960 resultados para Improved sequential algebraic algorithm
Resumo:
Since the 1950s, the theory of deterministic and nondeterministic finite automata (DFAs and NFAs, respectively) has been a cornerstone of theoretical computer science. In this dissertation, our main object of study is minimal NFAs. In contrast with minimal DFAs, minimal NFAs are computationally challenging: first, there can be more than one minimal NFA recognizing a given language; second, the problem of converting an NFA to a minimal equivalent NFA is NP-hard, even for NFAs over a unary alphabet. Our study is based on the development of two main theories, inductive bases and partials, which in combination form the foundation for an incremental algorithm, ibas, to find minimal NFAs. An inductive basis is a collection of languages with the property that it can generate (through union) each of the left quotients of its elements. We prove a fundamental characterization theorem which says that a language can be recognized by an n-state NFA if and only if it can be generated by an n-element inductive basis. A partial is an incompletely-specified language. We say that an NFA recognizes a partial if its language extends the partial, meaning that the NFA’s behavior is unconstrained on unspecified strings; it follows that a minimal NFA for a partial is also minimal for its language. We therefore direct our attention to minimal NFAs recognizing a given partial. Combining inductive bases and partials, we generalize our characterization theorem, showing that a partial can be recognized by an n-state NFA if and only if it can be generated by an n-element partial inductive basis. We apply our theory to develop and implement ibas, an incremental algorithm that finds minimal partial inductive bases generating a given partial. In the case of unary languages, ibas can often find minimal NFAs of up to 10 states in about an hour of computing time; with brute-force search this would require many trillions of years.
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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.
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This thesis describes the development of an adaptive control algorithm for Computerized Numerical Control (CNC) machines implemented in a multi-axis motion control board based on the TMS320C31 DSP chip. The adaptive process involves two stages: Plant Modeling and Inverse Control Application. The first stage builds a non-recursive model of the CNC system (plant) using the Least-Mean-Square (LMS) algorithm. The second stage consists of the definition of a recursive structure (the controller) that implements an inverse model of the plant by using the coefficients of the model in an algorithm called Forward-Time Calculation (FTC). In this way, when the inverse controller is implemented in series with the plant, it will pre-compensate for the modification that the original plant introduces in the input signal. The performance of this solution was verified at three different levels: Software simulation, implementation in a set of isolated motor-encoder pairs and implementation in a real CNC machine. The use of the adaptive inverse controller effectively improved the step response of the system in all three levels. In the simulation, an ideal response was obtained. In the motor-encoder test, the rise time was reduced by as much as 80%, without overshoot, in some cases. Even with the larger mass of the actual CNC machine, decrease of the rise time and elimination of the overshoot were obtained in most cases. These results lead to the conclusion that the adaptive inverse controller is a viable approach to position control in CNC machinery.
Resumo:
Since the 1950s, the theory of deterministic and nondeterministic finite automata (DFAs and NFAs, respectively) has been a cornerstone of theoretical computer science. In this dissertation, our main object of study is minimal NFAs. In contrast with minimal DFAs, minimal NFAs are computationally challenging: first, there can be more than one minimal NFA recognizing a given language; second, the problem of converting an NFA to a minimal equivalent NFA is NP-hard, even for NFAs over a unary alphabet. Our study is based on the development of two main theories, inductive bases and partials, which in combination form the foundation for an incremental algorithm, ibas, to find minimal NFAs. An inductive basis is a collection of languages with the property that it can generate (through union) each of the left quotients of its elements. We prove a fundamental characterization theorem which says that a language can be recognized by an n-state NFA if and only if it can be generated by an n-element inductive basis. A partial is an incompletely-specified language. We say that an NFA recognizes a partial if its language extends the partial, meaning that the NFA's behavior is unconstrained on unspecified strings; it follows that a minimal NFA for a partial is also minimal for its language. We therefore direct our attention to minimal NFAs recognizing a given partial. Combining inductive bases and partials, we generalize our characterization theorem, showing that a partial can be recognized by an n-state NFA if and only if it can be generated by an n-element partial inductive basis. We apply our theory to develop and implement ibas, an incremental algorithm that finds minimal partial inductive bases generating a given partial. In the case of unary languages, ibas can often find minimal NFAs of up to 10 states in about an hour of computing time; with brute-force search this would require many trillions of years.
Resumo:
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.
Resumo:
The authors would like to express their gratitude to organizations and people that supported this research. Piotr Omenzetter’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research. Ben Ryder of Aurecon and Graeme Cummings of HEB Construction assisted in obtaining access to the bridge and information for modelling. Luke Williams and Graham Bougen, undergraduate research students, assisted with testing.
Resumo:
Piotr Omenzetter and Simon Hoell’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.
Resumo:
Medical imaging technologies are experiencing a growth in terms of usage and image resolution, namely in diagnostics systems that require a large set of images, like CT or MRI. Furthermore, legal restrictions impose that these scans must be archived for several years. These facts led to the increase of storage costs in medical image databases and institutions. Thus, a demand for more efficient compression tools, used for archiving and communication, is arising. Currently, the DICOM standard, that makes recommendations for medical communications and imaging compression, recommends lossless encoders such as JPEG, RLE, JPEG-LS and JPEG2000. However, none of these encoders include inter-slice prediction in their algorithms. This dissertation presents the research work on medical image compression, using the MRP encoder. MRP is one of the most efficient lossless image compression algorithm. Several processing techniques are proposed to adapt the input medical images to the encoder characteristics. Two of these techniques, namely changing the alignment of slices for compression and a pixel-wise difference predictor, increased the compression efficiency of MRP, by up to 27.9%. Inter-slice prediction support was also added to MRP, using uni and bi-directional techniques. Also, the pixel-wise difference predictor was added to the algorithm. Overall, the compression efficiency of MRP was improved by 46.1%. Thus, these techniques allow for compression ratio savings of 57.1%, compared to DICOM encoders, and 33.2%, compared to HEVC RExt Random Access. This makes MRP the most efficient of the encoders under study.
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Optical coherence tomography (OCT) is a noninvasive three-dimensional interferometric imaging technique capable of achieving micrometer scale resolution. It is now a standard of care in ophthalmology, where it is used to improve the accuracy of early diagnosis, to better understand the source of pathophysiology, and to monitor disease progression and response to therapy. In particular, retinal imaging has been the most prevalent clinical application of OCT, but researchers and companies alike are developing OCT systems for cardiology, dermatology, dentistry, and many other medical and industrial applications.
Adaptive optics (AO) is a technique used to reduce monochromatic aberrations in optical instruments. It is used in astronomical telescopes, laser communications, high-power lasers, retinal imaging, optical fabrication and microscopy to improve system performance. Scanning laser ophthalmoscopy (SLO) is a noninvasive confocal imaging technique that produces high contrast two-dimensional retinal images. AO is combined with SLO (AOSLO) to compensate for the wavefront distortions caused by the optics of the eye, providing the ability to visualize the living retina with cellular resolution. AOSLO has shown great promise to advance the understanding of the etiology of retinal diseases on a cellular level.
Broadly, we endeavor to enhance the vision outcome of ophthalmic patients through improved diagnostics and personalized therapy. Toward this end, the objective of the work presented herein was the development of advanced techniques for increasing the imaging speed, reducing the form factor, and broadening the versatility of OCT and AOSLO. Despite our focus on applications in ophthalmology, the techniques developed could be applied to other medical and industrial applications. In this dissertation, a technique to quadruple the imaging speed of OCT was developed. This technique was demonstrated by imaging the retinas of healthy human subjects. A handheld, dual depth OCT system was developed. This system enabled sequential imaging of the anterior segment and retina of human eyes. Finally, handheld SLO/OCT systems were developed, culminating in the design of a handheld AOSLO system. This system has the potential to provide cellular level imaging of the human retina, resolving even the most densely packed foveal cones.
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The effectiveness of an optimization algorithm can be reduced to its ability to navigate an objective function’s topology. Hybrid optimization algorithms combine various optimization algorithms using a single meta-heuristic so that the hybrid algorithm is more robust, computationally efficient, and/or accurate than the individual algorithms it is made of. This thesis proposes a novel meta-heuristic that uses search vectors to select the constituent algorithm that is appropriate for a given objective function. The hybrid is shown to perform competitively against several existing hybrid and non-hybrid optimization algorithms over a set of three hundred test cases. This thesis also proposes a general framework for evaluating the effectiveness of hybrid optimization algorithms. Finally, this thesis presents an improved Method of Characteristics Code with novel boundary conditions, which better characterizes pipelines than previous codes. This code is coupled with the hybrid optimization algorithm in order to optimize the operation of real-world piston pumps.
Resumo:
The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost. Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation. In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.
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This paper describes a fast integer sorting algorithm, herein referred to as Bit-index sort, which does not use comparisons and is intended to sort partial permutations. Experimental results exhibit linear complexity order in execution time. Bit-index sort uses a bit-array to classify input sequences of distinct integers, and exploits built-in bit functions in C compilers, supported by machine hardware, to retrieve the ordered output sequence. Results show that Bit-index sort outperforms quicksort and counting sort algorithms when compared in their execution time. A parallel approach for Bit-index sort using two simultaneous threads is also included, which obtains further speedups of up to 1.6 compared to its sequential case.
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Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.
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Thesis (Ph.D.)--University of Washington, 2016-08
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Multilevel algorithms are a successful class of optimisation techniques which address the mesh partitioning problem. They usually combine a graph contraction algorithm together with a local optimisation method which refines the partition at each graph level. In this paper we present an enhancement of the technique which uses imbalance to achieve higher quality partitions. We also present a formulation of the Kernighan-Lin partition optimisation algorithm which incorporates load-balancing. The resulting algorithm is tested against a different but related state-of the-art partitioner and shown to provide improved results.