46 research outputs found
Adaptive and learning-based formation control of swarm robots
Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Neural-Kalman Schemes for Non-Stationary Channel Tracking and Learning
This Thesis focuses on channel tracking in Orthogonal Frequency-Division Multiplexing (OFDM), a
widely-used method of data transmission in wireless communications, when abrupt changes occur
in the channel. In highly mobile applications, new dynamics appear that might make channel
tracking non-stationary, e.g. channels might vary with location, and location rapidly varies with
time. Simple examples might be the di erent channel dynamics a train receiver faces when it is
close to a station vs. crossing a bridge vs. entering a tunnel, or a car receiver in a route that
grows more tra c-dense. Some of these dynamics can be modelled as channel taps dying or being
reborn, and so tap birth-death detection is of the essence.
In order to improve the quality of communications, we delved into mathematical methods to
detect such abrupt changes in the channel, such as the mathematical areas of Sequential Analysis/
Abrupt Change Detection and Random Set Theory (RST), as well as the engineering advances
in Neural Network schemes. This knowledge helped us nd a solution to the problem of abrupt
change detection by informing and inspiring the creation of low-complexity implementations for
real-world channel tracking. In particular, two such novel trackers were created: the Simpli-
ed Maximum A Posteriori (SMAP) and the Neural-Network-switched Kalman Filtering (NNKF)
schemes.
The SMAP is a computationally inexpensive, threshold-based abrupt-change detector. It applies
the three following heuristics for tap birth-death detection: a) detect death if the tap gain
jumps into approximately zero (memoryless detection); b) detect death if the tap gain has slowly
converged into approximately zero (memory detection); c) detect birth if the tap gain is far from
zero.
The precise parameters for these three simple rules can be approximated with simple theoretical
derivations and then ne-tuned through extensive simulations. The status detector for each
tap using only these three computationally inexpensive threshold comparisons achieves an error
reduction matching that of a close-to-perfect path death/birth detection, as shown in simulations.
This estimator was shown to greatly reduce channel tracking error in the target Signal-to-Noise
Ratio (SNR) range at a very small computational cost, thus outperforming previously known systems.
The underlying RST framework for the SMAP was then extended to combined death/birth
and SNR detection when SNR is dynamical and may drift. We analyzed how di erent quasi-ideal
SNR detectors a ect the SMAP-enhanced Kalman tracker's performance. Simulations showed
SMAP is robust to SNR drift in simulations, although it was also shown to bene t from an accurate
SNR detection.
The core idea behind the second novel tracker, NNKFs, is similar to the SMAP, but now the tap
birth/death detection will be performed via an arti cial neuronal network (NN). Simulations show
that the proposed NNKF estimator provides extremely good performance, practically identical to a detector with 100% accuracy.
These proposed Neural-Kalman schemes can work as novel trackers for multipath channels,
since they are robust to wide variations in the probabilities of tap birth and death. Such robustness
suggests a single, low-complexity NNKF could be reusable over di erent tap indices and
communication environments.
Furthermore, a di erent kind of abrupt change was proposed and analyzed: energy shifts from
one channel tap to adjacent taps (partial tap lateral hops). This Thesis also discusses how to
model, detect and track such changes, providing a geometric justi cation for this and additional
non-stationary dynamics in vehicular situations, such as road scenarios where re ections on trucks
and vans are involved, or the visual appearance/disappearance of drone swarms. An extensive
literature review of empirically-backed abrupt-change dynamics in channel modelling/measuring
campaigns is included.
For this generalized framework of abrupt channel changes that includes partial tap lateral
hopping, a neural detector for lateral hops with large energy transfers is introduced. Simulation
results suggest the proposed NN architecture might be a feasible lateral hop detector, suitable for
integration in NNKF schemes.
Finally, the newly found understanding of abrupt changes and the interactions between Kalman
lters and neural networks is leveraged to analyze the neural consequences of abrupt changes
and brie y sketch a novel, abrupt-change-derived stochastic model for neural intelligence, extract
some neuro nancial consequences of unstereotyped abrupt dynamics, and propose a new
portfolio-building mechanism in nance: Highly Leveraged Abrupt Bets Against Failing Experts
(HLABAFEOs). Some communication-engineering-relevant topics, such as a Bayesian stochastic
stereotyper for hopping Linear Gauss-Markov (LGM) models, are discussed in the process.
The forecasting problem in the presence of expert disagreements is illustrated with a hopping
LGM model and a novel structure for a Bayesian stereotyper is introduced that might eventually
solve such problems through bio-inspired, neuroscienti cally-backed mechanisms, like dreaming
and surprise (biological Neural-Kalman). A generalized framework for abrupt changes and expert
disagreements was introduced with the novel concept of Neural-Kalman Phenomena. This Thesis
suggests mathematical (Neural-Kalman Problem Category Conjecture), neuro-evolutionary and
social reasons why Neural-Kalman Phenomena might exist and found signi cant evidence for their
existence in the areas of neuroscience and nance.
Apart from providing speci c examples, practical guidelines and historical (out)performance
for some HLABAFEO investing portfolios, this multidisciplinary research suggests that a Neural-
Kalman architecture for ever granular stereotyping providing a practical solution for continual
learning in the presence of unstereotyped abrupt dynamics would be extremely useful in communications
and other continual learning tasks.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretaria: Ana García Armada.- Vocal: José Antonio Portilla Figuera
The impact of macroeconomic leading indicators on inventory management
Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study
Modelling and Simulation of Human-Environment Interactions
Computational models provide intelligent environmental decision support systems to understand how human decisions are shaped by, and contribute to changes in, the environment. These models provide essential tools to tackle the important issues raised by climate change, including migrations and conflicts due to resource scarcity (e.g., water resources), while accounting for the necessity of co-managing ecosystems across a population of stakeholders with diverse goals. Such socio-environmental systems are characterized by their complexity, which is reflected by an abundance of open questions. This book explores several of these open questions, based on the contributions from over 50 authors. While several books account for methodological developments in modeling socio-environmental systems, our book is unique in combining case studies, methodological innovations, and a holistic approach to training the next generation of modelers. One chapter covers the ontological, epistemological, and ethical issues raised at the intersection of sustainability research and social simulation. In another chapter, we show that the benefits of simulations are not limited to managing complex eco-systems, as they can also serve an educational mission in teaching essential rules and thus improve systems thinking competencies in the broader population
Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), volume 1
This document contains papers presented at the Space Operations, Applications and Research Symposium (SOAR) Symposium hosted by NASA/Johnson Space Center (JSC) on August 3-5, 1993, and held at JSC Gilruth Recreation Center. SOAR included NASA and USAF programmatic overview, plenary session, panel discussions, panel sessions, and exhibits. It invited technical papers in support of U.S. Army, U.S. Navy, Department of Energy, NASA, and USAF programs in the following areas: robotics and telepresence, automation and intelligent systems, human factors, life support, and space maintenance and servicing. SOAR was concerned with Government-sponsored research and development relevant to aerospace operations. More than 100 technical papers, 17 exhibits, a plenary session, several panel discussions, and several keynote speeches were included in SOAR '93
Performance Evaluation of Smart Decision Support Systems on Healthcare
Medical activity requires responsibility not only from clinical knowledge and skill but
also on the management of an enormous amount of information related to patient care. It is
through proper treatment of information that experts can consistently build a healthy wellness
policy. The primary objective for the development of decision support systems (DSSs) is
to provide information to specialists when and where they are needed. These systems provide
information, models, and data manipulation tools to help experts make better decisions in a
variety of situations.
Most of the challenges that smart DSSs face come from the great difficulty of dealing
with large volumes of information, which is continuously generated by the most diverse types
of devices and equipment, requiring high computational resources. This situation makes this
type of system susceptible to not recovering information quickly for the decision making. As a
result of this adversity, the information quality and the provision of an infrastructure capable
of promoting the integration and articulation among different health information systems (HIS)
become promising research topics in the field of electronic health (e-health) and that, for this
same reason, are addressed in this research. The work described in this thesis is motivated
by the need to propose novel approaches to deal with problems inherent to the acquisition,
cleaning, integration, and aggregation of data obtained from different sources in e-health environments,
as well as their analysis.
To ensure the success of data integration and analysis in e-health environments, it
is essential that machine-learning (ML) algorithms ensure system reliability. However, in this
type of environment, it is not possible to guarantee a reliable scenario. This scenario makes
intelligent SAD susceptible to predictive failures, which severely compromise overall system
performance. On the other hand, systems can have their performance compromised due to the
overload of information they can support.
To solve some of these problems, this thesis presents several proposals and studies
on the impact of ML algorithms in the monitoring and management of hypertensive disorders
related to pregnancy of risk. The primary goals of the proposals presented in this thesis are
to improve the overall performance of health information systems. In particular, ML-based
methods are exploited to improve the prediction accuracy and optimize the use of monitoring
device resources. It was demonstrated that the use of this type of strategy and methodology
contributes to a significant increase in the performance of smart DSSs, not only concerning precision
but also in the computational cost reduction used in the classification process.
The observed results seek to contribute to the advance of state of the art in methods
and strategies based on AI that aim to surpass some challenges that emerge from the integration
and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to
quickly and automatically analyze a larger volume of complex data and focus on more accurate
results, providing high-value predictions for a better decision making in real time and without
human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento
e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações
relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações
que os especialistas podem consistentemente construir uma política saudável de bem-estar. O
principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações
aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações,
modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores
decisões em diversas situações.
A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade
de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos
tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação
torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a
tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão
de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas
de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde
eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho
descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar
com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de
diferentes fontes em ambientes de e-saúde, bem como sua análise.
Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é
importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade
do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário
totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas
de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os
sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que
podem suportar.
Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e
estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos
relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta
tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os
métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o
uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo
de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD
inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional
utilizado no processo de classificação.
Os resultados observados buscam contribuir para o avanço do estado da arte em métodos
e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que
advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados
em inteligência artificial é possível analisar de forma rápida e automática um volume maior de
dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana
IoT Applications Computing
The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing
Dynamics in Logistics
This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions