6,297 research outputs found
Spatial Database For Intersections
Deciding which intersections in the state of Kentucky warrant safety improvements requires a comprehensive inventory with information on every intersection in the public roadway network. The Kentucky Transportation Cabinet (KYTC) had previously catalogued only those intersections where state-maintained roadways met. However, this inventory did not account for intersections between state- and locally-maintained routes, nor was it designed to accommodate regular updates. As such, the Kentucky Transportation Center (KTC) at the University of Kentucky developed a methodology to create and maintain a full inventory of every intersection in the state. The database contains precise location information as well as several safety and operational attributes for each point of an intersection. By replicating the topology factors used in the Highway Safety Manual (HSM), the research team categorized every intersection type, and developed. Safety Performance Functions (SPF) for each intersection type. The SPFs were used to rank each intersection. It is anticipated that this project’s deliverables will be used to increase KYTC’s ability to effectively allocate funds to maintain and improve intersection safety. Making the database available to expert users will allow continuous improvements. In the future, AADT data and traffic control information could be included
Large Genomes Assembly Using MAPREDUCE Framework
Knowing the genome sequence of an organism is the essential step toward understanding its genomic and genetic characteristics. Currently, whole genome shotgun (WGS) sequencing is the most widely used genome sequencing technique to determine the entire DNA sequence of an organism. Recent advances in next-generation sequencing (NGS) techniques have enabled biologists to generate large DNA sequences in a high-throughput and low-cost way. However, the assembly of NGS reads faces significant challenges due to short reads and an enormously high volume of data. Despite recent progress in genome assembly, current NGS assemblers cannot generate high-quality results or efficiently handle large genomes with billions of reads. In this research, we proposed a new Genome Assembler based on MapReduce (GAMR), which tackles both limitations. GAMR is based on a bi-directed de Bruijn graph and implemented using the MapReduce framework. We designed a distributed algorithm for each step in GAMR, making it scalable in assembling large-scale genomes. We also proposed novel gap-filling algorithms to improve assembly results to achieve higher accuracy and more extended continuity. We evaluated the assembly performance of GAMR using benchmark data and compared it against other NGS assemblers. We also demonstrated the scalability of GAMR by using it to assemble loblolly pine (~22Gbp). The results showed that GAMR finished the assembly much faster and with a much lower requirement of computing resources
Applied deep learning in intelligent transportation systems and embedding exploration
Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization and data retrieval.
The first part of this dissertation is about a Spatio-TEmporal Fuzzy neural Network (STEF-Net) which accurately predicts passenger demand by incorporating the complex interaction of all known important factors, such as temporal, spatial and external information. Specifically, a convolutional long short-term memory network is employed to simultaneously capture spatio-temporal feature interaction, and a fuzzy neural network to model external factors. A novel feature fusion method with convolution and an attention layer is proposed to keep the temporal relation and discriminative spatio-temporal feature interaction. Experiments on a large-scale real-world dataset show the proposed model outperforms the state-of-the-art approaches.
The second part is a light-weight and energy-efficient system which detects transportation modes using only accelerometer sensors in smartphones. Understanding people’s transportation modes is beneficial to many civilian applications, such as urban transportation planning. The system collects accelerometer data in an efficient way and leverages a convolutional neural network to determine transportation modes. Different architectures and classification methods are tested with the proposed convolutional neural network to optimize the system design. Performance evaluation shows that the proposed approach achieves better accuracy than existing work in detecting people’s transportation modes.
The third component of this dissertation is a deep reinforcement learning model, based on Q learning, to control the traffic light. Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. In the proposed model, the complex traffic scenario is quantified as states by collecting data and dividing the whole intersection into grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map states to rewards, which is further optimized by several components, such as dueling network, target network, double Q-learning network, and prioritized experience replay. The simulation results in Simulation of Urban MObility (SUMO) show the efficiency of the proposed model in controlling traffic lights.
The last part of this dissertation studies the hierarchical structure in an embedding system. Traditional embedding approaches associate a real-valued embedding vector with each symbol or data point, which generates storage-inefficient representation and fails to effectively encode the internal semantic structure of data. A regularized autoencoder framework is proposed to learn compact Hierarchical K-way D-dimensional (HKD) discrete embedding of data points, aiming at capturing semantic structures of data. Experimental results on synthetic and real-world datasets show that the proposed HKD embedding can effectively reveal the semantic structure of data via visualization and greatly reduce the search space of nearest neighbor retrieval while preserving high accuracy
The Quantum PCP Conjecture
The classical PCP theorem is arguably the most important achievement of
classical complexity theory in the past quarter century. In recent years,
researchers in quantum computational complexity have tried to identify
approaches and develop tools that address the question: does a quantum version
of the PCP theorem hold? The story of this study starts with classical
complexity and takes unexpected turns providing fascinating vistas on the
foundations of quantum mechanics, the global nature of entanglement and its
topological properties, quantum error correction, information theory, and much
more; it raises questions that touch upon some of the most fundamental issues
at the heart of our understanding of quantum mechanics. At this point, the jury
is still out as to whether or not such a theorem holds. This survey aims to
provide a snapshot of the status in this ongoing story, tailored to a general
theory-of-CS audience.Comment: 45 pages, 4 figures, an enhanced version of the SIGACT guest column
from Volume 44 Issue 2, June 201
Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
Detailed forest inventories are critical for sustainable and flexible
management of forest resources, to conserve various ecosystem services. Modern
airborne laser scanners deliver high-density point clouds with great potential
for fine-scale forest inventory and analysis, but automatically partitioning
those point clouds into meaningful entities like individual trees or tree
components remains a challenge. The present study aims to fill this gap and
introduces a deep learning framework, termed ForAINet, that is able to perform
such a segmentation across diverse forest types and geographic regions. From
the segmented data, we then derive relevant biophysical parameters of
individual trees as well as stands. The system has been tested on FOR-Instance,
a dataset of point clouds that have been acquired in five different countries
using surveying drones. The segmentation back-end achieves over 85% F-score for
individual trees, respectively over 73% mean IoU across five semantic
categories: ground, low vegetation, stems, live branches and dead branches.
Building on the segmentation results our pipeline then densely calculates
biophysical features of each individual tree (height, crown diameter, crown
volume, DBH, and location) and properties per stand (digital terrain model and
stand density). Especially crown-related features are in most cases retrieved
with high accuracy, whereas the estimates for DBH and location are less
reliable, due to the airborne scanning setup
Fully automated urban traffic system
The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible
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