Basin structure of optimization based state and parameter estimation

Most data based state and parameter estimation methods require suitable initial values or guesses to achieve convergence to the desired solution, which typically is a global minimum of some cost function. Unfortunately, however, other stable solutions (e.g., local minima) may exist and provide suboptimal or even wrong estimates. Here we demonstrate for a 9-dimensional Lorenz-96 model how to characterize the basin size of the global minimum when applying some particular optimization based estimation algorithm. We compare three different strategies for generating suitable initial guesses and we investigate the dependence of the solution on the given trajectory segment (underlying the measured time series). To address the question of how many state variables have to be measured for optimal performance, different types of multivariate time series are considered consisting of 1, 2, or 3 variables. Based on these time series the local observability of state variables and parameters of the Lorenz-96 model is investigated and confirmed using delay coordinates. This result is in good agreement with the observation that correct state and parameter estimation results are obtained if the optimization algorithm is initialized with initial guesses close to the true solution. In contrast, initialization with other exact solutions of the model equations (different from the true solution used to generate the time series) typically fails, i.e. the optimization procedure ends up in local minima different from the true solution. Initialization using random values in a box around the attractor exhibits success rates depending on the number of observables and the available time series (trajectory segment).Comment: 15 pages, 2 figure

Collective Perception: A Safety Perspective

Vehicle-to-everything (V2X) communication is seen as one of the main enabling technol-ogies for automated vehicles. Collective perception is especially promising, as it allows connected traffic participants to “see through the eyes of others” by sharing sensor-detected objects via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, redun-dancy, and awareness. To determine the safety improvement thanks to collective perception, the authors introduce new metrics, which quantify the environmental risk awareness of the traffic par-ticipants. The performance of the V2X service is then analyzed with the help of the test platform TEPLITS, using real traffic traces from German highways, amounting to over 100 h of total driving time. The results in the considered scenarios clearly show that collective perception not only con-tributes to the accuracy and integrity of the vehicles’ environmental perception, but also that a V2X market penetration of at least 25% is necessary to increase traffic safety from a “risk of serious traffic accidents” to a “residual hypothetical risk of collisions without minor injuries” for traffic participants equipped with non-redundant 360° sensor systems. These results support the ongoing world-wide standardization efforts of the collective perception service

A simple stress test of experimenter demand effects

As a stress test of experimenter demand effects, we run an experiment where subjects can physically destroy coupons awarded to them. About one subject out of three does. Giving money back to the experimenter is possible in a separate task but is more consistent with an experimenter demand effect than an explanation based on altruism towards the experimenter. A measure of sensitivity to social pressure helps predict destruction when social information is provided

KIC InnoEnergy Project Neptune: development of a floating LiDAR buoy for wind, wave and current measurements

The KIC-InnoEnergy project “NEPTUNE” develops a floating Lidar buoy and a hindcast- and forecast model for wind- wave- and current measurements of offshore wind farms. In this paper just the lidar buoy is presented and discussed: Main challenges, the design ideas and the steps to develop, test and prototype this product, which – according to the KIC-InnoEnergy project idea – should be commercialized after the project end, foreseen for the end of 2014. KIC-InnoEnergy is funded from the European Institute of Technology, EIT.Peer ReviewedPostprint (published version

Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks

Architectures that first convert point clouds to a grid representation and then apply convolutional neural networks achieve good performance for radar-based object detection. However, the transfer from irregular point cloud data to a dense grid structure is often associated with a loss of information, due to the discretization and aggregation of points. In this paper, we propose a novel architecture, multi-scale KPPillarsBEV, that aims to mitigate the negative effects of grid rendering. Specifically, we propose a novel grid rendering method, KPBEV, which leverages the descriptive power of kernel point convolutions to improve the encoding of local point cloud contexts during grid rendering. In addition, we propose a general multi-scale grid rendering formulation to incorporate multi-scale feature maps into convolutional backbones of detection networks with arbitrary grid rendering methods. We perform extensive experiments on the nuScenes dataset and evaluate the methods in terms of detection performance and computational complexity. The proposed multi-scale KPPillarsBEV architecture outperforms the baseline by 5.37% and the previous state of the art by 2.88% in Car AP4.0 (average precision for a matching threshold of 4 meters) on the nuScenes validation set. Moreover, the proposed single-scale KPBEV grid rendering improves the Car AP4.0 by 2.90% over the baseline while maintaining the same inference speed.Comment: (c) 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Some genetic markers in creole bovine of Argentina 1. Inmunogenetics

Dado el carácter de "primitivismo" del Bovino Criollo, motivo de esta investigación, se realiza un somero estudio filogenético a los efectos de ubicar este tipo de ganado en estudios inmunogénéticos futuros vinculados a poblaciones de hábitats regionales, en correspondencia a distintas zonas de la República Argentina y países limítrofes. En base al origen Ibérico común del Longhorn Americano u Bovino Criollo se hace una primera investigación tentativa con animales de la Estación Experimental Agropecuaria Famaillá (INTA) de Leales, Tucumán, orientada a verificar la existencia de algunos "marcadores genéticos" que fueran coincidentes con los descubiertos por MILLER en Longhorn (Miller, 1966). Esta primera etapa permitió comprobar 15 fenogrupos del Sistema B, involucrados en el total de 27 detectados por MILLER. En general hay acuerdo respecto a los otros sistemas, con pequeñas diferencias de las frecuencias génicas en algunos casos.Given the primitivism of the character of the Creole Bovine, we made a superficial phylogenic study to place this type of cattle in future imnunnogenetics studies which will involve regional habitat populations of the Argentine Republic and bounding countries. Because of the common Iberian origin of the American Longhorn and "Bovino Criollo", a first tentative investigation has been performed with animals of the Estación Experimental Agropecnaria Famaillá (Leales, Tucumán) to verify the existence of some "genetic markers" that would be coincident with MILLER's disclosure in Longhorns (Miller, 1966). Detected were 15 phenogroups of the B System which are held in common with 15 B phenogroups of the American Longhon detected by MILLER, with general agreement concerning to the other systems.Facultad de Ciencias Veterinaria

Some genetic markers in creole bovine of Argentina 1. Inmunogenetics

Dado el carácter de "primitivismo" del Bovino Criollo, motivo de esta investigación, se realiza un somero estudio filogenético a los efectos de ubicar este tipo de ganado en estudios inmunogénéticos futuros vinculados a poblaciones de hábitats regionales, en correspondencia a distintas zonas de la República Argentina y países limítrofes. En base al origen Ibérico común del Longhorn Americano u Bovino Criollo se hace una primera investigación tentativa con animales de la Estación Experimental Agropecuaria Famaillá (INTA) de Leales, Tucumán, orientada a verificar la existencia de algunos "marcadores genéticos" que fueran coincidentes con los descubiertos por MILLER en Longhorn (Miller, 1966). Esta primera etapa permitió comprobar 15 fenogrupos del Sistema B, involucrados en el total de 27 detectados por MILLER. En general hay acuerdo respecto a los otros sistemas, con pequeñas diferencias de las frecuencias génicas en algunos casos.Given the primitivism of the character of the Creole Bovine, we made a superficial phylogenic study to place this type of cattle in future imnunnogenetics studies which will involve regional habitat populations of the Argentine Republic and bounding countries. Because of the common Iberian origin of the American Longhorn and "Bovino Criollo", a first tentative investigation has been performed with animals of the Estación Experimental Agropecnaria Famaillá (Leales, Tucumán) to verify the existence of some "genetic markers" that would be coincident with MILLER's disclosure in Longhorns (Miller, 1966). Detected were 15 phenogroups of the B System which are held in common with 15 B phenogroups of the American Longhon detected by MILLER, with general agreement concerning to the other systems.Facultad de Ciencias Veterinaria

Exploiting Sparsity in Automotive Radar Object Detection Networks

Having precise perception of the environment is crucial for ensuring the secure and reliable functioning of autonomous driving systems. Radar object detection networks are one fundamental part of such systems. CNN-based object detectors showed good performance in this context, but they require large compute resources. This paper investigates sparse convolutional object detection networks, which combine powerful grid-based detection with low compute resources. We investigate radar specific challenges and propose sparse kernel point pillars (SKPP) and dual voxel point convolutions (DVPC) as remedies for the grid rendering and sparse backbone architectures. We evaluate our SKPP-DPVCN architecture on nuScenes, which outperforms the baseline by 5.89% and the previous state of the art by 4.19% in Car AP4.0. Moreover, SKPP-DPVCN reduces the average scale error (ASE) by 21.41% over the baseline