Performance Analysis and Four-Objective Optimization of an Irreversible Rectangular Cycle

Based on the established model of the irreversible rectangular cycle in the previous literature, in this paper, finite time thermodynamics theory is applied to analyze the performance characteristics of an irreversible rectangular cycle by firstly taking power density and effective power as the objective functions. Then, four performance indicators of the cycle, that is, the thermal efficiency, dimensionless power output, dimensionless effective power, and dimensionless power density, are optimized with the cycle expansion ratio as the optimization variable by applying the nondominated sorting genetic algorithm II (NSGA-II) and considering four-objective, three-objective, and two-objective optimization combinations. Finally, optimal results are selected through three decision-making methods. The results show that although the efficiency of the irreversible rectangular cycle under the maximum power density point is less than that at the maximum power output point, the cycle under the maximum power density point can acquire a smaller size parameter. The efficiency at the maximum effective power point is always larger than that at the maximum power output point. When multi-objective optimization is performed on dimensionless power output, dimensionless effective power, and dimensionless power density, the deviation index obtained from the technique for order preference by similarity to an ideal solution (TOPSIS) decision-making method is the smallest value, which means the result is the best.

Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities

The use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential characteristic to be taken into account for the accurate estimation of roof geometry: area, orientation and slope. This paper presents a comparative study between LiDAR data of different point densities: 0.5, 1, 2 and 14 points/m2 for the measurement of the area of roofs of residential and industrial buildings. The data used for the study are the LiDAR data freely available by the Spanish Institute of Geography (IGN), which is offered according to the INSPIRE Directive. The results obtained show different behaviors for roofs with an area below and over 200 m2. While the use of low-density point clouds (0.5 point/m2) presents significant errors in the estimation of the area, the use of point clouds with higher density (1 or 2 points/m2) implies a great improvement in the area results, with no significant difference among them. The use of high-density point clouds (14 points/m2) also implies an improvement of the results, although the accuracy does not increase in the same ratio as the increase in density regarding 1 or 2 points/m2. Thus, the conclusion reached is that the geometrical characterization of roofs requires data acquisition with point density of 1 or 2 points/m2, and that higher point densities do not improve the results with the same intensity as they increase computation time.

Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments

The advancements in the robotic field have made it possible for service robots to increasingly become part of everyday indoor scenarios. Their ability to operate and reach defined goals depends on the perception and understanding of their surrounding environment. Detecting and positioning objects as well as people in an accurate semantic map are, therefore, essential tasks that a robot needs to carry out. In this work, we walk an alternative path to build semantic maps of indoor scenarios. Instead of relying on high-density sensory input, like the one provided by an RGB-D camera, and resource-intensive processing algorithms, like the ones based on deep learning, we investigate the use of low-density point-clouds provided by 3D LiDARs together with a set of practical segmentation methods for the detection of objects. By focusing on the physical structure of the objects of interest, it is possible to remove complex training phases and exploit sensors with lower resolution but wider Field of View (FoV). Our evaluation shows that our approach can achieve comparable (if not better) performance in object labeling and positioning with a significant decrease in processing time than established approaches based on deep learning methods. As a side-effect of using low-density point-clouds, we also better support people privacy as the lower resolution inherently prevents the use of techniques like face recognition.

Geomorphological mapping of an alpine rock glacier with multi-temporal UAV-based high density point cloud comparison

<p>The acquisition of high-resolution topographic data is a widely used tool for studies related to the processes and dynamics of the Earth's surface. In this work, we present the results of the repeated acquisition of photogrammetric data by Unmanned Aerial Vehicle (UAV) in order to detect the topographic evolution of an alpine rock glaciers located in Valtournenche (AO, Italy). Field monitoring conducted in recent years has shown significant variations in the behaviour of these landforms, with an increasing trend of their dynamism, raising questions about their stability in changing climatic conditions.</p><p> </p><p>The photogrammetric shots were taken with a DJ Phantom 4 UAV equipped with a compact RGB digital camera. The acquisitions were performed yearly from 2012 up to 2019 with a ground sampling distance never exceeding 5 cm/px. Contemporary to the acquisitions, approximately 20 Ground Control Points were placed on the rock glacier and on the surrounding areas and their coordinates were measured with a differential GPS (dGPS) for georeferencing UAV images. Moreover, in 2014, 2015 and 2019 geophysical campaigns were carried out for the detection of ice lenses under the debris cover of the rock glacier.</p><p> </p><p>Structure-from-motion techniques were applied on overlapping images to create high-density point clouds, than converted in orthophotos and digital surface models of the Earth’s surface.</p><p>The point clouds were analysed using the M3C2 (Multiscale Model to Model Cloud Comparison) plug-in, freely available in the CloudCompare software. Maps of surface changes between acquisition pairs in the period from 2015-2019 have been created. The comparison allowed the identification of "material supply" and "material removal" zones, slightly variable from one year to the next. The major accumulation zones are concentrated along the frontal sector of the rock glacier, more focused on the western sector (black lobe) and secondly on the right side of the rock glacier (white lobe). The removal of material is mainly concentrated on the higher altitude of the body but also in correspondence to the systems of crevasses and scarps and on the central part of the black lobe.</p><p>The surface displacement analysis of the rock glacier was also performed selecting manually several clearly identifiable features on the orthomosaics collected. Blocks and ridges-and-furrows complex were marked on the 2019 orthomosaic and found them on the 2015 orthomosaic. This approach allows improving and quantifying the dynamics of the different portions of the individual apparatus.</p><p>The velocity fields’ patterns highlight non-homogeneous displacements between the West (black lobe) and East part (white lobe) of the whole rock glacier. Specifically, the black lobe showed an average horizontal displacement of around 1 m/y while the white lobe moved significantly slower than the previous one (approximately 0.5 m/y). Overall, the rock glacier moved downslope at an average horizontal velocity of 0.60 m/y in the frontal tongue, 0.48 m/y in the central portion and 0.30 m/y in the upper zone.</p>

Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

Monitoring the crisis of a rock glacier with repeated UAV surveys

In this study, rapid topographic changes and high creeping rates caused by the destabilisation of an active rock glacier in a steep mountain flank were investigated in detail with five unmanned aerial vehicle (UAV) surveys between June 2016 and September 2017. State-of-the-art photogrammetric techniques were employed to derived high-density point clouds and high-resolution orthophoto mosaics from the studied landform. The accuracy of the co-registration of subsequent point clouds was carefully examined and adjusted based on comparing stable areas outside the rock glacier, which minimised 3-D alignment errors to a mean of 0.12 m. Elevation and volumetric changes in the destabilised rock glacier were quantified over the study period. Surface kinematics were estimated with a combination of image correlation algorithms and visual inspection of the orthophoto mosaics. Between June 2016 and September 2017, the destabilised part of the rock glacier advanced up to 60–75 m and mobilised a volume of around 27 000 m3 of material which was dumped over the lower talus slope. This study has demonstrated a robust and customisable monitoring approach that allows a detailed study of rock glacier geometric changes during a crisis phase.