高可靠性红外车辆分离器设计与应用

本文给出了一种针对公路自动收费系统的车辆分离检测而设计的,利用红外线发射和接收所形成的光幕,完全消除跟车现象,并能将半挂车、全挂车、单车可靠分离的红外车辆分离器。高可靠性红外车辆分离器的设计很好克服了传统红外车辆分离器环境适应性差、故障率高等缺点。本文还介绍了光幕形成的基本原理,并阐述了硬件关键点及实现方法,给出了系统硬件原理图、软件基本设计思想。

大城市内部商业环境满意度评价与消费区位决策研究-以北京市西城区和海淀区为例

Since 1990s, commercial conditions in China including commercial environment, retail types, scale of retail enterprises, spatial structure of retail and shopping decision making factors have changed. In order to keep up with these changes, commercial geography should set up new perspectives, theories and methods to analyze its internal mechanism and changing rules, and thus provide reasonable and practical scientific basis to commercial planning, location decision of retail enterprises and commercial environment construction. Taking Xicheng and Haidian District of Beijing as research case, which is a sector region from city center to rural region, this paper selects 12 commercial centers as most important study objects of this sector. This paper mainly makes use of the methods of Modeling, Pearson Bivaiiate Correlations Analysis, Factor Analysis and Logit model. Based on 1300 questionnaires and fieldwork, this paper focuses on modeling of Consumer Satisfaction of Commercial Environment (CSCE), evaluation of commercial environment and driving factors of consumers' shopping location decision. Firstly, this paper discusses the development of commercial geography and commercial environment evaluation, the new characteristics and trends of commercial development in Beijing and physical commercial environment of Xicheng and Haidian District of Beijing from chapter 1 to chapter 4. Secondly, this paper summarizes characteristics of residents' shopping behavior in chapter 5. Thirdly, this paper sets up an evaluative model of CSCE, and analyzes consumer satisfaction indexes of commercial environment and their spatial features in chapter 6. Fourthly, this paper infers how residents' attributes and shopping behaviors affect their preferences of shopping location and what are residents' shopping location decisions and their influencing factors in chapter 7. Fifthly, this paper constructs a significant index model and a pyramidal framework of CSCE, and further analyzes the diversity and competitive advantage of commercial environment in chapter 8. Finally, some conclusions are drawn as follows: 1. Characteristics of residents' shopping behavior mostly embody residents' time distance preference, commodity consumption preference, shopping time distribution and shopping activity characteristics. The important factors that influence shopping location choice of residents are distance, transportation, commodity price, commodity types and commodity quality. However, the important factors, which influence shopping location re-choice of residents, are commodity price, commodity quality, commodity types and transportation. 2. CSCE indexes of 12 commercial centers show us significant spatial characteristics, such as spatial differences of "Center-fringe region", spatial characteristics of axes, spatial diversity of ring roads and so on. 3. Influencing factors including factor endowments, relative establishment factor and location and transportation factor of commercial environment are of importance for CSCE. 4. Logit model 1 indicates that shopping behavior of residents is significantly and positively related to working in high-tech companies, high income and by car and positively related to high school diploma, by bus and subway. 5. Logit model 2 indicates that residents' shopping location decision is significantly and positively related to leisure establishment and relative restaurant and entertainment establishment and negatively related to commercial location, commodity price, service quality, parking site. 6. The significant index model and the pyramidal framework of CSCE indicate competitive advantages are crucial to attractive capability of commercial center, and competitive weakness limits development of commercial centers, in particular the weakness of service quality and parking site now is the chief factors restricting development of commercial centers

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.