Encoding bird's trajectory using Recurrent Neural Networks

Ardakani, Ilya; Hashimoto, Koichi

doi:10.1109/icma.2017.8016063

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Among the various types of electric motors, the permanent magnet synchronous motor stands out as a popular choice for modern electric vehicles. Its attributes, including a high power-to-weight ratio, exceptional efficiency, robust design, low torque ripple, and additional reluctance torque, generation capabilities make PMSMs well-suited for electric propulsion systems [1]. Efficient control of the drive motor significantly impacts a vehicle's maneuverability, safety, and ride comfort.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

sime,

Aluvada,

Habtamu

et al. 2024

Preprint

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This study presents a novel approach to enhance the speed control performance of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicles (EVs) through the implementation of a Genetic Algorithm (GA)-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFOPID) controller. PMSM technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in EV drive systems. However, the inherent non-linearity, dynamics, and uncertainties of PMSMs pose significant control challenges. The proposed GA-AFFOPID controller, tuned using a genetic algorithm, exhibits superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances. Comparative analysis with traditional control methods demonstrates the exceptional performance of the GA-AFFOPID controller, achieving a 1.796% lower overshoot, 0.97% faster rise time, 4.25% lower steady-state error, and 0.35% faster settling time compared to the adaptive fuzzy fractional order PID controller. These results highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications, paving the way for improved energy efficiency and overall performance of electric vehicle propulsion systems.

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Section: Introductionmentioning

confidence: 99%

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

sime,

Aluvada,

Habtamu

et al. 2024

Preprint

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“…Fewer studies have explored deep learning for animal trajectory data. Recurrent neural networks have been used for movement prediction [38,39], and for the identification of representative movement patterns [40]. Very recently, an attention network has also been proposed for comparative analysis of animal trajectories [41].…”

Section: Introductionmentioning

confidence: 99%

Deep inference of seabird dives from GPS-only records: Performance and generalization properties

2022

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At-sea behaviour of seabirds have received significant attention in ecology over the last decades as it is a key process in the ecology and fate of these populations. It is also, through the position of top predator that these species often occupy, a relevant and integrative indicator of the dynamics of the marine ecosystems they rely on. Seabird trajectories are recorded through the deployment of GPS, and a variety of statistical approaches have been tested to infer probable behaviours from these location data. Recently, deep learning tools have shown promising results for the segmentation and classification of animal behaviour from trajectory data. Yet, these approaches have not been widely used and investigation is still needed to identify optimal network architecture and to demonstrate their generalization properties. From a database of about 300 foraging trajectories derived from GPS data deployed simultaneously with pressure sensors for the identification of dives, this work has benchmarked deep neural network architectures trained in a supervised manner for the prediction of dives from trajectory data. It first confirms that deep learning allows better dive prediction than usual methods such as Hidden Markov Models. It also demonstrates the generalization properties of the trained networks for inferring dives distribution for seabirds from other colonies and ecosystems. In particular, convolutional networks trained on Peruvian boobies from a specific colony show great ability to predict dives of boobies from other colonies and from distinct ecosystems. We further investigate accross-species generalization using a transfer learning strategy known as ‘fine-tuning’. Starting from a convolutional network pre-trained on Guanay cormorant data reduced by two the size of the dataset needed to accurately predict dives in a tropical booby from Brazil. We believe that the networks trained in this study will provide relevant starting point for future fine-tuning works for seabird trajectory segmentation.

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“…Fewer studies have explored deep learning for animal trajectory data. Recurrent neural networks (RNNs) have been used for movement prediction [1,41], and for the identification of representative movement patterns [39]. Very recently, an attention network has also been proposed for comparative analysis of animal trajectories [30].…”

Section: Introductionmentioning

confidence: 99%

“…Future work may further investigate how distance matrix representation could be of interest for other deep learning approaches to trajectory data, including among others Recurrent Neural Network (RNN), and Generative Adversarial Networks (GAN) for trajectory prediction and simulation [1,17,22,41].…”

mentioning

confidence: 99%

Deep inference of seabird dives from GPS-only records: performance and generalization properties

Roy

Fablet

Lanco-Bertrand

2021

Preprint

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Seabirds are considered as suitable indicators for the study of marine ecosystems, since their foraging strategies provide a real-time response to complex ecosystem dynamics. By deploying GPS sensors on seabirds it is possible to obtain their trajectories, and deep learning have recently shown promising results for the classification of animal behaviour from trajectory data. Yet there is still lot of investigation needed in terms of network architectures, data representation but also to demonstrate the generalization properties of these approaches. From a database of about 250 foraging trajectories derived from GPS data deployed simultaneously with pressure sensors for the identification of dives, this work has consisted in training deep networks in a supervised manner for the prediction of dives from trajectory data. In this study, we confirm that deep learning allows better dive prediction than usual methods such as Hidden Markov Models for two distinct seabirds species. We propose a novel deep learning model for trajectory data. It combines the computational efficiency of convolutional neural networks to distance-matrix-based representations of trajectory data. Our model considerably increases the ability of deep networks to infer behaviour, as well as their stability to different data inputs. The considered trajectory data representation might enable deep networks to better capture spatial information than from longitude and latitude time-series considered in previous works.

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Encoding bird's trajectory using Recurrent Neural Networks

Cited by 5 publications

References 4 publications

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

Deep inference of seabird dives from GPS-only records: Performance and generalization properties

Deep inference of seabird dives from GPS-only records: performance and generalization properties

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