Abstract. Three methods, return period, power-law frequency plot (concentration-area) and local singularity index, are introduced in the paper for characterizing peak flow events from river flow data for the past 100 years from 1900 to 2000 recorded at 25 selected gauging stations on rivers in the Oak Ridges Moraine (ORM) area, Canada. First a traditional method, return period, was applied to the maximum annual river flow data. Whereas the Pearson III distribution generally fits the values, a power-law frequency plot (C-A) on the basis of self-similarity principle provides an effective mean for distinguishing "extremely" large flow events from the regular flow events. While the latter show a power-law distribution, about 10 large flow events manifest departure from the power-law distribution and these flow events can be classified into a separate group most of which are related to flood events. It is shown that the relation between the average water releases over a time period after flow peak and the time duration may follow a power-law distribution. The exponent of the power-law or singularity index estimated from this power-law relation may be used to characterize non-linearity of peak flow recessions. Viewing large peak flow events or floods as singular processes can anticipate the application of power-law models not only for characterizing the frequency distribution of peak flow events, for example, power-law relation between the number and size of floods, but also for describing local singularity of processes such as power-law relation between the amount of water released versus releasing time. With the introduction and validation of singularity of peak flow events, alternative power-law models can be used to depict the recession property as well as other types of non-linear properties.
ABSTRACT:A web-based system based on the 3DTown project was proposed using Google Earth plug-in that brings information from indoor positioning devices and real-time sensors into an integrated 3D indoor and outdoor virtual world to visualize the dynamics of urban life within the 3D context of a city. We addressed limitation of the 3DTown project with particular emphasis on video surveillance camera used for indoor tracking purposes. The proposed solution was to utilize wireless local area network (WLAN) WiFi as a replacement technology for localizing objects of interest due to the wide spread availability and large coverage area of WiFi in indoor building spaces. Indoor positioning was performed using WiFi without modifying existing building infrastructure or introducing additional access points (AP)s. A hybrid probabilistic approach was used for indoor positioning based on previously recorded WiFi fingerprint database in the Petrie Science and Engineering building at York University. In addition, we have developed a 3D building modeling module that allows for efficient reconstruction of outdoor building models to be integrated with indoor building models; a sensor module for receiving, distributing, and visualizing real-time sensor data; and a web-based visualization module for users to explore the dynamic urban life in a virtual world. In order to solve the problems in the implementation of the proposed system, we introduce approaches for integration of indoor building models with indoor positioning data, as well as real-time sensor information and visualization on the web-based system. In this paper we report the preliminary results of our prototype system, demonstrating the system's capability for implementing a dynamic 3D indoor and outdoor virtual world that is composed of discrete modules connected through pre-determined communication protocols.
ABSTRACT:This paper presents a Maximum Sequential Similarity Reasoning (MSSR) algorithm based method for co-registration of 3D TLS data and 2D floor plans. The co-registration consists of two tasks: estimating a transformation between the two datasets and finding the vertical locations of windows and doors. The method first extracts TLS line sequences and floor plan line sequences from a series of horizontal cross-section bands of the TLS points and floor plans respectively. Then each line sequence is further decomposed into column vectors defined by using local transformation invariant information between two neighbouring line segments. Based on a normalized cross-correlation based similarity score function, the proposed MSSR algorithm is then used to iteratively estimate the vertical and horizontal locations of each floor plan by finding the longest matched consecutive column vectors between floor plan line sequences and TLS line sequences. A group matching algorithm is applied to simultaneously determine final matching results across floor plans and estimate the transformation parameters between floor plans and TLS points. With real datasets, the proposed method demonstrates its ability to deal with occlusions and multiple matching problems. It also shows the potential to detect conflict between floor plan and as-built, which makes it a promising method that can find many applications in many industrial fields. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.
A b s t r a c t This paper describes a more efficient approach to estimating an impulse or step response model of a linear system which involves first obtaining its frequency response using the frequency sampling filter (FSF) model. We show that the FSF model has an effective or true model order which is in general lower than that of its time domain counterparts, particularly in a fast sampling environment.
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