Handwritten and machine-printed text discrimination using a template matching approach

We propose a novel template matching approach for the discrimination of handwritten and machine-printed text. We first pre-process the scanned document images by performing denoising, circles/lines exclusion and word-block level segmentation. We then align and match characters in a flexible sized gallery with the segmented regions, using parallelised normalised cross-correlation. The experimental results over the Pattern Recognition & Image Analysis Research Lab-Natural History Museum (PRImA-NHM) dataset show remarkably high robustness of the algorithm in classifying cluttered, occluded and noisy samples, in addition to those with significant high missing data. The algorithm, which gives 84.0% classification rate with false positive rate 0.16 over the dataset, does not require training samples and generates compelling results as opposed to the training-based approaches, which have used the same benchmark.

Re-written by machine and new technology: Did the Internet kill the Video Star

The traditional way of understanding television content consumption and viewer reactions may be simply summarised: information about the program, viewing at airing time, and interpersonal discussion after the program. In our digital media environment due to crossmedia consumption and platform shifts, the actual trend in audiovisual, and traditionally television content consumption is changing, the viewer’s journey is different across contents and platforms. Content is becoming independent from the platform and the medium is increasingly in the hands of technologically empowered viewers. Our objective is to uncover how traditional content expressly manufactured for television (series, reality shows, sports) can now be consumed via other platforms, and how and to what extent audiovisual content consumption is complemented or replaced by other forms (text, audio). In our exploratory research we identify the typical patterns of interaction and synergies of consumption across classical media content. In this study we used a multimethodology qualitative research design with three research phases including focus groups, online content analysis, and viewers’ narratives. Overall, the Video Star stays alive, but has to deal with immediate reactions and has to change according to his or her audiences’ wishes

High frequency electric machine models for the evaluation of operational impacts in integrated drives

A high frequency physical phase variable electric machine model was developed using FE analysis. The model was implemented in a machine drive environment with hardware-in-the-loop. The novelty of the proposed model is that it is derived based on the actual geometrical and other physical information of the motor, considering each individual turn in the winding. This is the first attempt to develop such a model to obtain high frequency machine parameters without resorting to expensive experimental procedures currently in use. The model was used in a dynamic simulation environment to predict inverter-motor interaction. This includes motor terminal overvoltage, current spikes, as well as switching effects. In addition, a complete drive model was developed for electromagnetic interference (EMI) analysis and evaluation. This consists of the lumped parameter models of different system components, such as cable, inverter, and motor. The lumped parameter models enable faster simulations. The results obtained were verified by experimental measurements and excellent agreements were obtained. A change in the winding arrangement and its influence on the motor high frequency behavior has also been investigated. This was shown to have a little effect on the parameter values and in the motor high frequency behavior for equal number of turns. An accurate prediction of overvoltage and EMI in the design stages of the drive system would reduce the time required for the design modifications as well as for the evaluation of EMC compliance issues. The model can be utilized in the design optimization and insulation selection for motors. Use of this procedure could prove economical, as it would help designers develop and test new motor designs for the evaluation of operational impacts in various motor drive applications.

Makespan minimization in a job shop with a batch processing machine

A job shop with one batch processing and several discrete machines is analyzed. Given a set of jobs, their process routes, processing requirements, and size, the objective is to schedule the jobs such that the makespan is minimized. The batch processing machine can process a batch of jobs as long as the machine capacity is not violated. The batch processing time is equal to the longest processing job in the batch. The problem under study can be represented as Jm:batch:Cmax. If no batches were formed, the scheduling problem under study reduces to the classical job shop scheduling problem (i.e. Jm:: Cmax), which is known to be NP-hard. This research extends the scheduling literature by combining Jm::Cmax with batch processing. The primary contributions are the mathematical formulation, a new network representation and several solution approaches. The problem under study is observed widely in metal working and other industries, but received limited or no attention due to its complexity. A novel network representation of the problem using disjunctive and conjunctive arcs, and a mathematical formulation are proposed to minimize the makespan. Besides that, several algorithms, like batch forming heuristics, dispatching rules, Modified Shifting Bottleneck, Tabu Search (TS) and Simulated Annealing (SA), were developed and implemented. An experimental study was conducted to evaluate the proposed heuristics, and the results were compared to those from a commercial solver (i.e., CPLEX). TS and SA, with the combination of MWKR-FF as the initial solution, gave the best solutions among all the heuristics proposed. Their results were close to CPLEX; and for some larger instances, with total operations greater than 225, they were competitive in terms of solution quality and runtime. For some larger problem instances, CPLEX was unable to report a feasible solution even after running for several hours. Between SA and the experimental study indicated that SA produced a better average Cmax for all instances. The solution approaches proposed will benefit practitioners to schedule a job shop (with both discrete and batch processing machines) more efficiently. The proposed solution approaches are easier to implement and requires short run times to solve large problem instances.

Document understanding using data mining and machine learning techniques

With the explosive growth of the volume and complexity of document data (e.g., news, blogs, web pages), it has become a necessity to semantically understand documents and deliver meaningful information to users. Areas dealing with these problems are crossing data mining, information retrieval, and machine learning. For example, document clustering and summarization are two fundamental techniques for understanding document data and have attracted much attention in recent years. Given a collection of documents, document clustering aims to partition them into different groups to provide efficient document browsing and navigation mechanisms. One unrevealed area in document clustering is that how to generate meaningful interpretation for the each document cluster resulted from the clustering process. Document summarization is another effective technique for document understanding, which generates a summary by selecting sentences that deliver the major or topic-relevant information in the original documents. How to improve the automatic summarization performance and apply it to newly emerging problems are two valuable research directions. To assist people to capture the semantics of documents effectively and efficiently, the dissertation focuses on developing effective data mining and machine learning algorithms and systems for (1) integrating document clustering and summarization to obtain meaningful document clusters with summarized interpretation, (2) improving document summarization performance and building document understanding systems to solve real-world applications, and (3) summarizing the differences and evolution of multiple document sources.

Design optimization of modern machine drive systems for maximum fault tolerant and optimal operation

Modern electric machine drives, particularly three phase permanent magnet machine drive systems represent an indispensable part of high power density products. Such products include; hybrid electric vehicles, large propulsion systems, and automation products. Reliability and cost of these products are directly related to the reliability and cost of these systems. The compatibility of the electric machine and its drive system for optimal cost and operation has been a large challenge in industrial applications. The main objective of this dissertation is to find a design and control scheme for the best compromise between the reliability and optimality of the electric machine-drive system. The effort presented here is motivated by the need to find new techniques to connect the design and control of electric machines and drive systems. ^ A highly accurate and computationally efficient modeling process was developed to monitor the magnetic, thermal, and electrical aspects of the electric machine in its operational environments. The modeling process was also utilized in the design process in form finite element based optimization process. It was also used in hardware in the loop finite element based optimization process. The modeling process was later employed in the design of a very accurate and highly efficient physics-based customized observers that are required for the fault diagnosis as well the sensorless rotor position estimation. Two test setups with different ratings and topologies were numerically and experimentally tested to verify the effectiveness of the proposed techniques. ^ The modeling process was also employed in the real-time demagnetization control of the machine. Various real-time scenarios were successfully verified. It was shown that this process gives the potential to optimally redefine the assumptions in sizing the permanent magnets of the machine and DC bus voltage of the drive for the worst operating conditions. ^ The mathematical development and stability criteria of the physics-based modeling of the machine, design optimization, and the physics-based fault diagnosis and the physics-based sensorless technique are described in detail. ^ To investigate the performance of the developed design test-bed, software and hardware setups were constructed first. Several topologies of the permanent magnet machine were optimized inside the optimization test-bed. To investigate the performance of the developed sensorless control, a test-bed including a 0.25 (kW) surface mounted permanent magnet synchronous machine example was created. The verification of the proposed technique in a range from medium to very low speed, effectively show the intelligent design capability of the proposed system. Additionally, to investigate the performance of the developed fault diagnosis system, a test-bed including a 0.8 (kW) surface mounted permanent magnet synchronous machine example with trapezoidal back electromotive force was created. The results verify the use of the proposed technique under dynamic eccentricity, DC bus voltage variations, and harmonic loading condition make the system an ideal case for propulsion systems.^

V-Commerce: Vending Machine Technology

A Popular auxiliary service provided by hospitality businesses is automatic merchandising, more commonly known as vending. Recent advancement in vending technology (v-commerce) has changed the way vending machines are monitored, replenished, maintained, and reconciled. As the hospitality industry searches to reduce its reliance on labor intensive processes, automatic merchandising represents and effective way to provide unattended points of sale and service. Smart machines featuring quality products with high levels of auditabile control may me more appealing to the hospitality industry. While a hospitality manager does not need to have knowleds of the vending distribution channel or machine maintenance, it is important to understand available technology and the opportunity it provides for operational efficiencies and revenue enhancement.

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Outer rotor mechanical and dynamic performance analysis for high-speed machine

Due to high-speed rotation, the problems about rotor mechanics and dynamics for outer rotor high-speed machine are more serious than conventional ones, in view of above problems the mechanical and dynamics analysis for an outer rotor high-speed permanent magnet claw pole motor are carried out. The rotor stress analytical calculation model was derived, then the stress distribution is calculated by finite element method also, which is coincided with that calculated by analytical model. In addition, the stress distribution of outer rotor yoke and PMs considering centrifugal force and temperature effect has been calculated, some influence factors on rotor stress distribution have been analyzed such as pole-arc coefficient and speed. The rotor natural frequency and critical speed were calculated by vibration mode analysis, and its dynamics characteristics influenced by gyroscope effect were analyzed based on Campbell diagram. Based on the analysis results above an outer rotor permanent magnet high-speed claw pole motor is design and verified.

Kerr-induced nonlinearity reduction in coherent optical OFDM by low complexity support vector machine regression-based equalization

We experimentally demonstrate 7-dB reduction of nonlinearity penalty in 40-Gb/s CO-OFDM at 2000-km using support vector machine regression-based equalization. Simulation in WDM-CO-OFDM shows up to 12-dB enhancement in Q-factor compared to linear equalization.

Machine translation quality in an audiovisual context

The volume of Audiovisual Translation (AVT) is increasing to meet the rising demand for data that needs to be accessible around the world. Machine Translation (MT) is one of the most innovative technologies to be deployed in the field of translation, but it is still too early to predict how it can support the creativity and productivity of professional translators in the future. Currently, MT is more widely used in (non-AV) text translation than in AVT. In this article, we discuss MT technology and demonstrate why its use in AVT scenarios is particularly challenging. We also present some potentially useful methods and tools for measuring MT quality that have been developed primarily for text translation. The ultimate objective is to bridge the gap between the tech-savvy AVT community, on the one hand, and researchers and developers in the field of high-quality MT, on the other.

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Dimensionality hyper-reduction and machine learning for dynamical systems with varying parameters

Thesis (Ph.D.)--University of Washington, 2016-08

Machine learning and data decompositions for complex networked dynamical systems

Thesis (Ph.D.)--University of Washington, 2016-08

Essays on Machine Learning and Hedonic Models

Thesis (Ph.D.)--University of Washington, 2016-08