physics are appeared quite naturally in the framework of suggested theory.Comment: LaTex, 42 pages, email [email protected]

Learning multi-modal features for dense matching-based confidence estimation

In recent years, the ability to assess the uncertainty of depth estimates in the context of dense stereo matching has received increased attention due to its potential to detect erroneous estimates. Especially, the introduction of deep learning approaches greatly improved general performance, with feature extraction from multiple modalities proving to be highly advantageous due to the unique and different characteristics of each modality. However, most work in the literature focuses on using only mono- or bi- or rarely tri-modal input, not considering the potential effectiveness of modalities, going beyond tri-modality. To further advance the idea of combining different types of features for confidence estimation, in this work, a CNN-based approach is proposed, exploiting uncertainty cues from up to four modalities. For this purpose, a state-of-the-art local-global approach is used as baseline and extended accordingly. Additionally, a novel disparity-based modality named warped difference is presented to support uncertainty estimation at common failure cases of dense stereo matching. The general validity and improved performance of the proposed approach is demonstrated and compared against the bi-modal baseline in an evaluation on three datasets using two common dense stereo matching techniques

Incremental map refinement of building information using lidar point clouds

For autonomous systems, an accurate and precise map of the environment is of importance. Mapping from LiDAR point clouds is one of the promising ways to generate 3D environment models. However, there are many problems caused by inaccurate data, missing areas, low density of points and sensor noise. Also, it is often not possible or accurate enough to generate a map from only one measurement campaign. In this paper, we propose a method to incrementally refine the map by several measurements from different campaigns and represent the map in a hierarchical way with a measure indicating uncertainty and the level of detail for objects. The idea is thus to store all captured information with a tentative semantics and uncertainty - even when it is not yet complete. Hence, occulated areas are presented as well, which can be possibly improved by the supplemental observation from the next measurement campaign. The proposed 3D environment model framework and the incremental update method are evaluated using LiDAR scans obtained from Riegl Mobile Mapping System

Comap: A synthetic dataset for collective multi-agent perception of autonomous driving

Collective perception of connected vehicles can sufficiently increase the safety and reliability of autonomous driving by sharing perception information. However, collecting real experimental data for such scenarios is extremely expensive. Therefore, we built a computational efficient co-simulation synthetic data generator through CARLA and SUMO simulators. The simulated data contain image and point cloud data as well as ground truth for object detection and semantic segmentation tasks. To verify the superior performance gain of collective perception over single-vehicle perception, we conducted experiments of vehicle detection, which is one of the most important perception tasks for autonomous driving, on this data set. A 3D object detector and a Bird's Eye View (BEV) detector are trained and then test with different configurations of the number of cooperative vehicles and vehicle communication ranges. The experiment results showed that collective perception can not only dramatically increase the overall mean detection accuracy but also the localization accuracy of detected bounding boxes. Besides, a vehicle detection comparison experiment showed that the detection performance drop caused by sensor observation noise can be canceled out by redundant information collected by multiple vehicles

State-wide calculation of terrain-visualisations and automatic map generation for archaeological objects

Airborne laser scanning (ALS) became very popular in the last two decades for archaeological prospection. With the state-wide availability of ALS-data in Lower Saxony, Germany, about 48,000 km2;, we needed flexible and scalable approaches to process the data. First, we produced a state-wide digital terrain model (DTM) and some visualisations of it to use it in standard GIS software. Some of these visualisations are available as web maps and used for prospection also by volunteers. In a second approach, we automatically generate maps for all known archaeological objects. This is mainly used for the documentation of the 130,000 known objects in Lower Saxony, but also for object-by-object revision of the database. These Maps will also be presented in the web portal "Denkmalatlas Niedersachsen", an open data imitative of the state Lower Saxony.In the first part of this paper, we show how the state-wide DTM and its visualisations can be calculated using tiles. In the second part, we describe the automatic map generation process. All implementations were done with ArcGIS and its scripting interface ArcPy