2,869 research outputs found
Deep generative models for network data synthesis and monitoring
Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network.
Although networks inherently
have abundant amounts of monitoring data, its access and effective measurement is
another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset
without leaking commercial sensitive information. Second, it could be very expensive
to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of
flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources
in the network element that can be applied to support the measurement function are
too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex
structure. Various emerging optimization-based solutions (e.g., compressive sensing)
or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet
meet the current network requirements.
The contributions made in this thesis significantly advance the state of the art in
the domain of network measurement and monitoring techniques. Overall, we leverage
cutting-edge machine learning technology, deep generative modeling, throughout the
entire thesis. First, we design and realize APPSHOT , an efficient city-scale network
traffic sharing with a conditional generative model, which only requires open-source
contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we
design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time
network telemetry system with latent GANs and spectral-temporal networks. Finally,
we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through
this research are summarized, and interesting topics are discussed for future work in
this domain. All proposed solutions have been evaluated with real-world datasets and
applied to support different applications in real systems
Climate Change and Critical Agrarian Studies
Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
AI: Limits and Prospects of Artificial Intelligence
The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Modelação e Negociação de Flexibilidade em Comunidades de Energia Renovável
The progressive replacement of traditional generation resources with intermittent
resources has reduced the available supply-side flexibility and increased the need to unlock
flexibility on the demand-side. At the same time, the rising electricity consumption in
residential buildings requires an analysis of the potential flexibility of the loads within them
to contribute to the operation needs of electrical grids. Lastly, regulations governing self consumption have allowed end consumers to form energy communities based on local
electricity markets. This is an additional incentive to define strategies for trading available
flexibility at local level, in separate but simultaneously integrated structures within
wholesale electricity markets.
The proposed dissertation work focuses on studying the flexibility of energy
production and consumption by prosumers within a Renewable Energy Community (REC).
The objective is to investigate how residential flexibility can be determined, modeled, and
aggregated for trading in a local market created for this purpose. The work to be developed
will present a two-stage model that determines residential technical flexibility and
establishes a local market only for its transaction.
In the first stage, the optimal scheduling of domestic devices (flexible units or FUs)
for each prosumer is determined, serving as a baseline for comparison, along with the
technical limits of flexibility (maximum and minimum possible consumption profiles) for
each FU.
In the second stage, a market model is established only for flexibility exchanges. The
technical flexibility determined in the first stage is offered to the Community Manager (CM)
as flexibility offer, with an associated price. This entity acts as an aggregator and
simultaneously as the operator of the local market. At this level, the Distribution System
Operator (DSO) submits its flexibility requirements for the next day to the CM, who is
responsible for executing the clearing process. The pricing of the flexibility offered by
prosumers in the market is based on the base energy tariff they are subject to, which
corresponds to the cost of their optimal scheduling obtained in the first stage, without
considering this flexibility. Therefore, offering flexibility becomes an incentive to reduce prosumers energy costs or increase their utility, complementing their mere participation in
energy markets.
A case study based on a renewable energy community with a strong penetration of
emerging technologies is used to validate and demonstrate the relevance of the proposed
approach in terms of determining and activating residential FU flexibility. The obtained
results show that participation in the local flexibility market leads to a reduction in prosumers
energy costs, around 4.5%, in average. It can be an incentive for prosumers to join RECs
that would not only have local energy trading structures but also mechanisms for negotiating
and sharing flexibility. In addition, it was evidenced that the impact of electric vehicle
chargers and battery energy storage systems on the total flexibility offered and accepted in
the market is much greater than that the impact of other small loads studied. This not only
constitutes an incentive for the study of the operational flexibility of these resources but also
for investments in these emerging technologies.A substituição progressiva dos recursos de geração tradicionais por recursos
intermitentes tem reduzido a flexibilidade disponível do lado da oferta e aumentado a
necessidade de desbloqueá-la do lado da procura. Ao mesmo tempo, o aumento do consumo
de eletricidade nos edifícios residenciais obriga a que seja analisada a flexibilidade potencial
das cargas que o constituem, de modo a contribuir para as necessidades de operação das
redes elétricas. Por último, a regulamentação do autoconsumo, tem permitido aos
consumidores finais constituir comunidades energéticas baseadas em mercados locais de
eletricidade. Isto torna ainda mais importante a definição de estratégias para comercializar a
flexibilidade disponível a esse nível, em estruturas de mercado local separadas, mas
simultaneamente integradas nos mercados grossistas de eletricidade.
O trabalho proposto para dissertação assenta no estudo da flexibilidade da produção
e consumo de energia por parte dos prosumidores de uma Comunidade de Energia
Renovável. O objetivo é estudar como a flexibilidade residencial pode ser determinada,
modelada e agregada de modo a ser transacionada num mercado local criado para esse fim.
Assim, o trabalho a ser desenvolvido apresentará um modelo de dois estágios que determina
a flexibilidade técnica residencial e cria um mercado local exclusivo para transaciona-la.
Numa primeira fase, determina-se o escalonamento óptimo dos dispositivos
domésticos (unidades flexíveis ou UF) de cada prosumidor, o que constitui uma baseline de
comparação, bem como os limites técnicos de flexibilidade (perfis de consumo máximos e
mínimos possíveis) de cada UF.
Num segundo estágio, é estabelecido um modelo de mercado apenas para trocas de
flexibilidade. A flexibilidade técnica determinada no primeiro estágio é disponibilizada ao
Gestor de Comunidade (CM), enquanto oferta de flexibilidade, com um preço associado.
Esta entidade desempenha as funções de agregador e simultaneamente de operador do
mercado local. A este nível, o Operador do Sistema de Distribuição (ORD) submete os seus
requisitos de flexibilidade, para o dia seguinte, ao CM, que é responsável pelo executar o
clearing. A precificação da flexibilidade oferecida pelos prosumidores em mercado é feita
com base no valor da tarifa base de energia a que estão sujeitos, que corresponde ao custo do seu escalonamento ótimo, obtido no primeiro estágio, que não considera essa mesma
flexibilidade. Portanto, oferecer flexibilidade torna-se um incentivo para reduzir os custos
energéticos dos prosumidores ou aumentar a sua utilidade, o que complementa a sua mera
participação nos mercados de energia.
Um caso de estudo baseado numa comunidade de energia com forte penetração de
tecnologias emergentes é utilizado e valida a metodologia desenvolvida. Para além disso é
evidenciada a relevância da abordagem proposta em termos de determinação e ativação da
flexibilidade de UFs residenciais os impactos das mesmas no fecho de mercado. Os
resultados evidenciam que participação no mercado local de flexibilidade induz uma redução
dos custos energéticos dos prosumidores, na casa 4.5%, em média. O impacto dos
carregadores de veículos elétricos e dos sistemas de armazenamento de energia em baterias
na flexibilidade total oferecida e aceite em mercado é muito superior ao de outras pequenas
cargas estudadas. Tudo isto pode vir a resultar num incentivo ao investimento nos recursos
referidos, bem como à associação de prosumidores em comunidades de energia renovável,
onde para além de estruturas locais de comercialização de energia, existam outras que
permitam a negociação e partilha de flexibilidade
An Intelligent Time and Performance Efficient Algorithm for Aircraft Design Optimization
Die Optimierung des Flugzeugentwurfs erfordert die Beherrschung der komplexen Zusammenhänge mehrerer Disziplinen. Trotz seiner Abhängigkeit von einer Vielzahl unabhängiger Variablen zeichnet sich dieses komplexe Entwurfsproblem durch starke indirekte Verbindungen und eine daraus resultierende geringe Anzahl lokaler Minima aus. Kürzlich entwickelte intelligente Methoden, die auf selbstlernenden Algorithmen basieren, ermutigten die Suche nach einer diesem Bereich zugeordneten neuen Methode. Tatsächlich wird der in dieser Arbeit entwickelte Hybrid-Algorithmus (Cavus) auf zwei Hauptdesignfälle im Luft- und Raumfahrtbereich angewendet: Flugzeugentwurf- und Flugbahnoptimierung. Der implementierte neue Ansatz ist in der Lage, die Anzahl der Versuchspunkte ohne große Kompromisse zu reduzieren. Die Trendanalyse zeigt, dass der Cavus-Algorithmus für die komplexen Designprobleme, mit einer proportionalen Anzahl von Prüfpunkten konservativer ist, um die erfolgreichen Muster zu finden.
Aircraft Design Optimization requires mastering of the complex interrelationships of multiple disciplines. Despite its dependency on a diverse number of independent variables, this complex design problem has favourable nature as having strong indirect links and as a result a low number of local minimums. Recently developed intelligent methods that are based on self-learning algorithms encouraged finding a new method dedicated to this area. Indeed, the hybrid (Cavus) algorithm developed in this thesis is applied two main design cases in aerospace area: aircraft design optimization and trajectory optimization. The implemented new approach is capable of reducing the number of trial points without much compromise. The trend analysis shows that, for the complex design problems the Cavus algorithm is more conservative with a proportional number of trial points in finding the successful patterns
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Policy options for food system transformation in Africa and the role of science, technology and innovation
As recognized by the Science, Technology and Innovation Strategy for Africa – 2024 (STISA-2024), science, technology and innovation (STI) offer many opportunities for addressing the main constraints to embracing transformation in Africa, while important lessons can be learned from successful interventions, including policy and institutional innovations, from those African countries that have already made significant progress towards food system transformation. This chapter identifies opportunities for African countries and the region to take proactive steps to harness the potential of the food and agriculture sector so as to ensure future food and nutrition security by applying STI solutions and by drawing on transformational policy and institutional innovations across the continent. Potential game-changing solutions and innovations for food system transformation serving people and ecology apply to (a) raising production efficiency and restoring and sustainably managing degraded resources; (b) finding innovation in the storage, processing and packaging of foods; (c) improving human nutrition and health; (d) addressing equity and vulnerability at the community and ecosystem levels; and (e) establishing preparedness and accountability systems. To be effective in these areas will require institutional coordination; clear, food safety and health-conscious regulatory environments; greater and timely access to information; and transparent monitoring and accountability systems
Tools for efficient Deep Learning
In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption.
We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work.
This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C.
Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets.
All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces
A robotic platform for precision agriculture and applications
Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited.
As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles.
The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances.
Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles.
The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed.
Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork.
The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.Le tecniche agricole sono state migliorate nel corso dei secoli per soddisfare la crescente domanda di aumento della popolazione mondiale. I recenti progressi tecnologici in termini di piattaforme robotiche possono essere sfruttati in questo contesto.
Poiché la gestione del frutteto è una delle applicazioni più impegnative, a causa della sua struttura arborea e della necessaria interazione con l'ambiente, è stata oggetto di ricerca per fornire una soluzione personalizzata che sviluppi un nuovo concetto di veicolo agricolo.
Il risultato si è concretizzato in un veicolo cingolato leggero, capace di effettuare una navigazione autonoma sia nello scenario di pieno campo che all'interno dei frutteti (navigazione interfilare). La progettazione meccanica, insieme all'implementazione del software, sono stati dettagliati per evidenziarne i punti di forza, accanto ad alcuni ulteriori miglioramenti previsti per incrementarne le prestazioni complessive.
I test di stabilità statica hanno dimostrato che il veicolo può resistere a ripidi pendii. Sono stati inoltre studiati miglioramenti per affinare la stima dello slittamento che si verifica durante le manovre di svolta, tipico dei veicoli cingolati.
L'architettura software è stata implementata utilizzando il framework Robot Operating System (ROS), in modo da sfruttare i pacchetti disponibili relativi a componenti base, come le interfacce dei sensori, e consentendo al contempo un'implementazione personalizzata degli algoritmi di navigazione sviluppati.
I test in condizioni reali all'interno dei frutteti sperimentali dell'università hanno dimostrato la robustezza e la stabilità della soluzione con oltre 800 ore di lavoro sul campo.
Il veicolo ha permesso di attivare e svolgere un'ampia gamma di attività agricole in maniera autonoma, come l'irrorazione, la falciatura e la raccolta di dati sul campo. Questi ultimi possono essere sfruttati per stimare automaticamente le proprietà più rilevanti del frutteto, come il conteggio e la calibratura dei frutti, la stima delle proprietà della chioma e la raccolta autonoma dei frutti con stime post-raccolta
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