Intelligent Search and Automatic Document Classification and Cataloging Based on Ontology Approach

This paper presents an approach to development of intelligent search system and automatic document classification and cataloging tools for CASE-system based on metadata. The described method uses advantages of ontology approach and traditional approach based on keywords. The method has powerful intelligent means and it can be integrated with existing document search systems.

Automatic Translation of MSC Diagrams into Petri Nets

Development-engineers use in their work languages intended for software or hardware systems design, and test engineers utilize languages effective in verification, analysis of the systems properties and testing. Automatic interfaces between languages of these kinds are necessary in order to avoid ambiguous understanding of specification of models of the systems and inconsistencies in the initial requirements for the systems development. Algorithm of automatic translation of MSC (Message Sequence Chart) diagrams compliant with MSC’2000 standard into Petri Nets is suggested in this paper. Each input MSC diagram is translated into Petri Net (PN), obtained PNs are sequentially composed in order to synthesize a whole system in one final combined PN. The principle of such composition is defined through the basic element of MSC language — conditions. While translating reference table is developed for maintenance of consistent coordination between the input system’s descriptions in MSC language and in PN format. This table is necessary to present the results of analysis and verification on PN in suitable for the development-engineer format of MSC diagrams. The proof of algorithm correctness is based on the use of process algebra ACP. The most significant feature of the given algorithm is the way of handling of conditions. The direction for future work is the development of integral, partially or completely automated technological process, which will allow designing system, testing and verifying its various properties in the one frame.

Automatic Generation of Titles for a Corpus of Questions

This paper describes the followed methodology to automatically generate titles for a corpus of questions that belong to sociological opinion polls. Titles for questions have a twofold function: (1) they are the input of user searches and (2) they inform about the whole contents of the question and possible answer options. Thus, generation of titles can be considered as a case of automatic summarization. However, the fact that summarization had to be performed over very short texts together with the aforementioned quality conditions imposed on new generated titles led the authors to follow knowledge-rich and domain-dependent strategies for summarization, disregarding the more frequent extractive techniques for summarization.

Automatic ECG Analysis for Preliminary and Detailed Diagnostics Based on Scale-space Representation

A novel approach of automatic ECG analysis based on scale-scale signal representation is proposed. The approach uses curvature scale-space representation to locate main ECG waveform limits and peaks and may be used to correct results of other ECG analysis techniques or independently. Moreover dynamic matching of ECG CSS representations provides robust preliminary recognition of ECG abnormalities which has been proven by experimental results.

Automatic Discovery of Word Semantic Relations

In this paper, we propose an unsupervised methodology to automatically discover pairs of semantically related words by highlighting their local environment and evaluating their semantic similarity in local and global semantic spaces. This proposal di®ers from previous research as it tries to take the best of two different methodologies i.e. semantic space models and information extraction models. It can be applied to extract close semantic relations, it limits the search space and it is unsupervised.

Study of the uncertainty of angle measurement for a rotary-laser automatic theodolite (R-LAT)

This paper shows how the angular uncertainties can be determined for a rotary-laser automatic theodolite of the type used in (indoor-GPS) iGPS networks. Initially, the fundamental physics of the rotating head device is used to propagate uncertainties using Monte Carlo simulation. This theoretical element of the study shows how the angular uncertainty is affected by internal parameters, the actual values of which are estimated. Experiments are then carried out to determine the actual uncertainty in the azimuth angle. Results are presented that show that uncertainty decreases with sampling duration. Other significant findings are that uncertainty is relatively constant throughout the working volume and that the uncertainty value is not dependent on the size of the reference angle. © 2009 IMechE.

Automatic Classification of Bulgarian Adjectives on Emotional Semantic Axes

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Application of automatic load transfer on feeder utilisation balance

This paper looks at how automatic load transfer may be used as a possible planning tool to help deliver faster connections for customers. A trial on an area of overhead line Network is presented to show how improvements in % feeder utilisation may be realised by changing the location of the open point. The reported Network data is compared to calculated data under two different configurations over a two week trial period. The results show that ALT open point determination in the presence of generation is different from a load only circuit and that the open points may not be fixed with time. Looking at improvements in Network headroom may not be conducive to other improvements in the network such as loss reduction or improving voltage profiles.

Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.

The Emotional Intelligence of National Automatic Merchandising Association (NAMA) Vending and Coffee Services Industries Executives: A Pilot Study

The authors report the pilot study focused on identifying the emotional intelligence (El) of leaders in the automatic merchandising and coffee service industries. The data were collected from 39 executives, members of National Automatic Merchandising Association (NM), who attended 2005 Executive Development Program on the campus of Michigan State University. Three elements of EI- IN, OUT, RELATIONSHIP for these leaders are discussed.

Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

An automatic method of above-water iceberg 3-D profiling based on autonomous surface craft (ASC) using surface images and LIDAR

This thesis reports on a novel method to build a 3-D model of the above-water portion of icebergs using surface imaging. The goal is to work towards the automation of iceberg surveys, allowing an Autonomous Surface Craft (ASC) to acquire shape and size information. After collecting data and images, the core software algorithm is made up of three parts: occluding contour finding, volume intersection, and parameter estimation. A software module is designed that could be used on the ASC to perform automatic and fast processing of above-water surface image data to determine iceberg shape and size measurement and determination. The resolution of the method is calculated using data from the iceberg database of the Program of Energy Research and Development (PERD). The method was investigated using data from field trials conducted through the summer of 2014 by surveying 8 icebergs during 3 expeditions. The results were analyzed to determine iceberg characteristics. Limitations of this method are addressed including its accuracy. Surface imaging system and LIDAR system are developed to profile the above-water iceberg in 2015.