3,759 research outputs found
aTLP: a color-based model of uncertainty to evaluate the risk of decisions based on prototypes
Clustering techniques find homogeneous and distinguishable prototypes. Careful interpretation of these prototypes is crucial to assist the experts to better organize this know-how and to really improve their decision-making processes. The Traffic Lights Panel was introduced in 2009 as a postprocessing tool to provide understanding of clustering prototypes. In this work, annotated Traffic Lights Panel (aTLP) is presented as an enrichment of the TLP to manage the intrinsic uncertainty related with prototypes themselves. The aTLP handles uncertainty through a quantification of the prototypes' purity based on the variation coefficients (VC) and an associated color-based uncertainty model, with two dimensions - tone and saturation - representing nominal trend and purity of the prototype. An application to a waste-water treatment plant in Slovenia, in a discrete and continuous approach, suggests that aTLP seems a useful and friendly tool able to reduce the gap between data mining and effective decision support, towards informed-decisions.Peer ReviewedPostprint (author's final draft
Variable selection for meaningful clustering of multitopic territorial data
This paper proposes a new methodology to improve territorial cohesion in clustering processes where many variables from different topics are considered. Clustering techniques provide added value to identify typologies, but there are still unsolved challenges when data contain an unbalanced number of variables from different topics. The territorial feature selection method (TFSM) is presented as a method to select the representative variable of each topic such that the interpretability of resulting clusters is preserved and the geographical cohesion is improved with respect to classical approaches. This paper also introduces the thermometer as a new knowledge acquisition tool that allows experts to transfer semantics to the data mining process. TFSM proposes the index of potential explainability ( ) as the criteria to select the most promising variables for clustering. is based on the combination of inferential testing and metrics such as support. The proposal is applied with the INSESS-COVID19 database, where territorial groups of vulnerable populations were found. A set of 195 variables with 21 unbalanced thematic blocks is used to compare the results with a traditional multiview clustering analysis with promising results from both the geographical and the thematic point of view and the capacity to support further decision making.Peer ReviewedPostprint (published version
The DWD climate predictions website: Towards a seamless outlook based on subseasonal, seasonal and decadal predictions
The climate predictions website of the Deutscher Wetterdienst (DWD, https://www.dwd.de/climatepredictions) presents a consistent operational outlook for the coming weeks, months and years, focusing on the needs of German users. At global scale, subseasonal predictions from the European Centre of Medium-Range Weather Forecasts as well as seasonal and decadal predictions from the DWD are used. Statistical downscaling is applied to achieve high resolution over Germany. Lead-time dependent bias correction is performed on all time scales. Additionally, decadal predictions are recalibrated.
The website offers ensemble mean and probabilistic predictions for temperature and precipitation combined with their skill (mean squared error skill score, ranked probability skill score). Two levels of complexity are offered: basic climate predictions display simple, regionally averaged information for Germany, German regions and cities as maps, time series and tables. The skill is presented as traffic light. Expert climate predictions show complex, gridded predictions for Germany (at high resolution), Europe and the world as maps and time series. The skill is displayed as the size of dots. Their color is related to the signal in the prediction.
The website was developed in cooperation with users from different sectors via surveys, workshops and meetings to guarantee its understandability and usability. The users realize the potential of climate predictions, but some need advice in using probabilistic predictions and skill. Future activities will include the further development of predictions to improve skill (multi-model ensembles, teleconnections), the introduction of additional products (data provision, extremes) and the further clarification of the information (interactivity, video clips)
Perception and intelligent localization for autonomous driving
Mestrado em Engenharia de Computadores e TelemÑticaVisão por computador e fusão sensorial são temas relativamente recentes, no entanto largamente adoptados no desenvolvimento de robôs autónomos que exigem adaptabilidade ao seu ambiente envolvente. Esta dissertação foca-se numa abordagem a estes dois temas para alcançar percepção no contexto de condução autónoma. O uso de cÒmaras para atingir este fim é um
processo bastante complexo. Ao contrΓ‘rio dos meios sensoriais clΓ‘ssicos que fornecem sempre o mesmo tipo de informação precisa e atingida de forma determinΓstica, as sucessivas imagens adquiridas por uma cΓ’mara estΓ£o repletas
da mais variada informação e toda esta ambΓgua e extremamente difΓcil de extrair. A utilização de cΓ’maras como meio sensorial em robΓ³tica
Γ© o mais prΓ³ximo que chegamos na semelhanΓ§a com aquele que Γ© o de maior importΓ’ncia no processo de percepção humana, o sistema de visΓ£o. VisΓ£o por computador Γ© uma disciplina cientΓfica que engloba Γ reas como: processamento
de sinal, inteligΓͺncia artificial, matemΓ‘tica, teoria de controlo, neurobiologia e fΓsica.
A plataforma de suporte ao estudo desenvolvido no Òmbito desta dissertação é o ROTA (RObô Triciclo Autónomo) e todos os elementos que consistem
o seu ambiente. No contexto deste, são descritas abordagens que foram introduzidas com fim de desenvolver soluçáes para todos os desafios que o
robΓ΄ enfrenta no seu ambiente: detecção de linhas de estrada e consequente percepção desta, detecção de obstΓ‘culos, semΓ‘foros, zona da passadeira e zona de obras. Γ tambΓ©m descrito um sistema de calibração e aplicação da remoção da perspectiva da imagem, desenvolvido de modo a mapear os elementos percepcionados em distΓ’ncias reais. Em consequΓͺncia do sistema
de percepção, é ainda abordado o desenvolvimento de auto-localização integrado
numa arquitectura distribuΓda incluindo navegação com planeamento inteligente. Todo o trabalho desenvolvido no decurso da dissertação Γ© essencialmente centrado no desenvolvimento de percepção robΓ³tica no contexto de condução autΓ³noma.Computer vision and sensor fusion are subjects that are quite recent, however widely adopted in the development of autonomous robots that require
adaptability to their surrounding environment. This thesis gives an approach on both in order to achieve perception in the scope of autonomous driving.
The use of cameras to achieve this goal is a rather complex subject.
Unlike the classic sensorial devices that provide the same type of information with precision and achieve this in a deterministic way, the successive
images acquired by a camera are replete with the most varied information, that this ambiguous and extremely dificult to extract. The use of cameras
for robotic sensing is the closest we got within the similarities with what is of most importance in the process of human perception, the vision system. Computer vision is a scientific discipline that encompasses areas such as signal processing, artificial intelligence, mathematics, control theory,
neurobiology and physics.
The support platform in which the study within this thesis was developed, includes ROTA (RObΓ΄ Triciclo AutΓ³nomo) and all elements comprising its
environment. In its context, are described approaches that introduced in the platform in order to develop solutions for all the challenges facing the robot in its environment: detection of lane markings and its consequent perception, obstacle detection, trafic lights, crosswalk and road maintenance area. It is also described a calibration system and implementation for the removal of the image perspective, developed in order to map the
elements perceived in actual real world distances. As a result of the perception system development, it is also addressed self-localization integrated in
a distributed architecture that allows navigation with long term planning.
All the work developed in the course of this work is essentially focused on robotic perception in the context of autonomous driving
Front-of-pack nutrition labelling schemes: a comprehensive review
This JRC Science for Policy report was produced in support of a Commission report on front-of-pack (FOP) nutrition labelling. It provides a review of the scientific literature concerning FOP nutrition labelling and its effects on consumers, food business operators, and the single market. A major emphasis is placed on consumer attention, preferences, and understanding of different FOP schemes, as well as effects on food purchasing and implications for diet and health. The report also considers in how far producer efforts on food reformulation and innovation may be affected by the introduction of FOP nutrition labelling schemes, describes potential unintended consequences of introducing FOP nutrition labelling, and highlights knowledge gaps and directions for future research. An extensive, yet non-exhaustive overview of FOP schemes around the globe complements the literature review.JRC.F.1-Health in Societ
Discovering And Labelling Of Temporal Granularity Patterns In Electric Power Demand With A Brazilian Case Study
Clustering is commonly used to group data in order to represent the behaviour of a system as accurately as possible by obtaining patterns and profiles. In this paper, clustering is applied with partitioning-clustering techniques, specifically, Partitioning around Medoids (PAM) to analyse load curves from a city of South-eastern Brazil in SΓ£o Paulo state. A top-down approach in time granularity is performed to detect and to label profiles which could be affected by seasonal trends and daily/hourly time blocks. Time-granularity patterns are useful to support the improvement of activities related to distribution, transmission and scheduling of energy supply. Results indicated four main patterns which were post-processed in hourly blocks by using shades of grey to help final-user to understand demand thresholds according to the meaning of dark grey, light grey and white colours. A particular and different behaviour of load curve was identified for the studied city if it is compared to the classical behaviour of urban cities.36357559
Retrospective Examination of Demand-side Energy-efficiency Policies
Energy efficiency policies are a primary avenue for reducing carbon emissions, with potential additional benefits from improved air quality and energy security. We review literature on a broad range of existing non-transportation energy efficiency policies covering appliance standards, financial incentives, information and voluntary programs, and government energy use (building and professional codes are not included). Estimates indicate these programs are likely to have collectively saved up to 4 quads of energy annually, with appliance standards and utility demand-side management likely making up at least half these savings. Energy Star, Climate Challenge, and 1605b voluntary emissions reductions may also contribute significantly to aggregate energy savings, but how much of these savings would have occurred absent these programs is less clear. Although even more uncertain, reductions in CO2, NOX, SO2, and PM-10 associated with energy savings may contribute about 10% more to the value of energy savings.energy efficiency policy, appliance standards, information, incentives, voluntary programs
Flight deck automation: Promises and realities
Issues of flight deck automation are multifaceted and complex. The rapid introduction of advanced computer-based technology onto the flight deck of transport category aircraft has had considerable impact both on aircraft operations and on the flight crew. As part of NASA's responsibility to facilitate an active exchange of ideas and information among members of the aviation community, a NASA/FAA/Industry workshop devoted to flight deck automation, organized by the Aerospace Human Factors Research Division of NASA Ames Research Center. Participants were invited from industry and from government organizations responsible for design, certification, operation, and accident investigation of transport category, automated aircraft. The goal of the workshop was to clarify the implications of automation, both positive and negative. Workshop panels and working groups identified issues regarding the design, training, and procedural aspects of flight deck automation, as well as the crew's ability to interact and perform effectively with the new technology. The proceedings include the invited papers and the panel and working group reports, as well as the summary and conclusions of the conference
Evidence Report: Risk of Inadequate Human-Computer Interaction
Human-computer interaction (HCI) encompasses all the methods by which humans and computer-based systems communicate, share information, and accomplish tasks. When HCI is poorly designed, crews have difficulty entering, navigating, accessing, and understanding information. HCI has rarely been studied in an operational spaceflight context, and detailed performance data that would support evaluation of HCI have not been collected; thus, we draw much of our evidence from post-spaceflight crew comments, and from other safety-critical domains like ground-based power plants, and aviation. Additionally, there is a concern that any potential or real issues to date may have been masked by the fact that crews have near constant access to ground controllers, who monitor for errors, correct mistakes, and provide additional information needed to complete tasks. We do not know what types of HCI issues might arise without this "safety net". Exploration missions will test this concern, as crews may be operating autonomously due to communication delays and blackouts. Crew survival will be heavily dependent on available electronic information for just-in-time training, procedure execution, and vehicle or system maintenance; hence, the criticality of the Risk of Inadequate HCI. Future work must focus on identifying the most important contributing risk factors, evaluating their contribution to the overall risk, and developing appropriate mitigations. The Risk of Inadequate HCI includes eight core contributing factors based on the Human Factors Analysis and Classification System (HFACS): (1) Requirements, policies, and design processes, (2) Information resources and support, (3) Allocation of attention, (4) Cognitive overload, (5) Environmentally induced perceptual changes, (6) Misperception and misinterpretation of displayed information, (7) Spatial disorientation, and (8) Displays and controls
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