Investigation of uncoordinated coexisting IEEE 802.15.4 networks with sleep mode for machine-to-machine communications

The low-energy consumption of IEEE 802.15.4 networks makes it a strong candidate for machine-to-machine (M2M) communications. As multiple M2M applications with 802.15.4 networks may be deployed closely and independently in residential or enterprise areas, supporting reliable and timely M2M communications can be a big challenge especially when potential hidden terminals appear. In this paper, we investigate two scenarios of 802.15.4 network-based M2M communication. An analytic model is proposed to understand the performance of uncoordinated coexisting 802.15.4 networks. Sleep mode operations of the networks are taken into account. Simulations verified the analytic model. It is observed that reducing sleep time and overlap ratio can increase the performance of M2M communications. When the networks are uncoordinated, reducing the overlap ratio can effectively improve the network performance. © 2012 Chao Ma et al.

Uncoordinated coexisting IEEE 802.15.4 networks for machine to machine communications

IEEE 802.15.4 standard has been proposed for low power wireless personal area networks. It can be used as an important component in machine to machine (M2M) networks for data collection, monitoring and controlling functions. With an increasing number of machine devices enabled by M2M technology and equipped with 802.15.4 radios, it is likely that multiple 802.15.4 networks may be deployed closely, for example, to collect data for smart metering at residential or enterprise areas. In such scenarios, supporting reliable communications for monitoring and controlling applications is a big challenge. The problem becomes more severe due to the potential hidden terminals when the operations of multiple 802.15.4 networks are uncoordinated. In this paper, we investigate this problem from three typical scenarios and propose an analytic model to reveal how performance of coexisting 802.15.4 networks may be affected by uncoordinated operations under these scenarios. Simulations will be used to validate the analytic model. It is observed that uncoordinated operations may lead to a significant degradation of system performance in M2M applications. With the proposed analytic model, we also investigate the performance limits of the 802.15.4 networks, and the conditions under which coordinated operations may be required to support M2M applications. © 2012 Springer Science + Business Media, LLC.

Distribute machine learning

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Reliability monitoring, feedback and improvement systems:their application in the machine tool industry

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The direction of motion of machine tool controls

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Single-phase active rectifier selection for fault tolerant machine

A fault tolerant, 5-phase PM generator has been developed for use on the low pressure (LP) shaft of an aircraft gas turbine engine. The machine operates at variable speed and therefore has a variable voltage, variable frequency electrical output (VVVF). The generator is to be used to provide a 350V DC bus for distribution throughout the aircraft, and a study has been carried out that identifies the most suitable AC-DC converter topology for this machine in terms of losses, electrical component ratings, filtering requirements and circuit complexity.

Translating Nietzsche, mediating literature: Alexander Tille and the limits of Anglo-German intercultural transfer

Dr. Alexander Tille (1866–1912) was one of the key-figures in Anglo-German intercultural transfer towards the end of the 19th century. As a lecturer in German at Glasgow University he was the first to translate and edit Nietzsche’s work into English. Writers such as W. B. Yeats were influenced by Nietzsche and used Tille’s translations. Tille’s social Darwinist reading of the philosopher’s oeuvre, however, had a narrowing impact on the reception of Nietzsche in the Anglo-Saxon world for decades. Through numerous publications Tille disseminated knowledge about British authors (e.g., Robert Louis Stevenson, William Wordsworth) in Germany and about German authors (e.g., Johann Wolfgang von Goethe) in Britain. His role as mediator also extended into areas such as history, religion, and industry. During the Boer war, however, Tille’s outspoken pro-German nationalism brought him in conflict with his British host society. After being physically attacked by his students he returned to Germany and published a highly anglophobic monograph. Tille personifies the paradox of Anglo-German relations in the pre-war years, which deteriorated despite an increase in intercultural transfer and knowledge about the respective Other.

Translating specialised texts:an overview of key factors involved in translator training for specialized translation

Translators wishing to work on translating specialised texts are traditionally recommended to spend much time and effort acquiring specialist knowledge of the domain involved, and for some areas of specialised activity, this is clearly essential. For other types of translation-based, domain-specific of communication, however, it is possible to develop a systematic approach to the task which will allow for the production of target texts which are adequate for purpose, in a range of specialised domains, without necessarily having formal qualifications in those areas. For Esselink (2000) translation agencies, and individual clients, would tend to prefer a subject expert who also happens to have competence in one or more languages over a trained translator with a high degree of translation competence, including the ability to deal with specialised translation tasks. The problem, for the would-be translator, is persuading prospective clients that he or she is capable of this. This paper will offer an overview of the principles used to design training intended to teach trainee translators how to use a systematic approach to specialised translation, in order to extend the range of areas in which they can tackle translation, without compromising quality or reliability. This approach will be described within the context of the functionalist approach developed in particular by Reiss and Vermeer (1984), Nord (1991, 1997) inter alia.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Wireless machine-to-machine networks 2013

Editorial

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.