Real-time error compensation of a three-axis machine tool using a laser tracker

The concept of measurement-enabled production is based on integrating metrology systems into production processes and generated significant interest in industry, due to its potential to increase process capability and accuracy, which in turn reduces production times and eliminates defective parts. One of the most promising methods of integrating metrology into production is the usage of external metrology systems to compensate machine tool errors in real time. The development and experimental performance evaluation of a low-cost, prototype three-axis machine tool that is laser tracker assisted are described in this paper. Real-time corrections of the machine tool's absolute volumetric error have been achieved. As a result, significant increases in static repeatability and accuracy have been demonstrated, allowing the low-cost three-axis machine tool to reliably reach static positioning accuracies below 35 μm throughout its working volume without any prior calibration or error mapping. This is a significant technical development that demonstrated the feasibility of the proposed methods and can have wide-scale industrial applications by enabling low-cost and structural integrity machine tools that could be deployed flexibly as end-effectors of robotic automation, to achieve positional accuracies that were the preserve of large, high-precision machine tools.

The application of uncertainty evaluating software for the utilisation of machine tool systems for final inspection

As machine tools continue to become increasingly repeatable and accurate, high-precision manufacturers may be tempted to consider how they might utilise machine tools as measurement systems. In this paper, we have explored this paradigm by attempting to repurpose state-of-the-art coordinate measuring machine Uncertainty Evaluating Software (UES) for a machine tool application. We performed live measurements on all the systems in question. Our findings have highlighted some gaps with UES when applied to machine tools, and we have attempted to identify the sources of variation which have led to discrepancies. Implications of this research include requirements to evolve the algorithms within the UES if it is to be adapted for on-machine measurement, improve the robustness of the input parameters, and most importantly, clarify expectations.

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.

Simulation and experimental analysis of a brushless electrically excited synchronous machine with a hybrid rotor

Electrically excited synchronous machines with brushes and slip rings are popular but hardly used in inflammable and explosive environments. This paper proposes a new brushless electrically excited synchronous motor with a hybrid rotor. It eliminates the use of brushes and slip rings so as to improve the reliability and cost-effectiveness of the traction drive. The proposed motor is characterized with two sets of stator windings with two different pole numbers to provide excitation and drive torque independently. This paper introduces the structure and operating principle of the machine, followed by the analysis of the air-gap magnetic field using the finite-element method. The influence of the excitation winding's pole number on the coupling capability is studied and the operating characteristics of the machine are simulated. These are further examined by the experimental tests on a 16 kW prototype motor. The machine is proved to have good static and dynamic performance, which meets the stringent requirements for traction applications.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

DFIG machine design for maximizing power output based on surrogate optimization algorithm

This paper presents a surrogate-model-based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine's previous operational performance, the DFIG's stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization-based surrogate optimization techniques are used in conjunction with the finite element method to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Rotor retaining sleeve design for a 1.12-MW high-speed PM machine

Permanent-magnet (PM) synchronous machines (PMSMs) can provide excellent performance in terms of torque density, energy efficiency, and controllability. However, PMs on the rotor are prone to centrifugal force, which may break their physical integrity, particularly at high-speed operation. Typically, PMs are bound with carbon fiber or retained by alloy sleeves on the rotor surface. This paper is concerned with the design of a rotor retaining sleeve for a 1.12-MW 18-kr/min PM machine; its electromagnetic performance is investigated by the 2-D finite-element method (FEM). Theoretical and numerical analyses of the rotor stress are carried out. For the carbon fiber protective measure, the stresses of three PM configurations and three pole filler materials are compared in terms of operating temperature, rotor speed, retaining sleeve thickness, and interference fit. Then, a new hybrid protective measure is proposed and analyzed by the 2-D FEM for operational speeds up to 22 kr/min (1.2 times the rated speed). The rotor losses and machine temperatures with the carbon fiber retaining sleeve and the hybrid retaining sleeve are compared, and the sleeve design is refined. Two rotors using both designs are prototyped and experimentally tested to validate the effectiveness of the developed techniques for PM machines. The developed retaining sleeve makes it possible to operate megawatt PM machines at high speeds of 22 kr/min. This opens doors for many high-power high-speed applications such as turbo-generator, aerospace, and submarine motor drives.

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.

Translating international norms: Filters to women's rights in Lebanon

In 1979 the United Nations passed the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), an international bill of rights for women. Much scholarship has focused on the degree to which states have adopted these new international gender norms, but have paid little attention to the fact that norms change in the processes of implementation. This dissertation focuses on that process assessing the translation of international gender equality norm in Lebanon.^ The study traces global gender equality norms as they are translated into a complex context characterized by a political structure that divides powers according to confessional groups, a social structure that empowers men as heads of families, and a geopolitical structure that opposes a secular West to the Muslim East. Through a comparison of three campaigns – the campaign to combat violence against women, the campaign to change personal status codes, and the campaign to give women equal rights to pass on their nationality – the study traces different ways in which norms are translated as activists negotiate the structures that make up the Lebanese context. Through ethnographic research, the process of norm translation was found to produce various filters, i.e., constellations of arguments put forward by activists as they seek to match international norms to the local context. The dissertation identifies six such filters and finds that these filters often have created faithless translations of international norms.^

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.^