ALBERT

Beschreibung: The Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) of the United Nations mentions that extreme rainfalls might increase their intensity and frequency in most mid-latitude locations and tropical regions by the end of this century, as a consequence of the rise of the average global surface temperature. Human action has given way to global warming which manifests with an increase in extreme rainfall. If these climatic conditions are added to the waterproofing that cities have been experiencing as a result of urban development, a scenario of growing concern for the managers of drainage systems is generated. The objective of drainage networks is preventing the accumulation of rainwater on the surface. Under the new conditions of climate change, these need to be modified and adapted to provide cities with the security they demand. The following article describes a method for flood control by using a rehabilitation model that connects the Storm Water Management Model (SWMM) 5 model with a genetic algorithm to find the best solutions to the flood problem. The final analysis is performed using the Pareto efficiency criteria. The innovation of this method is the inclusion of a local head loss in the drainage network, allowing the upstream flow to be retained by decreasing the downstream concentration time. These elements called hydraulic controls improve system performance and are installed in the initial part of some pipes coming out of storm tanks. As a case study, the developed method has been applied in a section of the drainage network of the city of Bogotá.

Beschreibung: Health monitoring of civil and industrial structures has been gaining importance since the collapse of the bridge in Genoa (Italy). It is vital for the creation and maintenance of reliable infrastructure. Traditional manual inspections for this task are crucial but time consuming. We present a novel approach for combining Unmanned Aerial Vehicles (UAVs) and artificial intelligence to tackle the above-mentioned challenges. Modern architectures in Convolutional Neural Networks (CNNs) were adapted to the special characteristics of data streams gathered from UAV visual sensors. The approach allows for automated detection and localization of various damages to steel structures, coatings, and fasteners, e.g., cracks or corrosion, under uncertain and real-life environments. The proposed model is based on a multi-stage cascaded classifier to account for the variety of detail level from the optical sensor captured during an UAV flight. This allows for reconciliation of the characteristics of gathered image data and crucial aspects from a steel engineer’s point of view. To improve performance of the system and minimize manual data annotation, we use transfer learning based on the well-known COCO dataset combined with field inspection images. This approach provides a solid data basis for object localization and classification with state-of-the-art CNN architectures.

Beschreibung: Hadrontherapy has been constantly evolving in leaps and bounds since the 1950s, when the use of heavy particles was proposed as an alternative treatment to radiotherapy with gamma rays or electrons. The main objective of this treatment is to maximize the dose applied to the tumour, avoiding damage to the surrounding tissue. One of the keys to the success of hadrontherapy is to achieve instantaneous monitoring of the energy deposition in the environment. Since energy deposition leads to the generation of a thermoacoustic pulse, acoustic technologies have been tested with successful results. However, for this purpose, it is essential to increase the sensitivity of the sensors for the acoustical signal and, therefore, to optimize their geometry as a function of the beam that would be used. We have studied a PTZ material in volumetric and surface volumes through experimental measures and FEM methods. In this text, we start with numerical studies which determine the dependence of the thermoacoustic signal frequency with the energy and duration of the hadron beam.

Beschreibung: A full chain simulation of the acoustic hadron therapy monitoring for brain tumors is presented in this work. For the study, a proton beam of 100 MeV was considered. In the first stage, Geant4 was used to simulate the energy deposition and to study the behavior of the Bragg peak. The energy deposition in the medium produced local heating that can be considered instantaneous with respect to the hydrodynamic time scale producing a sound pressure wave. The resulting thermoacoustic signal was subsequently obtained by solving the thermoacoustic equation. The acoustic propagation was simulated by the Finite Element Method (FEM) in the brain and the skull, where a set of piezoelectric sensors were placed. Lastly, the final received signals in the sensors were processed in order to reconstruct the position of the thermal source and, thus, to determine the feasibility and accuracy of acoustic beam monitoring in hadron therapy.

Beschreibung: Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise winding temperature. With the development of new technologies and research on non-destructive techniques (NDT) for fault diagnoses in IMs, it is relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this study is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrograms. The piezoelectric sensor makes NDT feasible, as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analyses of acoustic emission signals. In this NDT, two sensors were coupled on both sides of an induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different VU levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain a desired identification that confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines.

Beschreibung: Robot mapping and exploration is basic to many robotic applications such as search and rescue operations in disaster scenarios, warehouse management, service robotics, patrolling and autonomous driving. With recent advances in robot navigation and sensor compactness, single robot systems can accurately model the environment and perform complex autonomous navigation tasks. On the other hand, multi-robot systems can speed up mapping and exploration tasks in emergency situations, such as rescue missions, by making use of distributed sensors, thereby increasing the range of exploration tasks to an extent that is not possible with a single robot. Each robot explores and maps different areas of the same environment that are finally merged and connected to make a global map. To build a map of an unknown environment, each robot must perform SLAM, or Simultaneous Localization and Mapping. A big challenge with a multi-robot SLAM system is the transfer of shared map information between multiple robots. There is a possibility of transferring individual measurement errors to the global map, resulting in excess computation and memory required to store such maps. To overcome this problem, we propose to use topological feature map representation that can store information into nodes and edges and does not have any large memory requirements. We present a combined metric-topological mapping approach to multi-robot SLAM. This method maintains a topological pose graph with sensor information stored in nodes and edges that can be optimized globally with reduced information sharing. By combining local metric and topological maps built by individual robots, the reduced graph structure can be merged and extended to map large areas effectively. To robustly merge local maps into global one, we used visual features from each robot that are matched in a distributed system. The graph node-edge structure is used for path planning and navigation. At the same time information sharing between robots results in optimized task distribution between multi-robots.

Beschreibung: Development of indoor location systems that use smartphone sensors has been a topic of interest to industry and academia. In this paper, we describe an experiment that was performed to evaluate the feasibility of creating a mobile indoor localization model based on data from participatory sensing. To achieve it, seven smartphone users used their integrated magnetometers to collected magnetic field information on a building. The data collected are utilized to train three machine learning algorithms: The k Nearest Neighbors (KNN), Decision Trees (J48), and Naïve Bayes algorithms. The performance of the algorithms was measured through the accuracy and kappa statistics. Our results indicate that it is possible to create an infrastructure-less indoor localization model at room level using data from participatory sensing. The model with the most significant performance was obtained with the KNN, since it offers an accuracy of 97.12%, while the model with the most reduced performance was Naïve Bayes, since it offers an accuracy of 50.79%.

Beschreibung: Multiphase gravity separators are widely used in the petroleum industry to separate the produced stream of oil, water and natural gas into pure (single-phase) streams. These equipment work based on the density differences of each fluid which tend to settle into layers when dwelled for some time in the separator. It is fundamental to monitor the levels of these phases inside the vessel, so that this information can be used in control strategies in order to increase the efficiency and safety of the process. In this work, we present a novel multiphase level sensor based on capacitance and inductance measurements of planar multi-electrodes and multi-coils. The sensor is low-cost, fast and does not apply ionizing radiation, being therefore simple to operate. The prototype sensor was constructed in standard printed-circuit board (PCB) technology and highly sensitive capacitance and inductance measurements are acquired with modern integrated circuit devices. Since the capacitive sensor readings depend on the water salinity, we perform simultaneous inductance measurements to compensate for such dependence. We have tested the prototype from two different approaches: varying the water salinity and for different water/oil mixtures. Preliminary results have shown the capability of the sensor to differentiate each one of the produced fluids, i.e., salty water, oil and gas, as well as the interfaces between them. A 16 electrodes capacitive-only sensor prototype was also built and tested.

Beschreibung: Wireless networks are technologies with a growing interest in the area of telecommunications, such as the case of MANETs. Despite its advantages, MANETs present several challenges in the transmission of information due to the limited bandwidth, high error rate, energy consumption restriction, and variable topologies. The transmission power can significantly influence some of the aforementioned issues. This paper proposes Density Power Control Mechanism (DPCM), which employs a cross-layering approach between AODV (Ad-hoc On-Demand Distance Vector) routing protocol and the physical layer to adapt power transmission. DPCM aims to reduce collisions, maintain or improve the performance of AODV as well as to save power in the nodes. Our results indicate that our proposal can improve the performance of the network and save power at the same time. Moreover, it is especially useful for low- and medium-density scenarios.

Beschreibung: An inertial measurement unit (IMU) typically has three accelerometers and three gyroscopes. The output of those inertial sensors is used by an inertial navigation system to calculate the navigation solution–position, velocity and attitude. Since the sensor measurements contain noise, the navigation solution drifts over time. When considering low cost sensors, multiple IMUs can be used to improve the performance of a single unit. In this paper, we describe our designed 32 multi-IMU (MIMU) architecture and present experimental results using this system. To analyze the sensory data, a dedicated software tool, capable of addressing MIMUs inputs, was developed. Using the MIMU hardware and software tool we examined and evaluated the MIMUs for: (1) navigation solution accuracy (2) sensor outlier rejection (3) stationary calibration performance (4) coarse alignment accuracy and (5) the effect of different MIMUs locations in the architecture. Our experimental results show that 32 IMUs obtained better performance than a single IMU for all testcases examined. In addition, we show that performance was improved gradually as the number of IMUs was increased in the architecture.