遮挡范式下碰撞时间估计的特性研究

The estimation of Time to Collision (TTC) related to avoiding collisions or making interceptions is an important cognitive ability for individuals. A number of studies have been carried out on this topic and related theories are developed. One of the most famous theories is the τ Theory. Based on the τ Theory, researches have found that visual information and physical information of moving objects would influent the TTC estimation. Are there any other factors that can affect people’s TTC estimation? A mixed design was used to examine the TTC estimation by different types of participants (professional drivers / people can not drive), with different moving objects (car/tricycle), under different speed (slow/fast) and direction (left to right / right to left) in Occlusion Paradigm. There were 21 professional drivers and 20 individuals who cannot drive participated in the experiment which was displayed on a computer. Participants were asked to click the button when he/she believed that the moving object had just contacted the red line. E-prime was used to establish the whole experimental environment and the RT was recorded at the same time. The results revealed that: (1) there is significant different TTC estimation between car and tricycle; (2) the professional drivers have more accurate TTC estimation than people do not drive. We can come to conclusion that conceptual information and driving skill could affect TTC estimation.

注意对驾驶场景客体-位置联系内隐学习的影响

The purpose of this study is to investigate the influence of attention resourse requirement and allocation on implicit memory and explicit memory for object-location associations in driving situation based on Adams theory on the function of implicit knowledge in the Situation Awareness(SA). This study adopted Musen’s implicit learning of object-location associations, sysmemtly manipulated the type and difficuty of the naming task. This research includes three studies and ten experiments. Their aim are separately to explore the influence of attention on implicit and explicit memory for object-loction assocaitons in simple stimulus and the driving situation. And it is needed to confirme the condition and the influencing factors of implicit memory for car-location association in different condition. It is also our aim to explore the feasibility of introduce of implicit learning methods in SA measurement. The results indicted that: ⑴ The influence of attention resourse allocation ,the difficulty of naming task , the deepness of processing on on implicit memory for object-location associations in driving situation are different . the dissociated results support the standpoint that there are two independent knowledge system; ⑵ The type of naming task more influenced the implicit and explicit memory for object-location associations than the difficulty of the naming task. The attention resourse requirement of the different type can not be compared; ⑶ The implicit memory seldom appears in the location naming task resulted from the defiency of processing on object-location association, and not as a results of the overtaxed; ⑷ The reaction time methods in the implicit learning could be used in SA measurement , it is a complementarity of the existing explicit SA measurement. These findings not only contribute to resolve ongoing debates about the process of cognition and mechanism of SA structure, but also have significant practical application in traffic safety.

Context-Based Vision System for Place and Object Recognition

While navigating in an environment, a vision system has to be able to recognize where it is and what the main objects in the scene are. In this paper we present a context-based vision system for place and object recognition. The goal is to identify familiar locations (e.g., office 610, conference room 941, Main Street), to categorize new environments (office, corridor, street) and to use that information to provide contextual priors for object recognition (e.g., table, chair, car, computer). We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and how such contextual information introduces strong priors that simplify object recognition. We have trained the system to recognize over 60 locations (indoors and outdoors) and to suggest the presence and locations of more than 20 different object types. The algorithm has been integrated into a mobile system that provides real-time feedback to the user.

Virtual Visual Hulls: Example-Based 3D Shape Estimation from a Single Silhouette

Recovering a volumetric model of a person, car, or other object of interest from a single snapshot would be useful for many computer graphics applications. 3D model estimation in general is hard, and currently requires active sensors, multiple views, or integration over time. For a known object class, however, 3D shape can be successfully inferred from a single snapshot. We present a method for generating a ``virtual visual hull''-- an estimate of the 3D shape of an object from a known class, given a single silhouette observed from an unknown viewpoint. For a given class, a large database of multi-view silhouette examples from calibrated, though possibly varied, camera rigs are collected. To infer a novel single view input silhouette's virtual visual hull, we search for 3D shapes in the database which are most consistent with the observed contour. The input is matched to component single views of the multi-view training examples. A set of viewpoint-aligned virtual views are generated from the visual hulls corresponding to these examples. The 3D shape estimate for the input is then found by interpolating between the contours of these aligned views. When the underlying shape is ambiguous given a single view silhouette, we produce multiple visual hull hypotheses; if a sequence of input images is available, a dynamic programming approach is applied to find the maximum likelihood path through the feasible hypotheses over time. We show results of our algorithm on real and synthetic images of people.

Konstrukcje awansu społecznego w Stanach Zjednoczonych. Między orientacją na dyplom uniwersytecki a sukcesem medialnym

Wydział Studiów Edukacyjnych

Charakterystyka topoklimatyczna fragmentu wybrzeża klifowego w rejonie Białej Góry (Woliński Park Narodowy)

The Meteorological Section at the scientific camp 2009–2010 conducted a series of meteorological measurements in the region of Biała Góra. The exploration area is located about 2 km east of Międzyzdroje, at the research station of the AMU Faculty of Geographical and Geological Sciences. Members of the section made measurements in the six selected points. The location of points had to reflect the specifics of the area (from the beach to the car park at the research station). The section focused on three basic measurements: air temperature (2009–2010), relative humidity (2009–2010) and atmospheric pressure (2009). This article aims to analyse a topoclimate section of cliff coast in the Wolin National Park. The compilation recognised the impact of various land surfaces, sea and altitude on the variability of air temperature and relative humidity. It notes the varied course of the daily meteorological elements analysed, which is directly related to the value of radiation balance dependent upon the intensity of direct solar radiation. In this article, particular emphasis is applied to the analysis of temperature amplitudes and humidity at different measuring points.

Modelo de engenharia de software para o desenvolvimento de jogos e simulações interactivas

Dissertação apresentada à Universidade Fernando Pessoa como partes dos requisitos para a obtenção do grau de Mestre em Engenharia Informática, ramo de Computação Móvel

Os blogues corporativos como meio de comunicação organizacional

Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências da Comunicação, especialização em Relações Públicas.

Competências estratégicas de negociação em gestão comercial

Dissertação de Mestrado apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Empresariais

Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.

Transport energy demand: techno-economic modelling and scenarios for Irish climate policy

The case for energy policy modelling is strong in Ireland, where stringent EU climate targets are projected to be overshot by 2015. Policy targets aiming to deliver greenhouse gas and renewable energy targets have been made, but it is unclear what savings are to be achieved and from which sectors. Concurrently, the growth of personal mobility has caused an astonishing increase in CO2 emissions from private cars in Ireland, a 37% rise between 2000 and 2008, and while there have been improvements in the efficiency of car technology, there was no decrease in the energy intensity of the car fleet in the same period. This thesis increases the capacity for evidenced-based policymaking in Ireland by developing techno-economic transport energy models and using them to analyse historical trends and to project possible future scenarios. A central focus of this thesis is to understand the effect of the car fleet‘s evolving technical characteristics on energy demand. A car stock model is developed to analyse this question from three angles: Firstly, analysis of car registration and activity data between 2000 and 2008 examines the trends which brought about the surge in energy demand. Secondly, the car stock is modelled into the future and is used to populate a baseline “no new policy” scenario, looking at the impact of recent (2008-2011) policy and purchasing developments on projected energy demand and emissions. Thirdly, a range of technology efficiency, fuel switching and behavioural scenarios are developed up to 2025 in order to indicate the emissions abatement and renewable energy penetration potential from alternative policy packages. In particular, an ambitious car fleet electrification target for Ireland is examined. The car stock model‘s functionality is extended by linking it with other models: LEAP-Ireland, a bottom-up energy demand model for all energy sectors in the country; Irish TIMES, a linear optimisation energy system model; and COPERT, a pollution model. The methodology is also adapted to analyse trends in freight energy demand in a similar way. Finally, this thesis addresses the gap in the representation of travel behaviour in linear energy systems models. A novel methodology is developed and case studies for Ireland and California are presented using the TIMES model. Transport Energy

Matemáticas experimentales

En primer lugar, quiero manifestar mi más sincero agradecimiento a los organizadores de estos II Encuentros Extremeños por la amabilidad que han tenido al invitame. Antes que nada, una aclaración. Al hablar de matemáticas experimentales me estaré refiriendo siempre a la enseñanza de nuestra materia. Nada más lejos de mis posibilidades que intentar adjetivar las propias matemáticas. Hablaré de enseñanza de las matemáticas y, en general, de la utilización de recursos que posibiliten la acción del alumno y su protagonismo en el aprendizaje. No pretendo, obviamente, magnificar ni dar valor absoluto a nada; las matemáticas experimentales serán, simplemente, una propuesta metodológica y organizadora del espacio educativo.