On the trade-off between electrical power consumption and flight performance in fixed-wing UAV autopilots

This paper sets out a study of the autopilot design for fixed wing Unmanned Aerial Vehicles (UAVs) taking into account the aircraft stability, as well as the power consumption as a function of the selected control strategy. To provide some generality to the outcomes of this study, construction of a reference small-UAV model, based on averaging the main aircraft defining parameters, is proposed. Using such a reference model of small, fixed-wing UAVs, different control strategies are assessed, especially with a view towards enlarging the controllers' sampling time. A beneficial consequence of this sample time enlargement is that the clock rate of the UAV autopilots may be proportionally reduced. This reduction in turn leads directly to decreased electrical power consumption. Such energy saving becomes proportionally relevant as the size and power of the UAV decrease, with benefits of lengthening battery life and, therefore, the flight endurance. Additionally, through the averaged model, which is derived from both published data and computations made from actual data captured from real UAVs, it is shown that behavior predictions beyond that of any particular UAV model may be extrapolated.Peer ReviewedPostprint (author's final draft

Multi-camera trajectory forecasting : pedestrian trajectory prediction in a network of cameras

We introduce the task of multi-camera trajectory forecasting (MCTF), where the future trajectory of an object is predicted in a network of cameras. Prior works consider forecasting trajectories in a single camera view. Our work is the first to consider the challenging scenario of forecasting across multiple non-overlapping camera views. This has wide applicability in tasks such as re-identification and multi-target multi-camera tracking. To facilitate research in this new area, we release the Warwick-NTU Multi-camera Forecasting Database (WNMF), a unique dataset of multi-camera pedestrian trajectories from a network of 15 synchronized cameras. To accurately label this large dataset (600 hours of video footage), we also develop a semi-automated annotation method. An effective MCTF model should proactively anticipate where and when a person will re-appear in the camera network. In this paper, we consider the task of predicting the next camera a pedestrian will re-appear after leaving the view of another camera, and present several baseline approaches for this. The labeled database is available online https://github.com/olly-styles/Multi-Camera-Trajectory-Forecastin

Projection of truck traffic volumes at interstate permanent automatic traffic recorders

This study documents the development of a methodology and models to forecast truck traffic volumes on Interstate Highways at a selection of Permanent Automatic Traffic Recorder (PATR) sites. The models were developed using data collected over a period nine years (1995 to 2003) from sixteen permanent count stations located throughout the state. Eight sites were ultimately utilized, five along rural interstate highways and the others from urban interstate highways. Model development was based on the time series method, using two techniques: regression analysis and the growth factor technique. Both were analyzed and compared in order to select the most reliable technique to be used in the forecasting procedure. To further understand changes in truck traffic patterns, traffic was grouped according to the FHWA vehicle classification scheme. Models were developed for each site and for every truck classification in these sites as well. Due to the smaller effect of demographic characteristics on interstate highways models; these models were performed using as a predicted variable: the Annual Average Daily Truck Traffic data obtained directly from the counters, and time period as the unique independent variable. Validation was conducted using the coefficient of variation to measure the statistical significance of the results obtained. Further validation of models was conducted by the coefficient of regression, and by comparison between the based trends data with the predicted models. In the course of the study, regression models resulted as the appropriate predictor technique to be used at interstate highways. Models, growth factors and figures are reported by every site and truck classification, detailed tables containing these factors are presented in the report

Investigation of Truckload Prices in the U.S: Exploratory Analysis, Forecast Methods, and the Influences of Unemployment on Freight Prices

Truckload (TL) pricing is a major factor that influences the manufacturing and retail costs of products. In the U.S., trucks accounts for more than 90% of freight shipped based on value, and it is expected to grow in the following years. TL price setting is a very complex task for logistic companies as it depends on a number of factors including the logistics carriers\u27 business strategies and other social and economic variables. Understanding TL patterns across the U.S. is important not only for logistic companies, but also for policy makers. TL prices are commonly provided on a dollar per mile rate. Thus the total transportation costs on a route will be the product of the truckload price rate and the distance. More accurate prediction of TL price will enable logistic companies to develop more optimal strategies to operate their transportation activity across destinations and effectively allocate resources on potential demand locations. Freight and economic policy makers will also be able to use this information to explore different potential economic scenarios.;This research analyses private data sets (TL rates), and publicly available data such as diesel cost, unemployment, wages, population, and gross state product to understand trends in TL prices. TL rates are evaluated through exploratory and visualization techniques to obtain useful insights. Time series analysis (TSA) and spatial econometric analysis (SEA) are conducted for forecasting TL prices. TSA provides with a general model based on time and delivery distance between origin and destination. Spatial econometric panel models incorporate the spatial dependency, being used for drawing inferences across space, and also for forecasting TL prices. Results indicate that TL prices are closely associated with unemployment, which links the consumer spending with transportation cost. Diesel cost has not impacted TL prices significantly during the last years, as is evidenced in the TSA and SEA. Moreover, in low demand condition such as high unemployment, carriers are likely to serve larger delivery distance in order to reduce TL prices, which impact TL prices in neighboring locations. Increasing the delivery distance by 1.00% was found to reduce the price in dollar-per-mile by about -0.25%, and raise prices in neighboring locations by about +0.05%. Similarly, 1% increase in unemployment rate was found to reduce prices by about -0.30% and increase prices in neighboring locations by about +0.06%. Forecasting models indicate accurate TL price values, with MAPE values less than 10% for the TSA model for estimating an overall monthly price in the U.S.; and less than 20% for the SEA that consider spatial dependence for estimating a yearly price at each U.S. state. This research represents a benchmark in the analysis of freight prices, providing useful insights, identifying significant variables impacting TL prices, and potential methodologies for forecasting truckload prices

Análisis de los resultados obtenidos, sobre el uso de ácido giberélico en el cultivo de la “alcachofa” Cynara scolymus L. (Asteraceae) en diferentes zonas geográficas, entre los años 2001-2007

La “alcachofa” pertenece a la especie Cynara scolymus L. de la familia Asteraceae, a la que también pertenecen la lechuga, el girasol, el marigold, la dalia, la manzanilla y muchas otras especies alimenticias, medicinales y ornamentales, siendo genéticamente una especie de 34 cromosomas. Las principales zonas de producción de la alcachofa se encuentran distribuidas a lo largo de la región Junín, principalmente en la zona del Valle del Mantaro. Otras regiones donde también se cultiva son La Libertad, Ica, Lima y Apurímac. En esta investigación se realizó varias revisiones de los resultados de 4 investigaciones previas se ajustan al tipo de análisis descriptivo, por lo tanto, la metodología será la revisión de los resultados en base a: Zonas geográficas, dosis de ácido giberélico, momentos de aplicación. Según el momento de aplicación, los mejores rendimientos se obtuvieron con las aplicaciones que se iniciaron a los 58 días después del trasplante, logrando 36.39 t/ha. Según la dosis total aplicada como AG (Ácido giberélico), a 52 ppm obtuvo el mayor rendimiento en 3 aplicaciones fraccionadas cada 15 días con 36.39 t/ha. Según la zona geográfica los mejores rendimientos se obtienen en las ciudades de Lima con aplicaciones de concentraciones más elevadas que en España, esta respuesta está relacionada a la latitud en la que se ubican y el fotoperiodo que ocurre distintamente en cada uno, lo que nos lleva a concluir que en Perú necesita mayor inducción floral.The ""artichoke"" belongs to the species Cynara scolymus L. of the Asteraceae family, which also includes lettuce, sunflower, marigold, dahlia, chamomile and many other food, medicinal and ornamental species, being genetically a species of 34 chromosomes. The main production areas of the artichoke are distributed throughout the Junín region, mainly in the Mantaro Valley area. Other regions where it is also grown are La Libertad, Ica, Lima and Apurímac. In this investigation several revisions of the results of 4 previous investigations are made, they adjust to the descriptive analysis type, therefore, the methodology will be the revision of the results based on: Geographical zones, doses of gibberellic acid, moments of application. According to the moment of application, the best yields were obtained with the applications that started 58 days after the transplant, achieving 36.39 t / ha. According to the total dose applied as AG (Gibberellic acid), at 52 ppm it obtained the highest performance in 3 applications divided every 15 days with 36.39 t/ha. According to the geographical area, the best yields are obtained in the cities of Lima with applications of higher concentrations than in Spain, this response is related to the latitude in which they are located and the photoperiod that occurs distinctly in each one, which leads us to conclude that Peru needs more floral induction.Tesi