In this paper, a typical application of multimobile robots, i.e., twin hoisting-girder transporters cooperating to transport a giant concrete girder, is studied. An overall solution based on the ...hybrid network communication framework is presented and the key issues, such as the accurate posture estimation and coordinated control of the two independent transporters forming a team, are researched. In order to obtain the relative distance and orientation angle between two transporters while traveling, a measurement data fusion method using real-time kinematic global positioning system for kinematic locating and in-vehicle speed sensors for speed measuring is proposed. Furthermore, a distributed master-slave coordinated control strategy based on the hybrid network framework is proposed to meet the reliability and accuracy requirements. The application results show that the deviations of the relative distance and the orientation of two transporters during working are not more than 0.1 m and 0.2deg, respectively, and it can meet the requirements of the technological specifications.
•Considering all parameters resulted in low posterior probability of presence.•Removing salt marsh from model resulted in high posterior probability of presence.•The Bayesian expert system correctly ...identified stopover sites.
The study of stopover sites has received a lot of attention in avian ecology, being especially important for many long-distance migrants, some of which have to pause several times during migration. The survival of many migratory birds depends primarily on food availability at these stopovers. However, previous studies show that there is a lack of knowledge about site selection where migratory birds stop to refuel energy stores. In the present study, a Bayesian expert system has been used to incorporate environmental parameters, to determine their relationship with the presence of barnacle geese at stopover sites. Data on stopover sites was obtained from satellite-tracked barnacle geese (Branta leucopsis) for three different breeding populations in the Western Palearctic (i.e. Russian, Svalbard and Greenland). The results from the present study showed that the posterior probability of presence at the stopover sites obtained from the Bayesian model was close to one. Therefore, the Bayesian expert system detected the stopover sites of the geese correctly and can be used as a proper method for modelling the presence of barnacle geese at the stopover sites in the future. This study introduces a new method into movement ecology to identify and predict the importance of different environmental parameters for stopover site selection by migratory geese. This is particularly important from both a conservation and an agro-economic point of view with the goal of reducing possible conflicts between geese and agricultural interests.
Knowledge regarding the spatial behavior of the Eurasian lynx is mainly inferred from populations in Europe. We used GPS telemetry to record the spatial behavior of nine individuals in northwestern ...Anatolia obtaining eleven home ranges (HRs). Analyses revealed the smallest mean HR sizes (nHR♀ = 4) at 57 km2 (95% kernel utilization distribution, KUD) and 56 km2 (95% minimum convex polygon, MCP), ever reported for adult female Eurasian lynx. Adult males either occupied small permanent territories (nHR♂.T = 2), with a mean of 176 km2 (95% KUD) and 150 km2 (95% MCP), or were residents without territories (floaters, nHR♂.F = 2) roaming across large, stable HRs with a mean size of 2,419 km2 (95% KUD) and 1,888 km2 (95% MCP), comparable to HR sizes of Scandinavian lynx populations. Three disperser subadult males did not hold stable HRs (mean 95% KUD = 203 km2, mean 95% MCP = 272 km2). At 4.9 individuals per 100 km2, population density was one of the highest recorded, suggesting that the presence of adult male floaters was a consequence of a landscape fully occupied by territorials and revealing a flexibility of spatial behavior of Eurasian lynx not previously recognized. Such a high population density, small HRs, and behavioral flexibility may have been aided by the legal protection from and apparent low levels of poaching of this population. The observed spatial tactics are unlikely to be seen in most of the previously studied Eurasian lynx populations, as they either suffer medium to high levels of human‐caused mortality or were unlikely to be at carrying capacity. For effective and appropriate conservation planning, data from felid populations in a reasonably natural state such as ours, where space, density, prey, and pathogens are likely to be the key drivers of spatial dynamics, are therefore essential.
In this study, we report the home range (HR) size, spatial behavior, and tactics of Eurasian lynx L. l. dinniki using high frequency GPS tracking data and focusing on the potentially isolated yet unexploited northwest Anatolian population. We also consider the HR size and density of L. l. dinniki in the context of previously published data for autochthonous and reintroduced Eurasian lynx populations in Europe (subspecies L. l. lynx and L. l. carpathicus).
Intelligent transportation systems (ITS) play an important role in the quality of life of citizens in any metropolitan city. Despite various policies and strategies incorporated to increase the ...reliability and quality of service, public transportation authorities continue to face criticism from commuters largely due to irregularities in bus arrival times, most notably manifested in early or late arrivals. Due to these irregularities, commuters may miss important appointments, wait for too long at the bus stop, or arrive late for work. Therefore, accurate prediction models are needed to build better customer service solutions for transit systems, e.g. building accurate mobile apps for trip planning or sending bus delay/cancel notifications. Prediction models will also help in developing better appointment scheduling systems for doctors, dentists, and other businesses to take into account transit bus delays for their clients. In this paper, we seek to predict the occurrence of arrival time irregularities by mining GPS coordinates of transit buses provided by the Toronto Transit Commission (TTC) along with hourly weather data and using this data in machine learning models that we have developed. In our study, we compared the performance of a Long Short Term Memory Recurrent Neural Network (LSTM) model against four baseline models, an Artificial Neural Network (ANN), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA) and historical averages. We found that our LSTM model demonstrates the best prediction accuracy. The improved accuracy achieved by the LSTM model may lend itself to its ability to adjust and update the weights of neurons while accounting for long-term dependencies. In addition, we found that weather conditions play a significant role in improving the accuracy of our models. Therefore, we built a prediction model that combines an LSTM model with a Recurrent Neural Network Model (RNN) that focuses on the weather condition. Our findings also reveal that in nearly 37% of scheduled arrival times, buses either arrive early or late by a margin of more than 5 min, suggesting room for improvement in the current strategies employed by transit authorities.
PDF
