11 research outputs found
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Smart mobility management would be an important prerequisite for future fog
computing systems. In this research, we propose a learning-based handover
optimization for the Internet of Vehicles that would assist the smooth
transition of device connections and offloaded tasks between fog nodes. To
accomplish this, we make use of machine learning algorithms to learn from
vehicle interactions with fog nodes. Our approach uses a three-layer
feed-forward neural network to predict the correct fog node at a given location
and time with 99.2 % accuracy on a test set. We also implement a dual stacked
recurrent neural network (RNN) with long short-term memory (LSTM) cells capable
of learning the latency, or cost, associated with these service requests. We
create a simulation in JAMScript using a dataset of real-world vehicle
movements to create a dataset to train these networks. We further propose the
use of this predictive system in a smarter request routing mechanism to
minimize the service interruption during handovers between fog nodes and to
anticipate areas of low coverage through a series of experiments and test the
models' performance on a test set
Estimation des poids dâun rĂ©seau rĂ©current par ajustement rĂ©troactif
We consider another formulation of weight estimation in recurrent networks,proposing a notation for a large amount of recurrent network units that helpsformulating the estimation problem. Reusing a âgood oldâ control-theory principle,improved here using a backward-tuning numerical stabilization heuristic, we obtaina numerically stable and rather efficient second-order and distributed estimation,without any meta-parameter to adjust. The relation with existing technique is discussedat each step. The proposed method is validated using reverse engineeringtasks.Nous considĂ©rons une formulation alternative de lâestimation du poidsdans les rĂ©seaux rĂ©currents, proposant une notation integrant une grande quantitĂ©dâunitĂ©s de rĂ©seau rĂ©currentes qui aide Ă formuler ce problĂšme dâestimation.RĂ©utilisant un «bon vieux» principe de la thĂ©orie du contrĂŽle, amĂ©liorĂ© ici Ă lâaidedâune heuristique de stabilisation numĂ©rique rĂ©troactive, nous obtenons une estimationdistribuĂ©e du 2Ăšme ordre, numĂ©riquement stable et plutĂŽt efficace, sansaucun mĂ©ta-paramĂštre Ă ajuster. La relation avec les techniques existantes estdiscutĂ©e Ă chaque Ă©tape. La mĂ©thode proposĂ©e est validĂ©e en utilisant des tĂąchesdâingĂ©nierie inverse
Trajectory prediction of moving objects by means of neural networks
Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 1997Includes bibliographical references (leaves: 103-105)Text in English; Abstract: Turkish and Englishviii, 105 leavesEstimating the three-dimensional motion of an object from a sequence of object positions and orientation is of significant importance in variety of applications in control and robotics. For instance, autonomous navigation, manipulation, servo, tracking, planning and surveillance needs prediction of motion parameters. Although "motion estimation" is an old problem (the formulations date back to the beginning of the century), only recently scientists have provided with the tools from nonlinear system estimation theory to solve this problem eural Networks are the ones which have recently been used in many nonlinear dynamic system parameter estimation context. The approximating ability of the neural network is used to identifY the relation between system variables and parameters of a dynamic system. The position, velocity and acceleration of the object are estimated by several neural networks using the II most recent measurements of the object coordinates as input to the system Several neural network topologies with different configurations are introduced and utilized in the solution of the problem. Training schemes for each configuration are given in detail. Simulation results for prediction of motion having different characteristics via different architectures with alternative configurations are presented comparatively
Navigation coopérative de véhicules autonomes basée sur la communication V2X dans un réseau de 5Úme génération
In todayâs world, road transport is essential to our daily routines and business activities. However, the exponential growth in the number of vehicles has led to problems such as traffic congestion and road accidents. Vehicular communication presents an innovative solution, envisaging a future where vehicles communicate with each other, the road infrastructure, and even the road itself, sharing real-time data to optimize traffic flow and enhance safety. This thesis focuses on 5G and Beyond 5G (B5G) technologies, which promise to revolutionize Vehicle-to-Everything (V2X) communication. With the emergence of millimeter-wave (mmWave) communication, high-speed, low-latency data transmission is essential for vehicular networks. However, mmWave communication faces problems with signal attenuation and interference. Our research focuses on solving these problems using a deep learning-based approach. Three significant contributions are proposed.
First, we introduce a classical optimization technique, the simulated annealing algorithm, to improve beam alignment in 5G vehicular networks. This reduces latency and improves data transmission between millimeter-wave base stations and vehicles. Our second contribution is a new approach involving a hybrid deep-learning model that predicts optimal beam angles. Combining a 1D CNN and a BiLSTM improves th accuracy of the prediction and reduces errors. This approach eliminates time-consuming computations and iterations critical to the success of B5G vehicular networks. The third contribution introduces a BiLSTM-based model to select the optimal beam pair angles at the mmWave base station (mmBS) and the moving vehicle side. This approach improves
the reliability of data transmission while minimizing the error probabilities and overheads during beam search. This research contributes to advancing vehicular communications, offering innovative solutions for 5G and B5G networks. We aim to enhance the efficiency,
reduce the latency, and improve the reliability of communications for connected vehicles. This thesis explores beam alignment through classical and deep learning techniques and presents solutions for the challenges of millimeter-wave vehicular networks. Our research provides the foundation for the next generation of vehicular communication and its vital role in making road transport safer and more efficient
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional
sparse graph-structured data into low-dimensional dense vectors, which is a
fundamental task that has been widely studied in a range of fields, including
machine learning and data mining. Classic graph embedding methods follow the
basic idea that the embedding vectors of interconnected nodes in the graph can
still maintain a relatively close distance, thereby preserving the structural
information between the nodes in the graph. However, this is sub-optimal due
to: (i) traditional methods have limited model capacity which limits the
learning performance; (ii) existing techniques typically rely on unsupervised
learning strategies and fail to couple with the latest learning paradigms;
(iii) representation learning and downstream tasks are dependent on each other
which should be jointly enhanced. With the remarkable success of deep learning,
deep graph representation learning has shown great potential and advantages
over shallow (traditional) methods, there exist a large number of deep graph
representation learning techniques have been proposed in the past decade,
especially graph neural networks. In this survey, we conduct a comprehensive
survey on current deep graph representation learning algorithms by proposing a
new taxonomy of existing state-of-the-art literature. Specifically, we
systematically summarize the essential components of graph representation
learning and categorize existing approaches by the ways of graph neural network
architectures and the most recent advanced learning paradigms. Moreover, this
survey also provides the practical and promising applications of deep graph
representation learning. Last but not least, we state new perspectives and
suggest challenging directions which deserve further investigations in the
future
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Salience Estimation and Faithful Generation: Modeling Methods for Text Summarization and Generation
This thesis is focused on a particular text-to-text generation problem, automatic summarization, where the goal is to map a large input text to a much shorter summary text. The research presented aims to both understand and tame existing machine learning models, hopefully paving the way for more reliable text-to-text generation algorithms. Somewhat against the prevailing trends, we eschew end-to-end training of an abstractive summarization model, and instead break down the text summarization problem into its constituent tasks. At a high level, we divide these tasks into two categories: content selection, or âwhat to sayâ and content realization, or âhow to say itâ (McKeown, 1985). Within these categories we propose models and learning algorithms for the problems of salience estimation and faithful generation.
Salience estimation, that is, determining the importance of a piece of text relative to some context, falls into a problem of the former category, determining what should be selected for a summary. In particular, we experiment with a variety of popular or novel deep learning models for salience estimation in a single document summarization setting, and design several ablation experiments to gain some insight into which input signals are most important for making predictions. Understanding these signals is critical for designing reliable summarization models.
We then consider a more difficult problem of estimating salience in a large document stream, and propose two alternative approaches using classical machine learning techniques from both unsupervised clustering and structured prediction. These models incorporate salience estimates into larger text extraction algorithms that also consider redundancy and previous extraction decisions.
Overall, we find that when simple, position based heuristics are available, as in single document news or research summarization, deep learning models of salience often exploit them to make predictions, while ignoring the arguably more important content features of the input. In more demanding environments, like stream summarization, where heuristics are unreliable, more semantically relevant features become key to identifying salience content.
In part two, content realization, we assume content selection has already been performed and focus on methods for faithful generation (i.e., ensuring that output text utterances respect the semantics of the input content). Since they can generate very fluent and natural text, deep learning- based natural language generation models are a popular approach to this problem. However, they often omit, misconstrue, or otherwise generate text that is not semantically correct given the input content. In this section, we develop a data augmentation and self-training technique to mitigate this problem. Additionally, we propose a training method for making deep learning-based natural language generation models capable of following a content plan, allowing for more control over the output utterances generated by the model. Under a stress test evaluation protocol, we demonstrate some empirical limits on several neural natural language generation modelsâ ability to encode and properly realize a content plan.
Finally, we conclude with some remarks on future directions for abstractive summarization outside of the end-to-end deep learning paradigm. Our aim here is to suggest avenues for constructing abstractive summarization systems with transparent, controllable, and reliable behavior when it comes to text understanding, compression, and generation. Our hope is that this thesis inspires more research in this direction, and, ultimately, real tools that are broadly useful outside of the natural language processing community
Guidage et planification rĂ©active de trajectoire dâun drone monoculaire contrĂŽlĂ© par intelligence artificielle
RĂSUMĂ Le problĂšme de guidage autonome est un domaine de recherche en constante Ă©volution. La popularisation des drones a Ă©tendu ce domaine de recherche au cours des derniĂšres annĂ©es. La nature de ce type dâengins amĂšne plusieurs nouveaux dĂ©fis Ă surmonter, notamment en lien avec la variĂ©tĂ© dâenvironnements auxquels ils peuvent ĂȘtre confrontĂ©s. Contrairement aux voitures autonomes, les drones se retrouvent souvent dans des milieux inconnus non cartographiĂ©s et dĂ©pourvus de signal GPS. De nouvelles mĂ©thodes ont donc Ă©tĂ© dĂ©veloppĂ©es pour mitiger ces dĂ©fis.
Les solutions au problĂšme de guidage autonome dans la littĂ©rature peuvent dans ce mĂ©moire de maĂźtrise ĂȘtre classĂ©es dans deux catĂ©gories : le guidage rĂ©actif localement Ă des fins dâexploration et le guidage orientĂ©. La premiĂšre catĂ©gorie regroupe les solutions de guidage local dâengins naviguant sans destination prĂ©cise alors que la seconde regroupe celles de guidage tentant dâatteindre une destination. Les deux catĂ©gories de guidage en milieu inconnu utilisent majoritairement des approches incluant lâapprentissage par renforcement ainsi que lâapprentissage par imitation. Cependant, peu dâĂ©tudes abordent le problĂšme de guidage orientĂ© dans des environnements complexes de grandeur nature. Lâobjectif de ce projet de recherche est donc de concevoir un agent intelligent capable dâimiter la logique de guidage dâun humain dans un environnement inconnu complexe en se basant sur la vision de profondeur et une estimation de sa destination. Une approche utilisant lâapprentissage par imitation est employĂ©e pour minimiser les coĂ»ts et les temps de calcul. Un environnement de simulation sophistiquĂ© est donc mis sur place afin de crĂ©er un ensemble de donnĂ©es pour lâentraĂźnement par imitation. Lâensemble de donnĂ©es qui a Ă©tĂ© crĂ©Ă© comporte 624 trajectoires parmi 9 environnements diffĂ©rents effectuĂ©es par un expert suboptimal pour un total de 296 466 paires dâentraĂźnement. Lâattributif suboptimal est employĂ© pour qualifier lâhumain Ă imiter puisque ce dernier devra dresser les trajets au meilleur de ses capacitĂ©s sans avoir recours Ă des algorithmes de planification de trajectoire optimale. Un modĂšle de classification capable de prĂ©dire la prochaine commande de guidage Ă effectuer compte tenu des observations actuelles et prĂ©cĂ©dentes a Ă©tĂ© implĂ©mentĂ©. Le modĂšle est entraĂźnĂ© Ă encoder une reprĂ©sentation de lâimage de profondeur obtenue Ă partir de lâimage RGB ainsi quâune reprĂ©sentation des coordonnĂ©es relative Ă sa destination. Ces reprĂ©sentations sont traitĂ©es par un rĂ©seau rĂ©current Ă mĂ©moire court et long terme («Long Short-Term Memory» ou LSTM) ainsi quâun perceptron multicouches («Multilayer Perceptron» ou MLP) afin de prĂ©dire la direction Ă emprunter. Une fonction coĂ»t adaptĂ©e au problĂšme ainsi que des techniques dâaugmentation de lâensemble de donnĂ©es sont incorporĂ©es lors de lâentraĂźnement afin dâamĂ©liorer la prĂ©cision du modĂšle en validation et en test. Une recherche dâhyperparamĂštres de type grid search a Ă©tĂ© effectuĂ©e afin de sĂ©lectionner le meilleur modĂšle selon la prĂ©cision obtenue sur lâensemble de donnĂ©es de test. Des prĂ©cisions entre 77.10% et 82.59% ont Ă©tĂ© atteintes indiquant un impact significatif des mĂ©thodes dâaugmentation de lâensemble de donnĂ©es.----------ABSTRACT The autonomous guidance field is a continuously evolving research topic. The popularization of micro aerial vehicles such as quadcopters has contributed to the expansion of this research topic. Because of the wide range of different environments they can navigate into, quadcopters have many challenges on their own. In contrast with autonomous cars, quadcopters will most likely navigate more often in unknown environments with limited or no GPS service. New methods for autonomous guidance were needed for quadcopters. The literature review reveals two main categories relevant to the autonomous guidance problem: locally passive-reactive guidance and oriented guidance. The former includes all forms of guidance not aiming for a specific target while the latter focuses on reaching a destination. Both categories are considering guidance in unknown environments and use mostly reinforcement learning or imitation learning as a solving method. However, most of the studies on autonomous oriented guidance are not executed in a full size, complex environment setting. The objective of this research project is to create an intelligent agent capable of imitating a human guidance policy in a complex and unknown environment based on a depth map image and relative goal inputs. Considering the lower cost in development and computation time, the imitation learning approach was chosen. A sophisticated simulation environment was set up to create an imitation learning datasets. A total of 624 suboptimal demonstration paths from 9 different 3D environments were gathered, which represent 296 466 learning pairs. The demonstrations are qualified as suboptimal since the expert is a human trying its best to solve the guidance problem without any optimal planners. A classification model was introduced for predicting the appropriate guidance command based on the observations over time. The model learned a meaningful representation of its inputs which can be processed by a long short-term memory network (LSTM) followed by a fully connected network. In this way, the depth image obtained from the RGB original image along with the relative coordinates to the destination are converted into a guidance command at each time step. In order to improve the classification accuracy on the test set, a custom loss function and data augmentation techniques were implemented. A grid search over possible combination of dataset augmentation proportions was conveyed to optimize the hyperparameters. Accuracy ranging between 77.10% and 82.59% were obtained for this experiment, revealing a significant dependency to the augmentation technique
Technology 2002: the Third National Technology Transfer Conference and Exposition, Volume 1
The proceedings from the conference are presented. The topics covered include the following: computer technology, advanced manufacturing, materials science, biotechnology, and electronics
Proceedings of the 7th Sound and Music Computing Conference
Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010
Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires Advances in Remote Sensing and GIS Applications in Forest Fire Management Towards an Operational Use of Remote Sensing in Forest Fire Management
During the last two decades, interest in forest fire research has grown steadily, as more and more local and global impacts of burning are being identified. The definition of fire regimes as well as the identification of factors explaining spatial and temporal variations in these fire characteristics are recently hot fields of research. Changes in these fire regimes have important social and ecological implications. Whether these changes are mainly caused by land use or climate warming, greater efforts are demanded to manage forest fires at different temporal and spatial scales.
The European Association of Remote Sensing Laboratories (EARSeL)âs Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying Mediterranean fires in Europe. It has promoted five technical meetings and several specialised publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of fire risk estimation and the assessment of fire effect.
The aim of the 6th international workshop is to analyze the operational use of remote sensing in forest fire management, bringing together scientists and fire managers to promote the development of methods that may better serve the operational community. This idea clearly links with international programmes of a similar scope, such as the Global Monitoring for Environment and Security (GMES) and the Global Observation of Forest Cover/Land Dynamics (GOFC-GOLD) who, together with the Joint Research Center of the European Union sponsor this event.
Finally, I would like to thank the local organisers for the considerable lengths they have gone to in order to put this material together, and take care of all the details that the organization of this event requires.JRC.H.3-Global environement monitorin