WorldWideScience

Sample records for machine translation models

  1. Machine Translation

    Indian Academy of Sciences (India)

    Research Mt System Example: The 'Janus' Translating Phone Project. The Janus ... based on laptops, and simultaneous translation of two speakers in a dialogue. For more ..... The current focus in MT research is on using machine learning.

  2. Machine translation

    Energy Technology Data Exchange (ETDEWEB)

    Nagao, M

    1982-04-01

    Each language has its own structure. In translating one language into another one, language attributes and grammatical interpretation must be defined in an unambiguous form. In order to parse a sentence, it is necessary to recognize its structure. A so-called context-free grammar can help in this respect for machine translation and machine-aided translation. Problems to be solved in studying machine translation are taken up in the paper, which discusses subjects for semantics and for syntactic analysis and translation software. 14 references.

  3. Latent domain models for statistical machine translation

    NARCIS (Netherlands)

    Hoàng, C.

    2017-01-01

    A data-driven approach to model translation suffers from the data mismatch problem and demands domain adaptation techniques. Given parallel training data originating from a specific domain, training an MT system on the data would result in a rather suboptimal translation for other domains. But does

  4. Neural Machine Translation with Recurrent Attention Modeling

    OpenAIRE

    Yang, Zichao; Hu, Zhiting; Deng, Yuntian; Dyer, Chris; Smola, Alex

    2016-01-01

    Knowing which words have been attended to in previous time steps while generating a translation is a rich source of information for predicting what words will be attended to in the future. We improve upon the attention model of Bahdanau et al. (2014) by explicitly modeling the relationship between previous and subsequent attention levels for each word using one recurrent network per input word. This architecture easily captures informative features, such as fertility and regularities in relat...

  5. Modeling and prediction of human word search behavior in interactive machine translation

    Science.gov (United States)

    Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

    2017-12-01

    As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

  6. Machine Translation and Other Translation Technologies.

    Science.gov (United States)

    Melby, Alan

    1996-01-01

    Examines the application of linguistic theory to machine translation and translator tools, discusses the use of machine translation and translator tools in the real world of translation, and addresses the impact of translation technology on conceptions of language and other issues. Findings indicate that the human mind is flexible and linguistic…

  7. Syntactic discriminative language model rerankers for statistical machine translation

    NARCIS (Netherlands)

    Carter, S.; Monz, C.

    2011-01-01

    This article describes a method that successfully exploits syntactic features for n-best translation candidate reranking using perceptrons. We motivate the utility of syntax by demonstrating the superior performance of parsers over n-gram language models in differentiating between Statistical

  8. Typologically robust statistical machine translation : Understanding and exploiting differences and similarities between languages in machine translation

    NARCIS (Netherlands)

    Daiber, J.

    2018-01-01

    Machine translation systems often incorporate modeling assumptions motivated by properties of the language pairs they initially target. When such systems are applied to language families with considerably different properties, translation quality can deteriorate. Phrase-based machine translation

  9. Machine Translation from Text

    Science.gov (United States)

    Habash, Nizar; Olive, Joseph; Christianson, Caitlin; McCary, John

    Machine translation (MT) from text, the topic of this chapter, is perhaps the heart of the GALE project. Beyond being a well defined application that stands on its own, MT from text is the link between the automatic speech recognition component and the distillation component. The focus of MT in GALE is on translating from Arabic or Chinese to English. The three languages represent a wide range of linguistic diversity and make the GALE MT task rather challenging and exciting.

  10. Parsing statistical machine translation output

    NARCIS (Netherlands)

    Carter, S.; Monz, C.; Vetulani, Z.

    2009-01-01

    Despite increasing research into the use of syntax during statistical machine translation, the incorporation of syntax into language models has seen limited success. We present a study of the discriminative abilities of generative syntax-based language models, over and above standard n-gram models,

  11. Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

    Science.gov (United States)

    Zamora-Martinez, Francisco; Castro-Bleda, Maria Jose

    2018-02-22

    Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

  12. Modeling workflow to design machine translation applications for public health practice.

    Science.gov (United States)

    Turner, Anne M; Brownstein, Megumu K; Cole, Kate; Karasz, Hilary; Kirchhoff, Katrin

    2015-02-01

    Provide a detailed understanding of the information workflow processes related to translating health promotion materials for limited English proficiency individuals in order to inform the design of context-driven machine translation (MT) tools for public health (PH). We applied a cognitive work analysis framework to investigate the translation information workflow processes of two large health departments in Washington State. Researchers conducted interviews, performed a task analysis, and validated results with PH professionals to model translation workflow and identify functional requirements for a translation system for PH. The study resulted in a detailed description of work related to translation of PH materials, an information workflow diagram, and a description of attitudes towards MT technology. We identified a number of themes that hold design implications for incorporating MT in PH translation practice. A PH translation tool prototype was designed based on these findings. This study underscores the importance of understanding the work context and information workflow for which systems will be designed. Based on themes and translation information workflow processes, we identified key design guidelines for incorporating MT into PH translation work. Primary amongst these is that MT should be followed by human review for translations to be of high quality and for the technology to be adopted into practice. The time and costs of creating multilingual health promotion materials are barriers to translation. PH personnel were interested in MT's potential to improve access to low-cost translated PH materials, but expressed concerns about ensuring quality. We outline design considerations and a potential machine translation tool to best fit MT systems into PH practice. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Machine Translation Effect on Communication

    DEFF Research Database (Denmark)

    Jensen, Mika Yasuoka; Bjørn, Pernille

    2011-01-01

    Intercultural collaboration facilitated by machine translation has gradually spread in various settings. Still, little is known as for the practice of machine-translation mediated communication. This paper investigates how machine translation affects intercultural communication in practice. Based...... on communication in which multilingual communication system is applied, we identify four communication types and its’ influences on stakeholders’ communication process, especially focusing on establishment and maintenance of common ground. Different from our expectation that quality of machine translation results...

  14. Language Model Adaptation Using Machine-Translated Text for Resource-Deficient Languages

    Directory of Open Access Journals (Sweden)

    Sadaoki Furui

    2009-01-01

    Full Text Available Text corpus size is an important issue when building a language model (LM. This is a particularly important issue for languages where little data is available. This paper introduces an LM adaptation technique to improve an LM built using a small amount of task-dependent text with the help of a machine-translated text corpus. Icelandic speech recognition experiments were performed using data, machine translated (MT from English to Icelandic on a word-by-word and sentence-by-sentence basis. LM interpolation using the baseline LM and an LM built from either word-by-word or sentence-by-sentence translated text reduced the word error rate significantly when manually obtained utterances used as a baseline were very sparse.

  15. Machine Translation as a Model for Overcoming Some Common Errors in English-into-Arabic Translation among EFL University Freshmen

    Science.gov (United States)

    El-Banna, Adel I.; Naeem, Marwa A.

    2016-01-01

    This research work aimed at making use of Machine Translation to help students avoid some syntactic, semantic and pragmatic common errors in translation from English into Arabic. Participants were a hundred and five freshmen who studied the "Translation Common Errors Remedial Program" prepared by the researchers. A testing kit that…

  16. Automatic Evaluation of Machine Translation

    DEFF Research Database (Denmark)

    Martinez, Mercedes Garcia; Koglin, Arlene; Mesa-Lao, Bartolomé

    2015-01-01

    The availability of systems capable of producing fairly accurate translations has increased the popularity of machine translation (MT). The translation industry is steadily incorporating MT in their workflows engaging the human translator to post-edit the raw MT output in order to comply with a s...

  17. Machine Translation for Academic Purposes

    Science.gov (United States)

    Lin, Grace Hui-chin; Chien, Paul Shih Chieh

    2009-01-01

    Due to the globalization trend and knowledge boost in the second millennium, multi-lingual translation has become a noteworthy issue. For the purposes of learning knowledge in academic fields, Machine Translation (MT) should be noticed not only academically but also practically. MT should be informed to the translating learners because it is a…

  18. Machine Translation - A Gentle Introduction

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 3; Issue 7. Machine Translation - A Gentle Introduction. Durgesh D Rao. General Article Volume 3 Issue 7 July 1998 pp 61-70. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/003/07/0061-0070 ...

  19. An Overall Perspective of Machine Translation with its Shortcomings

    Directory of Open Access Journals (Sweden)

    Alireza Akbari

    2014-01-01

    Full Text Available The petition for language translation has strikingly augmented recently due to cross-cultural communication and exchange of information. In order to communicate well, text should be translated correctly and completely in each field such as legal documents, technical texts, scientific texts, publicity leaflets, and instructional materials. In this connection, Machine translation is of great importance in translation. The term “Machine Translation” was first proposed by George Artsrouni and Smirnov Troyanski (1933 to design a storage design on paper tape. This paper sought to investigate an overall perspective of Machine Translation models and its metrics in detail. Finally, it scrutinized the ins and outs shortcomings of Machine Translation.

  20. CloudLM: a Cloud-based Language Model for Machine Translation

    Directory of Open Access Journals (Sweden)

    Ferrández-Tordera Jorge

    2016-04-01

    Full Text Available Language models (LMs are an essential element in statistical approaches to natural language processing for tasks such as speech recognition and machine translation (MT. The advent of big data leads to the availability of massive amounts of data to build LMs, and in fact, for the most prominent languages, using current techniques and hardware, it is not feasible to train LMs with all the data available nowadays. At the same time, it has been shown that the more data is used for a LM the better the performance, e.g. for MT, without any indication yet of reaching a plateau. This paper presents CloudLM, an open-source cloud-based LM intended for MT, which allows to query distributed LMs. CloudLM relies on Apache Solr and provides the functionality of state-of-the-art language modelling (it builds upon KenLM, while allowing to query massive LMs (as the use of local memory is drastically reduced, at the expense of slower decoding speed.

  1. A Survey of Statistical Machine Translation

    Science.gov (United States)

    2007-04-01

    methods are notoriously sen- sitive to domain differences, however, so the move to informal text is likely to present many interesting challenges ...Och, Christoph Tillman, and Hermann Ney. Improved alignment models for statistical machine translation. In Proc. of EMNLP- VLC , pages 20–28, Jun 1999

  2. MSD Recombination Method in Statistical Machine Translation

    Science.gov (United States)

    Gros, Jerneja Žganec

    2008-11-01

    Freely available tools and language resources were used to build the VoiceTRAN statistical machine translation (SMT) system. Various configuration variations of the system are presented and evaluated. The VoiceTRAN SMT system outperformed the baseline conventional rule-based MT system in all English-Slovenian in-domain test setups. To further increase the generalization capability of the translation model for lower-coverage out-of-domain test sentences, an "MSD-recombination" approach was proposed. This approach not only allows a better exploitation of conventional translation models, but also performs well in the more demanding translation direction; that is, into a highly inflectional language. Using this approach in the out-of-domain setup of the English-Slovenian JRC-ACQUIS task, we have achieved significant improvements in translation quality.

  3. Machine Translation in Post-Contemporary Era

    Science.gov (United States)

    Lin, Grace Hui Chin

    2010-01-01

    This article focusing on translating techniques via personal computer or laptop reports updated artificial intelligence progresses before 2010. Based on interpretations and information for field of MT [Machine Translation] by Yorick Wilks' book, "Machine Translation, Its scope and limits," this paper displays understandable theoretical frameworks…

  4. Machine Translation Tools - Tools of The Translator's Trade

    DEFF Research Database (Denmark)

    Kastberg, Peter

    2012-01-01

    In this article three of the more common types of translation tools are presented, discussed and critically evaluated. The types of translation tools dealt with in this article are: Fully Automated Machine Translation (or FAMT), Human Aided Machine Translation (or HAMT) and Machine Aided Human...... Translation (or MAHT). The strengths and weaknesses of the different types of tools are discussed and evaluated by means of a number of examples. The article aims at two things: at presenting a sort of state of the art of what is commonly referred to as “machine translation” as well as at providing the reader...... with a sound basis for considering what translation tool (if any) is the most appropriate in order to meet his or her specific translation needs....

  5. An Evaluation of Output Quality of Machine Translation (Padideh Software vs. Google Translate)

    Science.gov (United States)

    Azer, Haniyeh Sadeghi; Aghayi, Mohammad Bagher

    2015-01-01

    This study aims to evaluate the translation quality of two machine translation systems in translating six different text-types, from English to Persian. The evaluation was based on criteria proposed by Van Slype (1979). The proposed model for evaluation is a black-box type, comparative and adequacy-oriented evaluation. To conduct the evaluation, a…

  6. Treatment of Markup in Statistical Machine Translation

    OpenAIRE

    Müller, Mathias

    2017-01-01

    We present work on handling XML markup in Statistical Machine Translation (SMT). The methods we propose can be used to effectively preserve markup (for instance inline formatting or structure) and to place markup correctly in a machine-translated segment. We evaluate our approaches with parallel data that naturally contains markup or where markup was inserted to create synthetic examples. In our experiments, hybrid reinsertion has proven the most accurate method to handle markup, while alignm...

  7. Integrating Automatic Speech Recognition and Machine Translation for Better Translation Outputs

    DEFF Research Database (Denmark)

    Liyanapathirana, Jeevanthi

    translations, combining machine translation with computer assisted translation has drawn attention in current research. This combines two prospects: the opportunity of ensuring high quality translation along with a significant performance gain. Automatic Speech Recognition (ASR) is another important area......, which caters important functionalities in language processing and natural language understanding tasks. In this work we integrate automatic speech recognition and machine translation in parallel. We aim to avoid manual typing of possible translations as dictating the translation would take less time...... to the n-best list rescoring, we also use word graphs with the expectation of arriving at a tighter integration of ASR and MT models. Integration methods include constraining ASR models using language and translation models of MT, and vice versa. We currently develop and experiment different methods...

  8. Translation Analysis on Civil Engineering Text Produced by Machine Translator

    Directory of Open Access Journals (Sweden)

    Sutopo Anam

    2018-01-01

    Full Text Available Translation is extremely needed in communication since people have serious problem in the language used. Translation activity is done by the person in charge for translating the material. Translation activity is also able to be done by machine. It is called machine translation, reflected in the programs developed by programmer. One of them is Transtool. Many people used Transtool for helping them in solving the problem related with translation activities. This paper wants to deliver how important is the Transtool program, how effective is Transtool program and how is the function of Transtool for human business. This study applies qualitative research. The sources of data were document and informant. This study used documentation and in dept-interviewing as the techniques for collecting data. The collected data were analyzed by using interactive analysis. The results of the study show that, first; Transtool program is helpful for people in translating the civil engineering text and it functions as the aid or helper, second; the working of Transtool software program is effective enough and third; the result of translation produced by Transtool is good for short and simple sentences and not readable, not understandable and not accurate for long sentences (compound, complex and compound complex thought the result is informative. The translated material must be edited by the professional translator.

  9. Translation Analysis on Civil Engineering Text Produced by Machine Translator

    Science.gov (United States)

    Sutopo, Anam

    2018-02-01

    Translation is extremely needed in communication since people have serious problem in the language used. Translation activity is done by the person in charge for translating the material. Translation activity is also able to be done by machine. It is called machine translation, reflected in the programs developed by programmer. One of them is Transtool. Many people used Transtool for helping them in solving the problem related with translation activities. This paper wants to deliver how important is the Transtool program, how effective is Transtool program and how is the function of Transtool for human business. This study applies qualitative research. The sources of data were document and informant. This study used documentation and in dept-interviewing as the techniques for collecting data. The collected data were analyzed by using interactive analysis. The results of the study show that, first; Transtool program is helpful for people in translating the civil engineering text and it functions as the aid or helper, second; the working of Transtool software program is effective enough and third; the result of translation produced by Transtool is good for short and simple sentences and not readable, not understandable and not accurate for long sentences (compound, complex and compound complex) thought the result is informative. The translated material must be edited by the professional translator.

  10. What does Attention in Neural Machine Translation Pay Attention to?

    NARCIS (Netherlands)

    Ghader, H.; Monz, C.; Kondrak, G.; Watanabe, T.

    2017-01-01

    Attention in neural machine translation provides the possibility to encode relevant parts of the source sentence at each translation step. As a result, attention is considered to be an alignment model as well. However, there is no work that specifically studies attention and provides analysis of

  11. Morphological Analysis for Statistical Machine Translation

    National Research Council Canada - National Science Library

    Lee, Young-Suk

    2004-01-01

    .... The technique improves Arabic-to-English translation qualities significantly when applied to IBM Model 1 and Phrase Translation Models trained on the training corpus size ranging from 3,500 to 3.3 million sentence pairs.

  12. Telemedicine as a special case of machine translation.

    Science.gov (United States)

    Wołk, Krzysztof; Marasek, Krzysztof; Glinkowski, Wojciech

    2015-12-01

    Machine translation is evolving quite rapidly in terms of quality. Nowadays, we have several machine translation systems available in the web, which provide reasonable translations. However, these systems are not perfect, and their quality may decrease in some specific domains. This paper examines the effects of different training methods when it comes to Polish-English Statistical Machine Translation system used for the medical data. Numerous elements of the EMEA parallel text corpora and not related OPUS Open Subtitles project were used as the ground for creation of phrase tables and different language models including the development, tuning and testing of these translation systems. The BLEU, NIST, METEOR, and TER metrics have been used in order to evaluate the results of various systems. Our experiments deal with the systems that include POS tagging, factored phrase models, hierarchical models, syntactic taggers, and other alignment methods. We also executed a deep analysis of Polish data as preparatory work before automatized data processing such as true casing or punctuation normalization phase. Normalized metrics was used to compare results. Scores lower than 15% mean that Machine Translation engine is unable to provide satisfying quality, scores greater than 30% mean that translations should be understandable without problems and scores over 50 reflect adequate translations. The average results of Polish to English translations scores for BLEU, NIST, METEOR, and TER were relatively high and ranged from 7058 to 8272. The lowest score was 6438. The average results ranges for English to Polish translations were little lower (6758-7897). The real-life implementations of presented high quality Machine Translation Systems are anticipated in general medical practice and telemedicine. Copyright © 2015. Published by Elsevier Ltd.

  13. Machine Translation Using Constraint-Based Synchronous Grammar

    Institute of Scientific and Technical Information of China (English)

    WONG Fai; DONG Mingchui; HU Dongcheng

    2006-01-01

    A synchronous grammar based on the formalism of context-free grammar was developed by generalizing the first component of production that models the source text. Unlike other synchronous grammars,the grammar allows multiple target productions to be associated to a single production rule which can be used to guide a parser to infer different possible translational equivalences for a recognized input string according to the feature constraints of symbols in the pattern. An extended generalized LR algorithm was adapted to the parsing of the proposed formalism to analyze the syntactic structure of a language. The grammar was used as the basis for building a machine translation system for Portuguese to Chinese translation. The empirical results show that the grammar is more expressive when modeling the translational equivalences of parallel texts for machine translation and grammar rewriting applications.

  14. Grammatical Metaphor, Controlled Languageand Machine Translation

    DEFF Research Database (Denmark)

    Møller, Margrethe

    2003-01-01

    It is a general assumption that 1) the readability and clarity of LSP texts written in a controlled language are better than uncontrolled texts and 2) that controlled languages produce better results with machine translation than uncontrolled languages. Controlled languages impose lexical...

  15. Using Linguistic Knowledge in Statistical Machine Translation

    Science.gov (United States)

    2010-09-01

    reproduced in (Belnap and Haeri, 1997)), a sociolinguistic phenomenon where the literary standard differs considerably from the vernacular varieties...Machine Translation Summit (MT-Summit). N. Haeri. 2000. Form and ideology: Arabic sociolinguistics and beyond. Annual Review of Anthropology, 29. D. Hakkani

  16. A MOOC on Approaches to Machine Translation

    Science.gov (United States)

    Costa-jussà, Mart R.; Formiga, Lluís; Torrillas, Oriol; Petit, Jordi; Fonollosa, José A. R.

    2015-01-01

    This paper describes the design, development, and analysis of a MOOC entitled "Approaches to Machine Translation: Rule-based, statistical and hybrid", and provides lessons learned and conclusions to be taken into account in the future. The course was developed within the Canvas platform, used by recognized European universities. It…

  17. Rule-based machine translation for Aymara

    NARCIS (Netherlands)

    Coler, Matthew; Homola, Petr; Jones, Mari

    2014-01-01

    This paper presents the ongoing result of an approach developed by the collaboration of a computational linguist with a field linguist that addresses one of the oft-overlooked keys to language maintenance: the development of modern language-learning tools. Although machine translation isn’t commonly

  18. An analysis of machine translation and speech synthesis in speech-to-speech translation system

    OpenAIRE

    Hashimoto, K.; Yamagishi, J.; Byrne, W.; King, S.; Tokuda, K.

    2011-01-01

    This paper provides an analysis of the impacts of machine translation and speech synthesis on speech-to-speech translation systems. The speech-to-speech translation system consists of three components: speech recognition, machine translation and speech synthesis. Many techniques for integration of speech recognition and machine translation have been proposed. However, speech synthesis has not yet been considered. Therefore, in this paper, we focus on machine translation and speech synthesis, ...

  19. Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation

    NARCIS (Netherlands)

    Callison-Burch, C.; Koehn, P.; Monz, C.; Peterson, K.; Przybocki, M.; Zaidan, O.F.

    2010-01-01

    This paper presents the results of the WMT10 and MetricsMATR10 shared tasks, which included a translation task, a system combination task, and an evaluation task. We conducted a large-scale manual evaluation of 104 machine translation systems and 41 system combination entries. We used the ranking of

  20. The Impact of Machine Translation and Computer-aided Translation on Translators

    Science.gov (United States)

    Peng, Hao

    2018-03-01

    Under the context of globalization, communications between countries and cultures are becoming increasingly frequent, which make it imperative to use some techniques to help translate. This paper is to explore the influence of computer-aided translation on translators, which is derived from the field of the computer-aided translation (CAT) and machine translation (MT). Followed by an introduction to the development of machine and computer-aided translation, it then depicts the technologies practicable to translators, which are trying to analyze the demand of designing the computer-aided translation so far in translation practice, and optimize the designation of computer-aided translation techniques, and analyze its operability in translation. The findings underline the advantages and disadvantages of MT and CAT tools, and the serviceability and future development of MT and CAT technologies. Finally, this thesis probes into the impact of these new technologies on translators in hope that more translators and translation researchers can learn to use such tools to improve their productivity.

  1. Findings of the 2014 Workshop on Statistical Machine Translation

    NARCIS (Netherlands)

    Bojar, O.; Buck, C.; Federmann, C.; Haddow, B.; Koehn, P.; Leveling, J.; Monz, C.; Pecina, P.; Post, M.; Saint-Amand, H.; Soricut, R.; Specia, L.; Tamchyna, A.

    2014-01-01

    This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were

  2. Findings of the 2011 workshop on statistical machine translation

    NARCIS (Netherlands)

    Callison-Burch, C.; Koehn, P.; Monz, C.; Zaidan, O.F.

    2011-01-01

    This paper presents the results of the WMT11 shared tasks, which included a translation task, a system combination task, and a task for machine translation evaluation metrics. We conducted a large-scale manual evaluation of 148 machine translation systems and 41 system combination entries. We used

  3. Evaluation of Hindi to Punjabi Machine Translation System

    OpenAIRE

    Goyal, Vishal; Lehal, Gurpreet Singh

    2009-01-01

    Machine Translation in India is relatively young. The earliest efforts date from the late 80s and early 90s. The success of every system is judged from its evaluation experimental results. Number of machine translation systems has been started for development but to the best of author knowledge, no high quality system has been completed which can be used in real applications. Recently, Punjabi University, Patiala, India has developed Punjabi to Hindi Machine translation system with high accur...

  4. Empirical Investigation of Optimization Algorithms in Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Bahar Parnia

    2017-06-01

    Full Text Available Training neural networks is a non-convex and a high-dimensional optimization problem. In this paper, we provide a comparative study of the most popular stochastic optimization techniques used to train neural networks. We evaluate the methods in terms of convergence speed, translation quality, and training stability. In addition, we investigate combinations that seek to improve optimization in terms of these aspects. We train state-of-the-art attention-based models and apply them to perform neural machine translation. We demonstrate our results on two tasks: WMT 2016 En→Ro and WMT 2015 De→En.

  5. Quantum neural network based machine translator for Hindi to English.

    Science.gov (United States)

    Narayan, Ravi; Singh, V P; Chakraverty, S

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

  6. Machine vs. human translation of SNOMED CT terms.

    Science.gov (United States)

    Schulz, Stefan; Bernhardt-Melischnig, Johannes; Kreuzthaler, Markus; Daumke, Philipp; Boeker, Martin

    2013-01-01

    In the context of past and current SNOMED CT translation projects we compare three kinds of SNOMED CT translations from English to German by: (t1) professional medical translators; (t2) a free Web-based machine translation service; (t3) medical students. 500 SNOMED CT fully specified names from the (English) International release were randomly selected. Based on this, German translations t1, t2, and t3 were generated. A German and an Austrian physician rated the translations for linguistic correctness and content fidelity. Kappa for inter-rater reliability was 0.4 for linguistic correctness and 0.23 for content fidelity. Average ratings of linguistic correctness did not differ significantly between human translation scenarios. Content fidelity was rated slightly better for student translators compared to professional translators. Comparing machine to human translation, the linguistic correctness differed about 0.5 scale units in favour of the human translation and about 0.25 regarding content fidelity, equally in favour of the human translation. The results demonstrate that low-cost translation solutions of medical terms may produce surprisingly good results. Although we would not recommend low-cost translation for producing standardized preferred terms, this approach can be useful for creating additional language-specific entry terms. This may serve several important use cases. We also recommend testing this method to bootstrap a crowdsourcing process, by which term translations are gathered, improved, maintained, and rated by the user community.

  7. Analysis of MultiWord Expression Translation Errors in Statistical Machine Translation

    DEFF Research Database (Denmark)

    Klyueva, Natalia; Liyanapathirana, Jeevanthi

    2015-01-01

    In this paper, we analyse the usage of multiword expressions (MWE) in Statistical Machine Translation (SMT). We exploit the Moses SMT toolkit to train models for French-English and Czech-Russian language pairs. For each language pair, two models were built: a baseline model without additional MWE...... data and the model enhanced with information on MWE. For the French-English pair, we tried three methods of introducing the MWE data. For Czech-Russian pair, we used just one method – adding automatically extracted data as a parallel corpus....

  8. Convolutional over Recurrent Encoder for Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Dakwale Praveen

    2017-06-01

    Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.

  9. Bean Soup Translation: Flexible, Linguistically-Motivated Syntax for Machine Translation

    Science.gov (United States)

    Mehay, Dennis Nolan

    2012-01-01

    Machine translation (MT) systems attempt to translate texts from one language into another by translating words from a "source language" and rearranging them into fluent utterances in a "target language." When the two languages organize concepts in very different ways, knowledge of their general sentence structure, or…

  10. Dictionary Based Machine Translation from Kannada to Telugu

    Science.gov (United States)

    Sindhu, D. V.; Sagar, B. M.

    2017-08-01

    Machine Translation is a task of translating from one language to another language. For the languages with less linguistic resources like Kannada and Telugu Dictionary based approach is the best approach. This paper mainly focuses on Dictionary based machine translation for Kannada to Telugu. The proposed methodology uses dictionary for translating word by word without much correlation of semantics between them. The dictionary based machine translation process has the following sub process: Morph analyzer, dictionary, transliteration, transfer grammar and the morph generator. As a part of this work bilingual dictionary with 8000 entries is developed and the suffix mapping table at the tag level is built. This system is tested for the children stories. In near future this system can be further improved by defining transfer grammar rules.

  11. Integrating source-language context into phrase-based statistical machine translation

    NARCIS (Netherlands)

    Haque, R.; Kumar Naskar, S.; Bosch, A.P.J. van den; Way, A.

    2011-01-01

    The translation features typically used in Phrase-Based Statistical Machine Translation (PB-SMT) model dependencies between the source and target phrases, but not among the phrases in the source language themselves. A swathe of research has demonstrated that integrating source context modelling

  12. Findings of the 2009 Workshop on Statistical Machine Translation

    NARCIS (Netherlands)

    Callison-Burch, C.; Koehn, P.; Monz, C.; Schroeder, J.; Callison-Burch, C.; Koehn, P.; Monz, C.; Schroeder, J.

    2009-01-01

    This paper presents the results of the WMT09 shared tasks, which included a translation task, a system combination task, and an evaluation task. We conducted a large-scale manual evaluation of 87 machine translation systems and 22 system combination entries. We used the ranking of these systems to

  13. Using example-based machine translation to translate DVD subtitles

    DEFF Research Database (Denmark)

    Flanagan, Marian

    between Swedish and Danish and Swedish and Norwegian subtitles, with the company already reporting a successful return on their investment. The hybrid EBMT/SMT system used in the current research, on the other hand, remains within the confines of academic research, and the real potential of the system...... allotted to produce the subtitles have both decreased. Therefore, this market is recognised as a potential real-world application of MT. Recent publications have introduced Corpus-Based MT approaches to translate subtitles. An SMT system has been implemented in a Swedish subtitling company to translate...

  14. The evolution and practical application of machine translation system (1)

    Science.gov (United States)

    Tominaga, Isao; Sato, Masayuki

    This paper describes a development, practical applicatioin, problem of a system, evaluation of practical system, and development trend of machine translation. Most recent system contains next four problems. 1) the vagueness of a text, 2) a difference of the definition of the terminology between different language, 3) the preparing of a large-scale translation dictionary, 4) the development of a software for the logical inference. Machine translation system is already used practically in many industry fields. However, many problems are not solved. The implementation of an ideal system will be after 15 years. Also, this paper described seven evaluation items detailedly. This English abstract was made by Mu system.

  15. Precise machine translation of computer science study

    CSIR Research Space (South Africa)

    Marais, L

    2015-07-01

    Full Text Available mobile (Android) application for translating discrete mathematics definitions between English and Afrikaans. The main component of the system is a Grammatical Framework (GF) application grammar which produces syntactically and semantically accurate...

  16. English to Sanskrit Machine Translation Using Transfer Based approach

    Science.gov (United States)

    Pathak, Ganesh R.; Godse, Sachin P.

    2010-11-01

    Translation is one of the needs of global society for communicating thoughts and ideas of one country with other country. Translation is the process of interpretation of text meaning and subsequent production of equivalent text, also called as communicating same meaning (message) in another language. In this paper we gave detail information on how to convert source language text in to target language text using Transfer Based Approach for machine translation. Here we implemented English to Sanskrit machine translator using transfer based approach. English is global language used for business and communication but large amount of population in India is not using and understand the English. Sanskrit is ancient language of India most of the languages in India are derived from Sanskrit. Sanskrit can be act as an intermediate language for multilingual translation.

  17. The Dostoevsky Machine in Georgetown: scientific translation in the Cold War.

    Science.gov (United States)

    Gordin, Michael D

    2016-04-01

    Machine Translation (MT) is now ubiquitous in discussions of translation. The roots of this phenomenon - first publicly unveiled in the so-called 'Georgetown-IBM Experiment' on 9 January 1954 - displayed not only the technological utopianism still associated with dreams of a universal computer translator, but was deeply enmeshed in the political pressures of the Cold War and a dominating conception of scientific writing as both the goal of machine translation as well as its method. Machine translation was created, in part, as a solution to a perceived crisis sparked by the massive expansion of Soviet science. Scientific prose was also perceived as linguistically simpler, and so served as the model for how to turn a language into a series of algorithms. This paper follows the rise of the Georgetown program - the largest single program in the world - from 1954 to the (as it turns out, temporary) collapse of MT in 1964.

  18. Machine translation with minimal reliance on parallel resources

    CERN Document Server

    Tambouratzis, George; Sofianopoulos, Sokratis

    2017-01-01

    This book provides a unified view on a new methodology for Machine Translation (MT). This methodology extracts information from widely available resources (extensive monolingual corpora) while only assuming the existence of a very limited parallel corpus, thus having a unique starting point to Statistical Machine Translation (SMT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER. Additionally, a free-to-use code is available, that allows the creation of new MT systems. The volume is addressed to both language professionals and researchers. Prerequisites for the readers are very limited and include a basic understanding of the machine translation as well as of the basic tools of natural language processing.

  19. A translator and simulator for the Burroughs D machine

    Science.gov (United States)

    Roberts, J.

    1972-01-01

    The D Machine is described as a small user microprogrammable computer designed to be a versatile building block for such diverse functions as: disk file controllers, I/O controllers, and emulators. TRANSLANG is an ALGOL-like language, which allows D Machine users to write microprograms in an English-like format as opposed to creating binary bit pattern maps. The TRANSLANG translator parses TRANSLANG programs into D Machine microinstruction bit patterns which can be executed on the D Machine simulator. In addition to simulation and translation, the two programs also offer several debugging tools, such as: a full set of diagnostic error messages, register dumps, simulated memory dumps, traces on instructions and groups of instructions, and breakpoints.

  20. Transliteration normalization for Information Extraction and Machine Translation

    Directory of Open Access Journals (Sweden)

    Yuval Marton

    2014-12-01

    Full Text Available Foreign name transliterations typically include multiple spelling variants. These variants cause data sparseness and inconsistency problems, increase the Out-of-Vocabulary (OOV rate, and present challenges for Machine Translation, Information Extraction and other natural language processing (NLP tasks. This work aims to identify and cluster name spelling variants using a Statistical Machine Translation method: word alignment. The variants are identified by being aligned to the same “pivot” name in another language (the source-language in Machine Translation settings. Based on word-to-word translation and transliteration probabilities, as well as the string edit distance metric, names with similar spellings in the target language are clustered and then normalized to a canonical form. With this approach, tens of thousands of high-precision name transliteration spelling variants are extracted from sentence-aligned bilingual corpora in Arabic and English (in both languages. When these normalized name spelling variants are applied to Information Extraction tasks, improvements over strong baseline systems are observed. When applied to Machine Translation tasks, a large improvement potential is shown.

  1. Foreign Developments in Information Processing and Machine Translation, No. 1

    Science.gov (United States)

    1960-09-29

    technicians] (Sestier (A.) -- La Traduction automatfguT"" des textes ecrits scJQntifiqaes ej^J^chplc^es dxun langage~ dans__un’"*""* ’’^t^’T^^i...are more and more comprehensible to others than machine translation technicians will result. Sketch of a proaram. This outline of work xtfiich will

  2. Translating DVD Subtitles English-German, English-Japanese, Using Example-based Machine Translation

    DEFF Research Database (Denmark)

    Armstrong, Stephen; Caffrey, Colm; Flanagan, Marian

    2006-01-01

    Due to limited budgets and an ever-diminishing time-frame for the production of subtitles for movies released in cinema and DVD, there is a compelling case for a technology-based translation solution for subtitles. In this paper we describe how an Example-Based Machine Translation (EBMT) approach...... to the translation of English DVD subtitles into German and Japanese can aid the subtitler. Our research focuses on an EBMT tool that produces fully automated translations, which in turn can be edited if required. To our knowledge this is the first time that any EBMT approach has been used with DVD subtitle...

  3. A GRAMMATICAL ADJUSTMENT ANALYSIS OF STATISTICAL MACHINE TRANSLATION METHOD USED BY GOOGLE TRANSLATE COMPARED TO HUMAN TRANSLATION IN TRANSLATING ENGLISH TEXT TO INDONESIAN

    Directory of Open Access Journals (Sweden)

    Eko Pujianto

    2017-04-01

    Full Text Available Google translate is a program which provides fast, free and effortless translating service. This service uses a unique method to translate. The system is called ―Statistical Machine Translation‖, the newest method in automatic translation. Machine translation (MT is an area of many kinds of different subjects of study and technique from linguistics, computers science, artificial intelligent (AI, translation theory, and statistics. SMT works by using statistical methods and mathematics to process the training data. The training data is corpus-based. It is a compilation of sentences and words of the languages (SL and TL from translation done by human. By using this method, Google let their machine discovers the rules for themselves. They do this by analyzing millions of documents that have already been translated by human translators and then generate the result based on the corpus/training data. However, questions arise when the results of the automatic translation prove to be unreliable in some extent. This paper questions the dependability of Google translate in comparison with grammatical adjustment that naturally characterizes human translators' specific advantage. The attempt is manifested through the analysis of the TL of some texts translated by the SMT. It is expected that by using the sample of TL produced by SMT we can learn the potential flaws of the translation. If such exists, the partial of more substantial undependability of SMT may open more windows to the debates of whether this service may suffice the users‘ need.

  4. Local health department translation processes: potential of machine translation technologies to help meet needs.

    Science.gov (United States)

    Turner, Anne M; Mandel, Hannah; Capurro, Daniel

    2013-01-01

    Limited English proficiency (LEP), defined as a limited ability to read, speak, write, or understand English, is associated with health disparities. Despite federal and state requirements to translate health information, the vast majority of health materials are solely available in English. This project investigates barriers to translation of health information and explores new technologies to improve access to multilingual public health materials. We surveyed all 77 local health departments (LHDs) in the Northwest about translation needs, practices, barriers and attitudes towards machine translation (MT). We received 67 responses from 45 LHDs. Translation of health materials is the principle strategy used by LHDs to reach LEP populations. Cost and access to qualified translators are principle barriers to producing multilingual materials. Thirteen LHDs have used online MT tools. Many respondents expressed concerns about the accuracy of MT. Overall, respondents were positive about its potential use, if low costs and quality could be assured.

  5. INTEGRATING MACHINE TRANSLATION AND SPEECH SYNTHESIS COMPONENT FOR ENGLISH TO DRAVIDIAN LANGUAGE SPEECH TO SPEECH TRANSLATION SYSTEM

    Directory of Open Access Journals (Sweden)

    J. SANGEETHA

    2015-02-01

    Full Text Available This paper provides an interface between the machine translation and speech synthesis system for converting English speech to Tamil text in English to Tamil speech to speech translation system. The speech translation system consists of three modules: automatic speech recognition, machine translation and text to speech synthesis. Many procedures for incorporation of speech recognition and machine translation have been projected. Still speech synthesis system has not yet been measured. In this paper, we focus on integration of machine translation and speech synthesis, and report a subjective evaluation to investigate the impact of speech synthesis, machine translation and the integration of machine translation and speech synthesis components. Here we implement a hybrid machine translation (combination of rule based and statistical machine translation and concatenative syllable based speech synthesis technique. In order to retain the naturalness and intelligibility of synthesized speech Auto Associative Neural Network (AANN prosody prediction is used in this work. The results of this system investigation demonstrate that the naturalness and intelligibility of the synthesized speech are strongly influenced by the fluency and correctness of the translated text.

  6. Using the TED Talks to Evaluate Spoken Post-editing of Machine Translation

    DEFF Research Database (Denmark)

    Liyanapathirana, Jeevanthi; Popescu-Belis, Andrei

    2016-01-01

    This paper presents a solution to evaluate spoken post-editing of imperfect machine translation output by a human translator. We compare two approaches to the combination of machine translation (MT) and automatic speech recognition (ASR): a heuristic algorithm and a machine learning method...

  7. Word Transition Entropy as an Indicator for Expected Machine Translation Quality

    DEFF Research Database (Denmark)

    Carl, Michael; Schaeffer, Moritz

    2014-01-01

    While most machine translation evaluation techniques (BLEU, NIST, TER, METEOR) assess translation quality based on a set of reference translations, we suggest to evaluate the literality of a set of (human or machine generated) translations to infer their potential quality. We provide evidence whi...

  8. Comparison of Three English-to-Dutch Machine Translations of SNOMED CT Procedures

    NARCIS (Netherlands)

    Cornet, Ronald; Hill, Carly; de Keizer, Nicolette

    2017-01-01

    Dutch interface terminologies are needed to use SNOMED CT in the Netherlands. Machine translation may support in their creation. The aim of our study is to compare different machine translations of procedures in SNOMED CT. Procedures were translated using Google Translate, Matecat, and Thot. Google

  9. VIRTUAL MODELING OF A NUMERICAL CONTROL MACHINE TOOL USED FOR COMPLEX MACHINING OPERATIONS

    Directory of Open Access Journals (Sweden)

    POPESCU Adrian

    2015-11-01

    Full Text Available This paper presents the 3D virtual model of the numerical control machine Modustar 100, in terms of machine elements. This is a CNC machine of modular construction, all components allowing the assembly in various configurations. The paper focused on the design of the subassemblies specific to the axes numerically controlled by means of CATIA v5, which contained different drive kinematic chains of different translation modules that ensures translation on X, Y and Z axis. Machine tool development for high speed and highly precise cutting demands employment of advanced simulation techniques witch it reflect on cost of total development of the machine.

  10. Student Modeling and Machine Learning

    OpenAIRE

    Sison , Raymund; Shimura , Masamichi

    1998-01-01

    After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine le...

  11. Machine Translation as a complex system, and the phenomenon of Esperanto

    NARCIS (Netherlands)

    Gobbo, F.

    2015-01-01

    The history of machine translation and the history of Esperanto have long been connected, as they are two different ways to deal with the same problem: the problem of communication across language barriers. Language can be considered a Complex Adaptive System (CAS), and machine translation too. In

  12. Formal modeling of virtual machines

    Science.gov (United States)

    Cremers, A. B.; Hibbard, T. N.

    1978-01-01

    Systematic software design can be based on the development of a 'hierarchy of virtual machines', each representing a 'level of abstraction' of the design process. The reported investigation presents the concept of 'data space' as a formal model for virtual machines. The presented model of a data space combines the notions of data type and mathematical machine to express the close interaction between data and control structures which takes place in a virtual machine. One of the main objectives of the investigation is to show that control-independent data type implementation is only of limited usefulness as an isolated tool of program development, and that the representation of data is generally dictated by the control context of a virtual machine. As a second objective, a better understanding is to be developed of virtual machine state structures than was heretofore provided by the view of the state space as a Cartesian product.

  13. Domain Adaptation for Machine Translation with Instance Selection

    Directory of Open Access Journals (Sweden)

    Biçici Ergun

    2015-04-01

    Full Text Available Domain adaptation for machine translation (MT can be achieved by selecting training instances close to the test set from a larger set of instances. We consider 7 different domain adaptation strategies and answer 7 research questions, which give us a recipe for domain adaptation in MT. We perform English to German statistical MT (SMT experiments in a setting where test and training sentences can come from different corpora and one of our goals is to learn the parameters of the sampling process. Domain adaptation with training instance selection can obtain 22% increase in target 2-gram recall and can gain up to 3:55 BLEU points compared with random selection. Domain adaptation with feature decay algorithm (FDA not only achieves the highest target 2-gram recall and BLEU performance but also perfectly learns the test sample distribution parameter with correlation 0:99. Moses SMT systems built with FDA selected 10K training sentences is able to obtain F1 results as good as the baselines that use up to 2M sentences. Moses SMT systems built with FDA selected 50K training sentences is able to obtain F1 point better results than the baselines.

  14. Adaptation of machine translation for multilingual information retrieval in the medical domain.

    Science.gov (United States)

    Pecina, Pavel; Dušek, Ondřej; Goeuriot, Lorraine; Hajič, Jan; Hlaváčová, Jaroslava; Jones, Gareth J F; Kelly, Liadh; Leveling, Johannes; Mareček, David; Novák, Michal; Popel, Martin; Rosa, Rudolf; Tamchyna, Aleš; Urešová, Zdeňka

    2014-07-01

    We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve effectiveness of cross-lingual IR. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech-English, German-English, and French-English. MT quality is evaluated on data sets created within the Khresmoi project and IR effectiveness is tested on the CLEF eHealth 2013 data sets. The search query translation results achieved in our experiments are outstanding - our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech-English, from 23.03 to 40.82 for German-English, and from 32.67 to 40.82 for French-English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French-English. For Czech-English and German-English, the increased MT quality does not lead to better IR results. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of

  15. Livestock models in translational medicine.

    Science.gov (United States)

    Roth, James A; Tuggle, Christopher K

    2015-01-01

    This issue of the ILAR Journal focuses on livestock models in translational medicine. Livestock models of selected human diseases present important advantages as compared with rodent models for translating fundamental breakthroughs in biology to useful preventatives and therapeutics for humans. Livestock reflect the complexity of applying medical advances in an outbred species. In many cases, the pathogenesis of infectious, metabolic, genetic, and neoplastic diseases in livestock species more closely resembles that in humans than does the pathogenesis of rodent models. Livestock models also provide the advantage of similar organ size and function and the ability to serially sample an animal throughout the study period. Research using livestock models for human disease often benefits not only human health but animal health and food production as well. This issue of the ILAR Journal presents information on translational research using livestock models in two broad areas: microbiology and infectious disease (transmissible spongiform encephalopathies, mycobacterial infections, influenza A virus infection, vaccine development and testing, the human microbiota) and metabolic, neoplastic, and genetic disorders (stem cell therapy, male germ line cell biology, pulmonary adenocarcinoma, muscular dystrophy, wound healing). In addition, there is a manuscript devoted to Institutional Animal Care and Use Committees' responsibilities for reviewing research using livestock models. Conducting translational research using livestock models requires special facilities and researchers with expertise in livestock. There are many institutions in the world with experienced researchers and facilities designed for livestock research; primarily associated with colleges of agriculture and veterinary medicine or government laboratories. © The Author 2015. Published by Oxford University Press on behalf of the Institute for Laboratory Animal Research. All rights reserved. For permissions

  16. Percussive drilling application of translational motion permanent magnet machine

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Shujun

    2012-07-01

    It is clear that percussive drills are very promising since they can increase the rate of penetration in hard rock formations. Any small improvements on the percussive drills can make a big contribution to lowering the drilling costs since drilling a well for the oil and gas industry is very costly. This thesis presents a percussive drilling system mainly driven by a tubular reciprocating translational motion permanent magnet synchronous motor (RTPMSM), which efficiently converts electric energy to kinetic energy for crushing the hard rock since there is no mechanical media. The thesis starts from state-of-the-art of percussive drilling techniques, reciprocating translational motion motors, and self-sensing control of electric motors and its implementation issues. The following chapters present modeling the hard rock, modeling the drill, the design issues of the drill, the RTPMSM and its control. A single-phase RTPMSM prototype is tested for the hard rock drilling. The presented variable voltage variable frequency control is also validated on it. The space vector control and self-sensing control are also explored on a three-phase RTPMSM prototype. The results show that the percussive drill can be implemented to the hard rock drilling applications. A detailed summarisation of contributions and future work is presented at the end of the thesis.(Author)

  17. A Conjoint Analysis Framework for Evaluating User Preferences in Machine Translation.

    Science.gov (United States)

    Kirchhoff, Katrin; Capurro, Daniel; Turner, Anne M

    2014-03-01

    Despite much research on machine translation (MT) evaluation, there is surprisingly little work that directly measures users' intuitive or emotional preferences regarding different types of MT errors. However, the elicitation and modeling of user preferences is an important prerequisite for research on user adaptation and customization of MT engines. In this paper we explore the use of conjoint analysis as a formal quantitative framework to assess users' relative preferences for different types of translation errors. We apply our approach to the analysis of MT output from translating public health documents from English into Spanish. Our results indicate that word order errors are clearly the most dispreferred error type, followed by word sense, morphological, and function word errors. The conjoint analysis-based model is able to predict user preferences more accurately than a baseline model that chooses the translation with the fewest errors overall. Additionally we analyze the effect of using a crowd-sourced respondent population versus a sample of domain experts and observe that main preference effects are remarkably stable across the two samples.

  18. Model-based machine learning.

    Science.gov (United States)

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

  19. An Evaluation of Online Machine Translation of Arabic into English News Headlines: Implications on Students' Learning Purposes

    Science.gov (United States)

    Kadhim, Kais A.; Habeeb, Luwaytha S.; Sapar, Ahmad Arifin; Hussin, Zaharah; Abdullah, Muhammad Ridhuan Tony Lim

    2013-01-01

    Nowadays, online Machine Translation (MT) is used widely with translation software, such as Google and Babylon, being easily available and downloadable. This study aims to test the translation quality of these two machine systems in translating Arabic news headlines into English. 40 Arabic news headlines were selected from three online sources,…

  20. ADAPTING HYBRID MACHINE TRANSLATION TECHNIQUES FOR CROSS-LANGUAGE TEXT RETRIEVAL SYSTEM

    Directory of Open Access Journals (Sweden)

    P. ISWARYA

    2017-03-01

    Full Text Available This research work aims in developing Tamil to English Cross - language text retrieval system using hybrid machine translation approach. The hybrid machine translation system is a combination of rule based and statistical based approaches. In an existing word by word translation system there are lot of issues and some of them are ambiguity, Out-of-Vocabulary words, word inflections, and improper sentence structure. To handle these issues, proposed architecture is designed in such a way that, it contains Improved Part-of-Speech tagger, machine learning based morphological analyser, collocation based word sense disambiguation procedure, semantic dictionary, and tense markers with gerund ending rules, and two pass transliteration algorithm. From the experimental results it is clear that the proposed Tamil Query based translation system achieves significantly better translation quality over existing system, and reaches 95.88% of monolingual performance.

  1. An Overall Perspective of Machine Translation with Its Shortcomings

    Science.gov (United States)

    Akbari, Alireza

    2014-01-01

    The petition for language translation has strikingly augmented recently due to cross-cultural communication and exchange of information. In order to communicate well, text should be translated correctly and completely in each field such as legal documents, technical texts, scientific texts, publicity leaflets, and instructional materials. In this…

  2. An Evaluative Study of Machine Translation in the EFL Scenario of Saudi Arabia

    Directory of Open Access Journals (Sweden)

    Raneem Khalid Al-Tuwayrish

    2016-02-01

    Full Text Available Artificial Intelligence or AI as it is popularly known and its corollary, Machine Translation (MT have long engaged scientists, thinkers and linguists alike in the twenty first century. However, the wider question that lies in the relation between technology and translation is, What does technology do to language? This is an important question in the current paradigm because new translation technologies, such as, translation memories, data-based machine translation, and collaborative translation, far from being just additional tools, are changing the very nature of the translators’ cognitive activity, social relations, and professional standing. In fact, in some translation situations such as when translating technical materials or subject matter that are not a specialization with human translators, one potentially needs technology.  The purview of this paper, however, is limited to the role of MT in day to day situations where the generic MT tools like Google Translate or Bing Translator are encouraged. Further, it endeavours to weigh and empirically demonstrate the pros and cons of MT with a view to recommending measures for better communication training in the EFL set up of Saudi Arabia. Keywords: AI, MT, translation, technology, necessity, communication

  3. Investigating Connectivity and Consistency Criteria for Phrase Pair Extraction in Statistical Machine Translation

    NARCIS (Netherlands)

    Martzoukos, S.; Costa Florêncio, C.; Monz, C.; Kornai, A.; Kuhlmann, M.

    2013-01-01

    The consistency method has been established as the standard strategy for extracting high quality translation rules in statistical machine translation (SMT). However, no attention has been drawn to why this method is successful, other than empirical evidence. Using concepts from graph theory, we

  4. The Integration of Project-Based Methodology into Teaching in Machine Translation

    Science.gov (United States)

    Madkour, Magda

    2016-01-01

    This quantitative-qualitative analytical research aimed at investigating the effect of integrating project-based teaching methodology into teaching machine translation on students' performance. Data was collected from the graduate students in the College of Languages and Translation, at Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi…

  5. An Evaluative Study of Machine Translation in the EFL Scenario of Saudi Arabia

    Science.gov (United States)

    Al-Tuwayrish, Raneem Khalid

    2016-01-01

    Artificial Intelligence or AI as it is popularly known and its corollary, Machine Translation (MT) have long engaged scientists, thinkers and linguists alike in the twenty first century. However, the wider question that lies in the relation between technology and translation is, What does technology do to language? This is an important question in…

  6. Recycling Texts: Human evaluation of example-based machine translation subtitles for DVD

    DEFF Research Database (Denmark)

    Flanagan, Marian

    2009-01-01

    This project focuses on translation reusability in audiovisual contexts. Specifically, the project seeks to establish (1) whether target language subtitles produced by an Example-Based Machine Translation (EBMT) system are considered intelligible and acceptable by viewers of movies on DVD, and (2...

  7. A Character Level Based and Word Level Based Approach for Chinese-Vietnamese Machine Translation

    Directory of Open Access Journals (Sweden)

    Phuoc Tran

    2016-01-01

    Full Text Available Chinese and Vietnamese have the same isolated language; that is, the words are not delimited by spaces. In machine translation, word segmentation is often done first when translating from Chinese or Vietnamese into different languages (typically English and vice versa. However, it is a matter for consideration that words may or may not be segmented when translating between two languages in which spaces are not used between words, such as Chinese and Vietnamese. Since Chinese-Vietnamese is a low-resource language pair, the sparse data problem is evident in the translation system of this language pair. Therefore, while translating, whether it should be segmented or not becomes more important. In this paper, we propose a new method for translating Chinese to Vietnamese based on a combination of the advantages of character level and word level translation. In addition, a hybrid approach that combines statistics and rules is used to translate on the word level. And at the character level, a statistical translation is used. The experimental results showed that our method improved the performance of machine translation over that of character or word level translation.

  8. A Character Level Based and Word Level Based Approach for Chinese-Vietnamese Machine Translation.

    Science.gov (United States)

    Tran, Phuoc; Dinh, Dien; Nguyen, Hien T

    2016-01-01

    Chinese and Vietnamese have the same isolated language; that is, the words are not delimited by spaces. In machine translation, word segmentation is often done first when translating from Chinese or Vietnamese into different languages (typically English) and vice versa. However, it is a matter for consideration that words may or may not be segmented when translating between two languages in which spaces are not used between words, such as Chinese and Vietnamese. Since Chinese-Vietnamese is a low-resource language pair, the sparse data problem is evident in the translation system of this language pair. Therefore, while translating, whether it should be segmented or not becomes more important. In this paper, we propose a new method for translating Chinese to Vietnamese based on a combination of the advantages of character level and word level translation. In addition, a hybrid approach that combines statistics and rules is used to translate on the word level. And at the character level, a statistical translation is used. The experimental results showed that our method improved the performance of machine translation over that of character or word level translation.

  9. Technology: English Learners and Machine Translation, Part 2

    Science.gov (United States)

    Van Horn, Royal

    2004-01-01

    In this article, the author touches on the ways that technology can come to the aid of teachers with students who don't speak English. He discusses different word processors that successfully translate foreign text.

  10. Translational invariance in bag model

    International Nuclear Information System (INIS)

    Megahed, F.

    1981-10-01

    In this thesis, the effect of restoring the translational invariance to an approximation to the MIT bag model on the calculation of deep inelastic structure functions is investigated. In chapter one, the model and its major problems are reviewed and Dirac's method of quantisation is outlined. This method is used in chapter two to quantise a two-dimensional complex scalar bag and formal expressions for the form factor and the structure functions are obtained. In chapter three, the expression for the structure function away from the Bjorken limit is studied. The corrections to the L 0 - approximation to the structure function is calculated in chapter four and it is shown to be large. Finally, in chapter five, a bag-like model for kinematic corrections to structure functions is introduced and agreement with data between 2 and 6 (GeV/C) 2 is obtained. (author)

  11. Predicting post-translational lysine acetylation using support vector machines

    DEFF Research Database (Denmark)

    Gnad, Florian; Ren, Shubin; Choudhary, Chunaram

    2010-01-01

    spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico...

  12. Bombsights and Adding Machines: Translating Wartime Technology into Peacetime Sales

    Science.gov (United States)

    Tremblay, Michael

    2010-01-01

    On 10 February 1947, A.C. Buehler, the president of the Victor Adding Machine Company presented Norden Bombsight #4120 to the Smithsonian Institute. This sight was in service on board the Enola Gay when it dropped the first atomic bomb on Hiroshima. Through this public presentation, Buehler forever linked his company to the Norden Bombsight, the…

  13. Data extraction from machine-translated versus original language randomized trial reports: a comparative study.

    Science.gov (United States)

    Balk, Ethan M; Chung, Mei; Chen, Minghua L; Chang, Lina Kong Win; Trikalinos, Thomas A

    2013-11-07

    Google Translate offers free Web-based translation, but it is unknown whether its translation accuracy is sufficient to use in systematic reviews to mitigate concerns about language bias. We compared data extraction from non-English language studies with extraction from translations by Google Translate of 10 studies in each of five languages (Chinese, French, German, Japanese and Spanish). Fluent speakers double-extracted original-language articles. Researchers who did not speak the given language double-extracted translated articles along with 10 additional English language trials. Using the original language extractions as a gold standard, we estimated the probability and odds ratio of correctly extracting items from translated articles compared with English, adjusting for reviewer and language. Translation required about 30 minutes per article and extraction of translated articles required additional extraction time. The likelihood of correct extractions was greater for study design and intervention domain items than for outcome descriptions and, particularly, study results. Translated Spanish articles yielded the highest percentage of items (93%) that were correctly extracted more than half the time (followed by German and Japanese 89%, French 85%, and Chinese 78%) but Chinese articles yielded the highest percentage of items (41%) that were correctly extracted >98% of the time (followed by Spanish 30%, French 26%, German 22%, and Japanese 19%). In general, extractors' confidence in translations was not associated with their accuracy. Translation by Google Translate generally required few resources. Based on our analysis of translations from five languages, using machine translation has the potential to reduce language bias in systematic reviews; however, pending additional empirical data, reviewers should be cautious about using translated data. There remains a trade-off between completeness of systematic reviews (including all available studies) and risk of

  14. Some Problems in German to English Machine Translation

    Science.gov (United States)

    1974-12-01

    fron Benanti^e is a slippery business, especially when I have just clalwsd to subscribe to the idea that the structure of an utterance is intinately...from the English translation on page 15, the example paragraph can be divided Into elm 134 sections. These diviaions can be characterized at

  15. ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

    Science.gov (United States)

    Cao, Renzhi; Freitas, Colton; Chan, Leong; Sun, Miao; Jiang, Haiqing; Chen, Zhangxin

    2017-10-17

    With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.

  16. A user-based usability assessment of raw machine translated technical instructions

    OpenAIRE

    Doherty, Stephen; O'Brien, Sharon

    2012-01-01

    Despite the growth of statistical machine translation (SMT) research and development in recent years, it remains somewhat out of reach for the translation community where programming expertise and knowledge of statistics tend not to be commonplace. While the concept of SMT is relatively straightforward, its implementation in functioning systems remains difficult for most, regardless of expertise. More recently, however, developments such as SmartMATE have emerged which aim to assist users in ...

  17. Finding Translation Examples for Under-Resourced Language Pairs or for Narrow Domains; the Case for Machine Translation

    Directory of Open Access Journals (Sweden)

    Dan Tufis

    2012-07-01

    Full Text Available The cyberspace is populated with valuable information sources, expressed in about 1500 different languages and dialects. Yet, for the vast majority of WEB surfers this wealth of information is practically inaccessible or meaningless. Recent advancements in cross-lingual information retrieval, multilingual summarization, cross-lingual question answering and machine translation promise to narrow the linguistic gaps and lower the communication barriers between humans and/or software agents. Most of these language technologies are based on statistical machine learning techniques which require large volumes of cross lingual data. The most adequate type of cross-lingual data is represented by parallel corpora, collection of reciprocal translations. However, it is not easy to find enough parallel data for any language pair might be of interest. When required parallel data refers to specialized (narrow domains, the scarcity of data becomes even more acute. Intelligent information extraction techniques from comparable corpora provide one of the possible answers to this lack of translation data.

  18. Improving the quality of automated DVD subtitles via example-based machine translation

    DEFF Research Database (Denmark)

    Armstrong, Stephen; Caffrey, Colm; Flanagan, Marian

    Denoual (2005) discovered that, contrary to popular belief, an Example-Based Machine Translation system trained on heterogeneous data produced significantly better results than a system trained on homogeneous data. Using similar evaluation metrics and a few additional ones, in this paper we show...

  19. Crawl and crowd to bring machine translation to under-resourced languages

    NARCIS (Netherlands)

    Toral Ruiz, Antonio

    2017-01-01

    We present a widely applicable methodology to bring machine translation (MT) to under-resourced languages in a cost-effective and rapid manner. Our proposal relies on web crawling to automatically acquire parallel data to train statistical MT systems if any such data can be found for the language

  20. Efficient accurate syntactic direct translation models: one tree at a time

    NARCIS (Netherlands)

    Hassan, H.; Sima'an, K.; Way, A.

    2011-01-01

    A challenging aspect of Statistical Machine Translation from Arabic to English lies in bringing the Arabic source morpho-syntax to bear on the lexical as well as word-order choices of the English target string. In this article, we extend the feature-rich discriminative Direct Translation Model 2

  1. Machine translation (MT): qualità, produttività, customer satisfaction

    OpenAIRE

    Fellet, Anna

    2010-01-01

    The aim of the present research is to examine the impact of recent technological developments in machine translation (MT) in the language industry. The objectives are: 1. To define the value of MT in terms of suitability and convenience in meeting expressed requirements in those cases where MT is demanded; 2. To examine the potential increase in productivity through a conscious use of the tool; 3. To analyse those activities aimed at satisfying the customer’s explicit and impli...

  2. Handbook of natural language processing and machine translation DARPA global autonomous language exploitation

    CERN Document Server

    Olive, Joseph P; McCary, John

    2011-01-01

    This comprehensive handbook, written by leading experts in the field, details the groundbreaking research conducted under the breakthrough GALE program - The Global Autonomous Language Exploitation within the Defense Advanced Research Projects Agency (DARPA), while placing it in the context of previous research in the fields of natural language and signal processing, artificial intelligence and machine translation. The most fundamental contrast between GALE and its predecessor programs was its holistic integration of previously separate or sequential processes. In earlier language research pro

  3. Evaluating Translational Research: A Process Marker Model

    Science.gov (United States)

    Trochim, William; Kane, Cathleen; Graham, Mark J.; Pincus, Harold A.

    2011-01-01

    Abstract Objective: We examine the concept of translational research from the perspective of evaluators charged with assessing translational efforts. One of the major tasks for evaluators involved in translational research is to help assess efforts that aim to reduce the time it takes to move research to practice and health impacts. Another is to assess efforts that are intended to increase the rate and volume of translation. Methods: We offer an alternative to the dominant contemporary tendency to define translational research in terms of a series of discrete “phases.”Results: We contend that this phased approach has been confusing and that it is insufficient as a basis for evaluation. Instead, we argue for the identification of key operational and measurable markers along a generalized process pathway from research to practice. Conclusions: This model provides a foundation for the evaluation of interventions designed to improve translational research and the integration of these findings into a field of translational studies. Clin Trans Sci 2011; Volume 4: 153–162 PMID:21707944

  4. Parallel Boltzmann machines : a mathematical model

    NARCIS (Netherlands)

    Zwietering, P.J.; Aarts, E.H.L.

    1991-01-01

    A mathematical model is presented for the description of parallel Boltzmann machines. The framework is based on the theory of Markov chains and combines a number of previously known results into one generic model. It is argued that parallel Boltzmann machines maximize a function consisting of a

  5. MODEL OF TEACHING PROFESSION SPECIFIC BILATERAL TRANSLATION

    Directory of Open Access Journals (Sweden)

    Yana Fabrychna

    2017-03-01

    Full Text Available The article deals with the author’s interpretation of the process of teaching profession specific bilateral translation to student teacher of English in the Master’s program. The goal of the model of teaching profession specific bilateral translation development is to determine the logical sequence of educational activities of the teacher as the organizer of the educational process and students as its members. English and Ukrainian texts on methods of foreign languages and cultures teaching are defined as the object of study. Learning activities aimed at the development of student teachers of English profession specific competence in bilateral translation and Translation Proficiency Language Portfolio for Student Teachers of English are suggested as teaching tools. The realization of the model of teaching profession specific bilateral translation to student teachers of English in the Master’s program is suggested within the module topics of the academic discipline «Practice of English as the first foreign language»: Globalization; Localization; Education; Work; The role of new communication technologies in personal and professional development. We believe that the amount of time needed for efficient functioning of the model is 48 academic hours, which was determined by calculating the total number of academic hours allotted for the academic discipline «Practice of English as the first foreign language» in Ukrainian universities. Peculiarities of the model realization as well as learning goals and content of class activities and home self-study work of students are outlined.

  6. Model-Agnostic Interpretability of Machine Learning

    OpenAIRE

    Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos

    2016-01-01

    Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred f...

  7. Our Policies, Their Text: German Language Students' Strategies with and Beliefs about Web-Based Machine Translation

    Science.gov (United States)

    White, Kelsey D.; Heidrich, Emily

    2013-01-01

    Most educators are aware that some students utilize web-based machine translators for foreign language assignments, however, little research has been done to determine how and why students utilize these programs, or what the implications are for language learning and teaching. In this mixed-methods study we utilized surveys, a translation task,…

  8. Preliminary study of online machine translation use of nursing literature: quality evaluation and perceived usability

    Directory of Open Access Journals (Sweden)

    Anazawa Ryoko

    2012-11-01

    Full Text Available Abstract Background Japanese nurses are increasingly required to read published international research in clinical, educational, and research settings. Language barriers are a significant obstacle, and online machine translation (MT is a tool that can be used to address this issue. We examined the quality of Google Translate® (English to Japanese and Korean to Japanese, which is a representative online MT, using a previously verified evaluation method. We also examined the perceived usability and current use of online MT among Japanese nurses. Findings Randomly selected nursing abstracts were translated and then evaluated for intelligibility and usability by 28 participants, including assistants and research associates from nursing universities throughout Japan. They answered a questionnaire about their online MT use. From simple comparison of mean scores between two language pairs, translation quality was significantly better, with respect to both intelligibility and usability, for Korean-Japanese than for English-Japanese. Most respondents perceived a language barrier. Online MT had been used by 61% of the respondents and was perceived as not useful enough. Conclusion Nursing articles translated from Korean into Japanese by an online MT system could be read at an acceptable level of comprehension, but the same could not be said for English-Japanese translations. Respondents with experience using online MT used it largely to grasp the overall meanings of the original text. Enrichment in technical terms appeared to be the key to better usability. Users will be better able to use MT outputs if they improve their foreign language proficiency as much as possible. Further research is being conducted with a larger sample size and detailed analysis.

  9. Extracting Date/Time Expressions in Super-Function Based Japanese-English Machine Translation

    Science.gov (United States)

    Sasayama, Manabu; Kuroiwa, Shingo; Ren, Fuji

    Super-Function Based Machine Translation(SFBMT) which is a type of Example-Based Machine Translation has a feature which makes it possible to expand the coverage of examples by changing nouns into variables, however, there were problems extracting entire date/time expressions containing parts-of-speech other than nouns, because only nouns/numbers were changed into variables. We describe a method for extracting date/time expressions for SFBMT. SFBMT uses noun determination rules to extract nouns and a bilingual dictionary to obtain correspondence of the extracted nouns between the source and the target languages. In this method, we add a rule to extract date/time expressions and then extract date/time expressions from a Japanese-English bilingual corpus. The evaluation results shows that the precision of this method for Japanese sentences is 96.7%, with a recall of 98.2% and the precision for English sentences is 94.7%, with a recall of 92.7%.

  10. Breaking the language barrier: machine assisted diagnosis using the medical speech translator.

    Science.gov (United States)

    Starlander, Marianne; Bouillon, Pierrette; Rayner, Manny; Chatzichrisafis, Nikos; Hockey, Beth Ann; Isahara, Hitoshi; Kanzaki, Kyoko; Nakao, Yukie; Santaholma, Marianne

    2005-01-01

    In this paper, we describe and evaluate an Open Source medical speech translation system (MedSLT) intended for safety-critical applications. The aim of this system is to eliminate the language barriers in emergency situation. It translates spoken questions from English into French, Japanese and Finnish in three medical subdomains (headache, chest pain and abdominal pain), using a vocabulary of about 250-400 words per sub-domain. The architecture is a compromise between fixed-phrase translation on one hand and complex linguistically-based systems on the other. Recognition is guided by a Context Free Grammar Language Model compiled from a general unification grammar, automatically specialised for the domain. We present an evaluation of this initial prototype that shows the advantages of this grammar-based approach for this particular translation task in term of both reliability and use.

  11. Thermal models of pulse electrochemical machining

    International Nuclear Information System (INIS)

    Kozak, J.

    2004-01-01

    Pulse electrochemical machining (PECM) provides an economical and effective method for machining high strength, heat-resistant materials into complex shapes such as turbine blades, die, molds and micro cavities. Pulse Electrochemical Machining involves the application of a voltage pulse at high current density in the anodic dissolution process. Small interelectrode gap, low electrolyte flow rate, gap state recovery during the pulse off-times lead to improved machining accuracy and surface finish when compared with ECM using continuous current. This paper presents a mathematical model for PECM and employs this model in a computer simulation of the PECM process for determination of the thermal limitation and energy consumption in PECM. The experimental results and discussion of the characteristics PECM are presented. (authors)

  12. Applications and modelling of bulk HTSs in brushless ac machines

    International Nuclear Information System (INIS)

    Barnes, G.J.

    2000-01-01

    The use of high temperature superconducting material in its bulk form for engineering applications is attractive due to the large power densities that can be achieved. In brushless electrical machines, there are essentially four properties that can be exploited; their hysteretic nature, their flux shielding properties, their ability to trap large flux densities and their ability to produce levitation. These properties translate to hysteresis machines, reluctance machines, trapped-field synchronous machines and linear motors respectively. Each one of these machines is addressed separately and computer simulations that reveal the current and field distributions within the machines are used to explain their operation. (author)

  13. Proposal and Evaluation of Sequencing Words in Chat Conversation between Japanese and Chinese using Machine Translation

    OpenAIRE

    李, 芬慧; 由井薗, 隆也

    2010-01-01

    日中翻訳チャットにおいて単語を並べた会話によるチャットコミュニケーションを提案する.比較評価のために,通常の文章チャットによる評価実験も行った.その結果,日中翻訳チャットにおいて,(1)単語チャットは会話速度や会話内容の理解において文章チャットと同等に使えること,(2)利用者は,単語チャットよりは文章チャットを好む傾向があること,(3)翻訳された会話の理解は日本人と中国人とで文化的違いがある可能性が得られた.今後は単語チャットの応用を検討する予定である. : We propose a chat conversation between Japanese and Chinese using machine translation by sequencing words. By comparison with a conventional chat using machine translation, it is showed that (1) sequencing words in the chat is as same speed and understanding as the...

  14. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

  15. The Modelling of Axially Translating Flexible Beams

    Science.gov (United States)

    Theodore, R. J.; Arakeri, J. H.; Ghosal, A.

    1996-04-01

    The axially translating flexible beam with a prismatic joint can be modelled by using the Euler-Bernoulli beam equation together with the convective terms. In general, the method of separation of variables cannot be applied to solve this partial differential equation. In this paper, a non-dimensional form of the Euler Bernoulli beam equation is presented, obtained by using the concept of group velocity, and also the conditions under which separation of variables and assumed modes method can be used. The use of clamped-mass boundary conditions leads to a time-dependent frequency equation for the translating flexible beam. A novel method is presented for solving this time dependent frequency equation by using a differential form of the frequency equation. The assume mode/Lagrangian formulation of dynamics is employed to derive closed form equations of motion. It is shown by using Lyapunov's first method that the dynamic responses of flexural modal variables become unstable during retraction of the flexible beam, which the dynamic response during extension of the beam is stable. Numerical simulation results are presented for the uniform axial motion induced transverse vibration for a typical flexible beam.

  16. Man-machine analysis of translation and work tasks of Skylab films

    Science.gov (United States)

    Hosler, W. W.; Boelter, J. G.; Morrow, J. R., Jr.; Jackson, J. T.

    1979-01-01

    An objective approach to determine the concurrent validity of computer-graphic models is real time film analysis. This technique was illustrated through the procedures and results obtained in an evaluation of translation of Skylab mission astronauts. The quantitative analysis was facilitated by the use of an electronic film analyzer, minicomputer, and specifically supportive software. The uses of this technique for human factors research are: (1) validation of theoretical operator models; (2) biokinetic analysis; (3) objective data evaluation; (4) dynamic anthropometry; (5) empirical time-line analysis; and (6) consideration of human variability. Computer assisted techniques for interface design and evaluation have the potential for improving the capability for human factors engineering.

  17. Understanding and modelling Man-Machine Interaction

    International Nuclear Information System (INIS)

    Cacciabue, P.C.

    1991-01-01

    This paper gives an overview of the current state of the art in man machine systems interaction studies, focusing on the problems derived from highly automated working environments and the role of humans in the control loop. In particular, it is argued that there is a need for sound approaches to design and analysis of Man-Machine Interaction (MMI), which stem from the contribution of three expertises in interfacing domains, namely engineering, computer science and psychology: engineering for understanding and modelling plants and their material and energy conservation principles; psychology for understanding and modelling humans and their cognitive behaviours; computer science for converting models in sound simulations running in appropriate computer architectures. (author)

  18. Understanding and modelling man-machine interaction

    International Nuclear Information System (INIS)

    Cacciabue, P.C.

    1996-01-01

    This paper gives an overview of the current state of the art in man-machine system interaction studies, focusing on the problems derived from highly automated working environments and the role of humans in the control loop. In particular, it is argued that there is a need for sound approaches to the design and analysis of man-machine interaction (MMI), which stem from the contribution of three expertises in interfacing domains, namely engineering, computer science and psychology: engineering for understanding and modelling plants and their material and energy conservation principles; psychology for understanding and modelling humans an their cognitive behaviours; computer science for converting models in sound simulations running in appropriate computer architectures. (orig.)

  19. Use of Online Machine Translation for Nursing Literature: A Questionnaire-Based Survey

    Science.gov (United States)

    Anazawa, Ryoko; Ishikawa, Hirono; Takahiro, Kiuchi

    2013-01-01

    Background: The language barrier is a significant obstacle for nurses who are not native English speakers to obtain information from international journals. Freely accessible online machine translation (MT) offers a possible solution to this problem. Aim: To explore how Japanese nursing professionals use online MT and perceive its usability in reading English articles and to discuss what should be considered for better utilisation of online MT lessening the language barrier. Method: In total, 250 randomly selected assistants and research associates at nursing colleges across Japan answered a questionnaire examining the current use of online MT and perceived usability among Japanese nurses, along with the number of articles read in English and the perceived language barrier. The items were rated on Likert scales, and t-test, ANOVA, chi-square test, and Spearman’s correlation were used for analyses. Results: Of the participants, 73.8% had used online MT. More than half of them felt it was usable. The language barrier was strongly felt, and academic degrees and English proficiency level were associated factors. The perceived language barrier was related to the frequency of online MT use. No associated factor was found for the perceived usability of online MT. Conclusion: Language proficiency is an important factor for optimum utilisation of MT. A need for education in the English language, reading scientific papers, and online MT training was indicated. Cooperation with developers and providers of MT for the improvement of their systems is required. PMID:23459140

  20. Electromechanical model of machine for vibroabrasive treatment of machine parts

    OpenAIRE

    Gorbatiyk, Ruslan; Palamarchuk, Igor; Chubyk, Roman

    2015-01-01

    A lot of operations on trimming clean and finishing – stripping up treatment, first of all, removing of burrs, rounding and processing of borders, until recently time was carried out by hand, and hardly exposed to automation and became a serious obstacle in subsequent growth of the labor productivity. Machines with free kinematics connection between a tool and the treating parts is provided by the printing-down of all of the surface of the machine parts, that allows us to effectively treat bo...

  1. Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

    Science.gov (United States)

    Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

    2017-09-01

    This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

  2. PCI: A PATRAN-NASTRAN model translator

    Science.gov (United States)

    Sheerer, T. J.

    1990-01-01

    The amount of programming required to develop a PATRAN-NASTRAN translator was surprisingly small. The approach taken produced a highly flexible translator comparable with the PATNAS translator and superior to the PATCOS translator. The coding required varied from around ten lines for a shell element to around thirty for a bar element, and the time required to add a feature to the program is typically less than an hour. The use of a lookup table for element names makes the translator also applicable to other versions of NASTRAN. The saving in time as a result of using PDA's Gateway utilities was considerable. During the writing of the program it became apparent that, with a somewhat more complex structure, it would be possible to extend the element data file to contain all data required to define the translation from PATRAN to NASTRAN by mapping of data between formats. Similar data files on property, material and grid formats would produce a completely universal translator from PATRAN to any FEA program, or indeed any CAE system.

  3. comparative study of moore and mealy machine models adaptation

    African Journals Online (AJOL)

    user

    automata model was developed for ABS manufacturing process using Moore and Mealy Finite State Machines. Simulation ... The simulation results showed that the Mealy Machine is faster than the Moore ..... random numbers from MATLAB.

  4. A Flexible Statechart-to-Model-Checker Translator

    Science.gov (United States)

    Rouquette, Nicolas; Dunphy, Julia; Feather, Martin S.

    2000-01-01

    Many current-day software design tools offer some variant of statechart notation for system specification. We, like others, have built an automatic translator from (a subset of) statecharts to a model checker, for use to validate behavioral requirements. Our translator is designed to be flexible. This allows us to quickly adjust the translator to variants of statechart semantics, including problem-specific notational conventions that designers employ. Our system demonstration will be of interest to the following two communities: (1) Potential end-users: Our demonstration will show translation from statecharts created in a commercial UML tool (Rational Rose) to Promela, the input language of Holzmann's model checker SPIN. The translation is accomplished automatically. To accommodate the major variants of statechart semantics, our tool offers user-selectable choices among semantic alternatives. Options for customized semantic variants are also made available. The net result is an easy-to-use tool that operates on a wide range of statechart diagrams to automate the pathway to model-checking input. (2) Other researchers: Our translator embodies, in one tool, ideas and approaches drawn from several sources. Solutions to the major challenges of statechart-to-model-checker translation (e.g., determining which transition(s) will fire, handling of concurrent activities) are retired in a uniform, fully mechanized, setting. The way in which the underlying architecture of the translator itself facilitates flexible and customizable translation will also be evident.

  5. Screening for Prediabetes Using Machine Learning Models

    Directory of Open Access Journals (Sweden)

    Soo Beom Choi

    2014-01-01

    Full Text Available The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES were used, excluding subjects with diabetes. The KNHANES 2010 data (n=4685 were used for training and internal validation, while data from KNHANES 2011 (n=4566 were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN and support vector machine (SVM and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729 and the screening score model (0.712, respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.

  6. Machine Directional Register System Modeling for Shaft-Less Drive Gravure Printing Machines

    Directory of Open Access Journals (Sweden)

    Shanhui Liu

    2013-01-01

    Full Text Available In the latest type of gravure printing machines referred to as the shaft-less drive system, each gravure printing roller is driven by an individual servo motor, and all motors are electrically synchronized. The register error is regulated by a speed difference between the adjacent printing rollers. In order to improve the control accuracy of register system, an accurate mathematical model of the register system should be investigated for the latest machines. Therefore, the mathematical model of the machine directional register (MDR system is studied for the multicolor gravure printing machines in this paper. According to the definition of the MDR error, the model is derived, and then it is validated by the numerical simulation and experiments carried out in the experimental setup of the four-color gravure printing machines. The results show that the established MDR system model is accurate and reliable.

  7. Ausdruckskraft und Regelmaessigkeit: Was Esperanto fuer automatische Uebersetzung geeignet macht (Expressiveness and Formal Regularity: What Makes Esperanto Suitable for Machine Translation).

    Science.gov (United States)

    Schubert, Klaus

    1988-01-01

    Describes DLT, the multilingual machine translation system that uses Esperanto as an intermediate language in which substantial portions of the translation subprocesses are carried out. The criteria for choosing an intermediate language and the reasons for preferring Esperanto over other languages are explained. (Author/DJD)

  8. ASPECTS REGARDING THE METHOD OF REALIZING THE TECHNICAL EXPERTISE FOR REPAIRING THE TRANSLATION MECHANISM OF A M4A COAL-MINING MACHINE

    Directory of Open Access Journals (Sweden)

    Marius Liviu CÎRȚÎNĂ

    2018-05-01

    Full Text Available This paper presents the technical state of the mechanism of translation of the coalmining machine after the technical expertise. The rehabilitation to which the translation mechanism will be subjected will be carried out by performing the intervention works that will bring back into the normal operating parameters both the structural part and the functional part. The paper presents: the proposed solutions for repair after verification of the translation mechanism and the way of repairing the mechanism.

  9. Proposal for a telehealth concept in the translational research model.

    Science.gov (United States)

    Silva, Angélica Baptista; Morel, Carlos Médicis; Moraes, Ilara Hämmerli Sozzi de

    2014-04-01

    To review the conceptual relationship between telehealth and translational research. Bibliographical search on telehealth was conducted in the Scopus, Cochrane BVS, LILACS and MEDLINE databases to find experiences of telehealth in conjunction with discussion of translational research in health. The search retrieved eight studies based on analysis of models of the five stages of translational research and the multiple strands of public health policy in the context of telehealth in Brazil. The models were applied to telehealth activities concerning the Network of Human Milk Banks, in the Telemedicine University Network. The translational research cycle of human milk collected, stored and distributed presents several integrated telehealth initiatives, such as video conferencing, and software and portals for synthesizing knowledge, composing elements of an information ecosystem, mediated by information and communication technologies in the health system. Telehealth should be composed of a set of activities in a computer mediated network promoting the translation of knowledge between research and health services.

  10. Prototype-based models in machine learning.

    Science.gov (United States)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning. © 2016 Wiley Periodicals, Inc.

  11. QEFSM model and Markov Algorithm for translating Quran reciting rules into Braille code

    Directory of Open Access Journals (Sweden)

    Abdallah M. Abualkishik

    2015-07-01

    Full Text Available The Holy Quran is the central religious verbal text of Islam. Muslims are expected to read, understand, and apply the teachings of the Holy Quran. The Holy Quran was translated to Braille code as a normal Arabic text without having its reciting rules included. It is obvious that the users of this transliteration will not be able to recite the Quran the right way. Through this work, Quran Braille Translator (QBT presents a specific translator to translate Quran verses and their reciting rules into the Braille code. Quran Extended Finite State Machine (QEFSM model is proposed through this study as it is able to detect the Quran reciting rules (QRR from the Quran text. Basis path testing was used to evaluate the inner work for the model by checking all the test cases for the model. Markov Algorithm (MA was used for translating the detected QRR and Quran text into the matched Braille code. The data entries for QBT are Arabic letters and diacritics. The outputs of this study are seen in the double lines of Braille symbols; the first line is the proposed Quran reciting rules and the second line is for the Quran scripts.

  12. Sketch of a Noisy Channel Model for the Translation Process

    DEFF Research Database (Denmark)

    Carl, Michael

    default rendering" procedure, later conscious processes are triggered by a monitor who interferes when something goes wrong. An attempt is made to explain monitor activities with relevance theoretic concepts according to which a translator needs to ensure the similarity of explicatures and implicatures......The paper develops a Noisy Channel Model for the translation process that is based on actual user activity data. It builds on the monitor model and makes a distinction between early, automatic and late, conscious translation processes: while early priming processes are at the basis of a "literal...... of the source and the target texts. It is suggested that events and parameters in the model need be measurable and quantifiable in the user activity data so as to trace back monitoring activities in the translation process data. Michael Carl is a Professor with special responsibilities at the Department...

  13. Translation techniques for distributed-shared memory programming models

    Energy Technology Data Exchange (ETDEWEB)

    Fuller, Douglas James [Iowa State Univ., Ames, IA (United States)

    2005-01-01

    The high performance computing community has experienced an explosive improvement in distributed-shared memory hardware. Driven by increasing real-world problem complexity, this explosion has ushered in vast numbers of new systems. Each new system presents new challenges to programmers and application developers. Part of the challenge is adapting to new architectures with new performance characteristics. Different vendors release systems with widely varying architectures that perform differently in different situations. Furthermore, since vendors need only provide a single performance number (total MFLOPS, typically for a single benchmark), they only have strong incentive initially to optimize the API of their choice. Consequently, only a fraction of the available APIs are well optimized on most systems. This causes issues porting and writing maintainable software, let alone issues for programmers burdened with mastering each new API as it is released. Also, programmers wishing to use a certain machine must choose their API based on the underlying hardware instead of the application. This thesis argues that a flexible, extensible translator for distributed-shared memory APIs can help address some of these issues. For example, a translator might take as input code in one API and output an equivalent program in another. Such a translator could provide instant porting for applications to new systems that do not support the application's library or language natively. While open-source APIs are abundant, they do not perform optimally everywhere. A translator would also allow performance testing using a single base code translated to a number of different APIs. Most significantly, this type of translator frees programmers to select the most appropriate API for a given application based on the application (and developer) itself instead of the underlying hardware.

  14. From Translational Research to Translational Effectiveness: The “Patient-Centered Dental Home” Model

    Directory of Open Access Journals (Sweden)

    Francesco Chiappelli

    2011-06-01

    Full Text Available Toward revitalizing the Nation’s primary medical care system, the Agency for Health Research & Quality (AHRQ stated that new foundational measures must be crafted for achieving high-quality, accessible, efficient health care for all Americans. The efficiency of medical care is viewed along two dimensions: first, we must continue to pursue translational research; and second, we must translate research to optimize effectiveness in specific clinical settings. It is increasingly evident that the efficiency of both translational processes is critical to the revitalization of health care, and that it rests on the practical functionality of the nexus among three cardinal entities: the researcher, the clinician, and the patient. A novel model has evolved that encapsulates this notion, and that proposes the advanced pri-mary care “medical home”, more commonly referred to as the “patient-centered medical home” (PCMH. It is a promising model for transforming the organization and delivery of primary medical care, because it is not simply a place per se, but it is a function-ing unit that delivers medical care along the fundamental principles of being patient-centered, comprehensive, coordinated, and accessible. It is energized by translational research, and its principal aim and ultimate goal is translational effectiveness. The PCMH is a model that works well within the priorities set by the American Recovery and Reinvestment Act of 2009, and the Health Care Reform Act of 2010. However, while dentistry has a clearly defined place in both Acts, the PCMH is designed for medical and nursing care. A parallel model of the “patient-centered dental home” (PCDH must be realized.

  15. Simulation Tools for Electrical Machines Modelling: Teaching and ...

    African Journals Online (AJOL)

    Simulation tools are used both for research and teaching to allow a good comprehension of the systems under study before practical implementations. This paper illustrates the way MATLAB is used to model non-linearites in synchronous machine. The machine is modeled in rotor reference frame with currents as state ...

  16. Reading Strategies in a L2: A Study on Machine Translation

    Science.gov (United States)

    Karnal, Adriana Riess; Pereira, Vera Vanmacher

    2015-01-01

    This article aims at understanding cognitive strategies which are involved in reading academic texts in English as a L2/FL. Specifically, we focus on reading comprehension when a text is read either using Google translator or not. From this perspective we must consider the reading process in its complexity not only as a decoding process. We follow…

  17. Investigation of approximate models of experimental temperature characteristics of machines

    Science.gov (United States)

    Parfenov, I. V.; Polyakov, A. N.

    2018-05-01

    This work is devoted to the investigation of various approaches to the approximation of experimental data and the creation of simulation mathematical models of thermal processes in machines with the aim of finding ways to reduce the time of their field tests and reducing the temperature error of the treatments. The main methods of research which the authors used in this work are: the full-scale thermal testing of machines; realization of various approaches at approximation of experimental temperature characteristics of machine tools by polynomial models; analysis and evaluation of modelling results (model quality) of the temperature characteristics of machines and their derivatives up to the third order in time. As a result of the performed researches, rational methods, type, parameters and complexity of simulation mathematical models of thermal processes in machine tools are proposed.

  18. Mathematically modelling the power requirement for a vertical shaft mowing machine

    Directory of Open Access Journals (Sweden)

    Jorge Simón Pérez de Corcho Fuentes

    2008-09-01

    Full Text Available This work describes a mathematical model for determining the power demand for a vertical shaft mowing machine, particularly taking into account the influence of speed on cutting power, which is different from that of other models of mowers. The influence of the apparatus’ rotation and translation speeds was simulated in determining power demand. The results showed that no chan-ges in cutting power were produced by varying the knives’ angular speed (if translation speed was constant, while cutting power became increased if translation speed was increased. Variations in angular speed, however, influenced other parameters deter-mining total power demand. Determining this vertical shaft mower’s cutting pattern led to obtaining good crop stubble quality at the mower’s lower rotation speed, hence reducing total energy requirements.

  19. MT-ComparEval: Graphical evaluation interface for Machine Translation development

    Directory of Open Access Journals (Sweden)

    Klejch Ondřej

    2015-10-01

    Full Text Available The tool described in this article has been designed to help MT developers by implementing a web-based graphical user interface that allows to systematically compare and evaluate various MT engines/experiments using comparative analysis via automatic measures and statistics. The evaluation panel provides graphs, tests for statistical significance and n-gram statistics. We also present a demo server http://wmt.ufal.cz with WMT14 and WMT15 translations.

  20. Virtual NC machine model with integrated knowledge data

    International Nuclear Information System (INIS)

    Sidorenko, Sofija; Dukovski, Vladimir

    2002-01-01

    The concept of virtual NC machining was established for providing a virtual product that could be compared with an appropriate designed product, in order to make NC program correctness evaluation, without real experiments. This concept is applied in the intelligent CAD/CAM system named VIRTUAL MANUFACTURE. This paper presents the first intelligent module that enables creation of the virtual models of existed NC machines and virtual creation of new ones, applying modular composition. Creation of a virtual NC machine is carried out via automatic knowledge data saving (features of the created NC machine). (Author)

  1. Free Online Translators: A Comparative Assessment in Terms of Idioms and Phrasal Verbs

    OpenAIRE

    Marziyeh Taleghani; Ehsan Pazouki

    2018-01-01

    Free online translators are in fact statistical machine translators that create translator models using parallel corpora. Although it’s not a new subject and many works are reported on that in recent years, it still suffers from lots of shortcomings and has a long way ahead. While the literature on machine translators is vast, there are only a few that evaluate free online machine translators in specific terms like idioms. The aim of this paper is to evaluate and compare four free...

  2. Testing and Modeling of Machine Properties in Resistance Welding

    DEFF Research Database (Denmark)

    Wu, Pei

    The objective of this work has been to test and model the machine properties including the mechanical properties and the electrical properties in resistance welding. The results are used to simulate the welding process more accurately. The state of the art in testing and modeling machine properties...... as real projection welding tests, is easy to realize in industry, since tests may be performed in situ. In part II, an approach of characterizing the electrical properties of AC resistance welding machines is presented, involving testing and mathematical modelling of the weld current, the firing angle...... in resistance welding has been described based on a comprehensive literature study. The present thesis has been subdivided into two parts: Part I: Mechanical properties of resistance welding machines. Part II: Electrical properties of resistance welding machines. In part I, the electrode force in the squeeze...

  3. Testing and Modeling of Mechanical Characteristics of Resistance Welding Machines

    DEFF Research Database (Denmark)

    Wu, Pei; Zhang, Wenqi; Bay, Niels

    2003-01-01

    for both upper and lower electrode systems. This has laid a foundation for modeling the welding process and selecting the welding parameters considering the machine factors. The method is straightforward and easy to be applied in industry since the whole procedure is based on tests with no requirements......The dynamic mechanical response of resistance welding machine is very important to the weld quality in resistance welding especially in projection welding when collapse or deformation of work piece occurs. It is mainly governed by the mechanical parameters of machine. In this paper, a mathematical...... model for characterizing the dynamic mechanical responses of machine and a special test set-up called breaking test set-up are developed. Based on the model and the test results, the mechanical parameters of machine are determined, including the equivalent mass, damping coefficient, and stiffness...

  4. MODELING AND INVESTIGATION OF ASYNCHRONOUS TWO-MACHINE SYSTEM MODES

    Directory of Open Access Journals (Sweden)

    V. S. Safaryan

    2014-01-01

    Full Text Available The paper considers stationary and transient processes of an asynchronous two-machine system. A mathematical model for investigation of stationary and transient modes, static characteristics and research results of dynamic process pertaining to starting-up the asynchronous two-machine system has been given in paper.

  5. Boltzmann machines as a model for parallel annealing

    NARCIS (Netherlands)

    Aarts, E.H.L.; Korst, J.H.M.

    1991-01-01

    The potential of Boltzmann machines to cope with difficult combinatorial optimization problems is investigated. A discussion of various (parallel) models of Boltzmann machines is given based on the theory of Markov chains. A general strategy is presented for solving (approximately) combinatorial

  6. Implementation of the Lanczos algorithm for the Hubbard model on the Connection Machine system

    International Nuclear Information System (INIS)

    Leung, P.W.; Oppenheimer, P.E.

    1992-01-01

    An implementation of the Lanczos algorithm for the exact diagonalization of the two dimensional Hubbard model on a 4x4 square lattice on the Connection Machine CM-2 system is described. The CM-2 is a massively parallel machine with distributed memory. The program is written in C/PARIS. This implementation minimizes memory usage by generating the matrix elements as needed instead of storing them. The Lanczos vectors are stored across the local memory of the processors. Using translational symmetry only, the dimension of the Hilbert space at half filling is more than 10 million. A speed of about 2.4 min per iteration is achieved on a 64K CM-2. This implementation is scalable. Running it on a bigger machine with more processors speeds up the process. The performance analysis of this implementation is shown and discuss its advantages and disadvantages are discussed

  7. Discrete Model Reference Adaptive Control System for Automatic Profiling Machine

    Directory of Open Access Journals (Sweden)

    Peng Song

    2012-01-01

    Full Text Available Automatic profiling machine is a movement system that has a high degree of parameter variation and high frequency of transient process, and it requires an accurate control in time. In this paper, the discrete model reference adaptive control system of automatic profiling machine is discussed. Firstly, the model of automatic profiling machine is presented according to the parameters of DC motor. Then the design of the discrete model reference adaptive control is proposed, and the control rules are proven. The results of simulation show that adaptive control system has favorable dynamic performances.

  8. VOLUMETRIC ERROR COMPENSATION IN FIVE-AXIS CNC MACHINING CENTER THROUGH KINEMATICS MODELING OF GEOMETRIC ERROR

    Directory of Open Access Journals (Sweden)

    Pooyan Vahidi Pashsaki

    2016-06-01

    Full Text Available Accuracy of a five-axis CNC machine tool is affected by a vast number of error sources. This paper investigates volumetric error modeling and its compensation to the basis for creation of new tool path for improvement of work pieces accuracy. The volumetric error model of a five-axis machine tool with the configuration RTTTR (tilting head B-axis and rotary table in work piece side A΄ was set up taking into consideration rigid body kinematics and homogeneous transformation matrix, in which 43 error components are included. Volumetric error comprises 43 error components that can separately reduce geometrical and dimensional accuracy of work pieces. The machining accuracy of work piece is guaranteed due to the position of the cutting tool center point (TCP relative to the work piece. The cutting tool is deviated from its ideal position relative to the work piece and machining error is experienced. For compensation process detection of the present tool path and analysis of the RTTTR five-axis CNC machine tools geometrical error, translating current position of component to compensated positions using the Kinematics error model, converting newly created component to new tool paths using the compensation algorithms and finally editing old G-codes using G-code generator algorithm have been employed.

  9. Statistical and Machine Learning Models to Predict Programming Performance

    OpenAIRE

    Bergin, Susan

    2006-01-01

    This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

  10. Experimental force modeling for deformation machining stretching ...

    Indian Academy of Sciences (India)

    ARSHPREET SINGH

    requires different machining techniques such as use of long ... thin structure to a desired shape incrementally using com- .... 4.1c Influence of wall angle (a): The average resultant ..... [3] Agrawal A, Smith S, Woody B and Cao J 2012 Study of.

  11. Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices.

    Science.gov (United States)

    Tatjewski, Marcin; Kierczak, Marcin; Plewczynski, Dariusz

    2017-01-01

    Here, we present two perspectives on the task of predicting post translational modifications (PTMs) from local sequence fragments using machine learning algorithms. The first is the description of the fundamental steps required to construct a PTM predictor from the very beginning. These steps include data gathering, feature extraction, or machine-learning classifier selection. The second part of our work contains the detailed discussion of more advanced problems which are encountered in PTM prediction task. Probably the most challenging issues which we have covered here are: (1) how to address the training data class imbalance problem (we also present statistics describing the problem); (2) how to properly set up cross-validation folds with an approach which takes into account the homology of protein data records, to address this problem we present our folds-over-clusters algorithm; and (3) how to efficiently reach for new sources of learning features. Presented techniques and notes resulted from intense studies in the field, performed by our and other groups, and can be useful both for researchers beginning in the field of PTM prediction and for those who want to extend the repertoire of their research techniques.

  12. Integrated Features by Administering the Support Vector Machine (SVM of Translational Initiations Sites in Alternative Polymorphic Contex

    Directory of Open Access Journals (Sweden)

    Nurul Arneida Husin

    2012-04-01

    Full Text Available Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts.

  13. Measuring Difficulty in English-Chinese Translation: Towards a General Model of Translation Difficulty

    Science.gov (United States)

    Sun, Sanjun

    2012-01-01

    Accurate assessment of a text's level of translation difficulty is critical for translator training and accreditation, translation research, and the language industry as well. Traditionally, people rely on their general impression to gauge a text's translation difficulty level. If the evaluation process is to be more effective and the…

  14. Developing Parametric Models for the Assembly of Machine Fixtures for Virtual Multiaxial CNC Machining Centers

    Science.gov (United States)

    Balaykin, A. V.; Bezsonov, K. A.; Nekhoroshev, M. V.; Shulepov, A. P.

    2018-01-01

    This paper dwells upon a variance parameterization method. Variance or dimensional parameterization is based on sketching, with various parametric links superimposed on the sketch objects and user-imposed constraints in the form of an equation system that determines the parametric dependencies. This method is fully integrated in a top-down design methodology to enable the creation of multi-variant and flexible fixture assembly models, as all the modeling operations are hierarchically linked in the built tree. In this research the authors consider a parameterization method of machine tooling used for manufacturing parts using multiaxial CNC machining centers in the real manufacturing process. The developed method allows to significantly reduce tooling design time when making changes of a part’s geometric parameters. The method can also reduce time for designing and engineering preproduction, in particular, for development of control programs for CNC equipment and control and measuring machines, automate the release of design and engineering documentation. Variance parameterization helps to optimize construction of parts as well as machine tooling using integrated CAE systems. In the framework of this study, the authors demonstrate a comprehensive approach to parametric modeling of machine tooling in the CAD package used in the real manufacturing process of aircraft engines.

  15. On the Conditioning of Machine-Learning-Assisted Turbulence Modeling

    Science.gov (United States)

    Wu, Jinlong; Sun, Rui; Wang, Qiqi; Xiao, Heng

    2017-11-01

    Recently, several researchers have demonstrated that machine learning techniques can be used to improve the RANS modeled Reynolds stress by training on available database of high fidelity simulations. However, obtaining improved mean velocity field remains an unsolved challenge, restricting the predictive capability of current machine-learning-assisted turbulence modeling approaches. In this work we define a condition number to evaluate the model conditioning of data-driven turbulence modeling approaches, and propose a stability-oriented machine learning framework to model Reynolds stress. Two canonical flows, the flow in a square duct and the flow over periodic hills, are investigated to demonstrate the predictive capability of the proposed framework. The satisfactory prediction performance of mean velocity field for both flows demonstrates the predictive capability of the proposed framework for machine-learning-assisted turbulence modeling. With showing the capability of improving the prediction of mean flow field, the proposed stability-oriented machine learning framework bridges the gap between the existing machine-learning-assisted turbulence modeling approaches and the demand of predictive capability of turbulence models in real applications.

  16. Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain

    OpenAIRE

    Kurtulmus, A. Besir; Daniel, Kenny

    2018-01-01

    Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions won't have counterparty risk that they won't get paid fo...

  17. The Temple Translator's Workstation Project

    National Research Council Canada - National Science Library

    Vanni, Michelle; Zajac, Remi

    1996-01-01

    .... The Temple Translator's Workstation is incorporated into a Tipster document management architecture and it allows both translator/analysts and monolingual analysts to use the machine- translation...

  18. Modeling demagnetization effects in permanent magnet synchronous machines

    NARCIS (Netherlands)

    Kral, C.; Sprangers, R.L.J.; Waarma, J.; Haumer, A.; Winter, O.; Lomonova, E.

    2010-01-01

    This paper presents a permanent magnet model which takes temperature dependencies and demagnetization effects into account. The proposed model is integrated into a magnetic fundamental wave machine model using the model- ing language Modelica. For different rotor types permanent magnet models are

  19. Probabilistic models and machine learning in structural bioinformatics

    DEFF Research Database (Denmark)

    Hamelryck, Thomas

    2009-01-01

    . Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable. In this review, I will highlight some important recent developments in the prediction, analysis...

  20. Empirical model for estimating the surface roughness of machined ...

    African Journals Online (AJOL)

    Empirical model for estimating the surface roughness of machined ... as well as surface finish is one of the most critical quality measure in mechanical products. ... various cutting speed have been developed using regression analysis software.

  1. A Minimal Cognitive Model for Translating and Post-editing

    DEFF Research Database (Denmark)

    Schaeffer, Moritz; Carl, Michael

    2017-01-01

    This study investigates the coordination of reading (input) and writing (output) activities in from-scratch translation and post-editing. We segment logged eye movements and keylogging data into minimal units of reading and writing activity and model the process of post-editing and from-scratch t...

  2. Translating building information modeling to building energy modeling using model view definition.

    Science.gov (United States)

    Jeong, WoonSeong; Kim, Jong Bum; Clayton, Mark J; Haberl, Jeff S; Yan, Wei

    2014-01-01

    This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation into ModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated from Revit2Modelica and (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows that BIM2BEM (1) enables BIM models to be translated into ModelicaBEM models, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process.

  3. Translating Building Information Modeling to Building Energy Modeling Using Model View Definition

    Directory of Open Access Journals (Sweden)

    WoonSeong Jeong

    2014-01-01

    Full Text Available This paper presents a new approach to translate between Building Information Modeling (BIM and Building Energy Modeling (BEM that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica development for automatic BIM data translation into ModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1 the BIM-based Modelica models generated from Revit2Modelica and (2 BEM models manually created using LBNL Modelica Buildings library. Our implementation shows that BIM2BEM (1 enables BIM models to be translated into ModelicaBEM models, (2 enables system interface development based on the MVD for thermal simulation, and (3 facilitates the reuse of original BIM data into building energy simulation without an import/export process.

  4. Dual Numbers Approach in Multiaxis Machines Error Modeling

    Directory of Open Access Journals (Sweden)

    Jaroslav Hrdina

    2014-01-01

    Full Text Available Multiaxis machines error modeling is set in the context of modern differential geometry and linear algebra. We apply special classes of matrices over dual numbers and propose a generalization of such concept by means of general Weil algebras. We show that the classification of the geometric errors follows directly from the algebraic properties of the matrices over dual numbers and thus the calculus over the dual numbers is the proper tool for the methodology of multiaxis machines error modeling.

  5. Exploration of Disease Markers under Translational Medicine Model

    Directory of Open Access Journals (Sweden)

    Rajagopal Krishnamoorthy

    2015-06-01

    Full Text Available Disease markers are defined as the biomarkers with specific characteristics during the general physical, pathological or therapeutic process, the detection of which can inform the progression of present biological process of organisms. However, the exploration of disease markers is complicated and difficult, and only a few markers can be used in clinical practice and there is no significant difference in the mortality of cancers before and after biomarker exploration. Translational medicine focuses on breaking the blockage between basic medicine and clinical practice. In addition, it also establishes an effective association between researchers engaged on basic scientific discovery and clinical physicians well informed of patients' requirements, and gives particular attentions on how to translate the basic molecular biological research to the most effective and appropriate methods for the diagnosis, treatment and prevention of diseases, hoping to translate basic research into the new therapeutic methods in clinic. Therefore, this study mainly summarized the exploration of disease markers under translational medicine model so as to provide a basis for the translation of basic research results into clinical application.

  6. On feature augmentation for semantic argument classification of the Quran English translation using support vector machine

    Science.gov (United States)

    Khaira Batubara, Dina; Arif Bijaksana, Moch; Adiwijaya

    2018-03-01

    Research on the semantic argument classification requires semantically labeled data in large numbers, called corpus. Because building a corpus is costly and time-consuming, recently many studies have used existing corpus as the training data to conduct semantic argument classification research on new domain. But previous studies have proven that there is a significant decrease in performance when classifying semantic arguments on different domain between the training and the testing data. The main problem is when there is a new argument that found in the testing data but it is not found in the training data. This research carries on semantic argument classification on a new domain that is Quran English Translation by utilizing Propbank corpus as the training data. To recognize the new argument in the training data, this research proposes four new features for extending the argument features in the training data. By using SVM Linear, the experiment has proven that augmenting the proposed features to the baseline system with some combinations option improve the performance of semantic argument classification on Quran data using Propbank Corpus as training data.

  7. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Directory of Open Access Journals (Sweden)

    Saerom Park

    Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  8. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Science.gov (United States)

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  9. Model of cap-dependent translation initiation in sea urchin: a step towards the eukaryotic translation regulation network.

    Science.gov (United States)

    Bellé, Robert; Prigent, Sylvain; Siegel, Anne; Cormier, Patrick

    2010-03-01

    The large and rapid increase in the rate of protein synthesis following fertilization of the sea urchin egg has long been a paradigm of translational control, an important component of the regulation of gene expression in cells. This translational up-regulation is linked to physiological changes that occur upon fertilization and is necessary for entry into first cell division cycle. Accumulated knowledge on cap-dependent initiation of translation makes it suited and timely to start integrating the data into a system view of biological functions. Using a programming environment for system biology coupled with model validation (named Biocham), we have built an integrative model for cap-dependent initiation of translation. The model is described by abstract rules. It contains 51 reactions involved in 74 molecular complexes. The model proved to be coherent with existing knowledge by using queries based on computational tree logic (CTL) as well as Boolean simulations. The model could simulate the change in translation occurring at fertilization in the sea urchin model. It could also be coupled with an existing model designed for cell-cycle control. Therefore, the cap-dependent translation initiation model can be considered a first step towards the eukaryotic translation regulation network.

  10. Machine Learning Technologies Translates Vigilant Surveillance Satellite Big Data into Predictive Alerts for Environmental Stressors

    Science.gov (United States)

    Johnson, S. P.; Rohrer, M. E.

    2017-12-01

    The application of scientific research pertaining to satellite imaging and data processing has facilitated the development of dynamic methodologies and tools that utilize nanosatellites and analytical platforms to address the increasing scope, scale, and intensity of emerging environmental threats to national security. While the use of remotely sensed data to monitor the environment at local and global scales is not a novel proposition, the application of advances in nanosatellites and analytical platforms are capable of overcoming the data availability and accessibility barriers that have historically impeded the timely detection, identification, and monitoring of these stressors. Commercial and university-based applications of these technologies were used to identify and evaluate their capacity as security-motivated environmental monitoring tools. Presently, nanosatellites can provide consumers with 1-meter resolution imaging, frequent revisits, and customizable tasking, allowing users to define an appropriate temporal scale for high resolution data collection that meets their operational needs. Analytical platforms are capable of ingesting increasingly large and diverse volumes of data, delivering complex analyses in the form of interpretation-ready data products and solutions. The synchronous advancement of these technologies creates the capability of analytical platforms to deliver interpretable products from persistently collected high-resolution data that meet varying temporal and geographic scale requirements. In terms of emerging environmental threats, these advances translate into customizable and flexible tools that can respond to and accommodate the evolving nature of environmental stressors. This presentation will demonstrate the capability of nanosatellites and analytical platforms to provide timely, relevant, and actionable information that enables environmental analysts and stakeholders to make informed decisions regarding the prevention

  11. Modelling, Construction, and Testing of a Simple HTS Machine Demonstrator

    DEFF Research Database (Denmark)

    Jensen, Bogi Bech; Abrahamsen, Asger Bech

    2011-01-01

    This paper describes the construction, modeling and experimental testing of a high temperature superconducting (HTS) machine prototype employing second generation (2G) coated conductors in the field winding. The prototype is constructed in a simple way, with the purpose of having an inexpensive way...... of validating finite element (FE) simulations and gaining a better understanding of HTS machines. 3D FE simulations of the machine are compared to measured current vs. voltage (IV) curves for the tape on its own. It is validated that this method can be used to predict the critical current of the HTS tape...... installed in the machine. The measured torque as a function of rotor position is also reproduced by the 3D FE model....

  12. Research on Error Modelling and Identification of 3 Axis NC Machine Tools Based on Cross Grid Encoder Measurement

    International Nuclear Information System (INIS)

    Du, Z C; Lv, C F; Hong, M S

    2006-01-01

    A new error modelling and identification method based on the cross grid encoder is proposed in this paper. Generally, there are 21 error components in the geometric error of the 3 axis NC machine tools. However according our theoretical analysis, the squareness error among different guide ways affects not only the translation error component, but also the rotational ones. Therefore, a revised synthetic error model is developed. And the mapping relationship between the error component and radial motion error of round workpiece manufactured on the NC machine tools are deduced. This mapping relationship shows that the radial error of circular motion is the comprehensive function result of all the error components of link, worktable, sliding table and main spindle block. Aiming to overcome the solution singularity shortcoming of traditional error component identification method, a new multi-step identification method of error component by using the Cross Grid Encoder measurement technology is proposed based on the kinematic error model of NC machine tool. Firstly, the 12 translational error components of the NC machine tool are measured and identified by using the least square method (LSM) when the NC machine tools go linear motion in the three orthogonal planes: XOY plane, XOZ plane and YOZ plane. Secondly, the circular error tracks are measured when the NC machine tools go circular motion in the same above orthogonal planes by using the cross grid encoder Heidenhain KGM 182. Therefore 9 rotational errors can be identified by using LSM. Finally the experimental validation of the above modelling theory and identification method is carried out in the 3 axis CNC vertical machining centre Cincinnati 750 Arrow. The entire 21 error components have been successfully measured out by the above method. Research shows the multi-step modelling and identification method is very suitable for 'on machine measurement'

  13. Semiotics of Umberto Eco in a Literary Translation Class: The Model Reader as the Competent Translator

    Science.gov (United States)

    Ozturk Kasar, Sündüz; Can, Alize

    2017-01-01

    Classroom environment can be thought as an absolute place to practice and improve translation skills of students. They have the possibility to brainstorm and discuss problematic points they face with each other during a translation activity. It can be estimated in the same way in a literary translation class. Students who are supposed to become…

  14. An abstract machine model of dynamic module replacement

    OpenAIRE

    Walton, Chris; Kırlı, Dilsun; Gilmore, Stephen

    2000-01-01

    In this paper we define an abstract machine model for the mλ typed intermediate language. This abstract machine is used to give a formal description of the operation of run-time module replacement for the programming language Dynamic ML. The essential technical device which we employ for module replacement is a modification of two-space copying garbage collection. We show how the operation of module replacement could be applied to other garbage-collected languages such as Java.

  15. Machine translation of noun phrases from English to Igala using the ...

    African Journals Online (AJOL)

    Due to the structural differences between English and Igala, noun phrases coupled with the non- availability of large amount of parallel aligned corpus for English and Igala language, the rule based technology was adopted to develop the model. The model was implemented using VB.net programming language as front ...

  16. Modelling machine ensembles with discrete event dynamical system theory

    Science.gov (United States)

    Hunter, Dan

    1990-01-01

    Discrete Event Dynamical System (DEDS) theory can be utilized as a control strategy for future complex machine ensembles that will be required for in-space construction. The control strategy involves orchestrating a set of interactive submachines to perform a set of tasks for a given set of constraints such as minimum time, minimum energy, or maximum machine utilization. Machine ensembles can be hierarchically modeled as a global model that combines the operations of the individual submachines. These submachines are represented in the global model as local models. Local models, from the perspective of DEDS theory , are described by the following: a set of system and transition states, an event alphabet that portrays actions that takes a submachine from one state to another, an initial system state, a partial function that maps the current state and event alphabet to the next state, and the time required for the event to occur. Each submachine in the machine ensemble is presented by a unique local model. The global model combines the local models such that the local models can operate in parallel under the additional logistic and physical constraints due to submachine interactions. The global model is constructed from the states, events, event functions, and timing requirements of the local models. Supervisory control can be implemented in the global model by various methods such as task scheduling (open-loop control) or implementing a feedback DEDS controller (closed-loop control).

  17. Component based modelling of piezoelectric ultrasonic actuators for machining applications

    International Nuclear Information System (INIS)

    Saleem, A; Ahmed, N; Salah, M; Silberschmidt, V V

    2013-01-01

    Ultrasonically Assisted Machining (UAM) is an emerging technology that has been utilized to improve the surface finishing in machining processes such as turning, milling, and drilling. In this context, piezoelectric ultrasonic transducers are being used to vibrate the cutting tip while machining at predetermined amplitude and frequency. However, modelling and simulation of these transducers is a tedious and difficult task. This is due to the inherent nonlinearities associated with smart materials. Therefore, this paper presents a component-based model of ultrasonic transducers that mimics the nonlinear behaviour of such a system. The system is decomposed into components, a mathematical model of each component is created, and the whole system model is accomplished by aggregating the basic components' model. System parameters are identified using Finite Element technique which then has been used to simulate the system in Matlab/SIMULINK. Various operation conditions are tested and performed to demonstrate the system performance

  18. Learning About Climate and Atmospheric Models Through Machine Learning

    Science.gov (United States)

    Lucas, D. D.

    2017-12-01

    From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

  19. Translation and development of the BNWL-geosphere model

    International Nuclear Information System (INIS)

    Grundfelt, B.

    1977-02-01

    The report deals with the rate of radioactivity discharge from a repository for radioactive waste in a geologic formation to the biosphere. A BASIC language computer program called GETOUT has been developed in USA. It was obtained by the Swedish project Nuclear Fuel Safety and has thereafter been translated into FORTRAN. The main extension of the code, that was made during the translation, is a model for averaging the hydrodynamic and geochemical parameters for the case of non-uniform packing of the column (e.g. considering a repository in cracked rock with crack width, crack spacing etc. in different zones). The program has been outtested on an IBM model 360/75 computer. The migration is governed by three parameters i.e. the ground water velocity, the dispersion coefficient and the nuclide retentivities. (L.B.)

  20. A conceptual model for translating omic data into clinical action

    Directory of Open Access Journals (Sweden)

    Timothy M Herr

    2015-01-01

    Full Text Available Genomic, proteomic, epigenomic, and other "omic" data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called "the omic funnel." This model parallels the classic "Data, Information, Knowledge, Wisdom pyramid" and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice.

  1. Models of kulture in Nabokov's memoirs and translation memoirs in Serbian and Croatian language

    Directory of Open Access Journals (Sweden)

    Razdobudko-Čović Larisa I.

    2012-01-01

    Full Text Available The paper presents an analysis of two Serbian translations of V. Nabokov's memoirs, that is the translation of the novel 'Drugie berega' ('The Other Shores' published in Russian as an authorized translation from the original English version 'Conclusive Evidence', and the translation of Nabokov's authorized translation from Russian to English entitled 'Speak, Memory'. Creolization of three models of culture in translation from the two originals - Russian and English - Is presented. Specific features of the two Serbian translations are analyzed, and a survey of characteristic mistakes caused by some specific characteristics of the source language is given. Also, Nabokov's very original approach to translation which is quite interpretative is highlighted.

  2. Twin support vector machines models, extensions and applications

    CERN Document Server

    Jayadeva; Chandra, Suresh

    2017-01-01

    This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.

  3. Electric machines

    CERN Document Server

    Gross, Charles A

    2006-01-01

    BASIC ELECTROMAGNETIC CONCEPTSBasic Magnetic ConceptsMagnetically Linear Systems: Magnetic CircuitsVoltage, Current, and Magnetic Field InteractionsMagnetic Properties of MaterialsNonlinear Magnetic Circuit AnalysisPermanent MagnetsSuperconducting MagnetsThe Fundamental Translational EM MachineThe Fundamental Rotational EM MachineMultiwinding EM SystemsLeakage FluxThe Concept of Ratings in EM SystemsSummaryProblemsTRANSFORMERSThe Ideal n-Winding TransformerTransformer Ratings and Per-Unit ScalingThe Nonideal Three-Winding TransformerThe Nonideal Two-Winding TransformerTransformer Efficiency and Voltage RegulationPractical ConsiderationsThe AutotransformerOperation of Transformers in Three-Phase EnvironmentsSequence Circuit Models for Three-Phase Transformer AnalysisHarmonics in TransformersSummaryProblemsBASIC MECHANICAL CONSIDERATIONSSome General PerspectivesEfficiencyLoad Torque-Speed CharacteristicsMass Polar Moment of InertiaGearingOperating ModesTranslational SystemsA Comprehensive Example: The ElevatorP...

  4. Animal Models for Tuberculosis in Translational and Precision Medicine

    Directory of Open Access Journals (Sweden)

    Lingjun Zhan

    2017-05-01

    Full Text Available Tuberculosis (TB is a health threat to the global population. Anti-TB drugs and vaccines are key approaches for TB prevention and control. TB animal models are basic tools for developing biomarkers of diagnosis, drugs for therapy, vaccines for prevention and researching pathogenic mechanisms for identification of targets; thus, they serve as the cornerstone of comparative medicine, translational medicine, and precision medicine. In this review, we discuss the current use of TB animal models and their problems, as well as offering perspectives on the future of these models.

  5. Runtime Optimizations for Tree-Based Machine Learning Models

    NARCIS (Netherlands)

    N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

    2014-01-01

    htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

  6. Comparative study of Moore and Mealy machine models adaptation ...

    African Journals Online (AJOL)

    Information and Communications Technology has influenced the need for automated machines that can carry out important production procedures and, automata models are among the computational models used in design and construction of industrial processes. The production process of the popular African Black Soap ...

  7. Comparative analysis of various methods for modelling permanent magnet machines

    NARCIS (Netherlands)

    Ramakrishnan, K.; Curti, M.; Zarko, D.; Mastinu, G.; Paulides, J.J.H.; Lomonova, E.A.

    2017-01-01

    In this paper, six different modelling methods for permanent magnet (PM) electric machines are compared in terms of their computational complexity and accuracy. The methods are based primarily on conformal mapping, mode matching, and harmonic modelling. In the case of conformal mapping, slotted air

  8. Translational Models of Gambling-Related Decision-Making.

    Science.gov (United States)

    Winstanley, Catharine A; Clark, Luke

    Gambling is a harmless, recreational pastime that is ubiquitous across cultures. However, for some, gambling becomes a maladaptive and compulsive, and this syndrome is conceptualized as a behavioural addiction. Laboratory models that capture the key cognitive processes involved in gambling behaviour, and that can be translated across species, have the potential to make an important contribution to both decision neuroscience and the study of addictive disorders. The Iowa gambling task has been widely used to assess human decision-making under uncertainty, and this paradigm can be successfully modelled in rodents. Similar neurobiological processes underpin choice behaviour in humans and rats, and thus, a preference for the disadvantageous "high-risk, high-reward" options may reflect meaningful vulnerability for mental health problems. However, the choice behaviour operationalized by these tasks does not necessarily approximate the vulnerability to gambling disorder (GD) per se. We consider a number of psychological challenges that apply to modelling gambling in a translational way, and evaluate the success of the existing models. Heterogeneity in the structure of gambling games, as well as in the motivations of individuals with GD, is highlighted. The potential issues with extrapolating too directly from established animal models of drug dependency are discussed, as are the inherent difficulties in validating animal models of GD in the absence of any approved treatments for GD. Further advances in modelling the cognitive biases endemic in human decision-making, which appear to be exacerbated in GD, may be a promising line of research.

  9. A Multiple Model Prediction Algorithm for CNC Machine Wear PHM

    Directory of Open Access Journals (Sweden)

    Huimin Chen

    2011-01-01

    Full Text Available The 2010 PHM data challenge focuses on the remaining useful life (RUL estimation for cutters of a high speed CNC milling machine using measurements from dynamometer, accelerometer, and acoustic emission sensors. We present a multiple model approach for wear depth estimation of milling machine cutters using the provided data. The feature selection, initial wear estimation and multiple model fusion components of the proposed algorithm are explained in details and compared with several alternative methods using the training data. The final submission ranked #2 among professional and student participants and the method is applicable to other data driven PHM problems.

  10. A cognitive-pragmatic model for translation-shift analysis in ...

    African Journals Online (AJOL)

    A cognitive-pragmatic model for translation-shift analysis in descriptive case ... This model responds to the tendency of descriptive studies to analyse all translation shifts under the same rubric of neutrality. ... AJOL African Journals Online.

  11. A Model of Translator's Competence from an Educational Perspective

    Science.gov (United States)

    Eser, Oktay

    2015-01-01

    Translation as a business is a service. The concept of translation competence is a term covering the various skills and knowledge that a translator needs to have in order to translate functionally. The term which is often studied as a multi-componential concept in literature may not cover the necessary skills if it is taken from an organizational…

  12. Animal models of tic disorders: a translational perspective.

    Science.gov (United States)

    Godar, Sean C; Mosher, Laura J; Di Giovanni, Giuseppe; Bortolato, Marco

    2014-12-30

    Tics are repetitive, sudden movements and/or vocalizations, typically enacted as maladaptive responses to intrusive premonitory urges. The most severe tic disorder, Tourette syndrome (TS), is a childhood-onset condition featuring multiple motor and at least one phonic tic for a duration longer than 1 year. The pharmacological treatment of TS is mainly based on antipsychotic agents; while these drugs are often effective in reducing tic severity and frequency, their therapeutic compliance is limited by serious motor and cognitive side effects. The identification of novel therapeutic targets and development of better treatments for tic disorders is conditional on the development of animal models with high translational validity. In addition, these experimental tools can prove extremely useful to test hypotheses on the etiology and neurobiological bases of TS and related conditions. In recent years, the translational value of these animal models has been enhanced, thanks to a significant re-organization of our conceptual framework of neuropsychiatric disorders, with a greater focus on endophenotypes and quantitative indices, rather than qualitative descriptors. Given the complex and multifactorial nature of TS and other tic disorders, the selection of animal models that can appropriately capture specific symptomatic aspects of these conditions can pose significant theoretical and methodological challenges. In this article, we will review the state of the art on the available animal models of tic disorders, based on genetic mutations, environmental interventions as well as pharmacological manipulations. Furthermore, we will outline emerging lines of translational research showing how some of these experimental preparations have led to significant progress in the identification of novel therapeutic targets for tic disorders. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Animal models of tic disorders: A translational perspective

    Science.gov (United States)

    Godar, Sean C.; Mosher, Laura J.; Di Giovanni, Giuseppe; Bortolato, Marco

    2014-01-01

    Tics are repetitive, sudden movements and/or vocalizations, typically enacted as maladaptive responses to intrusive premonitory urges. The most severe tic disorder, Tourette syndrome (TS), is a childhood-onset condition featuring multiple motor and at least one phonic tic for a duration longer than 1 year. The pharmacological treatment of TS is mainly based on antipsychotic agents; while these drugs are often effective in reducing tic severity and frequency, their therapeutic compliance is limited by serious motor and cognitive side effects. The identification of novel therapeutic targets and development of better treatments for tic disorders is conditional on the development of animal models with high translational validity. In addition, these experimental tools can prove extremely useful to test hypotheses on the etiology and neurobiological bases of TS and related conditions. In recent years, the translational value of these animal models has been enhanced, thanks to a significant re-organization of our conceptual framework of neuropsychiatric disorders, with a greater focus on endophenotypes and quantitative indices, rather than qualitative descriptors. Given the complex and multifactorial nature of TS and other tic disorders, the selection of animal models that can appropriately capture specific symptomatic aspects of these conditions can pose significant theoretical and methodological challenges. In this article, we will review the state of the art on the available animal models of tic disorders, based on genetic mutations, environmental interventions as well as pharmacological manipulations. Furthermore, we will outline emerging lines of translational research showing how some of these experimental preparations have led to significant progress in the identification of novel therapeutic targets for tic disorders. PMID:25244952

  14. Analytical model for Stirling cycle machine design

    Energy Technology Data Exchange (ETDEWEB)

    Formosa, F. [Laboratoire SYMME, Universite de Savoie, BP 80439, 74944 Annecy le Vieux Cedex (France); Despesse, G. [Laboratoire Capteurs Actionneurs et Recuperation d' Energie, CEA-LETI-MINATEC, Grenoble (France)

    2010-10-15

    In order to study further the promising free piston Stirling engine architecture, there is a need of an analytical thermodynamic model which could be used in a dynamical analysis for preliminary design. To aim at more realistic values, the models have to take into account the heat losses and irreversibilities on the engine. An analytical model which encompasses the critical flaws of the regenerator and furthermore the heat exchangers effectivenesses has been developed. This model has been validated using the whole range of the experimental data available from the General Motor GPU-3 Stirling engine prototype. The effects of the technological and operating parameters on Stirling engine performance have been investigated. In addition to the regenerator influence, the effect of the cooler effectiveness is underlined. (author)

  15. An incremental anomaly detection model for virtual machines

    Science.gov (United States)

    Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

    2017-01-01

    Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform. PMID:29117245

  16. Innovative model of business process reengineering at machine building enterprises

    Science.gov (United States)

    Nekrasov, R. Yu; Tempel, Yu A.; Tempel, O. A.

    2017-10-01

    The paper provides consideration of business process reengineering viewed as amanagerial innovation accepted by present day machine building enterprises, as well as waysto improve its procedure. A developed innovative model of reengineering measures isdescribed and is based on the process approach and other principles of company management.

  17. An incremental anomaly detection model for virtual machines.

    Directory of Open Access Journals (Sweden)

    Hancui Zhang

    Full Text Available Self-Organizing Map (SOM algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

  18. Online State Space Model Parameter Estimation in Synchronous Machines

    Directory of Open Access Journals (Sweden)

    Z. Gallehdari

    2014-06-01

    The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.

  19. Assessing Implicit Knowledge in BIM Models with Machine Learning

    DEFF Research Database (Denmark)

    Krijnen, Thomas; Tamke, Martin

    2015-01-01

    architects and engineers are able to deduce non-explicitly explicitly stated information, which is often the core of the transported architectural information. This paper investigates how machine learning approaches allow a computational system to deduce implicit knowledge from a set of BIM models....

  20. Cutting force model for high speed machining process

    International Nuclear Information System (INIS)

    Haber, R. E.; Jimenez, J. E.; Jimenez, A.; Lopez-Coronado, J.

    2004-01-01

    This paper presents cutting force-based models able to describe a high speed machining process. The model considers the cutting force as output variable, essential for the physical processes that are taking place in high speed machining. Moreover, this paper shows the mathematical development to derive the integral-differential equations, and the algorithms implemented in MATLAB to predict the cutting force in real time MATLAB is a software tool for doing numerical computations with matrices and vectors. It can also display information graphically and includes many toolboxes for several research and applications areas. Two end mill shapes are considered (i. e. cylindrical and ball end mill) for real-time implementation of the developed algorithms. the developed models are validated in slot milling operations. The results corroborate the importance of the cutting force variable for predicting tool wear in high speed machining operations. The developed models are the starting point for future work related with vibration analysis, process stability and dimensional surface finish in high speed machining processes. (Author) 19 refs

  1. Modeling RHIC using the standard machine formal accelerator description

    International Nuclear Information System (INIS)

    Pilat, F.; Trahern, C.G.; Wei, J.

    1997-01-01

    The Standard Machine Format (SMF) is a structured description of accelerator lattices which supports both the hierarchy of beam lines and generic lattice objects as well as those deviations (field errors, alignment efforts, etc.) associated with each component of the as-installed machine. In this paper we discuss the use of SMF to describe the Relativistic Heavy Ion Collider (RHIC) as well as the ancillary data structures (such as field quality measurements) that are necessarily incorporated into the RHIC SMF model. Future applications of SMF are outlined, including its use in the RHIC operational environment

  2. Control of discrete event systems modeled as hierarchical state machines

    Science.gov (United States)

    Brave, Y.; Heymann, M.

    1991-01-01

    The authors examine a class of discrete event systems (DESs) modeled as asynchronous hierarchical state machines (AHSMs). For this class of DESs, they provide an efficient method for testing reachability, which is an essential step in many control synthesis procedures. This method utilizes the asynchronous nature and hierarchical structure of AHSMs, thereby illustrating the advantage of the AHSM representation as compared with its equivalent (flat) state machine representation. An application of the method is presented where an online minimally restrictive solution is proposed for the problem of maintaining a controlled AHSM within prescribed legal bounds.

  3. Compositional translation

    NARCIS (Netherlands)

    Appelo, Lisette; Janssen, Theo; Jong, de F.M.G.; Landsbergen, S.P.J.

    1994-01-01

    This book provides an in-depth review of machine translation by discussing in detail a particular method, called compositional translation, and a particular system, Rosetta, which is based on this method. The Rosetta project is a unique combination of fundamental research and large-scale

  4. Machine learning models in breast cancer survival prediction.

    Science.gov (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

    2016-01-01

    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

  5. Global ocean modeling on the Connection Machine

    International Nuclear Information System (INIS)

    Smith, R.D.; Dukowicz, J.K.; Malone, R.C.

    1993-01-01

    The authors have developed a version of the Bryan-Cox-Semtner ocean model (Bryan, 1969; Semtner, 1976; Cox, 1984) for massively parallel computers. Such models are three-dimensional, Eulerian models that use latitude and longitude as the horizontal spherical coordinates and fixed depth levels as the vertical coordinate. The incompressible Navier-Stokes equations, with a turbulent eddy viscosity, and mass continuity equation are solved, subject to the hydrostatic and Boussinesq approximations. The traditional model formulation uses a rigid-lid approximation (vertical velocity = 0 at the ocean surface) to eliminate fast surface waves. These waves would otherwise require that a very short time step be used in numerical simulations, which would greatly increase the computational cost. To solve the equations with the rigid-lid assumption, the equations of motion are split into two parts: a set of twodimensional ''barotropic'' equations describing the vertically-averaged flow, and a set of three-dimensional ''baroclinic'' equations describing temperature, salinity and deviations of the horizontal velocities from the vertically-averaged flow

  6. A comparative study of machine learning models for ethnicity classification

    Science.gov (United States)

    Trivedi, Advait; Bessie Amali, D. Geraldine

    2017-11-01

    This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

  7. Modeling Geomagnetic Variations using a Machine Learning Framework

    Science.gov (United States)

    Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

    2017-12-01

    We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

  8. Common Marmosets: A Potential Translational Animal Model of Juvenile Depression

    Directory of Open Access Journals (Sweden)

    Nicole Leite Galvão-Coelho

    2017-09-01

    Full Text Available Major depression is a psychiatric disorder with high prevalence in the general population, with increasing expression in adolescence, about 14% in young people. Frequently, it presents as a chronic condition, showing no remission even after several pharmacological treatments and persisting in adult life. Therefore, distinct protocols and animal models have been developed to increase the understanding of this disease or search for new therapies. To this end, this study investigated the effects of chronic social isolation and the potential antidepressant action of nortriptyline in juvenile Callithrix jacchus males and females by monitoring fecal cortisol, body weight, and behavioral parameters and searching for biomarkers and a protocol for inducing depression. The purpose was to validate this species and protocol as a translational model of juvenile depression, addressing all domain criteria of validation: etiologic, face, functional, predictive, inter-relational, evolutionary, and population. In both sexes and both protocols (IDS and DPT, we observed a significant reduction in cortisol levels in the last phase of social isolation, concomitant with increases in autogrooming, stereotyped and anxiety behaviors, and the presence of anhedonia. The alterations induced by chronic social isolation are characteristic of the depressive state in non-human primates and/or in humans, and were reversed in large part by treatment with an antidepressant drug (nortriptyline. Therefore, these results indicate C. jacchus as a potential translational model of juvenile depression by addressing all criteria of validation.

  9. Machine learning modelling for predicting soil liquefaction susceptibility

    Directory of Open Access Journals (Sweden)

    P. Samui

    2011-01-01

    Full Text Available This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN based on multi-layer perceptions (MLP that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N160] and cyclic stress ratio (CSR. Further, an attempt has been made to simplify the models, requiring only the two parameters [(N160 and peck ground acceleration (amax/g], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

  10. Translational Mouse Models of Autism: Advancing Toward Pharmacological Therapeutics

    Science.gov (United States)

    Kazdoba, Tatiana M.; Leach, Prescott T.; Yang, Mu; Silverman, Jill L.; Solomon, Marjorie

    2016-01-01

    Animal models provide preclinical tools to investigate the causal role of genetic mutations and environmental factors in the etiology of autism spectrum disorder (ASD). Knockout and humanized knock-in mice, and more recently knockout rats, have been generated for many of the de novo single gene mutations and copy number variants (CNVs) detected in ASD and comorbid neurodevelopmental disorders. Mouse models incorporating genetic and environmental manipulations have been employed for preclinical testing of hypothesis-driven pharmacological targets, to begin to develop treatments for the diagnostic and associated symptoms of autism. In this review, we summarize rodent behavioral assays relevant to the core features of autism, preclinical and clinical evaluations of pharmacological interventions, and strategies to improve the translational value of rodent models of autism. PMID:27305922

  11. Support vector machine based battery model for electric vehicles

    International Nuclear Information System (INIS)

    Wang Junping; Chen Quanshi; Cao Binggang

    2006-01-01

    The support vector machine (SVM) is a novel type of learning machine based on statistical learning theory that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish the relationship between the load voltage and the current under different temperatures and state of charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61%

  12. An Expectation-Maximization Method for Calibrating Synchronous Machine Models

    Energy Technology Data Exchange (ETDEWEB)

    Meng, Da; Zhou, Ning; Lu, Shuai; Lin, Guang

    2013-07-21

    The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (EM) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using maximum likelihood estimation (MLE) method. The EM method iterates over the preceding two steps to improve estimation accuracy. The proposed EM method’s performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using EM method are also presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

  13. Customer requirement modeling and mapping of numerical control machine

    Directory of Open Access Journals (Sweden)

    Zhongqi Sheng

    2015-10-01

    Full Text Available In order to better obtain information about customer requirement and develop products meeting customer requirement, it is necessary to systematically analyze and handle the customer requirement. This article uses the product service system of numerical control machine as research objective and studies the customer requirement modeling and mapping oriented toward configuration design. It introduces the conception of requirement unit, expounds the customer requirement decomposition rules, and establishes customer requirement model; it builds the house of quality using quality function deployment and confirms the weight of technical feature of product and service; it explores the relevance rules between data using rough set theory, establishes rule database, and solves the target value of technical feature of product. Using economical turning center series numerical control machine as an example, it verifies the rationality of proposed customer requirement model.

  14. Slow dynamics in translation-invariant quantum lattice models

    Science.gov (United States)

    Michailidis, Alexios A.; Žnidarič, Marko; Medvedyeva, Mariya; Abanin, Dmitry A.; Prosen, Tomaž; Papić, Z.

    2018-03-01

    Many-body quantum systems typically display fast dynamics and ballistic spreading of information. Here we address the open problem of how slow the dynamics can be after a generic breaking of integrability by local interactions. We develop a method based on degenerate perturbation theory that reveals slow dynamical regimes and delocalization processes in general translation invariant models, along with accurate estimates of their delocalization time scales. Our results shed light on the fundamental questions of the robustness of quantum integrable systems and the possibility of many-body localization without disorder. As an example, we construct a large class of one-dimensional lattice models where, despite the absence of asymptotic localization, the transient dynamics is exceptionally slow, i.e., the dynamics is indistinguishable from that of many-body localized systems for the system sizes and time scales accessible in experiments and numerical simulations.

  15. Building Better Ecological Machines: Complexity Theory and Alternative Economic Models

    Directory of Open Access Journals (Sweden)

    Jess Bier

    2016-12-01

    Full Text Available Computer models of the economy are regularly used to predict economic phenomena and set financial policy. However, the conventional macroeconomic models are currently being reimagined after they failed to foresee the current economic crisis, the outlines of which began to be understood only in 2007-2008. In this article we analyze the most prominent of this reimagining: Agent-Based models (ABMs. ABMs are an influential alternative to standard economic models, and they are one focus of complexity theory, a discipline that is a more open successor to the conventional chaos and fractal modeling of the 1990s. The modelers who create ABMs claim that their models depict markets as ecologies, and that they are more responsive than conventional models that depict markets as machines. We challenge this presentation, arguing instead that recent modeling efforts amount to the creation of models as ecological machines. Our paper aims to contribute to an understanding of the organizing metaphors of macroeconomic models, which we argue is relevant conceptually and politically, e.g., when models are used for regulatory purposes.

  16. Drosophila Melanogaster as an Emerging Translational Model of Human Nephrolithiasis

    Science.gov (United States)

    Miller, Joe; Chi, Thomas; Kapahi, Pankaj; Kahn, Arnold J.; Kim, Man Su; Hirata, Taku; Romero, Michael F.; Dow, Julian A.T.; Stoller, Marshall L.

    2013-01-01

    Purpose The limitations imposed by human clinical studies and mammalian models of nephrolithiasis have hampered the development of effective medical treatments and preventative measures for decades. The simple but elegant Drosophila melanogaster is emerging as a powerful translational model of human disease, including nephrolithiasis and may provide important information essential to our understanding of stone formation. We present the current state of research using D. melanogaster as a model of human nephrolithiasis. Materials and Methods A comprehensive review of the English language literature was performed using PUBMED. When necessary, authoritative texts on relevant subtopics were consulted. Results The genetic composition, anatomic structure and physiologic function of Drosophila Malpighian tubules are remarkably similar to those of the human nephron. The direct effects of dietary manipulation, environmental alteration, and genetic variation on stone formation can be observed and quantified in a matter of days. Several Drosophila models of human nephrolithiasis, including genetically linked and environmentally induced stones, have been developed. A model of calcium oxalate stone formation is among the most recent fly models of human nephrolithiasis. Conclusions The ability to readily manipulate and quantify stone formation in D. melanogaster models of human nephrolithiasis presents the urologic community with a unique opportunity to increase our understanding of this enigmatic disease. PMID:23500641

  17. Modelling of destructive ability of water-ice-jet while machine processing of machine elements

    Directory of Open Access Journals (Sweden)

    Burnashov Mikhail

    2017-01-01

    Full Text Available This paper represents the classification of the most common contaminants, appearing on the surfaces of machine elements after a long-term service.The existing well-known surface cleaning methods are described and analyzed in the framework of this paper. The article is intended to provide the reader with an understanding of the process of cleaning and removing contamination from machine elements surface by means of water-ice-jet with preprepared beforehand particles, as well as the process of water-ice-jet formation. The paper deals with the description of such advantages of this method as low costs, wastelessness, high quality of the surface, undergoing processing, minimization of harmful impact upon environment and eco-friendliness, which makes it differ radically from formerly known methods. The scheme of interection between the surface and ice particle is represented. A thermo-physical model of destruction of contaminants by means of a water-ice-jet cleaning technology was developed on its basis. The thermo-physical model allows us to make setting of processing mode and the parameters of water-ice-jet scientifically substantiated and well-grounded.

  18. Numerical modeling and optimization of machining duplex stainless steels

    Directory of Open Access Journals (Sweden)

    Rastee D. Koyee

    2015-01-01

    Full Text Available The shortcomings of the machining analytical and empirical models in combination with the industry demands have to be fulfilled. A three-dimensional finite element modeling (FEM introduces an attractive alternative to bridge the gap between pure empirical and fundamental scientific quantities, and fulfill the industry needs. However, the challenging aspects which hinder the successful adoption of FEM in the machining sector of manufacturing industry have to be solved first. One of the greatest challenges is the identification of the correct set of machining simulation input parameters. This study presents a new methodology to inversely calculate the input parameters when simulating the machining of standard duplex EN 1.4462 and super duplex EN 1.4410 stainless steels. JMatPro software is first used to model elastic–viscoplastic and physical work material behavior. In order to effectively obtain an optimum set of inversely identified friction coefficients, thermal contact conductance, Cockcroft–Latham critical damage value, percentage reduction in flow stress, and Taylor–Quinney coefficient, Taguchi-VIKOR coupled with Firefly Algorithm Neural Network System is applied. The optimization procedure effectively minimizes the overall differences between the experimentally measured performances such as cutting forces, tool nose temperature and chip thickness, and the numerically obtained ones at any specified cutting condition. The optimum set of input parameter is verified and used for the next step of 3D-FEM application. In the next stage of the study, design of experiments, numerical simulations, and fuzzy rule modeling approaches are employed to optimize types of chip breaker, insert shapes, process conditions, cutting parameters, and tool orientation angles based on many important performances. Through this study, not only a new methodology in defining the optimal set of controllable parameters for turning simulations is introduced, but also

  19. Impact of Model Detail of Synchronous Machines on Real-time Transient Stability Assessment

    DEFF Research Database (Denmark)

    Weckesser, Johannes Tilman Gabriel; Jóhannsson, Hjörtur; Østergaard, Jacob

    2013-01-01

    In this paper, it is investigated how detailed the model of a synchronous machine needs to be in order to assess transient stability using a Single Machine Equivalent (SIME). The results will show how the stability mechanism and the stability assessment are affected by the model detail. In order...... of the machine models is varied. Analyses of the results suggest that a 4th-order model may be sufficient to represent synchronous machines in transient stability studies....

  20. Credit Risk Analysis Using Machine and Deep Learning Models

    Directory of Open Access Journals (Sweden)

    Peter Martey Addo

    2018-04-01

    Full Text Available Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises.

  1. Modeling Music Emotion Judgments Using Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Naresh N. Vempala

    2018-01-01

    Full Text Available Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.

  2. Model-Driven Engineering of Machine Executable Code

    Science.gov (United States)

    Eichberg, Michael; Monperrus, Martin; Kloppenburg, Sven; Mezini, Mira

    Implementing static analyses of machine-level executable code is labor intensive and complex. We show how to leverage model-driven engineering to facilitate the design and implementation of programs doing static analyses. Further, we report on important lessons learned on the benefits and drawbacks while using the following technologies: using the Scala programming language as target of code generation, using XML-Schema to express a metamodel, and using XSLT to implement (a) transformations and (b) a lint like tool. Finally, we report on the use of Prolog for writing model transformations.

  3. Artificial emotional model based on finite state machine

    Institute of Scientific and Technical Information of China (English)

    MENG Qing-mei; WU Wei-guo

    2008-01-01

    According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform.And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

  4. Inverse Analysis and Modeling for Tunneling Thrust on Shield Machine

    Directory of Open Access Journals (Sweden)

    Qian Zhang

    2013-01-01

    Full Text Available With the rapid development of sensor and detection technologies, measured data analysis plays an increasingly important role in the design and control of heavy engineering equipment. The paper proposed a method for inverse analysis and modeling based on mass on-site measured data, in which dimensional analysis and data mining techniques were combined. The method was applied to the modeling of the tunneling thrust on shield machines and an explicit expression for thrust prediction was established. Combined with on-site data from a tunneling project in China, the inverse identification of model coefficients was carried out using the multiple regression method. The model residual was analyzed by statistical methods. By comparing the on-site data and the model predicted results in the other two projects with different tunneling conditions, the feasibility of the model was discussed. The work may provide a scientific basis for the rational design and control of shield tunneling machines and also a new way for mass on-site data analysis of complex engineering systems with nonlinear, multivariable, time-varying characteristics.

  5. Mapping research questions about translation to methods, measures, and models

    NARCIS (Netherlands)

    Berninger, V.; Rijlaarsdam, G.; Fayol, M.L.; Fayol, M.; Alamargot, D.; Berninger, V.W.

    2012-01-01

    About the book: Translation of cognitive representations into written language is one of the most important processes in writing. This volume provides a long-awaited updated overview of the field. The contributors discuss each of the commonly used research methods for studying translation; theorize

  6. On Interactive Teaching Model of Translation Course Based on Wechat

    Science.gov (United States)

    Lin, Wang

    2017-01-01

    Constructivism is a theory related to knowledge and learning, focusing on learners' subjective initiative, based on which the interactive approach has been proved to play a crucial role in language learning. Accordingly, the interactive approach can also be applied to translation teaching since translation itself is a bilingual transformational…

  7. MODEL RESEARCH OF THE ACIVE VIBROIZOLATION CABS MACHINE

    Directory of Open Access Journals (Sweden)

    Jerzy MARGIELEWICZ

    2014-03-01

    Full Text Available The study was carried out computer simulations of mechatronic model bridge crane, which is intended to theoretical evaluation of the possibility of eliminating the mechanical vibrations affecting the operator's cab driven machine. The model studies used fixed value control, the controlled variable is selected as the vertical displacement of the cab. Also included in the research model rheological model of the operator's body. We examined four overhead cranes with lifting capacity of 50T, which are classified in accordance with the directive of the European Union concerning the design of cranes, the four classes of cranes HC stiffness. The use of an active vibration isolation system in which distinguishes two negative feedback loops, very well eliminate mechanical vibration to the operator.

  8. Electric machines modeling, condition monitoring, and fault diagnosis

    CERN Document Server

    Toliyat, Hamid A; Choi, Seungdeog; Meshgin-Kelk, Homayoun

    2012-01-01

    With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condi

  9. Use of machine learning techniques for modeling of snow depth

    Directory of Open Access Journals (Sweden)

    G. V. Ayzel

    2017-01-01

    Full Text Available Snow exerts significant regulating effect on the land hydrological cycle since it controls intensity of heat and water exchange between the soil-vegetative cover and the atmosphere. Estimating of a spring flood runoff or a rain-flood on mountainous rivers requires understanding of the snow cover dynamics on a watershed. In our work, solving a problem of the snow cover depth modeling is based on both available databases of hydro-meteorological observations and easily accessible scientific software that allows complete reproduction of investigation results and further development of this theme by scientific community. In this research we used the daily observational data on the snow cover and surface meteorological parameters, obtained at three stations situated in different geographical regions: Col de Porte (France, Sodankyla (Finland, and Snoquamie Pass (USA.Statistical modeling of the snow cover depth is based on a complex of freely distributed the present-day machine learning models: Decision Trees, Adaptive Boosting, Gradient Boosting. It is demonstrated that use of combination of modern machine learning methods with available meteorological data provides the good accuracy of the snow cover modeling. The best results of snow cover depth modeling for every investigated site were obtained by the ensemble method of gradient boosting above decision trees – this model reproduces well both, the periods of snow cover accumulation and its melting. The purposeful character of learning process for models of the gradient boosting type, their ensemble character, and use of combined redundancy of a test sample in learning procedure makes this type of models a good and sustainable research tool. The results obtained can be used for estimating the snow cover characteristics for river basins where hydro-meteorological information is absent or insufficient.

  10. Advanced Machine Learning Emulators of Radiative Transfer Models

    Science.gov (United States)

    Camps-Valls, G.; Verrelst, J.; Martino, L.; Vicent, J.

    2017-12-01

    Physically-based model inversion methodologies are based on physical laws and established cause-effect relationships. A plethora of remote sensing applications rely on the physical inversion of a Radiative Transfer Model (RTM), which lead to physically meaningful bio-geo-physical parameter estimates. The process is however computationally expensive, needs expert knowledge for both the selection of the RTM, its parametrization and the the look-up table generation, as well as its inversion. Mimicking complex codes with statistical nonlinear machine learning algorithms has become the natural alternative very recently. Emulators are statistical constructs able to approximate the RTM, although at a fraction of the computational cost, providing an estimation of uncertainty, and estimations of the gradient or finite integral forms. We review the field and recent advances of emulation of RTMs with machine learning models. We posit Gaussian processes (GPs) as the proper framework to tackle the problem. Furthermore, we introduce an automatic methodology to construct emulators for costly RTMs. The Automatic Gaussian Process Emulator (AGAPE) methodology combines the interpolation capabilities of GPs with the accurate design of an acquisition function that favours sampling in low density regions and flatness of the interpolation function. We illustrate the good capabilities of our emulators in toy examples, leaf and canopy levels PROSPECT and PROSAIL RTMs, and for the construction of an optimal look-up-table for atmospheric correction based on MODTRAN5.

  11. Process Approach for Modeling of Machine and Tractor Fleet Structure

    Science.gov (United States)

    Dokin, B. D.; Aletdinova, A. A.; Kravchenko, M. S.; Tsybina, Y. S.

    2018-05-01

    The existing software complexes on modelling of the machine and tractor fleet structure are mostly aimed at solving the task of optimization. However, the creators, choosing only one optimization criterion and incorporating it in their software, provide grounds on why it is the best without giving a decision maker the opportunity to choose it for their enterprise. To analyze “bottlenecks” of machine and tractor fleet modelling, the authors of this article created a process model, in which they included adjustment to the plan of using machinery based on searching through alternative technologies. As a result, the following recommendations for software complex development have been worked out: the introduction of a database of alternative technologies; the possibility for a user to change the timing of the operations even beyond the allowable limits and in that case the calculation of the incurred loss; the possibility to rule out the solution of an optimization task, and if there is a necessity in it - the possibility to choose an optimization criterion; introducing graphical display of an annual complex of works, which could be enough for the development and adjustment of a business strategy.

  12. Modeling the Swift Bat Trigger Algorithm with Machine Learning

    Science.gov (United States)

    Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori

    2016-01-01

    To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift / BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of greater than or equal to 97 percent (less than or equal to 3 percent error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6 percent (10.4 percent error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n (sub 0) approaching 0.48 (sup plus 0.41) (sub minus 0.23) per cubic gigaparsecs per year with power-law indices of n (sub 1) approaching 1.7 (sup plus 0.6) (sub minus 0.5) and n (sub 2) approaching minus 5.9 (sup plus 5.7) (sub minus 0.1) for GRBs above and below a break point of z (redshift) (sub 1) approaching 6.8 (sup plus 2.8) (sub minus 3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.

  13. Modeling the Swift BAT Trigger Algorithm with Machine Learning

    Science.gov (United States)

    Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori

    2015-01-01

    To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online.

  14. Translation: Aids, Robots, and Automation.

    Science.gov (United States)

    Andreyewsky, Alexander

    1981-01-01

    Examines electronic aids to translation both as ways to automate it and as an approach to solve problems resulting from shortage of qualified translators. Describes the limitations of robotic MT (Machine Translation) systems, viewing MAT (Machine-Aided Translation) as the only practical solution and the best vehicle for further automation. (MES)

  15. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)

    2008-06-15

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

  16. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail: wlee@uta.edu

    2008-06-15

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

  17. Control volume based modelling of compressible flow in reciprocating machines

    DEFF Research Database (Denmark)

    Andersen, Stig Kildegård; Thomsen, Per Grove; Carlsen, Henrik

    2004-01-01

    , and multidimensional effects must be calculated using empirical correlations; correlations for steady state flow can be used as an approximation. A transformation that assumes ideal gas is presented for transforming equations for masses and energies in control volumes into the corresponding pressures and temperatures......An approach to modelling unsteady compressible flow that is primarily one dimensional is presented. The approach was developed for creating distributed models of machines with reciprocating pistons but it is not limited to this application. The approach is based on the integral form of the unsteady...... conservation laws for mass, energy, and momentum applied to a staggered mesh consisting of two overlapping strings of control volumes. Loss mechanisms can be included directly in the governing equations of models by including them as terms in the conservation laws. Heat transfer, flow friction...

  18. Coal demand prediction based on a support vector machine model

    Energy Technology Data Exchange (ETDEWEB)

    Jia, Cun-liang; Wu, Hai-shan; Gong, Dun-wei [China University of Mining & Technology, Xuzhou (China). School of Information and Electronic Engineering

    2007-01-15

    A forecasting model for coal demand of China using a support vector regression was constructed. With the selected embedding dimension, the output vectors and input vectors were constructed based on the coal demand of China from 1980 to 2002. After compared with lineal kernel and Sigmoid kernel, a radial basis function(RBF) was adopted as the kernel function. By analyzing the relationship between the error margin of prediction and the model parameters, the proper parameters were chosen. The support vector machines (SVM) model with multi-input and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM predictor has higher precision and greater generalization ability. In the end, the coal demand from 2003 to 2006 is accurately forecasted. l0 refs., 2 figs., 4 tabs.

  19. Machine learning based switching model for electricity load forecasting

    International Nuclear Information System (INIS)

    Fan Shu; Chen Luonan; Lee, Weijen

    2008-01-01

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

  20. The Abstract Machine Model for Transaction-based System Control

    Energy Technology Data Exchange (ETDEWEB)

    Chassin, David P.

    2003-01-31

    Recent work applying statistical mechanics to economic modeling has demonstrated the effectiveness of using thermodynamic theory to address the complexities of large scale economic systems. Transaction-based control systems depend on the conjecture that when control of thermodynamic systems is based on price-mediated strategies (e.g., auctions, markets), the optimal allocation of resources in a market-based control system results in an emergent optimal control of the thermodynamic system. This paper proposes an abstract machine model as the necessary precursor for demonstrating this conjecture and establishes the dynamic laws as the basis for a special theory of emergence applied to the global behavior and control of complex adaptive systems. The abstract machine in a large system amounts to the analog of a particle in thermodynamic theory. The permit the establishment of a theory dynamic control of complex system behavior based on statistical mechanics. Thus we may be better able to engineer a few simple control laws for a very small number of devices types, which when deployed in very large numbers and operated as a system of many interacting markets yields the stable and optimal control of the thermodynamic system.

  1. Subspace identification of Hammer stein models using support vector machines

    International Nuclear Information System (INIS)

    Al-Dhaifallah, Mujahed

    2011-01-01

    System identification is the art of finding mathematical tools and algorithms that build an appropriate mathematical model of a system from measured input and output data. Hammerstein model, consisting of a memoryless nonlinearity followed by a dynamic linear element, is often a good trade-off as it can represent some dynamic nonlinear systems very accurately, but is nonetheless quite simple. Moreover, the extensive knowledge about LTI system representations can be applied to the dynamic linear block. On the other hand, finding an effective representation for the nonlinearity is an active area of research. Recently, support vector machines (SVMs) and least squares support vector machines (LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In contrast with other approximation methods, SVMs do not require a-priori structural information. Furthermore, there are well established methods with guaranteed convergence (ordinary least squares, quadratic programming) for fitting LS-SVMs and SVMs. The general objective of this research is to develop new subspace algorithms for Hammerstein systems based on SVM regression.

  2. Hidden physics models: Machine learning of nonlinear partial differential equations

    Science.gov (United States)

    Raissi, Maziar; Karniadakis, George Em

    2018-03-01

    While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

  3. Conceptual models in man-machine design verification

    International Nuclear Information System (INIS)

    Rasmussen, J.

    1985-01-01

    The need for systematic methods for evaluation of design concepts for new man-machine systems has been rapidly increasing in consequence of the introduction of modern information technology. Direct empirical methods are difficult to apply when functions during rare conditions and support of operator decisions during emergencies are to be evaluated. In this paper, the problems of analytical evaluations based on conceptual models of the man-machine interaction are discussed, and the relations to system design and analytical risk assessment are considered. Finally, a conceptual framework for analytical evaluation is proposed, including several domains of description: 1. The problem space, in the form of a means-end hierarchy; 2. The structure of the decision process; 3. The mental strategies and heuristics used by operators; 4. The levels of cognitive control and the mechanisms related to human errors. Finally, the need for models representing operators' subjective criteria for choosing among available mental strategies and for accepting advice from intelligent interfaces is discussed

  4. Error modeling for surrogates of dynamical systems using machine learning

    Science.gov (United States)

    Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

    2017-12-01

    A machine-learning-based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (e.g., random forests, LASSO) to map a large set of inexpensively computed `error indicators' (i.e., features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering), and subsequently constructs a `local' regression model to predict the time-instantaneous error within each identified region of feature space. We consider two uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance, and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (e.g., time-integrated errors). We apply the proposed framework to model errors in reduced-order models of nonlinear oil--water subsurface flow simulations. The reduced-order models used in this work entail application of trajectory piecewise linearization with proper orthogonal decomposition. When the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.

  5. Translating the foundational model of anatomy into french using knowledge-based and lexical methods

    Directory of Open Access Journals (Sweden)

    Merabti Tayeb

    2011-10-01

    Full Text Available Abstract Background The Foundational Model of Anatomy (FMA is the reference ontology regarding human anatomy. FMA vocabulary was integrated into the Health Multi Terminological Portal (HMTP developed by CISMeF based on the CISMeF Information System which also includes 26 other terminologies and controlled vocabularies, mainly in French. However, FMA is primarily in English. In this context, the translation of FMA English terms into French could also be useful for searching and indexing French anatomy resources. Various studies have investigated automatic methods to assist the translation of medical terminologies or create multilingual medical vocabularies. The goal of this study was to facilitate the translation of FMA vocabulary into French. Methods We compare two types of approaches to translate the FMA terms into French. The first one is UMLS-based on the conceptual information of the UMLS metathesaurus. The second method is lexically-based on several Natural Language Processing (NLP tools. Results The UMLS-based approach produced a translation of 3,661 FMA terms into French whereas the lexical approach produced a translation of 3,129 FMA terms into French. A qualitative evaluation was made on 100 FMA terms translated by each method. For the UMLS-based approach, among the 100 translations, 52% were manually rated as "very good" and only 7% translations as "bad". For the lexical approach, among the 100 translations, 47% were rated as "very good" and 20% translations as "bad". Conclusions Overall, a low rate of translations were demonstrated by the two methods. The two approaches permitted us to semi-automatically translate 3,776 FMA terms from English into French, this was to added to the existing 10,844 French FMA terms in the HMTP (4,436 FMA French terms and 6,408 FMA terms manually translated.

  6. Attacking Machine Learning models as part of a cyber kill chain

    OpenAIRE

    Nguyen, Tam N.

    2017-01-01

    Machine learning is gaining popularity in the network security domain as many more network-enabled devices get connected, as malicious activities become stealthier, and as new technologies like Software Defined Networking emerge. Compromising machine learning model is a desirable goal. In fact, spammers have been quite successful getting through machine learning enabled spam filters for years. While previous works have been done on adversarial machine learning, none has been considered within...

  7. Functional networks inference from rule-based machine learning models.

    Science.gov (United States)

    Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

    2016-01-01

    Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

  8. Machine learning, computer vision, and probabilistic models in jet physics

    CERN Multimedia

    CERN. Geneva; NACHMAN, Ben

    2015-01-01

    In this talk we present recent developments in the application of machine learning, computer vision, and probabilistic models to the analysis and interpretation of LHC events. First, we will introduce the concept of jet-images and computer vision techniques for jet tagging. Jet images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing for the first time, improving the performance to identify highly boosted W bosons with respect to state-of-the-art methods, and providing a new way to visualize the discriminant features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods. Second, we will present Fuzzy jets: a new paradigm for jet clustering using machine learning methods. Fuzzy jets view jet clustering as an unsupervised learning task and incorporate a probabilistic assignment of particles to jets to learn new features of the jet structure. In particular, we wi...

  9. From theoretical model to practical use: an example of knowledge translation.

    Science.gov (United States)

    Bjørk, Ida Torunn; Lomborg, Kirsten; Nielsen, Carsten Munch; Brynildsen, Grethe; Frederiksen, Anne-Marie Skovsgaard; Larsen, Karin; Reierson, Inger Åse; Sommer, Irene; Stenholt, Britta

    2013-10-01

    To present a case of knowledge translation in nursing education and practice and discusses mechanisms relevant to bringing knowledge into action. The process of knowledge translation aspires to close the gap between theory and practice. Knowledge translation is a cyclic process involving both the creation and application of knowledge in several phases. The case presented in this paper is the translation of the Model of Practical Skill Performance into education and practice. Advantages and problems with the use of this model and its adaptation and tailoring to local contexts illustrate the cyclic and iterative process of knowledge translation. The cultivation of a three-sided relationship between researchers, educators, and clinical nurses was a major asset in driving the process of knowledge translation. The knowledge translation process gained momentum by replacing passive diffusion strategies with interaction and teamwork between stakeholders. The use of knowledge creates feedback that might have consequences for the refinement and tailoring of that same knowledge itself. With end-users in mind, several heuristics were used by the research group to increase clarity of the model and to tailor the implementation of knowledge to the users. This article illustrates the need for enduring collaboration between stakeholders to promote the process of knowledge translation. Translation of research knowledge into practice is a time-consuming process that is enhanced when appropriate support is given by leaders in the involved facilities. Knowledge translation is a time-consuming and collaborative endeavour. On the basis of our experience we advocate the implementation and use of a conceptual framework for the entire process of knowledge translation. More descriptions of knowledge translation in the nursing discipline are needed to inspire and advise in this process. © 2013 Blackwell Publishing Ltd.

  10. Visible Machine Learning for Biomedicine.

    Science.gov (United States)

    Yu, Michael K; Ma, Jianzhu; Fisher, Jasmin; Kreisberg, Jason F; Raphael, Benjamin J; Ideker, Trey

    2018-06-14

    A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology. Copyright © 2018. Published by Elsevier Inc.

  11. A Reference Model for Virtual Machine Launching Overhead

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Hao; Ren, Shangping; Garzoglio, Gabriele; Timm, Steven; Bernabeu, Gerard; Chadwick, Keith; Noh, Seo-Young

    2016-07-01

    Cloud bursting is one of the key research topics in the cloud computing communities. A well designed cloud bursting module enables private clouds to automatically launch virtual machines (VMs) to public clouds when more resources are needed. One of the main challenges in developing a cloud bursting module is to decide when and where to launch a VM so that all resources are most effectively and efficiently utilized and the system performance is optimized. However, based on system operational data obtained from FermiCloud, a private cloud developed by the Fermi National Accelerator Laboratory for scientific workflows, the VM launching overhead is not a constant. It varies with physical resource utilization, such as CPU and I/O device utilizations, at the time when a VM is launched. Hence, to make judicious decisions as to when and where a VM should be launched, a VM launching overhead reference model is needed. In this paper, we first develop a VM launching overhead reference model based on operational data we have obtained on FermiCloud. Second, we apply the developed reference model on FermiCloud and compare calculated VM launching overhead values based on the model with measured overhead values on FermiCloud. Our empirical results on FermiCloud indicate that the developed reference model is accurate. We believe, with the guidance of the developed reference model, efficient resource allocation algorithms can be developed for cloud bursting process to minimize the operational cost and resource waste.

  12. Modelling open pit shovel-truck systems using the Machine Repair Model

    Energy Technology Data Exchange (ETDEWEB)

    Krause, A.; Musingwini, C. [CBH Resources Ltd., Sydney, NSW (Australia). Endeaver Mine

    2007-08-15

    Shovel-truck systems for loading and hauling material in open pit mines are now routinely analysed using simulation models or off-the-shelf simulation software packages, which can be very expensive for once-off or occasional use. The simulation models invariably produce different estimations of fleet sizes due to their differing estimations of cycle time. No single model or package can accurately estimate the required fleet size because the fleet operating parameters are characteristically random and dynamic. In order to improve confidence in sizing the fleet for a mining project, at least two estimation models should be used. This paper demonstrates that the Machine Repair Model can be modified and used as a model for estimating truck fleet size in an open pit shovel-truck system. The modified Machine Repair Model is first applied to a virtual open pit mine case study. The results compare favourably to output from other estimation models using the same input parameters for the virtual mine. The modified Machine Repair Model is further applied to an existing open pit coal operation, the Kwagga Section of Optimum Colliery as a case study. Again the results confirm those obtained from the virtual mine case study. It is concluded that the Machine Repair Model can be an affordable model compared to off-the-shelf generic software because it is easily modelled in Microsoft Excel, a software platform that most mines already use.

  13. A Translational Model of Research-Practice Integration

    Science.gov (United States)

    Vivian, Dina; Hershenberg, Rachel; Teachman, Bethany A.; Drabick, Deborah A. G.; Goldfried, Marvin R.; Wolfe, Barry

    2013-01-01

    We propose a four-level, recursive Research-Practice Integration framework as a heuristic to (a) integrate and reflect on the articles in this Special Section as contributing to a bidirectional bridge between research and practice, and (b) consider additional opportunities to address the research–practice gap. Level 1 addresses Treatment Validation studies and includes an article by Lochman and colleagues concerning the programmatic adaptation, implementation, and dissemination of the empirically supported Coping Power treatment program for youth aggression. Level 2 translation, Training in Evidence-Based Practice, includes a paper by Hershenberg, Drabick, and Vivian, which focuses on the critical role that predoctoral training plays in bridging the research–practice gap. Level 3 addresses the Assessment of Clinical Utility and Feedback to Research aspects of translation. The articles by Lambert and Youn, Kraus, and Castonguay illustrate the use of commercial outcome packages that enable psychotherapists to integrate ongoing client assessment, thus enhancing the effectiveness of treatment implementation and providing data that can be fed back to researchers. Lastly, Level 4 translation, the Cross-Level Integrative Research and Communication, concerns research efforts that integrate data from clinical practice and all other levels of translation, as well as communication efforts among all stakeholders, such as researchers, psychotherapists, and clients. Using a two-chair technique as a framework for his discussion, Wolfe's article depicts the struggle inherent in research–practice integration efforts and proposes a rapprochement that highlights advancements in the field. PMID:22642522

  14. Omnibus risk assessment via accelerated failure time kernel machine modeling.

    Science.gov (United States)

    Sinnott, Jennifer A; Cai, Tianxi

    2013-12-01

    Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. © 2013, The International Biometric Society.

  15. Modeling of tool path for the CNC sheet cutting machines

    Science.gov (United States)

    Petunin, Aleksandr A.

    2015-11-01

    In the paper the problem of tool path optimization for CNC (Computer Numerical Control) cutting machines is considered. The classification of the cutting techniques is offered. We also propose a new classification of toll path problems. The tasks of cost minimization and time minimization for standard cutting technique (Continuous Cutting Problem, CCP) and for one of non-standard cutting techniques (Segment Continuous Cutting Problem, SCCP) are formalized. We show that the optimization tasks can be interpreted as discrete optimization problem (generalized travel salesman problem with additional constraints, GTSP). Formalization of some constraints for these tasks is described. For the solution GTSP we offer to use mathematical model of Prof. Chentsov based on concept of a megalopolis and dynamic programming.

  16. Selected translated abstracts of Russian-language climate-change publications. 4: General circulation models

    Energy Technology Data Exchange (ETDEWEB)

    Burtis, M.D. [comp.] [Oak Ridge National Lab., TN (United States). Carbon Dioxide Information Analysis Center; Razuvaev, V.N.; Sivachok, S.G. [All-Russian Research Inst. of Hydrometeorological Information--World Data Center, Obninsk (Russian Federation)

    1996-10-01

    This report presents English-translated abstracts of important Russian-language literature concerning general circulation models as they relate to climate change. Into addition to the bibliographic citations and abstracts translated into English, this report presents the original citations and abstracts in Russian. Author and title indexes are included to assist the reader in locating abstracts of particular interest.

  17. Comparison of Methods for Modeling a Hydraulic Loader Crane With Flexible Translational Links

    DEFF Research Database (Denmark)

    Pedersen, Henrik Clemmensen; Andersen, Torben O.; Nielsen, Brian K.

    2015-01-01

    not hold for translational links. Hence, special care has to be taken when including flexible translational links. In the current paper, different methods for modeling a hydraulic loader crane with a telescopic arm are investigated and compared using both the finite segment (FS) and AMs method...

  18. Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in-between?

    Science.gov (United States)

    Niederer, Steven A; Smith, Nic P

    2016-12-01

    Heart disease continues to be a significant clinical problem in Western society. Predictive models and simulations that integrate physiological understanding with patient information derived from clinical data have huge potential to contribute to improving our understanding of both the progression and treatment of heart disease. In particular they provide the potential to improve patient selection and optimisation of cardiovascular interventions across a range of pathologies. Currently a significant proportion of this potential is still to be realised. In this paper we discuss the opportunities and challenges associated with this realisation. Reviewing the successful elements of model translation for biophysically based models and the emerging supporting technologies, we propose three distinct modes of clinical translation. Finally we outline the challenges ahead that will be fundamental to overcome if the ultimate goal of fully personalised clinical cardiac care is to be achieved. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

  19. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

    Science.gov (United States)

    Cuperlovic-Culf, Miroslava

    2018-01-11

    Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

  20. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

    Science.gov (United States)

    Cuperlovic-Culf, Miroslava

    2018-01-01

    Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

  1. DG TO FT - AUTOMATIC TRANSLATION OF DIGRAPH TO FAULT TREE MODELS

    Science.gov (United States)

    Iverson, D. L.

    1994-01-01

    root node. A subtree is created for each of the inputs to the digraph terminal node and the root of those subtrees are added as children of the top node of the fault tree. Every node in the digraph upstream of the terminal node will be visited and converted. During the conversion process, the algorithm keeps track of the path from the digraph terminal node to the current digraph node. If a node is visited twice, then the program has found a cycle in the digraph. This cycle is broken by finding the minimal cut sets of the twice visited digraph node and forming those cut sets into subtrees. Another implementation of the algorithm resolves loops by building a subtree based on the digraph minimal cut sets calculation. It does not reduce the subtree to minimal cut set form. This second implementation produces larger fault trees, but runs much faster than the version using minimal cut sets since it does not spend time reducing the subtrees to minimal cut sets. The fault trees produced by DG TO FT will contain OR gates, AND gates, Basic Event nodes, and NOP gates. The results of a translation can be output as a text object description of the fault tree similar to the text digraph input format. The translator can also output a LISP language formatted file and an augmented LISP file which can be used by the FTDS (ARC-13019) diagnosis system, available from COSMIC, which performs diagnostic reasoning using the fault tree as a knowledge base. DG TO FT is written in C-language to be machine independent. It has been successfully implemented on a Sun running SunOS, a DECstation running ULTRIX, a Macintosh running System 7, and a DEC VAX running VMS. The RAM requirement varies with the size of the models. DG TO FT is available in UNIX tar format on a .25 inch streaming magnetic tape cartridge (standard distribution) or on a 3.5 inch diskette. It is also available on a 3.5 inch Macintosh format diskette or on a 9-track 1600 BPI magnetic tape in DEC VAX FILES-11 format. Sample input

  2. Dynamic modeling of an asynchronous squirrel-cage machine; Modelisation dynamique d'une machine asynchrone a cage

    Energy Technology Data Exchange (ETDEWEB)

    Guerette, D.

    2009-07-01

    This document presented a detailed mathematical explanation and validation of the steps leading to the development of an asynchronous squirrel-cage machine. The MatLab/Simulink software was used to model a wind turbine at variable high speeds. The asynchronous squirrel-cage machine is an electromechanical system coupled to a magnetic circuit. The resulting electromagnetic circuit can be represented as a set of resistances, leakage inductances and mutual inductances. Different models were used for a comparison study, including the Munteanu, Boldea, Wind Turbine Blockset, and SimPowerSystem. MatLab/Simulink modeling results were in good agreement with the results from other comparable models. Simulation results were in good agreement with analytical calculations. 6 refs, 2 tabs, 9 figs.

  3. Developing a PLC-friendly state machine model: lessons learned

    Science.gov (United States)

    Pessemier, Wim; Deconinck, Geert; Raskin, Gert; Saey, Philippe; Van Winckel, Hans

    2014-07-01

    Modern Programmable Logic Controllers (PLCs) have become an attractive platform for controlling real-time aspects of astronomical telescopes and instruments due to their increased versatility, performance and standardization. Likewise, vendor-neutral middleware technologies such as OPC Unified Architecture (OPC UA) have recently demonstrated that they can greatly facilitate the integration of these industrial platforms into the overall control system. Many practical questions arise, however, when building multi-tiered control systems that consist of PLCs for low level control, and conventional software and platforms for higher level control. How should the PLC software be structured, so that it can rely on well-known programming paradigms on the one hand, and be mapped to a well-organized OPC UA interface on the other hand? Which programming languages of the IEC 61131-3 standard closely match the problem domains of the abstraction levels within this structure? How can the recent additions to the standard (such as the support for namespaces and object-oriented extensions) facilitate a model based development approach? To what degree can our applications already take advantage of the more advanced parts of the OPC UA standard, such as the high expressiveness of the semantic modeling language that it defines, or the support for events, aggregation of data, automatic discovery, ... ? What are the timing and concurrency problems to be expected for the higher level tiers of the control system due to the cyclic execution of control and communication tasks by the PLCs? We try to answer these questions by demonstrating a semantic state machine model that can readily be implemented using IEC 61131 and OPC UA. One that does not aim to capture all possible states of a system, but rather one that attempts to organize the course-grained structure and behaviour of a system. In this paper we focus on the intricacies of this seemingly simple task, and on the lessons that we

  4. Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Krzysztof Gajowniczek

    2017-10-01

    Full Text Available Forecasting of electricity demand has become one of the most important areas of research in the electric power industry, as it is a critical component of cost-efficient power system management and planning. In this context, accurate and robust load forecasting is supposed to play a key role in reducing generation costs, and deals with the reliability of the power system. However, due to demand peaks in the power system, forecasts are inaccurate and prone to high numbers of errors. In this paper, our contributions comprise a proposed data-mining scheme for demand modeling through peak detection, as well as the use of this information to feed the forecasting system. For this purpose, we have taken a different approach from that of time series forecasting, representing it as a two-stage pattern recognition problem. We have developed a peak classification model followed by a forecasting model to estimate an aggregated demand volume. We have utilized a set of machine learning algorithms to benefit from both accurate detection of the peaks and precise forecasts, as applied to the Polish power system. The key finding is that the algorithms can detect 96.3% of electricity peaks (load value equal to or above the 99th percentile of the load distribution and deliver accurate forecasts, with mean absolute percentage error (MAPE of 3.10% and resistant mean absolute percentage error (r-MAPE of 2.70% for the 24 h forecasting horizon.

  5. Modeling the Virtual Machine Launching Overhead under Fermicloud

    Energy Technology Data Exchange (ETDEWEB)

    Garzoglio, Gabriele [Fermilab; Wu, Hao [Fermilab; Ren, Shangping [IIT, Chicago; Timm, Steven [Fermilab; Bernabeu, Gerard [Fermilab; Noh, Seo-Young [KISTI, Daejeon

    2014-11-12

    FermiCloud is a private cloud developed by the Fermi National Accelerator Laboratory for scientific workflows. The Cloud Bursting module of the FermiCloud enables the FermiCloud, when more computational resources are needed, to automatically launch virtual machines to available resources such as public clouds. One of the main challenges in developing the cloud bursting module is to decide when and where to launch a VM so that all resources are most effectively and efficiently utilized and the system performance is optimized. However, based on FermiCloud’s system operational data, the VM launching overhead is not a constant. It varies with physical resource (CPU, memory, I/O device) utilization at the time when a VM is launched. Hence, to make judicious decisions as to when and where a VM should be launched, a VM launch overhead reference model is needed. The paper is to develop a VM launch overhead reference model based on operational data we have obtained on FermiCloud and uses the reference model to guide the cloud bursting process.

  6. Translation of a High-Level Temporal Model into Lower Level Models: Impact of Modelling at Different Description Levels

    DEFF Research Database (Denmark)

    Kraft, Peter; Sørensen, Jens Otto

    2001-01-01

    given types of properties, and examine how descriptions on higher levels translate into descriptions on lower levels. Our example looks at temporal properties where the information is concerned with the existence in time. In a high level temporal model with information kept in a three-dimensional space...... the existences in time can be mapped precisely and consistently securing a consistent handling of the temporal properties. We translate the high level temporal model into an entity-relationship model, with the information in a two-dimensional graph, and finally we look at the translations into relational...... and other textual models. We also consider the aptness of models that include procedural mechanisms such as active and object databases...

  7. An improved modelling of asynchronous machine with skin-effect ...

    African Journals Online (AJOL)

    The conventional method of analysis of Asynchronous machine fails to give accurate results especially when the machine is operated under high rotor frequency. At high rotor frequency, skin-effect dominates causing the rotor impedance to be frequency dependant. This paper therefore presents an improved method of ...

  8. Modelling and Simulation of a Synchronous Machine with Power Electronic Systems

    DEFF Research Database (Denmark)

    Chen, Zhe; Blaabjerg, Frede

    2005-01-01

    is modelled in SIMULINK as well. The resulting model can more accurately represent non-idea situations such as non-symmetrical parameters of the electrical machines and unbalance conditions. The model may be used for both steady state and large-signal dynamic analysis. This is particularly useful......This paper reports the modeling and simulation of a synchronous machine with a power electronic interface in direct phase model. The implementation of a direct phase model of synchronous machines in MATLAB/SIMULINK is presented .The power electronic system associated with the synchronous machine...... in the systems where a detailed study is needed in order to assess the overall system stability. Simulation studies are performed under various operation conditions. It is shown that the developed model could be used for studies of various applications of synchronous machines such as in renewable and DG...

  9. Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

    Energy Technology Data Exchange (ETDEWEB)

    Reddy, M Mohan; Gorin, Alexander [School of Engineering and Science, Curtin University of Technology, Sarawak (Malaysia); Abou-El-Hossein, K A, E-mail: mohan.m@curtin.edu.my [Mechanical and Aeronautical Department, Nelson Mandela Metropolitan University, Port Elegebeth, 6031 (South Africa)

    2011-02-15

    Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

  10. Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

    International Nuclear Information System (INIS)

    Reddy, M Mohan; Gorin, Alexander; Abou-El-Hossein, K A

    2011-01-01

    Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

  11. Modeling and simulation of five-axis virtual machine based on NX

    Science.gov (United States)

    Li, Xiaoda; Zhan, Xianghui

    2018-04-01

    Virtual technology in the machinery manufacturing industry has shown the role of growing. In this paper, the Siemens NX software is used to model the virtual CNC machine tool, and the parameters of the virtual machine are defined according to the actual parameters of the machine tool so that the virtual simulation can be carried out without loss of the accuracy of the simulation. How to use the machine builder of the CAM module to define the kinematic chain and machine components of the machine is described. The simulation of virtual machine can provide alarm information of tool collision and over cutting during the process to users, and can evaluate and forecast the rationality of the technological process.

  12. Crystal structure representations for machine learning models of formation energies

    Energy Technology Data Exchange (ETDEWEB)

    Faber, Felix [Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, University of Basel Switzerland; Lindmaa, Alexander [Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping Sweden; von Lilienfeld, O. Anatole [Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, University of Basel Switzerland; Argonne Leadership Computing Facility, Argonne National Laboratory, 9700 S. Cass Avenue Lemont Illinois 60439; Armiento, Rickard [Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping Sweden

    2015-04-20

    We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization energies of organic molecules have been successful using a Coulomb matrix representation of the molecule. We consider three ways to generalize such representations to periodic systems: (i) a matrix where each element is related to the Ewald sum of the electrostatic interaction between two different atoms in the unit cell repeated over the lattice; (ii) an extended Coulomb-like matrix that takes into account a number of neighboring unit cells; and (iii) an ansatz that mimics the periodicity and the basic features of the elements in the Ewald sum matrix using a sine function of the crystal coordinates of the atoms. The representations are compared for a Laplacian kernel with Manhattan norm, trained to reproduce formation energies using a dataset of 3938 crystal structures obtained from the Materials Project. For training sets consisting of 3000 crystals, the generalization error in predicting formation energies of new structures corresponds to (i) 0.49, (ii) 0.64, and (iii) 0.37eV/atom for the respective representations.

  13. Analysis of the whole mitochondrial genome: translation of the Ion Torrent Personal Genome Machine system to the diagnostic bench?

    Science.gov (United States)

    Seneca, Sara; Vancampenhout, Kim; Van Coster, Rudy; Smet, Joél; Lissens, Willy; Vanlander, Arnaud; De Paepe, Boel; Jonckheere, An; Stouffs, Katrien; De Meirleir, Linda

    2015-01-01

    Next-generation sequencing (NGS), an innovative sequencing technology that enables the successful analysis of numerous gene sequences in a massive parallel sequencing approach, has revolutionized the field of molecular biology. Although NGS was introduced in a rather recent past, the technology has already demonstrated its potential and effectiveness in many research projects, and is now on the verge of being introduced into the diagnostic setting of routine laboratories to delineate the molecular basis of genetic disease in undiagnosed patient samples. We tested a benchtop device on retrospective genomic DNA (gDNA) samples of controls and patients with a clinical suspicion of a mitochondrial DNA disorder. This Ion Torrent Personal Genome Machine platform is a high-throughput sequencer with a fast turnaround time and reasonable running costs. We challenged the chemistry and technology with the analysis and processing of a mutational spectrum composed of samples with single-nucleotide substitutions, indels (insertions and deletions) and large single or multiple deletions, occasionally in heteroplasmy. The output data were compared with previously obtained conventional dideoxy sequencing results and the mitochondrial revised Cambridge Reference Sequence (rCRS). We were able to identify the majority of all nucleotide alterations, but three false-negative results were also encountered in the data set. At the same time, the poor performance of the PGM instrument in regions associated with homopolymeric stretches generated many false-positive miscalls demanding additional manual curation of the data.

  14. A generic method for automatic translation between input models for different versions of simulation codes

    International Nuclear Information System (INIS)

    Serfontein, Dawid E.; Mulder, Eben J.; Reitsma, Frederik

    2014-01-01

    A computer code was developed for the semi-automatic translation of input models for the VSOP-A diffusion neutronics simulation code to the format of the newer VSOP 99/05 code. In this paper, this algorithm is presented as a generic method for producing codes for the automatic translation of input models from the format of one code version to another, or even to that of a completely different code. Normally, such translations are done manually. However, input model files, such as for the VSOP codes, often are very large and may consist of many thousands of numeric entries that make no particular sense to the human eye. Therefore the task, of for instance nuclear regulators, to verify the accuracy of such translated files can be very difficult and cumbersome. This may cause translation errors not to be picked up, which may have disastrous consequences later on when a reactor with such a faulty design is built. Therefore a generic algorithm for producing such automatic translation codes may ease the translation and verification process to a great extent. It will also remove human error from the process, which may significantly enhance the accuracy and reliability of the process. The developed algorithm also automatically creates a verification log file which permanently record the names and values of each variable used, as well as the list of meanings of all the possible values. This should greatly facilitate reactor licensing applications

  15. A generic method for automatic translation between input models for different versions of simulation codes

    Energy Technology Data Exchange (ETDEWEB)

    Serfontein, Dawid E., E-mail: Dawid.Serfontein@nwu.ac.za [School of Mechanical and Nuclear Engineering, North West University (PUK-Campus), PRIVATE BAG X6001 (Internal Post Box 360), Potchefstroom 2520 (South Africa); Mulder, Eben J. [School of Mechanical and Nuclear Engineering, North West University (South Africa); Reitsma, Frederik [Calvera Consultants (South Africa)

    2014-05-01

    A computer code was developed for the semi-automatic translation of input models for the VSOP-A diffusion neutronics simulation code to the format of the newer VSOP 99/05 code. In this paper, this algorithm is presented as a generic method for producing codes for the automatic translation of input models from the format of one code version to another, or even to that of a completely different code. Normally, such translations are done manually. However, input model files, such as for the VSOP codes, often are very large and may consist of many thousands of numeric entries that make no particular sense to the human eye. Therefore the task, of for instance nuclear regulators, to verify the accuracy of such translated files can be very difficult and cumbersome. This may cause translation errors not to be picked up, which may have disastrous consequences later on when a reactor with such a faulty design is built. Therefore a generic algorithm for producing such automatic translation codes may ease the translation and verification process to a great extent. It will also remove human error from the process, which may significantly enhance the accuracy and reliability of the process. The developed algorithm also automatically creates a verification log file which permanently record the names and values of each variable used, as well as the list of meanings of all the possible values. This should greatly facilitate reactor licensing applications.

  16. A self-calibrating robot based upon a virtual machine model of parallel kinematics

    DEFF Research Database (Denmark)

    Pedersen, David Bue; Eiríksson, Eyþór Rúnar; Hansen, Hans Nørgaard

    2016-01-01

    A delta-type parallel kinematics system for Additive Manufacturing has been created, which through a probing system can recognise its geometrical deviations from nominal and compensate for these in the driving inverse kinematic model of the machine. Novelty is that this model is derived from...... a virtual machine of the kinematics system, built on principles from geometrical metrology. Relevant mathematically non-trivial deviations to the ideal machine are identified and decomposed into elemental deviations. From these deviations, a routine is added to a physical machine tool, which allows...

  17. Development of Mathematical Model for Lifecycle Management Process of New Type of Multirip Saw Machine

    Directory of Open Access Journals (Sweden)

    B. V. Phung

    2017-01-01

    Full Text Available The subject of research is a new type of the multirip saw machine with circular reciprocating saw blades. This machine has a number of advantages in comparison with other machines of similar purpose. The paper presents an overview of different types of saw equipment and describes basic characteristics of the machine under investigation.Using the concept of lifecycle management of the considered machine in a unified information space is necessary to improve quality and competitiveness in the current production environment. In this lifecycle all the members, namely designers, technologists, customers, etc., have a philosophy to tend to optimize the overall machine design as much as possible. However, it is not always possible to achieve. Conversely, at the boundary between the phases there are several mismatching situations, if not even conflicting inconsistencies. For example, improvement of mass characteristics can lead to poor stability and rigidity of the saw blade. Machine output improvement through increasing frequency of the machine motor rotation, on the other side, results in reducing stable ability of the saw blades and so on.In order to provide a coherent framework for the collaborative environment between the members of the life cycle, the article presents a technique to construct a mathematical model that allows combining all different members’ requirements in the unified information model. The article also gives analysis of kinematic and dynamic behavior and technological characteristics of the machine. Describes in detail all the controlled parameters, functional constraints, and quality criteria of the machine under consideration. Depending on the controlled parameters, the analytical relationships formulate functional constraints and quality criteria of the machine. The proposed algorithm allows fast and exact calculation of all the functional constraints and quality criteria of the machine for a given vector of the control

  18. Modelling tick abundance using machine learning techniques and satellite imagery

    DEFF Research Database (Denmark)

    Kjær, Lene Jung; Korslund, L.; Kjelland, V.

    satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

  19. Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process

    Directory of Open Access Journals (Sweden)

    Dazi Li

    2015-01-01

    Full Text Available A new strategy for internal model control (IMC is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM. Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS, while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.

  20. Multi-objective optimization model of CNC machining to minimize processing time and environmental impact

    Science.gov (United States)

    Hamada, Aulia; Rosyidi, Cucuk Nur; Jauhari, Wakhid Ahmad

    2017-11-01

    Minimizing processing time in a production system can increase the efficiency of a manufacturing company. Processing time are influenced by application of modern technology and machining parameter. Application of modern technology can be apply by use of CNC machining, one of the machining process can be done with a CNC machining is turning. However, the machining parameters not only affect the processing time but also affect the environmental impact. Hence, optimization model is needed to optimize the machining parameters to minimize the processing time and environmental impact. This research developed a multi-objective optimization to minimize the processing time and environmental impact in CNC turning process which will result in optimal decision variables of cutting speed and feed rate. Environmental impact is converted from environmental burden through the use of eco-indicator 99. The model were solved by using OptQuest optimization software from Oracle Crystal Ball.

  1. A machine learning model with human cognitive biases capable of learning from small and biased datasets.

    Science.gov (United States)

    Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

    2018-05-09

    Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

  2. Developing robust arsenic awareness prediction models using machine learning algorithms.

    Science.gov (United States)

    Singh, Sushant K; Taylor, Robert W; Rahman, Mohammad Mahmudur; Pradhan, Biswajeet

    2018-04-01

    Arsenic awareness plays a vital role in ensuring the sustainability of arsenic mitigation technologies. Thus far, however, few studies have dealt with the sustainability of such technologies and its associated socioeconomic dimensions. As a result, arsenic awareness prediction has not yet been fully conceptualized. Accordingly, this study evaluated arsenic awareness among arsenic-affected communities in rural India, using a structured questionnaire to record socioeconomic, demographic, and other sociobehavioral factors with an eye to assessing their association with and influence on arsenic awareness. First a logistic regression model was applied and its results compared with those produced by six state-of-the-art machine-learning algorithms (Support Vector Machine [SVM], Kernel-SVM, Decision Tree [DT], k-Nearest Neighbor [k-NN], Naïve Bayes [NB], and Random Forests [RF]) as measured by their accuracy at predicting arsenic awareness. Most (63%) of the surveyed population was found to be arsenic-aware. Significant arsenic awareness predictors were divided into three types: (1) socioeconomic factors: caste, education level, and occupation; (2) water and sanitation behavior factors: number of family members involved in water collection, distance traveled and time spent for water collection, places for defecation, and materials used for handwashing after defecation; and (3) social capital and trust factors: presence of anganwadi and people's trust in other community members, NGOs, and private agencies. Moreover, individuals' having higher social network positively contributed to arsenic awareness in the communities. Results indicated that both the SVM and the RF algorithms outperformed at overall prediction of arsenic awareness-a nonlinear classification problem. Lower-caste, less educated, and unemployed members of the population were found to be the most vulnerable, requiring immediate arsenic mitigation. To this end, local social institutions and NGOs could play a

  3. Modelling Machine Tools using Structure Integrated Sensors for Fast Calibration

    Directory of Open Access Journals (Sweden)

    Benjamin Montavon

    2018-02-01

    Full Text Available Monitoring of the relative deviation between commanded and actual tool tip position, which limits the volumetric performance of the machine tool, enables the use of contemporary methods of compensation to reduce tolerance mismatch and the uncertainties of on-machine measurements. The development of a primarily optical sensor setup capable of being integrated into the machine structure without limiting its operating range is presented. The use of a frequency-modulating interferometer and photosensitive arrays in combination with a Gaussian laser beam allows for fast and automated online measurements of the axes’ motion errors and thermal conditions with comparable accuracy, lower cost, and smaller dimensions as compared to state-of-the-art optical measuring instruments for offline machine tool calibration. The development is tested through simulation of the sensor setup based on raytracing and Monte-Carlo techniques.

  4. Effects of different per translational kinetics on the dynamics of a core circadian clock model.

    Science.gov (United States)

    Nieto, Paula S; Revelli, Jorge A; Garbarino-Pico, Eduardo; Condat, Carlos A; Guido, Mario E; Tamarit, Francisco A

    2015-01-01

    Living beings display self-sustained daily rhythms in multiple biological processes, which persist in the absence of external cues since they are generated by endogenous circadian clocks. The period (per) gene is a central player within the core molecular mechanism for keeping circadian time in most animals. Recently, the modulation PER translation has been reported, both in mammals and flies, suggesting that translational regulation of clock components is important for the proper clock gene expression and molecular clock performance. Because translational regulation ultimately implies changes in the kinetics of translation and, therefore, in the circadian clock dynamics, we sought to study how and to what extent the molecular clock dynamics is affected by the kinetics of PER translation. With this objective, we used a minimal mathematical model of the molecular circadian clock to qualitatively characterize the dynamical changes derived from kinetically different PER translational mechanisms. We found that the emergence of self-sustained oscillations with characteristic period, amplitude, and phase lag (time delays) between per mRNA and protein expression depends on the kinetic parameters related to PER translation. Interestingly, under certain conditions, a PER translation mechanism with saturable kinetics introduces longer time delays than a mechanism ruled by a first-order kinetics. In addition, the kinetic laws of PER translation significantly changed the sensitivity of our model to parameters related to the synthesis and degradation of per mRNA and PER degradation. Lastly, we found a set of parameters, with realistic values, for which our model reproduces some experimental results reported recently for Drosophila melanogaster and we present some predictions derived from our analysis.

  5. Towards an automatic model transformation mechanism from UML state machines to DEVS models

    Directory of Open Access Journals (Sweden)

    Ariel González

    2015-08-01

    Full Text Available The development of complex event-driven systems requires studies and analysis prior to deployment with the goal of detecting unwanted behavior. UML is a language widely used by the software engineering community for modeling these systems through state machines, among other mechanisms. Currently, these models do not have appropriate execution and simulation tools to analyze the real behavior of systems. Existing tools do not provide appropriate libraries (sampling from a probability distribution, plotting, etc. both to build and to analyze models. Modeling and simulation for design and prototyping of systems are widely used techniques to predict, investigate and compare the performance of systems. In particular, the Discrete Event System Specification (DEVS formalism separates the modeling and simulation; there are several tools available on the market that run and collect information from DEVS models. This paper proposes a model transformation mechanism from UML state machines to DEVS models in the Model-Driven Development (MDD context, through the declarative QVT Relations language, in order to perform simulations using tools, such as PowerDEVS. A mechanism to validate the transformation is proposed. Moreover, examples of application to analyze the behavior of an automatic banking machine and a control system of an elevator are presented.

  6. Making sense of the Sense Model: translation priming with Japanese-English bilinguals

    OpenAIRE

    Allen, David; Conklin, Kathy; Van Heuven, Walter J.B.

    2015-01-01

    Many studies have reported that first language (L1) translation primes speed responses to second language (L2) targets, whereas L2 translation primes generally do not speed up responses to L1 targets in lexical decision. According to the Sense Model (Finkbeiner, Forster, Nicol & Nakamura, 2004) this asymmetry is due to the proportion of senses activated by the prime. Because L2 primes activate only a subset of the L1 translations senses, priming is not observed. In this study we test the pred...

  7. Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model

    Science.gov (United States)

    Meilijson, Isaac; Kupiec, Martin; Ruppin, Eytan

    2011-01-01

    We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative (‘non-physical’) approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs. These quantities could not be inferred by previous predictors. We find that increasing the number of available ribosomes (or equivalently the initiation rate) increases the genomic translation rate and the mean ribosome density only up to a certain point, beyond which both saturate. Strikingly, assuming that the translation system is tuned to work at the pre-saturation point maximizes the predictive power of the model with respect to experimental data. This result suggests that in all organisms that were analyzed (from bacteria to Human), the global initiation rate is optimized to attain the pre-saturation point. The fact that similar results were not observed for heterologous genes indicates that this feature is under selection. Remarkably, the gap between the performance of the RFM and alternative predictors is strikingly large in the case of heterologous genes, testifying to the model's promising biotechnological value in predicting the abundance of heterologous proteins before expressing them in the desired host. PMID:21909250

  8. Multi-dimensional knowledge translation: enabling health informatics capacity audits using patient journey models.

    Science.gov (United States)

    Catley, Christina; McGregor, Carolyn; Percival, Jennifer; Curry, Joanne; James, Andrew

    2008-01-01

    This paper presents a multi-dimensional approach to knowledge translation, enabling results obtained from a survey evaluating the uptake of Information Technology within Neonatal Intensive Care Units to be translated into knowledge, in the form of health informatics capacity audits. Survey data, having multiple roles, patient care scenarios, levels, and hospitals, is translated using a structured data modeling approach, into patient journey models. The data model is defined such that users can develop queries to generate patient journey models based on a pre-defined Patient Journey Model architecture (PaJMa). PaJMa models are then analyzed to build capacity audits. Capacity audits offer a sophisticated view of health informatics usage, providing not only details of what IT solutions a hospital utilizes, but also answering the questions: when, how and why, by determining when the IT solutions are integrated into the patient journey, how they support the patient information flow, and why they improve the patient journey.

  9. Underlying finite state machine for the social engineering attack detection model

    CSIR Research Space (South Africa)

    Mouton, Francois

    2017-08-01

    Full Text Available one to have a clearer overview of the mental processing performed within the model. While the current model provides a general procedural template for implementing detection mechanisms for social engineering attacks, the finite state machine provides a...

  10. Machine learning in updating predictive models of planning and scheduling transportation projects

    Science.gov (United States)

    1997-01-01

    A method combining machine learning and regression analysis to automatically and intelligently update predictive models used in the Kansas Department of Transportations (KDOTs) internal management system is presented. The predictive models used...

  11. Development of an Empirical Model for Optimization of Machining Parameters to Minimize Power Consumption

    Science.gov (United States)

    Kant Garg, Girish; Garg, Suman; Sangwan, K. S.

    2018-04-01

    The manufacturing sector consumes huge energy demand and the machine tools used in this sector have very less energy efficiency. Selection of the optimum machining parameters for machine tools is significant for energy saving and for reduction of environmental emission. In this work an empirical model is developed to minimize the power consumption using response surface methodology. The experiments are performed on a lathe machine tool during the turning of AISI 6061 Aluminum with coated tungsten inserts. The relationship between the power consumption and machining parameters is adequately modeled. This model is used for formulation of minimum power consumption criterion as a function of optimal machining parameters using desirability function approach. The influence of machining parameters on the energy consumption has been found using the analysis of variance. The validation of the developed empirical model is proved using the confirmation experiments. The results indicate that the developed model is effective and has potential to be adopted by the industry for minimum power consumption of machine tools.

  12. Job shop scheduling model for non-identic machine with fixed delivery time to minimize tardiness

    Science.gov (United States)

    Kusuma, K. K.; Maruf, A.

    2016-02-01

    Scheduling non-identic machines problem with low utilization characteristic and fixed delivery time are frequent in manufacture industry. This paper propose a mathematical model to minimize total tardiness for non-identic machines in job shop environment. This model will be categorized as an integer linier programming model and using branch and bound algorithm as the solver method. We will use fixed delivery time as main constraint and different processing time to process a job. The result of this proposed model shows that the utilization of production machines can be increase with minimal tardiness using fixed delivery time as constraint.

  13. Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

    Science.gov (United States)

    Zur, Hadas; Tuller, Tamir

    2016-01-01

    mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field. PMID:27591251

  14. Parametric and non-parametric models for lifespan modeling of insulation systems in electrical machines

    OpenAIRE

    Salameh , Farah; Picot , Antoine; Chabert , Marie; Maussion , Pascal

    2017-01-01

    International audience; This paper describes an original statistical approach for the lifespan modeling of electric machine insulation materials. The presented models aim to study the effect of three main stress factors (voltage, frequency and temperature) and their interactions on the insulation lifespan. The proposed methodology is applied to two different insulation materials tested in partial discharge regime. Accelerated ageing tests are organized according to experimental optimization m...

  15. International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines

    CERN Document Server

    Belyaev, Alexander; Krommer, Michael

    2017-01-01

    The papers in this volume present and discuss the frontiers in the mechanics of controlled machines and structures. They are based on papers presented at the International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines held in Vienna in September 2015. The workshop continues a series of international workshops held in Linz (2008) and St. Petersburg (2010).

  16. The Effect of Unreliable Machine for Two Echelons Deteriorating Inventory Model

    Directory of Open Access Journals (Sweden)

    I Nyoman Sutapa

    2014-01-01

    Full Text Available Many researchers have developed two echelons supply chain, however only few of them consider deteriorating items and unreliable machine in their models In this paper, we develop an inventory deteriorating model for two echelons supply chain with unreliable machine. The unreliable machine time is assumed uniformly distributed. The model is solved using simple heuristic since a closed form model can not be derived. A numerical example is used to show how the model works. A sensitivity analysis is conducted to show effect of different lost sales cost in the model. The result shows that increasing lost sales cost will increase both manufacture and buyer costs however buyer’s total cost increase higher than manufacture’s total cost as manufacture’s machine is more unreliable.

  17. Why Translation Is Difficult

    DEFF Research Database (Denmark)

    Carl, Michael; Schaeffer, Moritz Jonas

    2017-01-01

    The paper develops a definition of translation literality that is based on the syntactic and semantic similarity of the source and the target texts. We provide theoretical and empirical evidence that absolute literal translations are easy to produce. Based on a multilingual corpus of alternative...... translations we investigate the effects of cross-lingual syntactic and semantic distance on translation production times and find that non-literality makes from-scratch translation and post-editing difficult. We show that statistical machine translation systems encounter even more difficulties with non-literality....

  18. Comparative study for different statistical models to optimize cutting parameters of CNC end milling machines

    International Nuclear Information System (INIS)

    El-Berry, A.; El-Berry, A.; Al-Bossly, A.

    2010-01-01

    In machining operation, the quality of surface finish is an important requirement for many work pieces. Thus, that is very important to optimize cutting parameters for controlling the required manufacturing quality. Surface roughness parameter (Ra) in mechanical parts depends on turning parameters during the turning process. In the development of predictive models, cutting parameters of feed, cutting speed, depth of cut, are considered as model variables. For this purpose, this study focuses on comparing various machining experiments which using CNC vertical machining center, work pieces was aluminum 6061. Multiple regression models are used to predict the surface roughness at different experiments.

  19. Spectral and scattering theory for translation invariant models in quantum field theory

    DEFF Research Database (Denmark)

    Rasmussen, Morten Grud

    This thesis is concerned with a large class of massive translation invariant models in quantum field theory, including the Nelson model and the Fröhlich polaron. The models in the class describe a matter particle, e.g. a nucleon or an electron, linearly coupled to a second quantised massive scalar...... by the physically relevant choices. The translation invariance implies that the Hamiltonian may be decomposed into a direct integral over the space of total momentum where the fixed momentum fiber Hamiltonians are given by , where denotes total momentum and is the Segal field operator. The fiber Hamiltonians...

  20. Mathematical model of five-phase induction machine

    Czech Academy of Sciences Publication Activity Database

    Schreier, Luděk; Bendl, Jiří; Chomát, Miroslav

    2011-01-01

    Roč. 56, č. 2 (2011), s. 141-157 ISSN 0001-7043 R&D Projects: GA ČR GA102/08/0424 Institutional research plan: CEZ:AV0Z20570509 Keywords : five-phase induction machines * symmetrical components * spatial wave harmonics Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering

  1. A note on the translation of conceptual data models into description logics: disjointness and covering assumptions

    CSIR Research Space (South Africa)

    Casini, G

    2012-10-01

    Full Text Available possibilities for conceptual data modeling. It also raises the question of how existing conceptual models using ER, UML or ORM could be translated into Description Logics (DLs), a family of logics that have proved to be particularly appropriate for formalizing...

  2. Murine models of osteosarcoma: A piece of the translational puzzle.

    Science.gov (United States)

    Walia, Mannu K; Castillo-Tandazo, Wilson; Mutsaers, Anthony J; Martin, Thomas John; Walkley, Carl R

    2018-06-01

    Osteosarcoma (OS) is the most common cancer of bone in children and young adults. Despite extensive research efforts, there has been no significant improvement in patient outcome for many years. An improved understanding of the biology of this cancer and how genes frequently mutated contribute to OS may help improve outcomes for patients. While our knowledge of the mutational burden of OS is approaching saturation, our understanding of how these mutations contribute to OS initiation and maintenance is less clear. Murine models of OS have now been demonstrated to be highly valid recapitulations of human OS. These models were originally based on the frequent disruption of p53 and Rb in familial OS syndromes, which are also common mutations in sporadic OS. They have been applied to significantly improve our understanding about the functions of recurrently mutated genes in disease. The murine models can be used as a platform for preclinical testing and identifying new therapeutic targets, in addition to testing the role of additional mutations in vivo. Most recently these models have begun to be used for discovery based approaches and screens, which hold significant promise in furthering our understanding of the genetic and therapeutic sensitivities of OS. In this review, we discuss the mouse models of OS that have been reported in the last 3-5 years and newly identified pathways from these studies. Finally, we discuss the preclinical utilization of the mouse models of OS for identifying and validating actionable targets to improve patient outcome. © 2017 Wiley Periodicals, Inc.

  3. Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

    KAUST Repository

    Magana-Mora, Arturo

    2017-04-29

    Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often result in extremely intricate ML models. Frequently, these models may have a poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first is to develop a generalizable classification methodology able to systematically derive competitive models despite the complexity and nature of the data. Although several algorithms for the induction of classification models have been proposed, the algorithms are data dependent. Consequently, we developed OmniGA, a novel and generalizable framework that uses different classification models in a treeXlike decision structure, along with a parallel GA for the optimization of the OmniGA structure. Results show that OmniGA consistently outperformed existing commonly used classification models. The second challenge is the prediction of translation initiation sites (TIS) in plants genomic DNA. We performed a statistical analysis of the genomic DNA and proposed a new set of discriminant features for this problem. We developed a wrapper method based on GAs for selecting an optimal feature subset, which, in conjunction with a classification model, produced the most accurate framework for the recognition of TIS in plants. Finally, results demonstrate that despite the evolutionary distance between different plants, our approach successfully identified conserved genomic elements that may serve as the starting point for the development of a generic model for prediction of TIS in eukaryotic organisms. Finally, the third challenge is the accurate prediction of polyadenylation signals in human genomic DNA. To achieve

  4. Non-linear hybrid control oriented modelling of a digital displacement machine

    DEFF Research Database (Denmark)

    Pedersen, Niels Henrik; Johansen, Per; Andersen, Torben O.

    2017-01-01

    Proper feedback control of digital fluid power machines (Pressure, flow, torque or speed control) requires a control oriented model, from where the system dynamics can be analyzed, stability can be proven and design criteria can be specified. The development of control oriented models for hydraulic...... Digital Displacement Machines (DDM) is complicated due to non-smooth machine behavior, where the dynamics comprises both analog, digital and non-linear elements. For a full stroke operated DDM the power throughput is altered in discrete levels based on the ratio of activated pressure chambers....... In this paper, a control oriented hybrid model is established, which combines the continuous non-linear pressure chamber dynamics and the discrete shaft position dependent activation of the pressure chambers. The hybrid machine model is further extended to describe the dynamics of a Digital Fluid Power...

  5. Modelling of human-machine interaction in equipment design of manufacturing cells

    Science.gov (United States)

    Cochran, David S.; Arinez, Jorge F.; Collins, Micah T.; Bi, Zhuming

    2017-08-01

    This paper proposes a systematic approach to model human-machine interactions (HMIs) in supervisory control of machining operations; it characterises the coexistence of machines and humans for an enterprise to balance the goals of automation/productivity and flexibility/agility. In the proposed HMI model, an operator is associated with a set of behavioural roles as a supervisor for multiple, semi-automated manufacturing processes. The model is innovative in the sense that (1) it represents an HMI based on its functions for process control but provides the flexibility for ongoing improvements in the execution of manufacturing processes; (2) it provides a computational tool to define functional requirements for an operator in HMIs. The proposed model can be used to design production systems at different levels of an enterprise architecture, particularly at the machine level in a production system where operators interact with semi-automation to accomplish the goal of 'autonomation' - automation that augments the capabilities of human beings.

  6. Current Translational Research and Murine Models For Duchenne Muscular Dystrophy

    Science.gov (United States)

    Rodrigues, Merryl; Echigoya, Yusuke; Fukada, So-ichiro; Yokota, Toshifumi

    2016-01-01

    Duchenne muscular dystrophy (DMD) is an X-linked genetic disorder characterized by progressive muscle degeneration. Mutations in the DMD gene result in the absence of dystrophin, a protein required for muscle strength and stability. Currently, there is no cure for DMD. Since murine models are relatively easy to genetically manipulate, cost effective, and easily reproducible due to their short generation time, they have helped to elucidate the pathobiology of dystrophin deficiency and to assess therapies for treating DMD. Recently, several murine models have been developed by our group and others to be more representative of the human DMD mutation types and phenotypes. For instance, mdx mice on a DBA/2 genetic background, developed by Fukada et al., have lower regenerative capacity and exhibit very severe phenotype. Cmah-deficient mdx mice display an accelerated disease onset and severe cardiac phenotype due to differences in glycosylation between humans and mice. Other novel murine models include mdx52, which harbors a deletion mutation in exon 52, a hot spot region in humans, and dystrophin/utrophin double-deficient (dko), which displays a severe dystrophic phenotype due the absence of utrophin, a dystrophin homolog. This paper reviews the pathological manifestations and recent therapeutic developments in murine models of DMD such as standard mdx (C57BL/10), mdx on C57BL/6 background (C57BL/6-mdx), mdx52, dystrophin/utrophin double-deficient (dko), mdxβgeo, Dmd-null, humanized DMD (hDMD), mdx on DBA/2 background (DBA/2-mdx), Cmah-mdx, and mdx/mTRKO murine models. PMID:27854202

  7. Conditions for Model Matching of Switched Asynchronous Sequential Machines with Output Feedback

    OpenAIRE

    Jung–Min Yang

    2016-01-01

    Solvability of the model matching problem for input/output switched asynchronous sequential machines is discussed in this paper. The control objective is to determine the existence condition and design algorithm for a corrective controller that can match the stable-state behavior of the closed-loop system to that of a reference model. Switching operations and correction procedures are incorporated using output feedback so that the controlled switched machine can show the ...

  8. Rotating magnetizations in electrical machines: Measurements and modeling

    Directory of Open Access Journals (Sweden)

    Andreas Thul

    2018-05-01

    Full Text Available This paper studies the magnetization process in electrical steel sheets for rotational magnetizations as they occur in the magnetic circuit of electrical machines. A four-pole rotational single sheet tester is used to generate the rotating magnetic flux inside the sample. A field-oriented control scheme is implemented to improve the control performance. The magnetization process of different non-oriented materials is analyzed and compared.

  9. Behavioral Modeling for Mental Health using Machine Learning Algorithms.

    Science.gov (United States)

    Srividya, M; Mohanavalli, S; Bhalaji, N

    2018-04-03

    Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.

  10. Rotating magnetizations in electrical machines: Measurements and modeling

    Science.gov (United States)

    Thul, Andreas; Steentjes, Simon; Schauerte, Benedikt; Klimczyk, Piotr; Denke, Patrick; Hameyer, Kay

    2018-05-01

    This paper studies the magnetization process in electrical steel sheets for rotational magnetizations as they occur in the magnetic circuit of electrical machines. A four-pole rotational single sheet tester is used to generate the rotating magnetic flux inside the sample. A field-oriented control scheme is implemented to improve the control performance. The magnetization process of different non-oriented materials is analyzed and compared.

  11. A Formal Model of Ambiguity and its Applications in Machine Translation

    Science.gov (United States)

    2010-01-01

    structure indicates linguisti- cally implausible segmentation that might be generated using dictionary - driven approaches...derivation. As was done in the monolingual case, the functions LHS, RHSi, RHSo and υ can be extended to a derivation δ. D(q) where q ∈V denotes the... monolingual parses. My algorithm runs more efficiently than O(n6) with many grammars (including those that required using heuristic search with other parsers

  12. Domain Adaptation of Translation Models for Multilingual Applications

    Science.gov (United States)

    2009-04-01

    employed. In the past two years, domain adaptation for NLP tasks has become an active research area [3, 38, 25, 23]. New domain adaptation tasks have...and unlabeled data in the target domain and learn a mixture model to adapt from the source domain. Other NLP tasks where domain adaptation has been...evaluation forum, http://www.clef-campaign.org. [13] K. Darwish and D. Oard, CLIR experiments at maryland for TREC-2002: Evidence combination for arabic

  13. Assessing the Quality of Persian Translation of Kite Runner based on House’s (2014 Functional Pragmatic Model

    Directory of Open Access Journals (Sweden)

    Fateme Kargarzadeh

    2017-03-01

    Full Text Available Translation quality assessment is at the heart of any theory of translation. It is used in the academic or teaching contexts to judge translations, to discuss their merits and demerits and to suggest solutions. However, literary translations needs more consideration in terms of quality and clarity as it is widely read form of translation. In this respect, Persian literary translation of Kite Runner was taken for investigation based on House’s (2014 functional pragmatic model of translation quality assessment. To this end, around 100 pages from the beginning of both English and Persian versions of the novel were selected and compared. Using House’s model, the profile of the source text register was created and the genre was recognized. The source text profile was compared to the translation text profile. The results were minute mismatches in field, tenor, and mode which accounted for as overt erroneous expressions and leading matches which were accounted for as covert translation. The mismatches were some mistranslations of tenses and selection of inappropriate meanings for the lexicon. Since the informal and culture specific terms were transferred thoroughly, the culture filter was not applied. Besides, as the translation was a covert one. The findings of the study have implications for translators, researchers and translator trainers.

  14. Equivalent model of a dually-fed machine for electric drive control systems

    Science.gov (United States)

    Ostrovlyanchik, I. Yu; Popolzin, I. Yu

    2018-05-01

    The article shows that the mathematical model of a dually-fed machine is complicated because of the presence of a controlled voltage source in the rotor circuit. As a method of obtaining a mathematical model, the method of a generalized two-phase electric machine is applied and a rotating orthogonal coordinate system is chosen that is associated with the representing vector of a stator current. In the chosen coordinate system in the operator form the differential equations of electric equilibrium for the windings of the generalized machine (the Kirchhoff equation) are written together with the expression for the moment, which determines the electromechanical energy transformation in the machine. Equations are transformed so that they connect the currents of the windings, that determine the moment of the machine, and the voltages on these windings. The structural diagram of the machine is assigned to the written equations. Based on the written equations and accepted assumptions, expressions were obtained for the balancing the EMF of windings, and on the basis of these expressions an equivalent mathematical model of a dually-fed machine is proposed, convenient for use in electric drive control systems.

  15. Prediction Model of Machining Failure Trend Based on Large Data Analysis

    Science.gov (United States)

    Li, Jirong

    2017-12-01

    The mechanical processing has high complexity, strong coupling, a lot of control factors in the machining process, it is prone to failure, in order to improve the accuracy of fault detection of large mechanical equipment, research on fault trend prediction requires machining, machining fault trend prediction model based on fault data. The characteristics of data processing using genetic algorithm K mean clustering for machining, machining feature extraction which reflects the correlation dimension of fault, spectrum characteristics analysis of abnormal vibration of complex mechanical parts processing process, the extraction method of the abnormal vibration of complex mechanical parts processing process of multi-component spectral decomposition and empirical mode decomposition Hilbert based on feature extraction and the decomposition results, in order to establish the intelligent expert system for the data base, combined with large data analysis method to realize the machining of the Fault trend prediction. The simulation results show that this method of fault trend prediction of mechanical machining accuracy is better, the fault in the mechanical process accurate judgment ability, it has good application value analysis and fault diagnosis in the machining process.

  16. A comparison of machine learning and Bayesian modelling for molecular serotyping.

    Science.gov (United States)

    Newton, Richard; Wernisch, Lorenz

    2017-08-11

    Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological

  17. Translation from mathematical model to data driven knowledge

    OpenAIRE

    Boixareu Fiol, Margarita

    2017-01-01

    Prove if with the acquisition of new samples of data the knowledge we can obtain is more accurate. The projects centers in the data obtained from the patient-specific calibration of a computer simulation of a human heart. Neural networks are used to understand the relation input-output of the data and they are compared with a physical model that relates them. Input data are some parameters of the heart and the output data is the elastance of the heart (variation of pressure/ variation of volu...

  18. Multi products single machine economic production quantity model with multiple batch size

    Directory of Open Access Journals (Sweden)

    Ata Allah Taleizadeh

    2011-04-01

    Full Text Available In this paper, a multi products single machine economic order quantity model with discrete delivery is developed. A unique cycle length is considered for all produced items with an assumption that all products are manufactured on a single machine with a limited capacity. The proposed model considers different items such as production, setup, holding, and transportation costs. The resulted model is formulated as a mixed integer nonlinear programming model. Harmony search algorithm, extended cutting plane and particle swarm optimization methods are used to solve the proposed model. Two numerical examples are used to analyze and to evaluate the performance of the proposed model.

  19. Statistical Machine Translation of Japanese

    Science.gov (United States)

    2007-03-01

    hiragana and katakana) syllabaries…………………….. 20 3.2 Sample Japanese sentence showing kanji and kana……………………... 21 3.5 Japanese formality example...syllabary. 19 Figure 3.1. Japanese kana syllabaries, hiragana for native Japanese words, word endings, and particles, and katakana for foreign...Figure 3.2. Simple Japanese sentence showing the use of kanji, hiragana , and katakana. Kanji is used for nouns and verb, adjective, and

  20. The companion dog as a unique translational model for aging.

    Science.gov (United States)

    Mazzatenta, Andrea; Carluccio, Augusto; Robbe, Domenico; Giulio, Camillo Di; Cellerino, Alessandro

    2017-10-01

    The dog is a unique species due to its wide variation among breeds in terms of size, morphology, behaviour and lifespan, coupled with a genetic structure that facilitates the dissection of the genetic architecture that controls these traits. Dogs and humans co-evolved and share recent evolutionary selection processes, such as adaptation to digest starch-rich diets. Many diseases of the dog have a human counterpart, and notably Alzheimer's disease, which is otherwise difficult to model in other organisms. Unlike laboratory animals, companion dogs share the human environment and lifestyle, are exposed to the same pollutants, and are faced with pathogens and infections. Dogs represented a very useful model to understand the relationship between size, insulin-like growth factor-1 genetic variation and lifespan, and have been used to test the effects of dietary restriction and immunotherapy for Alzheimer's disease. Very recently, rapamycin was tested in companion dogs outside the laboratory, and this approach where citizens are involved in research aimed at the benefit of dog welfare might become a game changer in geroscience. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. A modeling method for hybrid energy behaviors in flexible machining systems

    International Nuclear Information System (INIS)

    Li, Yufeng; He, Yan; Wang, Yan; Wang, Yulin; Yan, Ping; Lin, Shenlong

    2015-01-01

    Increasingly environmental and economic pressures have led to great concerns regarding the energy consumption of machining systems. Understanding energy behaviors of flexible machining systems is a prerequisite for improving energy efficiency of these systems. This paper proposes a modeling method to predict energy behaviors in flexible machining systems. The hybrid energy behaviors not only depend on the technical specification related of machine tools and workpieces, but are significantly affected by individual production scenarios. In the method, hybrid energy behaviors are decomposed into Structure-related energy behaviors, State-related energy behaviors, Process-related energy behaviors and Assignment-related energy behaviors. The modeling method for the hybrid energy behaviors is proposed based on Colored Timed Object-oriented Petri Net (CTOPN). The former two types of energy behaviors are modeled by constructing the structure of CTOPN, whist the latter two types of behaviors are simulated by applying colored tokens and associated attributes. Machining on two workpieces in the experimental workshop were undertaken to verify the proposed modeling method. The results showed that the method can provide multi-perspective transparency on energy consumption related to machine tools, workpieces as well as production management, and is particularly suitable for flexible manufacturing system when frequent changes in machining systems are often encountered. - Highlights: • Energy behaviors in flexible machining systems are modeled in this paper. • Hybrid characteristics of energy behaviors are examined from multiple viewpoints. • Flexible modeling method CTOPN is used to predict the hybrid energy behaviors. • This work offers a multi-perspective transparency on energy consumption

  2. A Study of Synchronous Machine Model Implementations in Matlab/Simulink Simulations for New and Renewable Energy Systems

    DEFF Research Database (Denmark)

    Chen, Zhe; Blaabjerg, Frede; Iov, Florin

    2005-01-01

    A direct phase model of synchronous machines implemented in MA TLAB/SIMULINK is presented. The effects of the machine saturation have been included. Simulation studies are performed under various conditions. It has been demonstrated that the MATLAB/SIMULINK is an effective tool to study the compl...... synchronous machine and the implemented model could be used for studies of various applications of synchronous machines including in renewable and DG generation systems....

  3. Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

    Science.gov (United States)

    Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

    2003-01-01

    Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

  4. Vacation model for Markov machine repair problem with two heterogeneous unreliable servers and threshold recovery

    Science.gov (United States)

    Jain, Madhu; Meena, Rakesh Kumar

    2018-03-01

    Markov model of multi-component machining system comprising two unreliable heterogeneous servers and mixed type of standby support has been studied. The repair job of broken down machines is done on the basis of bi-level threshold policy for the activation of the servers. The server returns back to render repair job when the pre-specified workload of failed machines is build up. The first (second) repairman turns on only when the work load of N1 (N2) failed machines is accumulated in the system. The both servers may go for vacation in case when all the machines are in good condition and there are no pending repair jobs for the repairmen. Runge-Kutta method is implemented to solve the set of governing equations used to formulate the Markov model. Various system metrics including the mean queue length, machine availability, throughput, etc., are derived to determine the performance of the machining system. To provide the computational tractability of the present investigation, a numerical illustration is provided. A cost function is also constructed to determine the optimal repair rate of the server by minimizing the expected cost incurred on the system. The hybrid soft computing method is considered to develop the adaptive neuro-fuzzy inference system (ANFIS). The validation of the numerical results obtained by Runge-Kutta approach is also facilitated by computational results generated by ANFIS.

  5. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

    Science.gov (United States)

    Luo, Wei; Phung, Dinh; Tran, Truyen; Gupta, Sunil; Rana, Santu; Karmakar, Chandan; Shilton, Alistair; Yearwood, John; Dimitrova, Nevenka; Ho, Tu Bao; Venkatesh, Svetha; Berk, Michael

    2016-12-16

    As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. ©Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.12.2016.

  6. Anatomy and bronchoscopy of the porcine lung. A model for translational respiratory medicine.

    LENUS (Irish Health Repository)

    Judge, Eoin P

    2014-09-01

    The porcine model has contributed significantly to biomedical research over many decades. The similar size and anatomy of pig and human organs make this model particularly beneficial for translational research in areas such as medical device development, therapeutics and xenotransplantation. In recent years, a major limitation with the porcine model was overcome with the successful generation of gene-targeted pigs and the publication of the pig genome. As a result, the role of this model is likely to become even more important. For the respiratory medicine field, the similarities between pig and human lungs give the porcine model particular potential for advancing translational medicine. An increasing number of lung conditions are being studied and modeled in the pig. Genetically modified porcine models of cystic fibrosis have been generated that, unlike mouse models, develop lung disease similar to human cystic fibrosis. However, the scientific literature relating specifically to porcine lung anatomy and airway histology is limited and is largely restricted to veterinary literature and textbooks. Furthermore, methods for in vivo lung procedures in the pig are rarely described. The aims of this review are to collate the disparate literature on porcine lung anatomy, histology, and microbiology; to provide a comparison with the human lung; and to describe appropriate bronchoscopy procedures for the pig lungs to aid clinical researchers working in the area of translational respiratory medicine using the porcine model.

  7. A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

    Science.gov (United States)

    Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

    2017-08-01

    In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

  8. Recent Advances in Translational Magnetic Resonance Imaging in Animal Models of Stress and Depression.

    Science.gov (United States)

    McIntosh, Allison L; Gormley, Shane; Tozzi, Leonardo; Frodl, Thomas; Harkin, Andrew

    2017-01-01

    Magnetic resonance imaging (MRI) is a valuable translational tool that can be used to investigate alterations in brain structure and function in both patients and animal models of disease. Regional changes in brain structure, functional connectivity, and metabolite concentrations have been reported in depressed patients, giving insight into the networks and brain regions involved, however preclinical models are less well characterized. The development of more effective treatments depends upon animal models that best translate to the human condition and animal models may be exploited to assess the molecular and cellular alterations that accompany neuroimaging changes. Recent advances in preclinical imaging have facilitated significant developments within the field, particularly relating to high resolution structural imaging and resting-state functional imaging which are emerging techniques in clinical research. This review aims to bring together the current literature on preclinical neuroimaging in animal models of stress and depression, highlighting promising avenues of research toward understanding the pathological basis of this hugely prevalent disorder.

  9. Computational Model for Impact-Resisting Critical Thickness of High-Speed Machine Outer Protective Plate

    Science.gov (United States)

    Wu, Huaying; Wang, Li Zhong; Wang, Yantao; Yuan, Xiaolei

    2018-05-01

    The blade or surface grinding blade of the hypervelocity grinding wheel may be damaged due to too high rotation rate of the spindle of the machine and then fly out. Its speed as a projectile may severely endanger the field persons. Critical thickness model of the protective plate of the high-speed machine is studied in this paper. For easy analysis, the shapes of the possible impact objects flying from the high-speed machine are simplified as sharp-nose model, ball-nose model and flat-nose model. Whose front ending shape to represent point, line and surface contacting. Impact analysis based on J-C model is performed for the low-carbon steel plate with different thicknesses in this paper. One critical thickness computational model for the protective plate of high-speed machine is established according to the damage characteristics of the thin plate to get relation among plate thickness and mass, shape and size and impact speed of impact object. The air cannon is used for impact test. The model accuracy is validated. This model can guide identification of the thickness of single-layer outer protective plate of a high-speed machine.

  10. Bayesian networks modeling for thermal error of numerical control machine tools

    Institute of Scientific and Technical Information of China (English)

    Xin-hua YAO; Jian-zhong FU; Zi-chen CHEN

    2008-01-01

    The interaction between the heat source location,its intensity,thermal expansion coefficient,the machine system configuration and the running environment creates complex thermal behavior of a machine tool,and also makes thermal error prediction difficult.To address this issue,a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented.The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques.Due to the effective combination of domain knowledge and sampled data,the BN method could adapt to the change of running state of machine,and obtain satisfactory prediction accuracy.Ex-periments on spindle thermal deformation were conducted to evaluate the modeling performance.Experimental results indicate that the BN method performs far better than the least squares(LS)analysis in terms of modeling estimation accuracy.

  11. Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools

    Science.gov (United States)

    Luo, Zhonghui; Peng, Bin; Xiao, Qijun; Bai, Lu

    2018-03-01

    Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.

  12. Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

    OpenAIRE

    Shan, Min

    2017-01-01

    With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

  13. Early-life stress origins of gastrointestinal disease: animal models, intestinal pathophysiology, and translational implications.

    Science.gov (United States)

    Pohl, Calvin S; Medland, Julia E; Moeser, Adam J

    2015-12-15

    Early-life stress and adversity are major risk factors in the onset and severity of gastrointestinal (GI) disease in humans later in life. The mechanisms by which early-life stress leads to increased GI disease susceptibility in adult life remain poorly understood. Animal models of early-life stress have provided a foundation from which to gain a more fundamental understanding of this important GI disease paradigm. This review focuses on animal models of early-life stress-induced GI disease, with a specific emphasis on translational aspects of each model to specific human GI disease states. Early postnatal development of major GI systems and the consequences of stress on their development are discussed in detail. Relevant translational differences between species and models are highlighted. Copyright © 2015 the American Physiological Society.

  14. Early-life stress origins of gastrointestinal disease: animal models, intestinal pathophysiology, and translational implications

    Science.gov (United States)

    Pohl, Calvin S.; Medland, Julia E.

    2015-01-01

    Early-life stress and adversity are major risk factors in the onset and severity of gastrointestinal (GI) disease in humans later in life. The mechanisms by which early-life stress leads to increased GI disease susceptibility in adult life remain poorly understood. Animal models of early-life stress have provided a foundation from which to gain a more fundamental understanding of this important GI disease paradigm. This review focuses on animal models of early-life stress-induced GI disease, with a specific emphasis on translational aspects of each model to specific human GI disease states. Early postnatal development of major GI systems and the consequences of stress on their development are discussed in detail. Relevant translational differences between species and models are highlighted. PMID:26451004

  15. An Investigation of Pun Translatability in English Translations of Sa'di's Ghazals Based on Delabastita's Proposed Model

    Science.gov (United States)

    Koochacki, Fahime

    2016-01-01

    The rich cultural connotations behind puns and the distinctive features of the puns' form, sound and meanings pose great challenges to the translator. Furthermore, given puns' non-negligible effects in Persian literary texts, it has been the aim of the present study to analyze and measure how puns in Sa'di's Ghazals have actually been treated in…

  16. Measuring and Modelling Delays in Robot Manipulators for Temporally Precise Control using Machine Learning

    DEFF Research Database (Denmark)

    Andersen, Thomas Timm; Amor, Heni Ben; Andersen, Nils Axel

    2015-01-01

    and separate. In this paper, we present a data-driven methodology for separating and modelling inherent delays during robot control. We show how both actuation and response delays can be modelled using modern machine learning methods. The resulting models can be used to predict the delays as well...

  17. An Introduction to Topic Modeling as an Unsupervised Machine Learning Way to Organize Text Information

    Science.gov (United States)

    Snyder, Robin M.

    2015-01-01

    The field of topic modeling has become increasingly important over the past few years. Topic modeling is an unsupervised machine learning way to organize text (or image or DNA, etc.) information such that related pieces of text can be identified. This paper/session will present/discuss the current state of topic modeling, why it is important, and…

  18. A hybrid analytical model for open-circuit field calculation of multilayer interior permanent magnet machines

    Science.gov (United States)

    Zhang, Zhen; Xia, Changliang; Yan, Yan; Geng, Qiang; Shi, Tingna

    2017-08-01

    Due to the complicated rotor structure and nonlinear saturation of rotor bridges, it is difficult to build a fast and accurate analytical field calculation model for multilayer interior permanent magnet (IPM) machines. In this paper, a hybrid analytical model suitable for the open-circuit field calculation of multilayer IPM machines is proposed by coupling the magnetic equivalent circuit (MEC) method and the subdomain technique. In the proposed analytical model, the rotor magnetic field is calculated by the MEC method based on the Kirchhoff's law, while the field in the stator slot, slot opening and air-gap is calculated by subdomain technique based on the Maxwell's equation. To solve the whole field distribution of the multilayer IPM machines, the coupled boundary conditions on the rotor surface are deduced for the coupling of the rotor MEC and the analytical field distribution of the stator slot, slot opening and air-gap. The hybrid analytical model can be used to calculate the open-circuit air-gap field distribution, back electromotive force (EMF) and cogging torque of multilayer IPM machines. Compared with finite element analysis (FEA), it has the advantages of faster modeling, less computation source occupying and shorter time consuming, and meanwhile achieves the approximate accuracy. The analytical model is helpful and applicable for the open-circuit field calculation of multilayer IPM machines with any size and pole/slot number combination.

  19. Multiphysics Modeling of an Permanent Magnet Synchronous Machine

    Directory of Open Access Journals (Sweden)

    MARTIS Claudia

    2012-10-01

    Full Text Available This paper analyzes the noise and vibration in PMSMs. There are three types of vibrations in electrical machines: electromagnetic,mechanical and aerodynamic. Electromagnetic force are the main cause of noise and vibration in PMSMs. It is very important to calculate precisely the natural frequencies of the stator system. If oneradial force (which are the main cause for electromagnetic vibration has the frequency close to the natural frequency of the stator system for the same order of vibrational mode, then this force canproduce dangerous vibration in the stator system. The natural frequencies for a stator system of a PMSM have been calculated. Finally a Structural Analysis has been made , pointing out the radialdisplacement and stress for the chosen PMSM .

  20. Product Quality Modelling Based on Incremental Support Vector Machine

    International Nuclear Information System (INIS)

    Wang, J; Zhang, W; Qin, B; Shi, W

    2012-01-01

    Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

  1. A Transparent Translation from Legacy System Model into Common Information Model: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Simpson, Jeffrey [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2018-04-27

    Advance in smart grid is forcing utilities towards better monitoring, control and analysis of distribution systems, and requires extensive cyber-based intelligent systems and applications to realize various functionalities. The ability of systems, or components within systems, to interact and exchange services or information with each other is the key to the success of smart grid technologies, and it requires efficient information exchanging and data sharing infrastructure. The Common Information Model (CIM) is a standard that allows different applications to exchange information about an electrical system, and it has become a widely accepted solution for information exchange among different platforms and applications. However, most existing legacy systems are not developed using CIM, but using their own languages. Integrating such legacy systems is a challenge for utilities, and the appropriate utilization of the integrated legacy systems is even more intricate. Thus, this paper has developed an approach and open-source tool in order to translate legacy system models into CIM format. The developed tool is tested for a commercial distribution management system and simulation results have proved its effectiveness.

  2. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

    Science.gov (United States)

    Zeng, Xueqiang; Luo, Gang

    2017-12-01

    Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

  3. A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model

    Directory of Open Access Journals (Sweden)

    Yanbing Liu

    2014-01-01

    Full Text Available Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM, the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.

  4. Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

    Science.gov (United States)

    Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

    2018-04-01

    A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

  5. A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

    Directory of Open Access Journals (Sweden)

    Jian Chai

    2015-01-01

    Full Text Available This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search, PSO (particle swarm optimization, and GA (genetic algorithm. Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.

  6. A rule-based approach to model checking of UML state machines

    Science.gov (United States)

    Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz

    2016-12-01

    In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.

  7. Translating Institutional Templates: A Historical Account of the Consequences of Importing Policing Models into Argentina

    Directory of Open Access Journals (Sweden)

    Matías Dewey

    2017-01-01

    Full Text Available This article focuses on the translation of the French and English law enforcement models into Argentina and analyzes its consequences in terms of social order. Whereas in the former two models the judiciary and police institutions originated in large-scale processes of historical consolidation, in the latter these institutions were implanted without the antecedents present in their countries of origin. The empirical references are Argentine police institutions, particularly the police of the Buenos Aires Province, observed at two moments in which the institutional import was particularly intense: towards the end of the nineteenth and beginning of the twentieth centuries, and at the end of the twentieth century. By way of tracing these processes of police constitution and reform, we show how new models of law enforcement and policing interacted with indigenous political structures and cultural frames, as well as how this constellation produced a social order in which legality and illegality are closely interwoven. The article is an attempt to go beyond the common observations regarding how an imported model failed; instead, it dissects the effects the translation actually produced and how the translated models transform into resources that reshape the new social order. A crucial element, the article shows, is that these resources can be instrumentalized according to »idiosyncrasies«, interests, and quotas of power.

  8. Respiratory nanoparticle-based vaccines and challenges associated with animal models and translation.

    Science.gov (United States)

    Renukaradhya, Gourapura J; Narasimhan, Balaji; Mallapragada, Surya K

    2015-12-10

    Vaccine development has had a huge impact on human health. However, there is a significant need to develop efficacious vaccines for several existing as well as emerging respiratory infectious diseases. Several challenges need to be overcome to develop efficacious vaccines with translational potential. This review focuses on two aspects to overcome some barriers - 1) the development of nanoparticle-based vaccines, and 2) the choice of suitable animal models for respiratory infectious diseases that will allow for translation. Nanoparticle-based vaccines, including subunit vaccines involving synthetic and/or natural polymeric adjuvants and carriers, as well as those based on virus-like particles offer several key advantages to help overcome the barriers to effective vaccine development. These include the ability to deliver combinations of antigens, target the vaccine formulation to specific immune cells, enable cross-protection against divergent strains, act as adjuvants or immunomodulators, allow for sustained release of antigen, enable single dose delivery, and potentially obviate the cold chain. While mouse models have provided several important insights into the mechanisms of infectious diseases, they are often a limiting step in translation of new vaccines to the clinic. An overview of different animal models involved in vaccine research for respiratory infections, with advantages and disadvantages of each model, is discussed. Taken together, advances in nanotechnology, combined with the right animal models for evaluating vaccine efficacy, has the potential to revolutionize vaccine development for respiratory infections. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. A hybrid analytical model for open-circuit field calculation of multilayer interior permanent magnet machines

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Zhen [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Xia, Changliang [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Tianjin Engineering Center of Electric Machine System Design and Control, Tianjin 300387 (China); Yan, Yan, E-mail: yanyan@tju.edu.cn [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Geng, Qiang [Tianjin Engineering Center of Electric Machine System Design and Control, Tianjin 300387 (China); Shi, Tingna [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

    2017-08-01

    Highlights: • A hybrid analytical model is developed for field calculation of multilayer IPM machines. • The rotor magnetic field is calculated by the magnetic equivalent circuit method. • The field in the stator and air-gap is calculated by subdomain technique. • The magnetic scalar potential on rotor surface is modeled as trapezoidal distribution. - Abstract: Due to the complicated rotor structure and nonlinear saturation of rotor bridges, it is difficult to build a fast and accurate analytical field calculation model for multilayer interior permanent magnet (IPM) machines. In this paper, a hybrid analytical model suitable for the open-circuit field calculation of multilayer IPM machines is proposed by coupling the magnetic equivalent circuit (MEC) method and the subdomain technique. In the proposed analytical model, the rotor magnetic field is calculated by the MEC method based on the Kirchhoff’s law, while the field in the stator slot, slot opening and air-gap is calculated by subdomain technique based on the Maxwell’s equation. To solve the whole field distribution of the multilayer IPM machines, the coupled boundary conditions on the rotor surface are deduced for the coupling of the rotor MEC and the analytical field distribution of the stator slot, slot opening and air-gap. The hybrid analytical model can be used to calculate the open-circuit air-gap field distribution, back electromotive force (EMF) and cogging torque of multilayer IPM machines. Compared with finite element analysis (FEA), it has the advantages of faster modeling, less computation source occupying and shorter time consuming, and meanwhile achieves the approximate accuracy. The analytical model is helpful and applicable for the open-circuit field calculation of multilayer IPM machines with any size and pole/slot number combination.

  10. Novel Simplified Model for Asynchronous Machine with Consideration of Frequency Characteristic

    Directory of Open Access Journals (Sweden)

    Changchun Cai

    2014-01-01

    Full Text Available The frequency characteristic of electric equipment should be considered in the digital simulation of power systems. The traditional asynchronous machine third-order transient model excludes not only the stator transient but also the frequency characteristics, thus decreasing the application sphere of the model and resulting in a large error under some special conditions. Based on the physical equivalent circuit and Park model for asynchronous machines, this study proposes a novel asynchronous third-order transient machine model with consideration of the frequency characteristic. In the new definitions of variables, the voltages behind the reactance are redefined as the linear equation of flux linkage. In this way, the rotor voltage equation is not associated with the derivative terms of frequency. However, the derivative terms of frequency should not always be ignored in the application of the traditional third-order transient model. Compared with the traditional third-order transient model, the novel simplified third-order transient model with consideration of the frequency characteristic is more accurate without increasing the order and complexity. Simulation results show that the novel third-order transient model for the asynchronous machine is suitable and effective and is more accurate than the widely used traditional simplified third-order transient model under some special conditions with drastic frequency fluctuations.

  11. Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

    Science.gov (United States)

    Luo, Gang; Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

    2017-08-29

    To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient's weight kept rising in the past year). This process becomes infeasible with limited budgets. This study's goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care

  12. Exaggerated Cap-Dependent Translation as a Mechanism for Corticostriatal Dysfunction in Fragile X Syndrome Model Mice

    Science.gov (United States)

    2017-11-01

    AWARD NUMBER: W81XWH-15-1-0361 TITLE: “Exaggerated Cap-Dependent Translation as a Mechanism for Corticostriatal Dysfunction in Fragile X...Annual 3. DATES COVERED 19Oct2016 - 18Oct2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER “Exaggerated Cap-Dependent Translation as a Mechanism for... translation inhibitors. Our specific tasks are centered on a proteomic study of FXS striatal synapses by using a transgenic mouse model that allows to

  13. Advanced Model of Squirrel Cage Induction Machine for Broken Rotor Bars Fault Using Multi Indicators

    Directory of Open Access Journals (Sweden)

    Ilias Ouachtouk

    2016-01-01

    Full Text Available Squirrel cage induction machine are the most commonly used electrical drives, but like any other machine, they are vulnerable to faults. Among the widespread failures of the induction machine there are rotor faults. This paper focuses on the detection of broken rotor bars fault using multi-indicator. However, diagnostics of asynchronous machine rotor faults can be accomplished by analysing the anomalies of machine local variable such as torque, magnetic flux, stator current and neutral voltage signature analysis. The aim of this research is to summarize the existing models and to develop new models of squirrel cage induction motors with consideration of the neutral voltage and to study the effect of broken rotor bars on the different electrical quantities such as the park currents, torque, stator currents and neutral voltage. The performance of the model was assessed by comparing the simulation and experimental results. The obtained results show the effectiveness of the model, and allow detection and diagnosis of these defects.

  14. Colour Model for Outdoor Machine Vision for Tropical Regions and its Comparison with the CIE Model

    Energy Technology Data Exchange (ETDEWEB)

    Sahragard, Nasrolah; Ramli, Abdul Rahman B [Institute of Advanced Technology, Universiti Putra Malaysia 43400 Serdang, Selangor (Malaysia); Marhaban, Mohammad Hamiruce [Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400 Serdang, Selangor (Malaysia); Mansor, Shattri B, E-mail: sahragard@yahoo.com [Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400 Serdang, Selangor (Malaysia)

    2011-02-15

    Accurate modeling of daylight and surface reflectance are very useful for most outdoor machine vision applications specifically those which are based on color recognition. Existing daylight CIE model has drawbacks that limit its ability to predict the color of incident light. These limitations include lack of considering ambient light, effects of light reflected off the ground, and context specific information. Previously developed color model is only tested for a few geographical places in North America and its accountability is under question for other places in the world. Besides, existing surface reflectance models are not easily applied to outdoor images. A reflectance model with combined diffuse and specular reflection in normalized HSV color space could be used to predict color. In this paper, a new daylight color model showing the color of daylight for a broad range of sky conditions is developed which will suit weather conditions of tropical places such as Malaysia. A comparison of this daylight color model and daylight CIE model will be discussed. The colors of matte and specular surfaces have been estimated by use of the developed color model and surface reflection function in this paper. The results are shown to be highly reliable.

  15. Colour Model for Outdoor Machine Vision for Tropical Regions and its Comparison with the CIE Model

    Science.gov (United States)

    Sahragard, Nasrolah; Ramli, Abdul Rahman B.; Hamiruce Marhaban, Mohammad; Mansor, Shattri B.

    2011-02-01

    Accurate modeling of daylight and surface reflectance are very useful for most outdoor machine vision applications specifically those which are based on color recognition. Existing daylight CIE model has drawbacks that limit its ability to predict the color of incident light. These limitations include lack of considering ambient light, effects of light reflected off the ground, and context specific information. Previously developed color model is only tested for a few geographical places in North America and its accountability is under question for other places in the world. Besides, existing surface reflectance models are not easily applied to outdoor images. A reflectance model with combined diffuse and specular reflection in normalized HSV color space could be used to predict color. In this paper, a new daylight color model showing the color of daylight for a broad range of sky conditions is developed which will suit weather conditions of tropical places such as Malaysia. A comparison of this daylight color model and daylight CIE model will be discussed. The colors of matte and specular surfaces have been estimated by use of the developed color model and surface reflection function in this paper. The results are shown to be highly reliable.

  16. Colour Model for Outdoor Machine Vision for Tropical Regions and its Comparison with the CIE Model

    International Nuclear Information System (INIS)

    Sahragard, Nasrolah; Ramli, Abdul Rahman B; Marhaban, Mohammad Hamiruce; Mansor, Shattri B

    2011-01-01

    Accurate modeling of daylight and surface reflectance are very useful for most outdoor machine vision applications specifically those which are based on color recognition. Existing daylight CIE model has drawbacks that limit its ability to predict the color of incident light. These limitations include lack of considering ambient light, effects of light reflected off the ground, and context specific information. Previously developed color model is only tested for a few geographical places in North America and its accountability is under question for other places in the world. Besides, existing surface reflectance models are not easily applied to outdoor images. A reflectance model with combined diffuse and specular reflection in normalized HSV color space could be used to predict color. In this paper, a new daylight color model showing the color of daylight for a broad range of sky conditions is developed which will suit weather conditions of tropical places such as Malaysia. A comparison of this daylight color model and daylight CIE model will be discussed. The colors of matte and specular surfaces have been estimated by use of the developed color model and surface reflection function in this paper. The results are shown to be highly reliable.

  17. Modeling human-machine interactions for operations room layouts

    Science.gov (United States)

    Hendy, Keith C.; Edwards, Jack L.; Beevis, David

    2000-11-01

    The LOCATE layout analysis tool was used to analyze three preliminary configurations for the Integrated Command Environment (ICE) of a future USN platform. LOCATE develops a cost function reflecting the quality of all human-human and human-machine communications within a workspace. This proof- of-concept study showed little difference between the efficacy of the preliminary designs selected for comparison. This was thought to be due to the limitations of the study, which included the assumption of similar size for each layout and a lack of accurate measurement data for various objects in the designs, due largely to their notional nature. Based on these results, the USN offered an opportunity to conduct a LOCATE analysis using more appropriate assumptions. A standard crew was assumed, and subject matter experts agreed on the communications patterns for the analysis. Eight layouts were evaluated with the concepts of coordination and command factored into the analysis. Clear differences between the layouts emerged. The most promising design was refined further by the USN, and a working mock-up built for human-in-the-loop evaluation. LOCATE was applied to this configuration for comparison with the earlier analyses.

  18. Magnetic saturation in semi-analytical harmonic modeling for electric machine analysis

    NARCIS (Netherlands)

    Sprangers, R.L.J.; Paulides, J.J.H.; Gysen, B.L.J.; Lomonova, E.

    2016-01-01

    A semi-analytical method based on the harmonic modeling (HM) technique is presented for the analysis of the magneto-static field distribution in the slotted structure of rotating electric machines. In contrast to the existing literature, the proposed model does not require the assumption of infinite

  19. Advanced induction machine model in phase coordinates for wind turbine applications

    DEFF Research Database (Denmark)

    Fajardo, L.A.; Iov, F.; Hansen, Anca Daniela

    2007-01-01

    In this paper an advanced phase coordinates squirrel cage induction machine model with time varying electrical parameters affected by magnetic saturation and rotor deep bar effects, is presented. The model uses standard data sheet for characterization of the electrical parameters, it is developed...

  20. Modeling timelines for translational science in cancer; the impact of technological maturation.

    Directory of Open Access Journals (Sweden)

    Laura M McNamee

    Full Text Available This work examines translational science in cancer based on theories of innovation that posit a relationship between the maturation of technologies and their capacity to generate successful products. We examined the growth of technologies associated with 138 anticancer drugs using an analytical model that identifies the point of initiation of exponential growth and the point at which growth slows as the technology becomes established. Approval of targeted and biological products corresponded with technological maturation, with first approval averaging 14 years after the established point and 44 years after initiation of associated technologies. The lag in cancer drug approvals after the increases in cancer funding and dramatic scientific advances of the 1970s thus reflects predictable timelines of technology maturation. Analytical models of technological maturation may be used for technological forecasting to guide more efficient translation of scientific discoveries into cures.

  1. Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

    Science.gov (United States)

    Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

    2015-01-01

    Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (pmachine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

  2. Translator-computer interaction in action

    DEFF Research Database (Denmark)

    Bundgaard, Kristine; Christensen, Tina Paulsen; Schjoldager, Anne

    2016-01-01

    perspective, this paper investigates the relationship between machines and humans in the field of translation, analysing a CAT process in which machine-translation (MT) technology was integrated into a translation-memory (TM) suite. After a review of empirical research into the impact of CAT tools......Though we lack empirically-based knowledge of the impact of computer-aided translation (CAT) tools on translation processes, it is generally agreed that all professional translators are now involved in some kind of translator-computer interaction (TCI), using O’Brien’s (2012) term. Taking a TCI......, the study indicates that the tool helps the translator conform to project and customer requirements....

  3. Modeling timelines for translational science in cancer; the impact of technological maturation

    OpenAIRE

    McNamee, Laura M.; Ledley, Fred D.

    2017-01-01

    This work examines translational science in cancer based on theories of innovation that posit a relationship between the maturation of technologies and their capacity to generate successful products. We examined the growth of technologies associated with 138 anticancer drugs using an analytical model that identifies the point of initiation of exponential growth and the point at which growth slows as the technology becomes established. Approval of targeted and biological products corresponded ...

  4. A proposed model for assessing service quality in small machining and industrial maintenance companies

    Directory of Open Access Journals (Sweden)

    Morvam dos Santos Netto

    2014-11-01

    Full Text Available Machining and industrial maintenance services include repair (corrective maintenance of equipments, activities involving the assembly-disassembly of equipments, fault diagnosis, machining operations, forming operations, welding processes, assembly and test of equipments. This article proposes a model for assessing the quality of services provided by small machining and industrial maintenance companies, since there is a gap in the literature regarding this issue and because the importance of small service companies in socio-economic development of the country. The model is an adaptation of the SERVQUAL instrument and the criteria determining the quality of services are designed according to the service cycle of a typical small machining and industrial maintenance company. In this sense, the Moments of Truth have been considered in the preparation of two separate questionnaires. The first questionnaire contains 24 statements that reflect the expectations of customers, and the second one contains 24 statements that measure perceptions of service performance. An additional item was included in each questionnaire to assess, respectively, the overall expectation about the services and the overall company performance. Therefore, it is a model that considers the interfaces of the client/supplier relationship, the peculiarities of the machining and industrial maintenance service sector and the company size.

  5. A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Hemphill, Geralyn M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-27

    Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to be an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.

  6. Modelling of Tool Wear and Residual Stress during Machining of AISI H13 Tool Steel

    Science.gov (United States)

    Outeiro, José C.; Umbrello, Domenico; Pina, José C.; Rizzuti, Stefania

    2007-05-01

    Residual stresses can enhance or impair the ability of a component to withstand loading conditions in service (fatigue, creep, stress corrosion cracking, etc.), depending on their nature: compressive or tensile, respectively. This poses enormous problems in structural assembly as this affects the structural integrity of the whole part. In addition, tool wear issues are of critical importance in manufacturing since these affect component quality, tool life and machining cost. Therefore, prediction and control of both tool wear and the residual stresses in machining are absolutely necessary. In this work, a two-dimensional Finite Element model using an implicit Lagrangian formulation with an automatic remeshing was applied to simulate the orthogonal cutting process of AISI H13 tool steel. To validate such model the predicted and experimentally measured chip geometry, cutting forces, temperatures, tool wear and residual stresses on the machined affected layers were compared. The proposed FE model allowed us to investigate the influence of tool geometry, cutting regime parameters and tool wear on residual stress distribution in the machined surface and subsurface of AISI H13 tool steel. The obtained results permit to conclude that in order to reduce the magnitude of surface residual stresses, the cutting speed should be increased, the uncut chip thickness (or feed) should be reduced and machining with honed tools having large cutting edge radii produce better results than chamfered tools. Moreover, increasing tool wear increases the magnitude of surface residual stresses.

  7. Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Hang-cheong Wong

    2012-01-01

    Full Text Available Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM, to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN and decremental least-squares support vector machine (DLSSVM. Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.

  8. Modeling and control of lateral vibration of an axially translating flexible link

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Heon Seop; Rhim, Sung Soo [Kyung Hee University, Yongin (Korea, Republic of)

    2015-01-15

    Manipulators used for the transportation of large panel-shape payloads often adopt long and slender links (or forks) with translational joins to carry the payloads. As the size of the payload increases, the length of the links also increases to hold the payload securely. The increased length of the link inevitably amplifies the effect of the flexure in the link. Intuitively, the translational motion of the link in its longitudinal direction should have no effect on the lateral vibration of the link because of the orthogonality between the direction of the translational motion and the lateral vibration. If, however, the link was flexible and translated horizontally (perpendicular to the gravitational field) the asymmetric deflection of the link caused by gravity would break the orthogonality between the two directions, and the longitudinal motion of the link would excite lateral motion in the link. In this paper, the lateral oscillatory motion of the flexible link in a large-scale solar cell panel handling robot is investigated where the links carry the panel in its longitudinal direction. The Newtonian approach in conjunction with the assumed modes method is used for derivation of the equation of motion for the flexible forks where non-zero control force is applied at the base of the link. The analysis illustrates the effect of longitudinal motion on the lateral vibration and dynamic stiffening effect (variation of the natural frequency) of the link due to the translational velocity. Lateral vibration behavior is simulated using the derived equations of the motion. A robust vibration control scheme, the input shaping filter technique, is implemented on the model and the effectiveness of the scheme is verified numerically.

  9. Modeling and control of lateral vibration of an axially translating flexible link

    International Nuclear Information System (INIS)

    Shin, Heon Seop; Rhim, Sung Soo

    2015-01-01

    Manipulators used for the transportation of large panel-shape payloads often adopt long and slender links (or forks) with translational joins to carry the payloads. As the size of the payload increases, the length of the links also increases to hold the payload securely. The increased length of the link inevitably amplifies the effect of the flexure in the link. Intuitively, the translational motion of the link in its longitudinal direction should have no effect on the lateral vibration of the link because of the orthogonality between the direction of the translational motion and the lateral vibration. If, however, the link was flexible and translated horizontally (perpendicular to the gravitational field) the asymmetric deflection of the link caused by gravity would break the orthogonality between the two directions, and the longitudinal motion of the link would excite lateral motion in the link. In this paper, the lateral oscillatory motion of the flexible link in a large-scale solar cell panel handling robot is investigated where the links carry the panel in its longitudinal direction. The Newtonian approach in conjunction with the assumed modes method is used for derivation of the equation of motion for the flexible forks where non-zero control force is applied at the base of the link. The analysis illustrates the effect of longitudinal motion on the lateral vibration and dynamic stiffening effect (variation of the natural frequency) of the link due to the translational velocity. Lateral vibration behavior is simulated using the derived equations of the motion. A robust vibration control scheme, the input shaping filter technique, is implemented on the model and the effectiveness of the scheme is verified numerically.

  10. Translation of overlay models of student knowledge for relative domains based on domain ontology mapping

    DEFF Research Database (Denmark)

    Sosnovsky, Sergey; Dolog, Peter; Henze, Nicola

    2007-01-01

    The effectiveness of an adaptive educational system in many respects depends on the precision of modeling assumptions it makes about a student. One of the well-known challenges in student modeling is to adequately assess the initial level of student's knowledge when s/he starts working...... with a system. Sometimes potentially handful data are available as a part of user model from a system used by the student before. The usage of external user modeling information is troublesome because of differences in system architecture, knowledge representation, modeling constraints, etc. In this paper, we...... argue that the implementation of underlying knowledge models in a sharable format, as domain ontologies - along with application of automatic ontology mapping techniques for model alignment - can help to overcome the "new-user" problem and will greatly widen opportunities for student model translation...

  11. Translational Rodent Models for Research on Parasitic Protozoa-A Review of Confounders and Possibilities.

    Science.gov (United States)

    Ehret, Totta; Torelli, Francesca; Klotz, Christian; Pedersen, Amy B; Seeber, Frank

    2017-01-01

    Rodents, in particular Mus musculus , have a long and invaluable history as models for human diseases in biomedical research, although their translational value has been challenged in a number of cases. We provide some examples in which rodents have been suboptimal as models for human biology and discuss confounders which influence experiments and may explain some of the misleading results. Infections of rodents with protozoan parasites are no exception in requiring close consideration upon model choice. We focus on the significant differences between inbred, outbred and wild animals, and the importance of factors such as microbiota, which are gaining attention as crucial variables in infection experiments. Frequently, mouse or rat models are chosen for convenience, e.g., availability in the institution rather than on an unbiased evaluation of whether they provide the answer to a given question. Apart from a general discussion on translational success or failure, we provide examples where infections with single-celled parasites in a chosen lab rodent gave contradictory or misleading results, and when possible discuss the reason for this. We present emerging alternatives to traditional rodent models, such as humanized mice and organoid primary cell cultures. So-called recombinant inbred strains such as the Collaborative Cross collection are also a potential solution for certain challenges. In addition, we emphasize the advantages of using wild rodents for certain immunological, ecological, and/or behavioral questions. The experimental challenges (e.g., availability of species-specific reagents) that come with the use of such non-model systems are also discussed. Our intention is to foster critical judgment of both traditional and newly available translational rodent models for research on parasitic protozoa that can complement the existing mouse and rat models.

  12. Translational Rodent Models for Research on Parasitic Protozoa—A Review of Confounders and Possibilities

    Directory of Open Access Journals (Sweden)

    Totta Ehret

    2017-06-01

    Full Text Available Rodents, in particular Mus musculus, have a long and invaluable history as models for human diseases in biomedical research, although their translational value has been challenged in a number of cases. We provide some examples in which rodents have been suboptimal as models for human biology and discuss confounders which influence experiments and may explain some of the misleading results. Infections of rodents with protozoan parasites are no exception in requiring close consideration upon model choice. We focus on the significant differences between inbred, outbred and wild animals, and the importance of factors such as microbiota, which are gaining attention as crucial variables in infection experiments. Frequently, mouse or rat models are chosen for convenience, e.g., availability in the institution rather than on an unbiased evaluation of whether they provide the answer to a given question. Apart from a general discussion on translational success or failure, we provide examples where infections with single-celled parasites in a chosen lab rodent gave contradictory or misleading results, and when possible discuss the reason for this. We present emerging alternatives to traditional rodent models, such as humanized mice and organoid primary cell cultures. So-called recombinant inbred strains such as the Collaborative Cross collection are also a potential solution for certain challenges. In addition, we emphasize the advantages of using wild rodents for certain immunological, ecological, and/or behavioral questions. The experimental challenges (e.g., availability of species-specific reagents that come with the use of such non-model systems are also discussed. Our intention is to foster critical judgment of both traditional and newly available translational rodent models for research on parasitic protozoa that can complement the existing mouse and rat models.

  13. Dynamical modeling of microRNA action on the protein translation process.

    Science.gov (United States)

    Zinovyev, Andrei; Morozova, Nadya; Nonne, Nora; Barillot, Emmanuel; Harel-Bellan, Annick; Gorban, Alexander N

    2010-02-24

    Protein translation is a multistep process which can be represented as a cascade of biochemical reactions (initiation, ribosome assembly, elongation, etc.), the rate of which can be regulated by small non-coding microRNAs through multiple mechanisms. It remains unclear what mechanisms of microRNA action are the most dominant: moreover, many experimental reports deliver controversial messages on what is the concrete mechanism actually observed in the experiment. Nissan and Parker have recently demonstrated that it might be impossible to distinguish alternative biological hypotheses using the steady state data on the rate of protein synthesis. For their analysis they used two simple kinetic models of protein translation. In contrary to the study by Nissan and Parker, we show that dynamical data allow discriminating some of the mechanisms of microRNA action. We demonstrate this using the same models as developed by Nissan and Parker for the sake of comparison but the methods developed (asymptotology of biochemical networks) can be used for other models. We formulate a hypothesis that the effect of microRNA action is measurable and observable only if it affects the dominant system (generalization of the limiting step notion for complex networks) of the protein translation machinery. The dominant system can vary in different experimental conditions that can partially explain the existing controversy of some of the experimental data. Our analysis of the transient protein translation dynamics shows that it gives enough information to verify or reject a hypothesis about a particular molecular mechanism of microRNA action on protein translation. For multiscale systems only that action of microRNA is distinguishable which affects the parameters of dominant system (critical parameters), or changes the dominant system itself. Dominant systems generalize and further develop the old and very popular idea of limiting step. Algorithms for identifying dominant systems in multiscale

  14. Dynamical modeling of microRNA action on the protein translation process

    Directory of Open Access Journals (Sweden)

    Barillot Emmanuel

    2010-02-01

    Full Text Available Abstract Background Protein translation is a multistep process which can be represented as a cascade of biochemical reactions (initiation, ribosome assembly, elongation, etc., the rate of which can be regulated by small non-coding microRNAs through multiple mechanisms. It remains unclear what mechanisms of microRNA action are the most dominant: moreover, many experimental reports deliver controversial messages on what is the concrete mechanism actually observed in the experiment. Nissan and Parker have recently demonstrated that it might be impossible to distinguish alternative biological hypotheses using the steady state data on the rate of protein synthesis. For their analysis they used two simple kinetic models of protein translation. Results In contrary to the study by Nissan and Parker, we show that dynamical data allow discriminating some of the mechanisms of microRNA action. We demonstrate this using the same models as developed by Nissan and Parker for the sake of comparison but the methods developed (asymptotology of biochemical networks can be used for other models. We formulate a hypothesis that the effect of microRNA action is measurable and observable only if it affects the dominant system (generalization of the limiting step notion for complex networks of the protein translation machinery. The dominant system can vary in different experimental conditions that can partially explain the existing controversy of some of the experimental data. Conclusions Our analysis of the transient protein translation dynamics shows that it gives enough information to verify or reject a hypothesis about a particular molecular mechanism of microRNA action on protein translation. For multiscale systems only that action of microRNA is distinguishable which affects the parameters of dominant system (critical parameters, or changes the dominant system itself. Dominant systems generalize and further develop the old and very popular idea of limiting step

  15. Model for bridging the translational "valleys of death" in spinal cord injury research

    Directory of Open Access Journals (Sweden)

    Barrable B

    2014-04-01

    Full Text Available Bill Barrable,1 Nancy Thorogood,1 Vanessa Noonan,1,2 Jocelyn Tomkinson,1 Phalgun Joshi,1 Ken Stephenson,1 John Barclay,1 Katharina Kovacs Burns3 1Rick Hansen Institute, 2Division of Spine, Department of Orthopaedics, University of British Columbia, Vancouver, BC, 3Health Sciences Council, University of Alberta, Edmonton, AB, Canada Abstract: To improve health care outcomes with cost-effective treatments and prevention initiatives, basic health research must be translated into clinical application and studied during implementation, a process commonly referred to as translational research. It is estimated that only 14% of health-related scientific discoveries enter into medical practice and that it takes an average of 17 years for them to do so. The transition from basic research to clinical knowledge and from clinical knowledge to practice or implementation is so fraught with obstacles that these transitions are often referred to as “valleys of death”. The Rick Hansen Institute has developed a unique praxis model for translational research in the field of spinal cord injury (SCI. The praxis model involves three components. The first is a coordinated program strategy of cure, care, consumer engagement, and commercialization. The second is a knowledge cycle that consists of four phases, ie, environmental scanning, knowledge generation and synthesis, knowledge validation, and implementation. The third is the provision of relevant resources and infrastructure to overcome obstacles in the “valleys of death”, ie, funding, clinical research operations, informatics, clinical research and best practice implementation, consumer engagement, collaborative networks, and strategic partnerships. This model, which is to be independently evaluated in 2018 to determine its strengths and limitations, has been used to advance treatments for pressure ulcers in SCI. The Rick Hansen Institute has developed an innovative solution to move knowledge into action by

  16. MODELS OF LIVE MIGRATION WITH ITERATIVE APPROACH AND MOVE OF VIRTUAL MACHINES

    Directory of Open Access Journals (Sweden)

    S. M. Aleksankov

    2015-11-01

    Full Text Available Subject of Research. The processes of live migration without shared storage with pre-copy approach and move migration are researched. Migration of virtual machines is an important opportunity of virtualization technology. It enables applications to move transparently with their runtime environments between physical machines. Live migration becomes noticeable technology for efficient load balancing and optimizing the deployment of virtual machines to physical hosts in data centres. Before the advent of live migration, only network migration (the so-called, «Move», has been used, that entails stopping the virtual machine execution while copying to another physical server, and, consequently, unavailability of the service. Method. Algorithms of live migration without shared storage with pre-copy approach and move migration of virtual machines are reviewed from the perspective of research of migration time and unavailability of services at migrating of virtual machines. Main Results. Analytical models are proposed predicting migration time of virtual machines and unavailability of services at migrating with such technologies as live migration with pre-copy approach without shared storage and move migration. In the latest works on the time assessment of unavailability of services and migration time using live migration without shared storage experimental results are described, that are applicable to draw general conclusions about the changes of time for unavailability of services and migration time, but not to predict their values. Practical Significance. The proposed models can be used for predicting the migration time and time of unavailability of services, for example, at implementation of preventive and emergency works on the physical nodes in data centres.

  17. Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity

    International Nuclear Information System (INIS)

    Stern, R.E.; Song, J.; Work, D.B.

    2017-01-01

    The two-terminal reliability problem in system reliability analysis is known to be computationally intractable for large infrastructure graphs. Monte Carlo techniques can estimate the probability of a disconnection between two points in a network by selecting a representative sample of network component failure realizations and determining the source-terminal connectivity of each realization. To reduce the runtime required for the Monte Carlo approximation, this article proposes an approximate framework in which the connectivity check of each sample is estimated using a machine-learning-based classifier. The framework is implemented using both a support vector machine (SVM) and a logistic regression based surrogate model. Numerical experiments are performed on the California gas distribution network using the epicenter and magnitude of the 1989 Loma Prieta earthquake as well as randomly-generated earthquakes. It is shown that the SVM and logistic regression surrogate models are able to predict network connectivity with accuracies of 99% for both methods, and are 1–2 orders of magnitude faster than using a Monte Carlo method with an exact connectivity check. - Highlights: • Surrogate models of network connectivity are developed by machine-learning algorithms. • Developed surrogate models can reduce the runtime required for Monte Carlo simulations. • Support vector machine and logistic regressions are employed to develop surrogate models. • Numerical example of California gas distribution network demonstrate the proposed approach. • The developed models have accuracies 99%, and are 1–2 orders of magnitude faster than MCS.

  18. Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

    Energy Technology Data Exchange (ETDEWEB)

    Racle, Julien; Hatzimanikatis, Vassily, E-mail: vassily.hatzimanikatis@epfl.ch [Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne (Switzerland); Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne (Switzerland); Stefaniuk, Adam Jan [Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne (Switzerland)

    2015-07-28

    Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA polymerases along their corresponding templates (messenger RNA (mRNA) and DNA). The large size of these systems, which scales with the number of genes, mRNA copies, codons per mRNA, and ribosomes, is responsible for some of the challenges. Additionally, one should be able to describe the dynamics of ribosome exchange between the free ribosome pool and those bound to mRNAs, as well as how mRNA species compete for ribosomes. We developed an efficient algorithm for stochastic simulations that addresses these issues and used it to study the contribution and trade-offs of noise to translation properties (rates, time delays, and rate-limiting steps). The algorithm scales linearly with the number of mRNA copies, which allowed us to study the importance of genome-scale competition between mRNAs for the same ribosomes. We determined that noise is minimized under conditions maximizing the specific synthesis rate. Moreover, sensitivity analysis of the stochastic system revealed the importance of the elongation rate in the resultant noise, whereas the translation initiation rate constant was more closely related to the average protein synthesis rate. We observed significant differences between our results and the noise properties of the most commonly used translation models. Overall, our studies demonstrate that the use of full mechanistic models is essential for the study of noise in translation and transcription.

  19. Improving wave forecasting by integrating ensemble modelling and machine learning

    Science.gov (United States)

    O'Donncha, F.; Zhang, Y.; James, S. C.

    2017-12-01

    Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

  20. Design ensemble machine learning model for breast cancer diagnosis.

    Science.gov (United States)

    Hsieh, Sheau-Ling; Hsieh, Sung-Huai; Cheng, Po-Hsun; Chen, Chi-Huang; Hsu, Kai-Ping; Lee, I-Shun; Wang, Zhenyu; Lai, Feipei

    2012-10-01

    In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

  1. LINEAR KERNEL SUPPORT VECTOR MACHINES FOR MODELING PORE-WATER PRESSURE RESPONSES

    Directory of Open Access Journals (Sweden)

    KHAMARUZAMAN W. YUSOF

    2017-08-01

    Full Text Available Pore-water pressure responses are vital in many aspects of slope management, design and monitoring. Its measurement however, is difficult, expensive and time consuming. Studies on its predictions are lacking. Support vector machines with linear kernel was used here to predict the responses of pore-water pressure to rainfall. Pore-water pressure response data was collected from slope instrumentation program. Support vector machine meta-parameter calibration and model development was carried out using grid search and k-fold cross validation. The mean square error for the model on scaled test data is 0.0015 and the coefficient of determination is 0.9321. Although pore-water pressure response to rainfall is a complex nonlinear process, the use of linear kernel support vector machine can be employed where high accuracy can be sacrificed for computational ease and time.

  2. Static Object Detection Based on a Dual Background Model and a Finite-State Machine

    Directory of Open Access Journals (Sweden)

    Heras Evangelio Rubén

    2011-01-01

    Full Text Available Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction; it can be implemented as a look-up table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or interactively, making it extremely suitable for real-life surveillance applications. The system was successfully validated with several public datasets.

  3. A Novel Machine Learning Strategy Based on Two-Dimensional Numerical Models in Financial Engineering

    Directory of Open Access Journals (Sweden)

    Qingzhen Xu

    2013-01-01

    Full Text Available Machine learning is the most commonly used technique to address larger and more complex tasks by analyzing the most relevant information already present in databases. In order to better predict the future trend of the index, this paper proposes a two-dimensional numerical model for machine learning to simulate major U.S. stock market index and uses a nonlinear implicit finite-difference method to find numerical solutions of the two-dimensional simulation model. The proposed machine learning method uses partial differential equations to predict the stock market and can be extensively used to accelerate large-scale data processing on the history database. The experimental results show that the proposed algorithm reduces the prediction error and improves forecasting precision.

  4. Effect of power quality on windings temperature of marine induction motors. Part I: Machine model

    Energy Technology Data Exchange (ETDEWEB)

    Gnacinski, P. [Gdynia Maritime Univ., Dept. of Ship Electrical Power Engineering, Morska Str. 83, 81-225 Gdynia (Poland)

    2009-10-15

    Marine induction machines are exposed to various power quality disturbances appearing simultaneously in ship power systems: frequency and voltage rms value deviation, voltage unbalance and voltage waveform distortions. As a result, marine induction motors can be seriously overheated due to lowered supply voltage quality. Improvement of the protection of marine induction machines requires an appropriate method of power quality assessment and modification of the power quality regulations of ship classification societies. This paper presents an analytical model of an induction cage machine supplied with voltage of lowered quality, used in part II of the work (effect of power quality on windings temperature of marine induction motors. Part II. Results of investigations and recommendations for related regulations) for power quality assessment in ship power systems, and for justification of the new power quality regulations proposal. The presented model is suitable for implementation in an on-line measurement system. (author)

  5. Photon beam modelling with Pinnacle3 Treatment Planning System for a Rokus M Co-60 Machine

    International Nuclear Information System (INIS)

    Dulcescu, Mihaela; Murgulet Cristian

    2008-01-01

    The basic relationships of the convolution/superposition dose calculation technique are reviewed, and a modelling technique that can be used for obtaining a satisfactory beam model for a commercially available convolution/superposition-based treatment planning system is described. A fluence energy spectrum for a Co-60 treatment machine obtained from a Monte Carlo simulation was used for modelling the fluence spectrum for a Rokus M machine. In order to achieve this model we measured the depth dose distribution and the dose profiles with a Wellhofer dosimetry system. The primary fluence was iteratively modelled by comparing the computed depth dose curves and beam profiles with the depth dose curves and crossbeam profiles measured in a water phantom. The objective of beam modelling is to build a model of the primary fluence that the patient is exposed to, which can then be used for the calculation of the dose deposited in the patient. (authors)

  6. Evaluation of discrete modeling efficiency of asynchronous electric machines

    OpenAIRE

    Byczkowska-Lipińska, Liliana; Stakhiv, Petro; Hoholyuk, Oksana; Vasylchyshyn, Ivanna

    2011-01-01

    In the paper the problem of effective mathematical macromodels in the form of state variables intended for asynchronous motor transient analysis is considered. Their comparing with traditional mathematical models of asynchronous motors including models built into MATLAB/Simulink software was carried out and analysis of their efficiency was conducted.

  7. A Data Flow Model to Solve the Data Distribution Changing Problem in Machine Learning

    Directory of Open Access Journals (Sweden)

    Shang Bo-Wen

    2016-01-01

    Full Text Available Continuous prediction is widely used in broad communities spreading from social to business and the machine learning method is an important method in this problem.When we use the machine learning method to predict a problem. We use the data in the training set to fit the model and estimate the distribution of data in the test set.But when we use machine learning to do the continuous prediction we get new data as time goes by and use the data to predict the future data, there may be a problem. As the size of the data set increasing over time, the distribution changes and there will be many garbage data in the training set.We should remove the garbage data as it reduces the accuracy of the prediction. The main contribution of this article is using the new data to detect the timeliness of historical data and remove the garbage data.We build a data flow model to describe how the data flow among the test set, training set, validation set and the garbage set and improve the accuracy of prediction. As the change of the data set, the best machine learning model will change.We design a hybrid voting algorithm to fit the data set better that uses seven machine learning models predicting the same problem and uses the validation set putting different weights on the learning models to give better model more weights. Experimental results show that, when the distribution of the data set changes over time, our time flow model can remove most of the garbage data and get a better result than the traditional method that adds all the data to the data set; our hybrid voting algorithm has a better prediction result than the average accuracy of other predict models

  8. A Novel Application of Machine Learning Methods to Model Microcontroller Upset Due to Intentional Electromagnetic Interference

    Science.gov (United States)

    Bilalic, Rusmir

    A novel application of support vector machines (SVMs), artificial neural networks (ANNs), and Gaussian processes (GPs) for machine learning (GPML) to model microcontroller unit (MCU) upset due to intentional electromagnetic interference (IEMI) is presented. In this approach, an MCU performs a counting operation (0-7) while electromagnetic interference in the form of a radio frequency (RF) pulse is direct-injected into the MCU clock line. Injection times with respect to the clock signal are the clock low, clock rising edge, clock high, and the clock falling edge periods in the clock window during which the MCU is performing initialization and executing the counting procedure. The intent is to cause disruption in the counting operation and model the probability of effect (PoE) using machine learning tools. Five experiments were executed as part of this research, each of which contained a set of 38,300 training points and 38,300 test points, for a total of 383,000 total points with the following experiment variables: injection times with respect to the clock signal, injected RF power, injected RF pulse width, and injected RF frequency. For the 191,500 training points, the average training error was 12.47%, while for the 191,500 test points the average test error was 14.85%, meaning that on average, the machine was able to predict MCU upset with an 85.15% accuracy. Leaving out the results for the worst-performing model (SVM with a linear kernel), the test prediction accuracy for the remaining machines is almost 89%. All three machine learning methods (ANNs, SVMs, and GPML) showed excellent and consistent results in their ability to model and predict the PoE on an MCU due to IEMI. The GP approach performed best during training with a 7.43% average training error, while the ANN technique was most accurate during the test with a 10.80% error.

  9. Translational mixed-effects PKPD modelling of recombinant human growth hormone - from hypophysectomized rat to patients

    DEFF Research Database (Denmark)

    Thorsted, A; Thygesen, P; Agersø, H

    2016-01-01

    BACKGROUND AND PURPOSE: We aimed to develop a mechanistic mixed-effects pharmacokinetic (PK)-pharmacodynamic (PD) (PKPD) model for recombinant human growth hormone (rhGH) in hypophysectomized rats and to predict the human PKPD relationship. EXPERIMENTAL APPROACH: A non-linear mixed-effects model...... was developed from experimental PKPD studies of rhGH and effects of long-term treatment as measured by insulin-like growth factor 1 (IGF-1) and bodyweight gain in rats. Modelled parameter values were scaled to human values using the allometric approach with fixed exponents for PKs and unscaled for PDs...... s.c. administration was over predicted. After correction of the human s.c. absorption model, the induction model for IGF-1 well described the human PKPD data. CONCLUSIONS: A translational mechanistic PKPD model for rhGH was successfully developed from experimental rat data. The model links...

  10. Rabbit models for the study of human atherosclerosis: from pathophysiological mechanisms to translational medicine.

    Science.gov (United States)

    Fan, Jianglin; Kitajima, Shuji; Watanabe, Teruo; Xu, Jie; Zhang, Jifeng; Liu, Enqi; Chen, Y Eugene

    2015-02-01

    Laboratory animal models play an important role in the study of human diseases. Using appropriate animals is critical not only for basic research but also for the development of therapeutics and diagnostic tools. Rabbits are widely used for the study of human atherosclerosis. Because rabbits have a unique feature of lipoprotein metabolism (like humans but unlike rodents) and are sensitive to a cholesterol diet, rabbit models have not only provided many insights into the pathogenesis and development of human atherosclerosis but also made a great contribution to translational research. In fact, rabbit was the first animal model used for studying human atherosclerosis, more than a century ago. Currently, three types of rabbit model are commonly used for the study of human atherosclerosis and lipid metabolism: (1) cholesterol-fed rabbits, (2) Watanabe heritable hyperlipidemic rabbits, analogous to human familial hypercholesterolemia due to genetic deficiency of LDL receptors, and (3) genetically modified (transgenic and knock-out) rabbits. Despite their importance, compared with the mouse, the most widely used laboratory animal model nowadays, the use of rabbit models is still limited. In this review, we focus on the features of rabbit lipoprotein metabolism and pathology of atherosclerotic lesions that make it the optimal model for human atherosclerotic disease, especially for the translational medicine. For the sake of clarity, the review is not an attempt to be completely inclusive, but instead attempts to summarize substantial information concisely and provide a guideline for experiments using rabbits. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Translational Assays for Assessment of Cognition in Rodent Models of Alzheimer's Disease and Dementia.

    Science.gov (United States)

    Shepherd, A; Tyebji, S; Hannan, A J; Burrows, E L

    2016-11-01

    Cognitive dysfunction appears as a core feature of dementia, which includes its most prevalent form, Alzheimer's disease (AD), as well as vascular dementia, frontotemporal dementia, and other brain disorders. AD alone affects more than 45 million people worldwide, with growing prevalence in aging populations. There is no cure, and therapeutic options remain limited. Gene-edited and transgenic animal models, expressing disease-specific gene mutations, illuminate pathogenic mechanisms leading to cognitive decline in AD and other forms of dementia. To date, cognitive tests in AD mouse models have not been directly relevant to the clinical presentation of AD, providing challenges for translation of findings to the clinic. Touchscreen testing in mice has enabled the assessment of specific cognitive domains in mice that are directly relevant to impairments described in human AD patients. In this review, we provide context for how cognitive decline is measured in the clinic, describe traditional methods for assessing cognition in mice, and outline novel approaches, including the use of the touchscreen platform for cognitive testing. We highlight the limitations of traditional memory-testing paradigms in mice, particularly their capacity for direct translation into cognitive testing of patients. While it is not possible to expect direct translation in testing methodologies, we can aim to develop tests that engage similar neural substrates in both humans and mice. Ultimately, that would enable us to better predict efficacy across species and therefore improve the chances that a treatment that works in mice will also work in the clinic.

  12. State Machine Modeling of the Space Launch System Solid Rocket Boosters

    Science.gov (United States)

    Harris, Joshua A.; Patterson-Hine, Ann

    2013-01-01

    The Space Launch System is a Shuttle-derived heavy-lift vehicle currently in development to serve as NASA's premiere launch vehicle for space exploration. The Space Launch System is a multistage rocket with two Solid Rocket Boosters and multiple payloads, including the Multi-Purpose Crew Vehicle. Planned Space Launch System destinations include near-Earth asteroids, the Moon, Mars, and Lagrange points. The Space Launch System is a complex system with many subsystems, requiring considerable systems engineering and integration. To this end, state machine analysis offers a method to support engineering and operational e orts, identify and avert undesirable or potentially hazardous system states, and evaluate system requirements. Finite State Machines model a system as a finite number of states, with transitions between states controlled by state-based and event-based logic. State machines are a useful tool for understanding complex system behaviors and evaluating "what-if" scenarios. This work contributes to a state machine model of the Space Launch System developed at NASA Ames Research Center. The Space Launch System Solid Rocket Booster avionics and ignition subsystems are modeled using MATLAB/Stateflow software. This model is integrated into a larger model of Space Launch System avionics used for verification and validation of Space Launch System operating procedures and design requirements. This includes testing both nominal and o -nominal system states and command sequences.

  13. Empirical model for estimating the surface roughness of machined ...

    African Journals Online (AJOL)

    Michael Horsfall

    one of the most critical quality measure in mechanical products. In the ... Keywords: cutting speed, centre lathe, empirical model, surface roughness, Mean absolute percentage deviation ... The factors considered were work piece properties.

  14. Credit Risk Analysis using Machine and Deep Learning models

    OpenAIRE

    Addo , Peter ,; Guegan , Dominique; Hassani , Bertrand

    2018-01-01

    URL des Documents de travail : https://centredeconomiesorbonne.univ-paris1.fr/documents-de-travail-du-ces/; Documents de travail du Centre d'Economie de la Sorbonne 2018.03 - ISSN : 1955-611X; Due to the hyper technology associated to Big Data, data availability and computing power, most banks or lending financial institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision making and transparency. In...

  15. Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

    Science.gov (United States)

    Lane, Thomas; Russo, Daniel P; Zorn, Kimberley M; Clark, Alex M; Korotcov, Alexandru; Tkachenko, Valery; Reynolds, Robert C; Perryman, Alexander L; Freundlich, Joel S; Ekins, Sean

    2018-04-26

    Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

  16. OPERATING OF MOBILE MACHINE UNITS SYSTEM USING THE MODEL OF MULTICOMPONENT COMPLEX MOVEMENT

    Directory of Open Access Journals (Sweden)

    A. Lebedev

    2015-07-01

    Full Text Available To solve the problems of mobile machine units system operating it is proposed using complex multi-component (composite movement physical models. Implementation of the proposed method is possible by creating of automatic operating systems of fuel supply to the engines using linear accelerometers. Some examples to illustrate the proposed method are offered.

  17. Operating of mobile machine units system using the model of multicomponent complex movement

    OpenAIRE

    A. Lebedev; R. Kaidalov; N. Artiomov; M. Shulyak; M. Podrigalo; D. Abramov; D. Klets

    2015-01-01

    To solve the problems of mobile machine units system operating it is proposed using complex multi-component (composite) movement physical models. Implementation of the proposed method is possible by creating of automatic operating systems of fuel supply to the engines using linear accelerometers. Some examples to illustrate the proposed method are offered.

  18. Model of large scale man-machine systems with an application to vessel traffic control

    NARCIS (Netherlands)

    Wewerinke, P.H.; van der Ent, W.I.; ten Hove, D.

    1989-01-01

    Mathematical models are discussed to deal with complex large-scale man-machine systems such as vessel (air, road) traffic and process control systems. Only interrelationships between subsystems are assumed. Each subsystem is controlled by a corresponding human operator (HO). Because of the

  19. A comparative study of machine learning classifiers for modeling travel mode choice

    NARCIS (Netherlands)

    Hagenauer, J; Helbich, M

    2017-01-01

    The analysis of travel mode choice is an important task in transportation planning and policy making in order to understand and predict travel demands. While advances in machine learning have led to numerous powerful classifiers, their usefulness for modeling travel mode choice remains largely

  20. Modelling and optimization of a permanent-magnet machine in a flywheel

    NARCIS (Netherlands)

    Holm, S.R.

    2003-01-01

    This thesis describes the derivation of an analytical model for the design and optimization of a permanent-magnet machine for use in an energy storage flywheel. A prototype of this flywheel is to be used as the peak-power unit in a hybrid electric city bus. The thesis starts by showing the

  1. Static stiffness modeling of a novel hybrid redundant robot machine

    International Nuclear Information System (INIS)

    Li Ming; Wu Huapeng; Handroos, Heikki

    2011-01-01

    This paper presents a modeling method to study the stiffness of a hybrid serial-parallel robot IWR (Intersector Welding Robot) for the assembly of ITER vacuum vessel. The stiffness matrix of the basic element in the robot is evaluated using matrix structural analysis (MSA); the stiffness of the parallel mechanism is investigated by taking account of the deformations of both hydraulic limbs and joints; the stiffness of the whole integrated robot is evaluated by employing the virtual joint method and the principle of virtual work. The obtained stiffness model of the hybrid robot is analytical and the deformation results of the robot workspace under certain external load are presented.

  2. A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

    International Nuclear Information System (INIS)

    Miao, Qiang; Huang, Hong Zhong; Fan, Xianfeng

    2007-01-01

    Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area

  3. Big data - modelling of midges in Europa using machine learning techniques and satellite imagery

    DEFF Research Database (Denmark)

    Cuellar, Ana Carolina; Kjær, Lene Jung; Skovgaard, Henrik

    2017-01-01

    coordinates of each trap, start and end dates of trapping. We used 120 environmental predictor variables together with Random Forest machine learning algorithms to predict the overall species distribution (probability of occurrence) and monthly abundance in Europe. We generated maps for every month...... and the Obsoletus group, although abundance was generally higher for a longer period of time for C. imicula than for the Obsoletus group. Using machine learning techniques, we were able to model the spatial distribution in Europe for C. imicola and the Obsoletus group in terms of abundance and suitability...

  4. Comparison of Models Needed for Conceptual Design of Man-Machine Systems in Different Application Domains

    DEFF Research Database (Denmark)

    Rasmussen, Jens

    1986-01-01

    and subjective preferences. For design of man-machine systems in process control, a framework has been developed in terms of separate representation of the problem domain, the decision task, and the information processing strategies required. The author analyzes the application of this framework to a number......For systematic and computer-aided design of man-machine systems, a consistent framework is needed, i. e. , a set of models which allows the selection of system characteristics which serve the individual user not only to satisfy his goal, but also to select mental processes that match his resources...

  5. Entrainment to periodic initiation and transition rates in a computational model for gene translation.

    Directory of Open Access Journals (Sweden)

    Michael Margaliot

    Full Text Available Periodic oscillations play an important role in many biomedical systems. Proper functioning of biological systems that respond to periodic signals requires the ability to synchronize with the periodic excitation. For example, the sleep/wake cycle is a manifestation of an internal timing system that synchronizes to the solar day. In the terminology of systems theory, the biological system must entrain or phase-lock to the periodic excitation. Entrainment is also important in synthetic biology. For example, connecting several artificial biological systems that entrain to a common clock may lead to a well-functioning modular system. The cell-cycle is a periodic program that regulates DNA synthesis and cell division. Recent biological studies suggest that cell-cycle related genes entrain to this periodic program at the gene translation level, leading to periodically-varying protein levels of these genes. The ribosome flow model (RFM is a deterministic model obtained via a mean-field approximation of a stochastic model from statistical physics that has been used to model numerous processes including ribosome flow along the mRNA. Here we analyze the RFM under the assumption that the initiation and/or transition rates vary periodically with a common period T. We show that the ribosome distribution profile in the RFM entrains to this periodic excitation. In particular, the protein synthesis pattern converges to a unique periodic solution with period T. To the best of our knowledge, this is the first proof of entrainment in a mathematical model for translation that encapsulates aspects such as initiation and termination rates, ribosomal movement and interactions, and non-homogeneous elongation speeds along the mRNA. Our results support the conjecture that periodic oscillations in tRNA levels and other factors related to the translation process can induce periodic oscillations in protein levels, and may suggest a new approach for re-engineering genetic

  6. Translation Theory 'Translated'

    DEFF Research Database (Denmark)

    Wæraas, Arild; Nielsen, Jeppe

    2016-01-01

    Translation theory has proved to be a versatile analytical lens used by scholars working from different traditions. On the basis of a systematic literature review, this study adds to our understanding of the ‘translations’ of translation theory by identifying the distinguishing features of the most...... common theoretical approaches to translation within the organization and management discipline: actor-network theory, knowledge-based theory, and Scandinavian institutionalism. Although each of these approaches already has borne much fruit in research, the literature is diverse and somewhat fragmented......, but also overlapping. We discuss the ways in which the three versions of translation theory may be combined and enrich each other so as to inform future research, thereby offering a more complete understanding of translation in and across organizational settings....

  7. Modeling and simulation of the fluid flow in wire electrochemical machining with rotating tool (wire ECM)

    Science.gov (United States)

    Klocke, F.; Herrig, T.; Zeis, M.; Klink, A.

    2017-10-01

    Combining the working principle of electrochemical machining (ECM) with a universal rotating tool, like a wire, could manage lots of challenges of the classical ECM sinking process. Such a wire-ECM process could be able to machine flexible and efficient 2.5-dimensional geometries like fir tree slots in turbine discs. Nowadays, established manufacturing technologies for slotting turbine discs are broaching and wire electrical discharge machining (wire EDM). Nevertheless, high requirements on surface integrity of turbine parts need cost intensive process development and - in case of wire-EDM - trim cuts to reduce the heat affected rim zone. Due to the process specific advantages, ECM is an attractive alternative manufacturing technology and is getting more and more relevant for sinking applications within the last few years. But ECM is also opposed with high costs for process development and complex electrolyte flow devices. In the past, few studies dealt with the development of a wire ECM process to meet these challenges. However, previous concepts of wire ECM were only suitable for micro machining applications. Due to insufficient flushing concepts the application of the process for machining macro geometries failed. Therefore, this paper presents the modeling and simulation of a new flushing approach for process assessment. The suitability of a rotating structured wire electrode in combination with an axial flushing for electrodes with high aspect ratios is investigated and discussed.

  8. Mathematical Model of Lifetime Duration at Insulation of Electrical Machines

    Directory of Open Access Journals (Sweden)

    Mihaela Răduca

    2009-10-01

    Full Text Available Abstract. This paper present a mathematical model of lifetime duration at hydro generator stator winding insulation when at hydro generator can be appear the damage regimes. The estimation to make by take of the programming and non-programming revisions, through the introduction and correlation of the new defined notions.

  9. Modelling rollover behaviour of exacavator-based forest machines

    Science.gov (United States)

    M.W. Veal; S.E. Taylor; Robert B. Rummer

    2003-01-01

    This poster presentation provides results from analytical and computer simulation models of rollover behaviour of hydraulic excavators. These results are being used as input to the operator protective structure standards development process. Results from rigid body mechanics and computer simulation methods agree well with field rollover test data. These results show...

  10. Modelling of Moving Coil Actuators in Fast Switching Valves Suitable for Digital Hydraulic Machines

    DEFF Research Database (Denmark)

    Nørgård, Christian; Roemer, Daniel Beck; Bech, Michael Møller

    2015-01-01

    an estimation of the eddy currents generated in the actuator yoke upon current rise, as they may have significant influence on the coil current response. The analytical model facilitates fast simulation of the transient actuator response opposed to the transient electro-magnetic finite element model which......The efficiency of digital hydraulic machines is strongly dependent on the valve switching time. Recently, fast switching have been achieved by using a direct electromagnetic moving coil actuator as the force producing element in fast switching hydraulic valves suitable for digital hydraulic...... machines. Mathematical models of the valve switching, targeted for design optimisation of the moving coil actuator, are developed. A detailed analytical model is derived and presented and its accuracy is evaluated against transient electromagnetic finite element simulations. The model includes...

  11. Identification and non-integer order modelling of synchronous machines operating as generator

    Directory of Open Access Journals (Sweden)

    Szymon Racewicz

    2012-09-01

    Full Text Available This paper presents an original mathematical model of a synchronous generator using derivatives of fractional order. In contrast to classical models composed of a large number of R-L ladders, it comprises half-order impedances, which enable the accurate description of the electromagnetic induction phenomena in a wide frequency range, while minimizing the order and number of model parameters. The proposed model takes into account the skin eff ect in damper cage bars, the eff ects of eddy currents in rotor solid parts, and the saturation of the machine magnetic circuit. The half-order transfer functions used for modelling these phenomena were verifi ed by simulation of ferromagnetic sheet impedance using the fi nite elements method. The analysed machine’s parameters were identified on the basis of SSFR (StandStill Frequency Response characteristics measured on a gradually magnetised synchronous machine.

  12. Free Online Translators: A Comparative Assessment in Terms of Idioms and Phrasal Verbs

    Directory of Open Access Journals (Sweden)

    Marziyeh Taleghani

    2018-03-01

    Full Text Available Free online translators are in fact statistical machine translators that create translator models using parallel corpora. Although it’s not a new subject and many works are reported on that in recent years, it still suffers from lots of shortcomings and has a long way ahead. While the literature on machine translators is vast, there are only a few that evaluate free online machine translators in specific terms like idioms. The aim of this paper is to evaluate and compare four free online translators in terms of translating English idioms (including idiomatic phrasal verbs into Persian. To that end, ten chosen texts from the book “oxford word Skills: idioms and phrasal verbs” were translated by four online translators, www.bing.com, www.translate.google.com , www.freetranslation.com and www.targoman.com , and the obtained results were compared in a subjectively method based on Aryanpur English to Persian dictionary. Comparison of the results shows that www.targoman.com has a better performance in translating idioms from English to Persian and as a result, it can be the best choice if the aim is to do so.

  13. Programming and machining of complex parts based on CATIA solid modeling

    Science.gov (United States)

    Zhu, Xiurong

    2017-09-01

    The complex parts of the use of CATIA solid modeling programming and simulation processing design, elaborated in the field of CNC machining, programming and the importance of processing technology. In parts of the design process, first make a deep analysis on the principle, and then the size of the design, the size of each chain, connected to each other. After the use of backstepping and a variety of methods to calculate the final size of the parts. In the selection of parts materials, careful study, repeated testing, the final choice of 6061 aluminum alloy. According to the actual situation of the processing site, it is necessary to make a comprehensive consideration of various factors in the machining process. The simulation process should be based on the actual processing, not only pay attention to shape. It can be used as reference for machining.

  14. Modeling of thermal spalling during electrical discharge machining of titanium diboride

    International Nuclear Information System (INIS)

    Gadalla, A.M.; Bozkurt, B.; Faulk, N.M.

    1991-01-01

    Erosion in electrical discharge machining has been described as occurring by melting and flushing the liquid formed. Recently, however, thermal spalling was reported as the mechanism for machining refractory materials with low thermal conductivity and high thermal expansion. The process is described in this paper by a model based on a ceramic surface exposed to a constant circular heating source which supplied a constant flux over the pulse duration. The calculations were based on TiB 2 mechanical properties along a and c directions. Theoretical predictions were verified by machining hexagonal TiB 2 . Large flakes of TiB 2 with sizes close to grain size and maximum thickness close to the predicted values were collected, together with spherical particles of Cu and Zn eroded from cutting wire. The cutting surfaces consist of cleavage planes sometimes contaminated with Cu, Zn, and impurities from the dielectric fluid

  15. Recent Advances in Translational Magnetic Resonance Imaging in Animal Models of Stress and Depression

    Directory of Open Access Journals (Sweden)

    Allison L. McIntosh

    2017-05-01

    Full Text Available Magnetic resonance imaging (MRI is a valuable translational tool that can be used to investigate alterations in brain structure and function in both patients and animal models of disease. Regional changes in brain structure, functional connectivity, and metabolite concentrations have been reported in depressed patients, giving insight into the networks and brain regions involved, however preclinical models are less well characterized. The development of more effective treatments depends upon animal models that best translate to the human condition and animal models may be exploited to assess the molecular and cellular alterations that accompany neuroimaging changes. Recent advances in preclinical imaging have facilitated significant developments within the field, particularly relating to high resolution structural imaging and resting-state functional imaging which are emerging techniques in clinical research. This review aims to bring together the current literature on preclinical neuroimaging in animal models of stress and depression, highlighting promising avenues of research toward understanding the pathological basis of this hugely prevalent disorder.

  16. Advancing Transdisciplinary and Translational Research Practice: Issues and Models of Doctoral Education in Public Health

    Directory of Open Access Journals (Sweden)

    Linda Neuhauser

    2007-01-01

    Full Text Available Finding solutions to complex health problems, such as obesity, violence, and climate change, will require radical changes in cross-disciplinary education, research, and practice. The fundamental determinants of health include many interrelated factors such as poverty, culture, education, environment, and government policies. However, traditional public health training has tended to focus more narrowly on diseases and risk factors, and has not adequately leveraged the rich contributions of sociology, anthropology, economics, geography, communication, political science, and other disciplines. Further, students are often not sufficiently trained to work across sectors to translate research findings into effective, large-scale sustainable actions.During the past 2 decades, national and international organizations have called for more effective interdisciplinary, transdisciplinary, and translational approaches to graduate education. Although it has been difficult to work across traditional academic boundaries, some promising models draw on pedagogical theory and feature cross-disciplinary training focused on real-world problems, linkage between research, professional practice, community action, and cultivation of leadership skills.We describe the development the Doctor of Public Health program at the University of California, Berkeley, USA and its efforts to improve transdisciplinary and translational research education. We stress the need for international collaboration to improve educational approaches and better evaluate their impact.

  17. The development of fully dynamic rotating machine models for nuclear training simulators

    International Nuclear Information System (INIS)

    Birsa, J.J.

    1990-01-01

    Prior to beginning the development of an enhanced set of electrical plant models for several nuclear training simulators, an extensive literature search was conducted to evaluate and select rotating machine models for use on these simulators. These models include the main generator, diesel generators, in-plant electric power distribution and off-side power. Form the results of this search, various models were investigated and several were selected for further evaluation. Several computer studies were performed on the selected models in order to determine their suitability for use in a training simulator environment. One surprising result of this study was that a number of established, classical models could not be made to reproduce actual plant steady-state data over the range necessary for a training simulator. This evaluation process and its results are presented in this paper. Various historical, as well as contemporary, electrical models of rotating machines are discussed. Specific criteria for selection of rotating machine models for training simulator use are presented

  18. Harmonic wave model of a permanent magnet synchronous machine for modeling partial demagnetization under short circuit conditions

    NARCIS (Netherlands)

    Kral, C.; Haumer, A.; Bogomolov, M.D.; Lomonova, E.

    2012-01-01

    This paper proposes a multi domain physical model of permanent magnet synchronous machines, considering electrical, magnetic, thermal and mechanical effects. For each component of the model, the main wave as well as lower and higher harmonic wave components of the magnetic flux and the magnetic

  19. MATHEMATICAL MODEL FOR THE STUDY AND DESIGN OF A ROTARY-VANE GAS REFRIGERATION MACHINE

    Directory of Open Access Journals (Sweden)

    V. V. Trandafilov

    2016-08-01

    Full Text Available This paper presents a mathematical model of calculating the main parameters the operating cycle, rotary-vane gas refrigerating machine that affect installation, machine control and working processes occurring in it at the specified criteria. A procedure and a graphical method for the rotary-vane gas refrigerating machine (RVGRM are proposed. A parametric study of the main geometric variables and temperature variables on the thermal behavior of the system is analyzed. The model considers polytrope index for the compression and expansion in the chamber. Graphs of the pressure and temperature in the chamber of the angle of rotation of the output shaft are received. The possibility of inclusion in the cycle regenerative heat exchanger is appreciated. The change of the coefficient of performance machine after turning the cycle regenerative heat exchanger is analyzed. It is shown that the installation of a regenerator RVGRM cycle results in increased COP more than 30%. The simulation results show that the proposed model can be used to design and optimize gas refrigerator Stirling.

  20. TREXMO: A Translation Tool to Support the Use of Regulatory Occupational Exposure Models.

    Science.gov (United States)

    Savic, Nenad; Racordon, Dimitri; Buchs, Didier; Gasic, Bojan; Vernez, David

    2016-10-01

    Occupational exposure models vary significantly in their complexity, purpose, and the level of expertise required from the user. Different parameters in the same model may lead to different exposure estimates for the same exposure situation. This paper presents a tool developed to deal with this concern-TREXMO or TRanslation of EXposure MOdels. TREXMO integrates six commonly used occupational exposure models, namely, ART v.1.5, STOFFENMANAGER(®) v.5.1, ECETOC TRA v.3, MEASE v.1.02.01, EMKG-EXPO-TOOL, and EASE v.2.0. By enabling a semi-automatic translation between the parameters of these six models, TREXMO facilitates their simultaneous use. For a given exposure situation, defined by a set of parameters in one of the models, TREXMO provides the user with the most appropriate parameters to use in the other exposure models. Results showed that, once an exposure situation and parameters were set in ART, TREXMO reduced the number of possible outcomes in the other models by 1-4 orders of magnitude. The tool should manage to reduce the uncertain entry or selection of parameters in the six models, improve between-user reliability, and reduce the time required for running several models for a given exposure situation. In addition to these advantages, registrants of chemicals and authorities should benefit from more reliable exposure estimates for the risk characterization of dangerous chemicals under Regulation, Evaluation, Authorisation and restriction of CHemicals (REACH). © The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

  1. A Comparative Study of "Google Translate" Translations: An Error Analysis of English-to-Persian and Persian-to-English Translations

    Science.gov (United States)

    Ghasemi, Hadis; Hashemian, Mahmood

    2016-01-01

    Both lack of time and the need to translate texts for numerous reasons brought about an increase in studying machine translation with a history spanning over 65 years. During the last decades, Google Translate, as a statistical machine translation (SMT), was in the center of attention for supporting 90 languages. Although there are many studies on…

  2. A Taxonomy of Human Translation Styles

    DEFF Research Database (Denmark)

    Carl, Michael; Dragsted, Barbara; Lykke Jakobsen, Arnt

    2011-01-01

    on the translators' activity data, we develop a taxonomy of translation styles. The taxonomy could serve to inform the development of advanced translation assistance tools and provide a basis for a felicitous and grounded integration of human machine interaction in translation.......While the translation profession becomes increasingly technological, we are still far from understanding how humans actually translate and how they could be best supported by machines. In this paper we outline a method which helps to uncover characteristics of human translation processes. Based...

  3. Shot-by-shot spectrum model for rod-pinch, pulsed radiography machines

    Directory of Open Access Journals (Sweden)

    Wm M. Wood

    2018-02-01

    Full Text Available A simplified model of bremsstrahlung production is developed for determining the x-ray spectrum output of a rod-pinch radiography machine, on a shot-by-shot basis, using the measured voltage, V(t, and current, I(t. The motivation for this model is the need for an agile means of providing shot-by-shot spectrum prediction, from a laptop or desktop computer, for quantitative radiographic analysis. Simplifying assumptions are discussed, and the model is applied to the Cygnus rod-pinch machine. Output is compared to wedge transmission data for a series of radiographs from shots with identical target objects. Resulting model enables variation of parameters in real time, thus allowing for rapid optimization of the model across many shots. “Goodness of fit” is compared with output from LSP Particle-In-Cell code, as well as the Monte Carlo Neutron Propagation with Xrays (“MCNPX” model codes, and is shown to provide an excellent predictive representation of the spectral output of the Cygnus machine. Improvements to the model, specifically for application to other geometries, are discussed.

  4. A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model.

    Science.gov (United States)

    Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F

    2014-06-01

    To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified). © 2013 Elsevier B.V. All rights reserved.

  5. Shot-by-shot spectrum model for rod-pinch, pulsed radiography machines

    Science.gov (United States)

    Wood, Wm M.

    2018-02-01

    A simplified model of bremsstrahlung production is developed for determining the x-ray spectrum output of a rod-pinch radiography machine, on a shot-by-shot basis, using the measured voltage, V(t), and current, I(t). The motivation for this model is the need for an agile means of providing shot-by-shot spectrum prediction, from a laptop or desktop computer, for quantitative radiographic analysis. Simplifying assumptions are discussed, and the model is applied to the Cygnus rod-pinch machine. Output is compared to wedge transmission data for a series of radiographs from shots with identical target objects. Resulting model enables variation of parameters in real time, thus allowing for rapid optimization of the model across many shots. "Goodness of fit" is compared with output from LSP Particle-In-Cell code, as well as the Monte Carlo Neutron Propagation with Xrays ("MCNPX") model codes, and is shown to provide an excellent predictive representation of the spectral output of the Cygnus machine. Improvements to the model, specifically for application to other geometries, are discussed.

  6. Modeling of the flow stress for AISI H13 Tool Steel during Hard Machining Processes

    Science.gov (United States)

    Umbrello, Domenico; Rizzuti, Stefania; Outeiro, José C.; Shivpuri, Rajiv

    2007-04-01

    In general, the flow stress models used in computer simulation of machining processes are a function of effective strain, effective strain rate and temperature developed during the cutting process. However, these models do not adequately describe the material behavior in hard machining, where a range of material hardness between 45 and 60 HRC are used. Thus, depending on the specific material hardness different material models must be used in modeling the cutting process. This paper describes the development of a hardness-based flow stress and fracture models for the AISI H13 tool steel, which can be applied for range of material hardness mentioned above. These models were implemented in a non-isothermal viscoplastic numerical model to simulate the machining process for AISI H13 with various hardness values and applying different cutting regime parameters. Predicted results are validated by comparing them with experimental results found in the literature. They are found to predict reasonably well the cutting forces as well as the change in chip morphology from continuous to segmented chip as the material hardness change.

  7. Modeling of the flow stress for AISI H13 Tool Steel during Hard Machining Processes

    International Nuclear Information System (INIS)

    Umbrello, Domenico; Rizzuti, Stefania; Outeiro, Jose C.; Shivpuri, Rajiv

    2007-01-01

    In general, the flow stress models used in computer simulation of machining processes are a function of effective strain, effective strain rate and temperature developed during the cutting process. However, these models do not adequately describe the material behavior in hard machining, where a range of material hardness between 45 and 60 HRC are used. Thus, depending on the specific material hardness different material models must be used in modeling the cutting process. This paper describes the development of a hardness-based flow stress and fracture models for the AISI H13 tool steel, which can be applied for range of material hardness mentioned above. These models were implemented in a non-isothermal viscoplastic numerical model to simulate the machining process for AISI H13 with various hardness values and applying different cutting regime parameters. Predicted results are validated by comparing them with experimental results found in the literature. They are found to predict reasonably well the cutting forces as well as the change in chip morphology from continuous to segmented chip as the material hardness change

  8. Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

    Directory of Open Access Journals (Sweden)

    Young Min Kim

    2016-06-01

    Full Text Available The current Building Energy Performance Simulation (BEPS tools are based on first principles. For the correct use of BEPS tools, simulationists should have an in-depth understanding of building physics, numerical methods, control logics of building systems, etc. However, it takes significant time and effort to develop a first principles-based simulation model for existing buildings—mainly due to the laborious process of data gathering, uncertain inputs, model calibration, etc. Rather than resorting to an expert’s effort, a data-driven approach (so-called “inverse” approach has received growing attention for the simulation of existing buildings. This paper reports a cross-comparison of three popular machine learning models (Artificial Neural Network (ANN, Support Vector Machine (SVM, and Gaussian Process (GP for predicting a chiller’s energy consumption in a virtual and a real-life building. The predictions based on the three models are sufficiently accurate compared to the virtual and real measurements. This paper addresses the following issues for the successful development of machine learning models: reproducibility, selection of inputs, training period, outlying data obtained from the building energy management system (BEMS, and validation of the models. From the result of this comparative study, it was found that SVM has a disadvantage in computation time compared to ANN and GP. GP is the most sensitive to a training period among the three models.

  9. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  10. Component simulation in problems of calculated model formation of automatic machine mechanisms

    Directory of Open Access Journals (Sweden)

    Telegin Igor

    2017-01-01

    Full Text Available The paper deals with the problems of the component simulation method application in the problems of the automation of the mechanical system model formation with the further possibility of their CAD-realization. The purpose of the investigations mentioned consists in the automation of the CAD-model formation of high-speed mechanisms in automatic machines and in the analysis of dynamic processes occurred in their units taking into account their elasto-inertial properties, power dissipation, gaps in kinematic pairs, friction forces, design and technological loads. As an example in the paper there are considered a formalization of stages in the computer model formation of the cutting mechanism in cold stamping automatic machine AV1818 and methods of for the computation of their parameters on the basis of its solid-state model.

  11. Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

    Science.gov (United States)

    Abellán-Nebot, J. V.; Liu, J.; Romero, F.

    2009-11-01

    The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

  12. Component simulation in problems of calculated model formation of automatic machine mechanisms

    OpenAIRE

    Telegin Igor; Kozlov Alexander; Zhirkov Alexander

    2017-01-01

    The paper deals with the problems of the component simulation method application in the problems of the automation of the mechanical system model formation with the further possibility of their CAD-realization. The purpose of the investigations mentioned consists in the automation of the CAD-model formation of high-speed mechanisms in automatic machines and in the analysis of dynamic processes occurred in their units taking into account their elasto-inertial properties, power dissipation, gap...

  13. Research on Modeling and Control of Regenerative Braking for Brushless DC Machines Driven Electric Vehicles

    OpenAIRE

    Jian-ping Wen; Chuan-wei Zhang

    2015-01-01

    In order to improve energy utilization rate of battery-powered electric vehicle (EV) using brushless DC machine (BLDCM), the model of braking current generated by regenerative braking and control method are discussed. On the basis of the equivalent circuit of BLDCM during the generative braking period, the mathematic model of braking current is established. By using an extended state observer (ESO) to observe actual braking current and the unknown disturbances of regenerative braking system, ...

  14. Direct Drive Synchronous Machine Models for Stability Assessment of Wind Farms

    Energy Technology Data Exchange (ETDEWEB)

    Poeller, Markus; Achilles, Sebastian [DIgSILENT GmbH, Gomaringen (Germany)

    2003-11-01

    The increasing size of wind farms requires power system stability analysis including dynamic wind generator models. For turbines above 1MW doubly-fed induction machines are the most widely used concept. However, especially in Germany, direct-drive wind generators based on converter-driven synchronous generator concepts have reached considerable market penetration. This paper presents converter driven synchronous generator models of various order that can be used for simulating transients and dynamics in a very wide time range.

  15. Fear Extinction as a Model for Translational Neuroscience: Ten Years of Progress

    Science.gov (United States)

    Milad, Mohammed R.; Quirk, Gregory J.

    2016-01-01

    The psychology of extinction has been studied for decades. Approximately 10 years ago, however, there began a concerted effort to understand the neural circuits of extinction of fear conditioning, in both animals and humans. Progress during this period has been facilitated by an unusual degree of coordination between rodent and human researchers examining fear extinction. This successful research program could serve as a model for translational research in other areas of behavioral neuroscience. Here we review the major advances and highlight new approaches to understanding and exploiting fear extinction. PMID:22129456

  16. Model for Investigation of Operational Wind Power Plant Regimes with Doubly–Fed Asynchronous Machine in Power System

    Directory of Open Access Journals (Sweden)

    R. I. Mustafayev

    2012-01-01

    Full Text Available The paper presents methodology for mathematical modeling of power system (its part when jointly operated with wind power plants (stations that contain asynchronous doubly-fed machines used as generators. The essence and advantage of the methodology is that it allows efficiently to mate equations of doubly-fed asynchronous machines, written in the axes that rotate with the machine rotor speed with the equations of external electric power system, written in synchronously rotating axes.

  17. Characteristics determination of Tanka X-ray Diagnostic Machine Model RTO-125

    International Nuclear Information System (INIS)

    Trijoko, Susetyo; Nasukha; Suyati; Nugroho, Agung.

    1993-01-01

    Characteristics determination of Tanka X-ray diagnostic machine model RTO-125. The characteristics of X-ray machine used for examining patient should be known. The characteristics studied in this paper include : X-ray beam profile, coincidence of the light field with radiation field, peak voltage, radiation quality, stability of exposures, and linearity of exposures against time. Beam profile and radiation-field alignment were determined using X-ray film. Winconsin kVp test cassette was used to measure peak voltage. The quality of the radiation, represented by half-value layer (HVL), was measured using aluminium step-wedge. Stability and linearity of exposures were measured using ionization chamber detector having an air volume of 40 cc. The results of this study were documented for the TANKA X-ray machine model RTO-125 of PSPKR BATAN, and the method of this study could be applied for X-ray diagnostic machine in general. (authors). 6 refs., 2 tabs., 6 figs

  18. Analytical Modeling of a Novel Transverse Flux Machine for Direct Drive Wind Turbine Applications: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Hasan, IIftekhar; Husain, Tausif; Uddin, Md Wasi; Sozer, Yilmaz; Husain; Iqbal; Muljadi, Eduard

    2015-08-24

    This paper presents a nonlinear analytical model of a novel double-sided flux concentrating Transverse Flux Machine (TFM) based on the Magnetic Equivalent Circuit (MEC) model. The analytical model uses a series-parallel combination of flux tubes to predict the flux paths through different parts of the machine including air gaps, permanent magnets, stator, and rotor. The two-dimensional MEC model approximates the complex three-dimensional flux paths of the TFM and includes the effects of magnetic saturation. The model is capable of adapting to any geometry that makes it a good alternative for evaluating prospective designs of TFM compared to finite element solvers that are numerically intensive and require more computation time. A single-phase, 1-kW, 400-rpm machine is analytically modeled, and its resulting flux distribution, no-load EMF, and torque are verified with finite element analysis. The results are found to be in agreement, with less than 5% error, while reducing the computation time by 25 times.

  19. Using cognitive modeling to improve the man-machine interface

    International Nuclear Information System (INIS)

    Newton, R.A.; Zyduck, R.C.; Johnson, D.R.

    1982-01-01

    A group of utilities from the Westinghouse Owners Group was formed in early 1980 to examine the interface requirements and to determine how they could be implemented. The products available from the major vendors were examined early in 1980 and judged not to be completely applicable. The utility group then decided to develop its own specifications for a Safety Assessment System (SAS) and, later in 1980, contracted with a company to develop the system, prepare the software and demonstrate the system on a simulator. The resulting SAS is a state-of-the-art system targeted for implementation on pressurized water reactor nuclear units. It has been designed to provide control room operators with centralized and easily understandable information from a computer-based data and display system. This paper gives an overview of the SAS plus a detailed description of one of its functional areas - called AIDS. The AIDS portion of SAS is an advanced concept which uses cognitive modeling of the operator as the basis for its design

  20. Experimental liver fibrosis research: update on animal models, legal issues and translational aspects

    Science.gov (United States)

    2013-01-01

    Liver fibrosis is defined as excessive extracellular matrix deposition and is based on complex interactions between matrix-producing hepatic stellate cells and an abundance of liver-resident and infiltrating cells. Investigation of these processes requires in vitro and in vivo experimental work in animals. However, the use of animals in translational research will be increasingly challenged, at least in countries of the European Union, because of the adoption of new animal welfare rules in 2013. These rules will create an urgent need for optimized standard operating procedures regarding animal experimentation and improved international communication in the liver fibrosis community. This review gives an update on current animal models, techniques and underlying pathomechanisms with the aim of fostering a critical discussion of the limitations and potential of up-to-date animal experimentation. We discuss potential complications in experimental liver fibrosis and provide examples of how the findings of studies in which these models are used can be translated to human disease and therapy. In this review, we want to motivate the international community to design more standardized animal models which might help to address the legally requested replacement, refinement and reduction of animals in fibrosis research. PMID:24274743

  1. A Novel Translational Model of Spinal Cord Injury in Nonhuman Primate.

    Science.gov (United States)

    Le Corre, Marine; Noristani, Harun N; Mestre-Frances, Nadine; Saint-Martin, Guillaume P; Coillot, Christophe; Goze-Bac, Christophe; Lonjon, Nicolas; Perrin, Florence E

    2017-11-27

    Spinal cord injuries (SCI) lead to major disabilities affecting > 2.5 million people worldwide. Major shortcomings in clinical translation result from multiple factors, including species differences, development of moderately predictive animal models, and differences in methodologies between preclinical and clinical studies. To overcome these obstacles, we first conducted a comparative neuroanatomical analysis of the spinal cord between mice, Microcebus murinus (a nonhuman primate), and humans. Next, we developed and characterized a new model of lateral spinal cord hemisection in M. murinus. Over a 3-month period after SCI, we carried out a detailed, longitudinal, behavioral follow-up associated with in vivo magnetic resonance imaging ( 1 H-MRI) monitoring. Then, we compared lesion extension and tissue alteration using 3 methods: in vivo 1 H-MRI, ex vivo 1 H-MRI, and classical histology. The general organization and glial cell distribution/morphology in the spinal cord of M. murinus closely resembles that of humans. Animals assessed at different stages following lateral hemisection of the spinal cord presented specific motor deficits and spinal cord tissue alterations. We also found a close correlation between 1 H-MRI signal and microglia reactivity and/or associated post-trauma phenomena. Spinal cord hemisection in M. murinus provides a reliable new nonhuman primate model that can be used to promote translational research on SCI and represents a novel and more affordable alternative to larger primates.

  2. Parallel phase model : a programming model for high-end parallel machines with manycores.

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Junfeng (Syracuse University, Syracuse, NY); Wen, Zhaofang; Heroux, Michael Allen; Brightwell, Ronald Brian

    2009-04-01

    This paper presents a parallel programming model, Parallel Phase Model (PPM), for next-generation high-end parallel machines based on a distributed memory architecture consisting of a networked cluster of nodes with a large number of cores on each node. PPM has a unified high-level programming abstraction that facilitates the design and implementation of parallel algorithms to exploit both the parallelism of the many cores and the parallelism at the cluster level. The programming abstraction will be suitable for expressing both fine-grained and coarse-grained parallelism. It includes a few high-level parallel programming language constructs that can be added as an extension to an existing (sequential or parallel) programming language such as C; and the implementation of PPM also includes a light-weight runtime library that runs on top of an existing network communication software layer (e.g. MPI). Design philosophy of PPM and details of the programming abstraction are also presented. Several unstructured applications that inherently require high-volume random fine-grained data accesses have been implemented in PPM with very promising results.

  3. Sensitivity analysis of machine-learning models of hydrologic time series

    Science.gov (United States)

    O'Reilly, A. M.

    2017-12-01

    Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

  4. Proposed Model for Translational Research at a Teaching-Intensive College of Pharmacy.

    Science.gov (United States)

    Ulrich, Erin; Grady, Sarah; Vonderhaar, Jacqueline; Ruplin, Andrew

    2017-08-08

    Many American colleges of pharmacy are small, private, teaching institutions. Faculty are required to maintain a research agenda, although the publication quota is less compared with their publicly funded college of pharmacy peers. Faculty at these smaller schools conduct research with very little internal or external funding. This tends to lead to smaller, less impactful research findings. Translational research is becoming popular for research faculty as it bridges theory to practice. The Knowledge-to-Action (KTA) framework presents the steps to conduct translational research. To apply and determine if the KTA framework would be able to produce practice-impactful research at an institution that does not depend on grant funding as part of faculty research agendas. An interdisciplinary team was formed with providers at the clinical faculty's practice site. As the team moved through the KTA steps, authors documented the roles of each team member. It was clear that many different types of teams were formed throughout the KTA process. These teams were then categorized according to the Interdisciplinary Teamwork System. The final result is a proposed model of types of teams and required member roles that are necessary within each KTA step for faculty to conduct practice-impactful research at a small, private, teaching institution without substantial grant funding awards. Applying the KTA framework, two impactful original research manuscripts were developed over two academic years. Furthermore, the practitioners at the clinical faculty member's site were very pleased with the ease of conducting research, as they were never required to take a lead role. In addition, both faculty members alternated lead and support role allowing for a decreased burden of workload while producing theory-driven research. The KTA framework can create a model for translational research and may be particularly beneficial to small teaching institutions to conduct impactful research. Copyright

  5. Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

    International Nuclear Information System (INIS)

    Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

    2017-01-01

    A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well

  6. A one-dimensional Q-machine model taking into account charge-exchange collisions

    International Nuclear Information System (INIS)

    Maier, H.; Kuhn, S.

    1992-01-01

    The Q-machine is a nontrivial bounded plasma system which is excellently suited not only for fundamental plasma physics investigations but also for the development and testing of new theoretical methods for modeling such systems. However, although Q-machines have now been around for over thirty years, it appears that there exist no comprehensive theoretical models taking into account their considerable geometrical and physical complexity with a reasonable degree of self-consistency. In the present context we are concerned with the low-density, single-emitter Q-machine, for which the most widely used model is probably the (one-dimensional) ''collisionless plane-diode model'', which has originally been developed for thermionic diodes. Although the validity of this model is restricted to certain ''axial'' phenomena, we consider it a suitable starting point for extensions of various kinds. While a generalization to two-dimensional geometry (with still collisionless plasma) is being reported elsewhere, the present work represents a first extension to collisional plasma (with still one-dimensional geometry). (author) 12 refs., 2 figs

  7. Law machines: scale models, forensic materiality and the making of modern patent law.

    Science.gov (United States)

    Pottage, Alain

    2011-10-01

    Early US patent law was machine made. Before the Patent Office took on the function of examining patent applications in 1836, questions of novelty and priority were determined in court, within the forum of the infringement action. And at all levels of litigation, from the circuit courts up to the Supreme Court, working models were the media through which doctrine, evidence and argument were made legible, communicated and interpreted. A model could be set on a table, pointed at, picked up, rotated or upended so as to display a point of interest to a particular audience within the courtroom, and, crucially, set in motion to reveal the 'mode of operation' of a machine. The immediate object of demonstration was to distinguish the intangible invention from its tangible embodiment, but models also'machined' patent law itself. Demonstrations of patent claims with models articulated and resolved a set of conceptual tensions that still make the definition and apprehension of the invention difficult, even today, but they resolved these tensions in the register of materiality, performativity and visibility, rather than the register of conceptuality. The story of models tells us something about how inventions emerge and subsist within the context of patent litigation and patent doctrine, and it offers a starting point for renewed reflection on the question of how technology becomes property.

  8. Contribution to the modelling of induction machines by fractional order; Contribution a la modelisation dynamique d'ordre non entier de la machine asynchrone a cage

    Energy Technology Data Exchange (ETDEWEB)

    Canat, S.

    2005-07-15

    Induction machine is most widespread in industry. Its traditional modeling does not take into account the eddy current in the rotor bars which however induce strong variations as well of the resistance as of the resistance of the rotor. This diffusive phenomenon, called 'skin effect' could be modeled by a compact transfer function using fractional derivative (non integer order). This report theoretically analyzes the electromagnetic phenomenon on a single rotor bar before approaching the rotor as a whole. This analysis is confirmed by the results of finite elements calculations of the magnetic field, exploited to identify a fractional order model of the induction machine (identification method of Levenberg-Marquardt). Then, the model is confronted with an identification of experimental results. Finally, an automatic method is carried out to approximate the dynamic model by integer order transfer function on a frequency band. (author)

  9. BRIDG: a domain information model for translational and clinical protocol-driven research.

    Science.gov (United States)

    Becnel, Lauren B; Hastak, Smita; Ver Hoef, Wendy; Milius, Robert P; Slack, MaryAnn; Wold, Diane; Glickman, Michael L; Brodsky, Boris; Jaffe, Charles; Kush, Rebecca; Helton, Edward

    2017-09-01

    It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data. To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG. BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models. With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains. The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov . © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  10. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms

    International Nuclear Information System (INIS)

    Liu, Hui; Tian, Hong-qi; Li, Yan-fei

    2015-01-01

    Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions

  11. Translational relevance of rodent models of hypothalamic-pituitary-adrenal function and stressors in adolescence

    Directory of Open Access Journals (Sweden)

    Cheryl M. McCormick

    2017-02-01

    Full Text Available Elevations in glucocorticoids that result from environmental stressors can have programming effects on brain structure and function when the exposure occurs during sensitive periods that involve heightened neural development. In recent years, adolescence has gained increasing attention as another sensitive period of development, a period in which pubertal transitions may increase the vulnerability to stressors. There are similarities in physical and behavioural development between humans and rats, and rats have been used effectively as an animal model of adolescence and the unique plasticity of this period of ontogeny. This review focuses on benefits and challenges of rats as a model for translational research on hypothalamic-pituitary-adrenal (HPA function and stressors in adolescence, highlighting important parallels and contrasts between adolescent rats and humans, and we review the main stress procedures that are used in investigating HPA stress responses and their consequences in adolescence in rats. We conclude that a greater focus on timing of puberty as a factor in research in adolescent rats may increase the translational relevance of the findings.

  12. Dynamic Modeling of GAIT System Reveals Transcriptome Expansion and Translational Trickle Control Device

    Science.gov (United States)

    Yao, Peng; Potdar, Alka A.; Arif, Abul; Ray, Partho Sarothi; Mukhopadhyay, Rupak; Willard, Belinda; Xu, Yichi; Yan, Jun; Saidel, Gerald M.; Fox, Paul L.

    2012-01-01

    SUMMARY Post-transcriptional regulatory mechanisms superimpose “fine-tuning” control upon “on-off” switches characteristic of gene transcription. We have exploited computational modeling with experimental validation to resolve an anomalous relationship between mRNA expression and protein synthesis. Differential GAIT (Gamma-interferon Activated Inhibitor of Translation) complex activation repressed VEGF-A synthesis to a low, constant rate despite high, variable VEGFA mRNA expression. Dynamic model simulations indicated the presence of an unidentified, inhibitory GAIT element-interacting factor. We discovered a truncated form of glutamyl-prolyl tRNA synthetase (EPRS), the GAIT constituent that binds the 3’-UTR GAIT element in target transcripts. The truncated protein, EPRSN1, prevents binding of functional GAIT complex. EPRSN1 mRNA is generated by a remarkable polyadenylation-directed conversion of a Tyr codon in the EPRS coding sequence to a stop codon (PAY*). By low-level protection of GAIT element-bearing transcripts, EPRSN1 imposes a robust “translational trickle” of target protein expression. Genome-wide analysis shows PAY* generates multiple truncated transcripts thereby contributing to transcriptome expansion. PMID:22386318

  13. Using Machine Learning as a fast emulator of physical processes within the Met Office's Unified Model

    Science.gov (United States)

    Prudden, R.; Arribas, A.; Tomlinson, J.; Robinson, N.

    2017-12-01

    The Unified Model is a numerical model of the atmosphere used at the UK Met Office (and numerous partner organisations including Korean Meteorological Agency, Australian Bureau of Meteorology and US Air Force) for both weather and climate applications.Especifically, dynamical models such as the Unified Model are now a central part of weather forecasting. Starting from basic physical laws, these models make it possible to predict events such as storms before they have even begun to form. The Unified Model can be simply described as having two components: one component solves the navier-stokes equations (usually referred to as the "dynamics"); the other solves relevant sub-grid physical processes (usually referred to as the "physics"). Running weather forecasts requires substantial computing resources - for example, the UK Met Office operates the largest operational High Performance Computer in Europe - and the cost of a typical simulation is spent roughly 50% in the "dynamics" and 50% in the "physics". Therefore there is a high incentive to reduce cost of weather forecasts and Machine Learning is a possible option because, once a machine learning model has been trained, it is often much faster to run than a full simulation. This is the motivation for a technique called model emulation, the idea being to build a fast statistical model which closely approximates a far more expensive simulation. In this paper we discuss the use of Machine Learning as an emulator to replace the "physics" component of the Unified Model. Various approaches and options will be presented and the implications for further model development, operational running of forecasting systems, development of data assimilation schemes, and development of ensemble prediction techniques will be discussed.

  14. Critical Appraisal of Translational Research Models for Suitability in Performance Assessment of Cancer Centers

    NARCIS (Netherlands)

    Rajan, Abinaya; Sullivan, Richard; Bakker, Suzanne; van Harten, Willem H.

    2012-01-01

    Background. Translational research is a complex cumulative process that takes time. However, the operating environment for cancer centers engaged in translational research is now financially insecure. Centers are challenged to improve results and reduce time from discovery to practice innovations.

  15. Critical appraisal of the suitability of translational research models for performance assessment of cancer institutions

    NARCIS (Netherlands)

    Rajan, A.; Sullivan, R.; Bakker, S.; van Harten, Willem H.

    2012-01-01

    Background. Translational research is a complex cumulative process that takes time. However, the operating environment for cancer centers engaged in translational research is now financially insecure. Centers are challenged to improve results and reduce time from discovery to practice innovations.

  16. On the Systematicity of Human Translation Processes

    DEFF Research Database (Denmark)

    Carl, Michael; Dragsted, Barbara; Lykke Jakobsen, Arnt

    While translation careers and the translation profession become more globalised and more technological, we are still far from understanding how humans actually translate and how they could be best supported by machines. In this paper we attempt to outline a method which helps to uncover character......While translation careers and the translation profession become more globalised and more technological, we are still far from understanding how humans actually translate and how they could be best supported by machines. In this paper we attempt to outline a method which helps to uncover...... characteristic steps in human translation processes. Based on the translators' activity data, we develop a taxonomy of translation styles, which are characteristic for different kinds of translators. The taxonomy could serve to inform the development of advanced translation assistance tools and provide a basis...

  17. Developing a new model for the invention and translation of neurotechnologies in academic neurosurgery.

    Science.gov (United States)

    Leuthardt, Eric C

    2013-01-01

    There is currently an acceleration of new scientific and technical capabilities that create new opportunities for academic neurosurgery. To engage these changing dynamics, the Center for Innovation in Neuroscience and Technology (CINT) was created on the premise that successful innovation of device-related ideas relies on collaboration between multiple disciplines. The CINT has created a unique model that integrates scientific, medical, engineering, and legal/business experts to participate in the continuum from idea generation to translation. To detail the method by which this model has been implemented in the Department of Neurological Surgery at Washington University in St. Louis and the experience that has been accrued thus far. The workflow is structured to enable cross-disciplinary interaction, both intramurally and extramurally between academia and industry. This involves a structured method for generating, evaluating, and prototyping promising device concepts. The process begins with the "invention session," which consists of a structured exchange between inventors from diverse technical and medical backgrounds. Successful ideas, which pass a separate triage mechanism, are then sent to industry-sponsored multidisciplinary fellowships to create functioning prototypes. After 3 years, the CINT has engaged 32 clinical and nonclinical inventors, resulting in 47 ideas, 16 fellowships, and 12 patents, for which 7 have been licensed to industry. Financial models project that if commercially successful, device sales could have a notable impact on departmental revenue. The CINT is a model that supports an integrated approach from the time an idea is created through its translational development. To date, the approach has been successful in creating numerous concepts that have led to industry licenses. In the long term, this model will create a novel revenue stream to support the academic neurosurgical mission.

  18. Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

    Science.gov (United States)

    Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

    2017-12-01

    Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

  19. Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development

    Science.gov (United States)

    Calvo, Roque; D’Amato, Roberto; Gómez, Emilio; Domingo, Rosario

    2016-01-01

    The development of an error compensation model for coordinate measuring machines (CMMs) and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included. PMID:27690052

  20. Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development

    Directory of Open Access Journals (Sweden)

    Roque Calvo

    2016-09-01

    Full Text Available The development of an error compensation model for coordinate measuring machines (CMMs and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included.

  1. Response surface modelling of tool electrode wear rate and material removal rate in micro electrical discharge machining of Inconel 718

    DEFF Research Database (Denmark)

    Puthumana, Govindan

    2017-01-01

    conductivity and high strength causing it extremely difficult tomachine. Micro-Electrical Discharge Machining (Micro-EDM) is a non-conventional method that has a potential toovercome these restrictions for machining of Inconel 718. Response Surface Method (RSM) was used for modelling thetool Electrode Wear...

  2. The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction

    DEFF Research Database (Denmark)

    Sabuncu, Mert R.; Van Leemput, Koen

    2012-01-01

    This paper presents the relevance voxel machine (RVoxM), a dedicated Bayesian model for making predictions based on medical imaging data. In contrast to the generic machine learning algorithms that have often been used for this purpose, the method is designed to utilize a small number of spatially...

  3. Extended Park's transformation for 2×3-phase synchronous machine and converter phasor model with representation of AC harmonics

    DEFF Research Database (Denmark)

    Knudsen, Hans

    1995-01-01

    A model of the 2×3-phase synchronous machine is presented using a new transformation based on the eigenvectors of the stator inductance matrix. The transformation fully decouples the stator inductance matrix, and this leads to an equivalent diagram of the machine with no mutual couplings...

  4. A Collaboration Model for Community-Based Software Development with Social Machines

    Directory of Open Access Journals (Sweden)

    Dave Murray-Rust

    2016-02-01

    Full Text Available Crowdsourcing is generally used for tasks with minimal coordination, providing limited support for dynamic reconfiguration. Modern systems, exemplified by social ma chines, are subject to continual flux in both the client and development communities and their needs. To support crowdsourcing of open-ended development, systems must dynamically integrate human creativity with machine support. While workflows can be u sed to handle structured, predictable processes, they are less suitable for social machine development and its attendant uncertainty. We present models and techniques for coordination of human workers in crowdsourced software development environments. We combine the Social Compute Unit—a model of ad-hoc human worker teams—with versatile coordination protocols expressed in the Lightweight Social Calculus. This allows us to combine coordination and quality constraints with dynamic assessments of end-user desires, dynamically discovering and applying development protocols.

  5. Efficiency of Iranian Translation Syllabus at BA Level; Deficiency: A New Comprehensive Model

    Science.gov (United States)

    Sohrabi, Sarah; Rahimi, Ramin; Arjmandi, Masoume

    2015-01-01

    This study aims at investigating the practicality of the current curriculum for translation studies at national level (Iranian curriculum). It is going to have a comprehensive idea of translation students and teachers (university lecturers) over the current translation syllabus at BA level in Iran. A researcher-made CEQ questionnaire (Curriculum…

  6. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

    The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

  7. Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

    OpenAIRE

    Pak Kin Wong; Hang Cheong Wong; Chi Man Vong; Tong Meng Iong; Ka In Wong; Xianghui Gao

    2015-01-01

    Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (...

  8. A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

    Energy Technology Data Exchange (ETDEWEB)

    Hamann, Hendrik F. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center

    2017-05-31

    The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

  9. Quantitative chemogenomics: machine-learning models of protein-ligand interaction.

    Science.gov (United States)

    Andersson, Claes R; Gustafsson, Mats G; Strömbergsson, Helena

    2011-01-01

    Chemogenomics is an emerging interdisciplinary field that lies in the interface of biology, chemistry, and informatics. Most of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand interaction is therefore central to drug discovery and design. In the subfield of chemogenomics known as proteochemometrics, protein-ligand-interaction models are induced from data matrices that consist of both protein and ligand information along with some experimentally measured variable. The two general aims of this quantitative multi-structure-property-relationship modeling (QMSPR) approach are to exploit sparse/incomplete information sources and to obtain more general models covering larger parts of the protein-ligand space, than traditional approaches that focuses mainly on specific targets or ligands. The data matrices, usually obtained from multiple sparse/incomplete sources, typically contain series of proteins and ligands together with quantitative information about their interactions. A useful model should ideally be easy to interpret and generalize well to new unseen protein-ligand combinations. Resolving this requires sophisticated machine-learning methods for model induction, combined with adequate validation. This review is intended to provide a guide to methods and data sources suitable for this kind of protein-ligand-interaction modeling. An overview of the modeling process is presented including data collection, protein and ligand descriptor computation, data preprocessing, machine-learning-model induction and validation. Concerns and issues specific for each step in this kind of data-driven modeling will be discussed. © 2011 Bentham Science Publishers

  10. Mapping Translation Technology Research in Translation Studies

    DEFF Research Database (Denmark)

    Schjoldager, Anne; Christensen, Tina Paulsen; Flanagan, Marian

    2017-01-01

    /Schjoldager 2010, 2011; Christensen 2011). Unfortunately, the increasing professional use of translation technology has not been mirrored within translation studies (TS) by a similar increase in research projects on translation technology (Munday 2009: 15; O’Hagan 2013; Doherty 2016: 952). The current thematic...... section aims to improve this situation by presenting new and innovative research papers that reflect on recent technological advances and their impact on the translation profession and translators from a diversity of perspectives and using a variety of methods. In Section 2, we present translation...... technology research as a subdiscipline of TS, and we define and discuss some basic concepts and models of the field that we use in the rest of the paper. Based on a small-scale study of papers published in TS journals between 2006 and 2016, Section 3 attempts to map relevant developments of translation...

  11. Advancing Control for Shield Tunneling Machine by Backstepping Design with LuGre Friction Model

    Directory of Open Access Journals (Sweden)

    Haibo Xie

    2014-01-01

    Full Text Available Shield tunneling machine is widely applied for underground tunnel construction. The shield machine is a complex machine with large momentum and ultralow advancing speed. The working condition underground is rather complicated and unpredictable, and brings big trouble in controlling the advancing speed. This paper focused on the advancing motion control on desired tunnel axis. A three-state dynamic model was established with considering unknown front face earth pressure force and unknown friction force. LuGre friction model was introduced to describe the friction force. Backstepping design was then proposed to make tracking error converge to zero. To have a comparison study, controller without LuGre model was designed. Tracking simulations of speed regulations and simulations when front face earth pressure changed were carried out to show the transient performances of the proposed controller. The results indicated that the controller had good tracking performance even under changing geological conditions. Experiments of speed regulations were carried out to have validations of the controllers.

  12. Study on intelligent processing system of man-machine interactive garment frame model

    Science.gov (United States)

    Chen, Shuwang; Yin, Xiaowei; Chang, Ruijiang; Pan, Peiyun; Wang, Xuedi; Shi, Shuze; Wei, Zhongqian

    2018-05-01

    A man-machine interactive garment frame model intelligent processing system is studied in this paper. The system consists of several sensor device, voice processing module, mechanical parts and data centralized acquisition devices. The sensor device is used to collect information on the environment changes brought by the body near the clothes frame model, the data collection device is used to collect the information of the environment change induced by the sensor device, voice processing module is used for speech recognition of nonspecific person to achieve human-machine interaction, mechanical moving parts are used to make corresponding mechanical responses to the information processed by data collection device.it is connected with data acquisition device by a means of one-way connection. There is a one-way connection between sensor device and data collection device, two-way connection between data acquisition device and voice processing module. The data collection device is one-way connection with mechanical movement parts. The intelligent processing system can judge whether it needs to interact with the customer, realize the man-machine interaction instead of the current rigid frame model.

  13. ROCK inhibition in models of neurodegeneration and its potential for clinical translation.

    Science.gov (United States)

    Koch, Jan Christoph; Tatenhorst, Lars; Roser, Anna-Elisa; Saal, Kim-Ann; Tönges, Lars; Lingor, Paul

    2018-04-03

    Neurodegenerative disorders like Parkinson's disease, Alzheimer's disease, or amyotrophic lateral sclerosis are affecting a rapidly increasing population worldwide. While common pathomechanisms such as protein aggregation, axonal degeneration, dysfunction of protein clearing and an altered immune response have been characterized, no disease-modifying therapies have been developed so far. Interestingly, a significant involvement of the Rho kinase (ROCK) signaling pathway has been described in all of these mechanisms making it a promising target for new therapeutic approaches. In this article, we first review current knowledge of the involvement of ROCK in neurodegenerative disorders and the utility of its inhibition as a disease-modifying therapy in different neurodegenerative disorders. After a detailed description of the biochemical characteristics of ROCK and its molecular interactors, differences of ROCK-expression under physiological and pathological conditions are compared. Next, different pharmacological and molecular-genetic strategies to inhibit ROCK-function are discussed, focusing on pharmacological ROCK-inhibitors. The role of the ROCK-pathway in cellular processes that are central in neurodegenerative disorders pathology like axonal degeneration, autophagy, synaptic and glial function is explained in detail. Finally, all available data on ROCK-inhibition in different animal models of neurodegenerative disorders is reviewed and first approaches for translation into human patients are discussed. Taken together, there is now extensive evidence from preclinical studies in several neurodegenerative disorders that characterize ROCK as a promising drug target for further translational research in neurodegenerative disorders. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

    Science.gov (United States)

    Fang, Xingang; Bagui, Sikha; Bagui, Subhash

    2017-08-01

    The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Scoping review identifies significant number of knowledge translation theories, models and frameworks with limited use.

    Science.gov (United States)

    Strifler, Lisa; Cardoso, Roberta; McGowan, Jessie; Cogo, Elise; Nincic, Vera; Khan, Paul A; Scott, Alistair; Ghassemi, Marco; MacDonald, Heather; Lai, Yonda; Treister, Victoria; Tricco, Andrea C; Straus, Sharon E

    2018-04-13

    To conduct a scoping review of knowledge translation (KT) theories, models and frameworks that have been used to guide dissemination or implementation of evidence-based interventions targeted to prevention and/or management of cancer or other chronic diseases. We used a comprehensive multistage search process from 2000-2016, which included traditional bibliographic database searching, searching using names of theories, models and frameworks, and cited reference searching. Two reviewers independently screened the literature and abstracted data. We found 596 studies reporting on the use of 159 KT theories, models or frameworks. A majority (87%) of the identified theories, models or frameworks were used in five or fewer studies, with 60% used once. The theories, models and frameworks were most commonly used to inform planning/design, implementation and evaluation activities, and least commonly used to inform dissemination and sustainability/scalability activities. Twenty-six were used across the full implementation spectrum (from planning/design to sustainability/scalability) either within or across studies. All were used for at least individual-level behavior change, while 48% were used for organization-level, 33% for community-level and 17% for system-level change. We found a significant number of KT theories, models and frameworks with a limited evidence base describing their use. Copyright © 2018. Published by Elsevier Inc.

  16. Characterization and modeling of 2D-glass micro-machining by spark-assisted chemical engraving (SACE) with constant velocity

    International Nuclear Information System (INIS)

    Didar, Tohid Fatanat; Dolatabadi, Ali; Wüthrich, Rolf

    2008-01-01

    Spark-assisted chemical engraving (SACE) is an unconventional micro-machining technology based on electrochemical discharge used for micro-machining nonconductive materials. SACE 2D micro-machining with constant speed was used to machine micro-channels in glass. Parameters affecting the quality and geometry of the micro-channels machined by SACE technology with constant velocity were presented and the effect of each of the parameters was assessed. The effect of chemical etching on the geometry of micro-channels under different machining conditions has been studied, and a model is proposed for characterization of the micro-channels as a function of machining voltage and applied speed

  17. Introducing the Interactive Model for the Training of Audiovisual Translators and Analysis of Multimodal Texts

    Directory of Open Access Journals (Sweden)

    Pietro Luigi Iaia

    2015-07-01

    Full Text Available Abstract – This paper introduces the ‘Interactive Model’ of audiovisual translation developed in the context of my PhD research on the cognitive-semantic, functional and socio-cultural features of the Italian-dubbing translation of a corpus of humorous texts. The Model is based on two interactive macro-phases – ‘Multimodal Critical Analysis of Scripts’ (MuCrAS and ‘Multimodal Re-Textualization of Scripts’ (MuReTS. Its construction and application are justified by a multidisciplinary approach to the analysis and translation of audiovisual texts, so as to focus on the linguistic and extralinguistic dimensions affecting both the reception of source texts and the production of target ones (Chaume 2004; Díaz Cintas 2004. By resorting to Critical Discourse Analysis (Fairclough 1995, 2001, to a process-based approach to translation and to a socio-semiotic analysis of multimodal texts (van Leeuwen 2004; Kress and van Leeuwen 2006, the Model is meant to be applied to the training of audiovisual translators and discourse analysts in order to help them enquire into the levels of pragmalinguistic equivalence between the source and the target versions. Finally, a practical application shall be discussed, detailing the Italian rendering of a comic sketch from the American late-night talk show Conan.Abstract – Questo studio introduce il ‘Modello Interattivo’ di traduzione audiovisiva sviluppato durante il mio dottorato di ricerca incentrato sulle caratteristiche cognitivo-semantiche, funzionali e socio-culturali della traduzione italiana per il doppiaggio di un corpus di testi comici. Il Modello è costituito da due fasi: la prima, di ‘Analisi critica e multimodale degli script’ (MuCrAS e la seconda, di ‘Ritestualizzazione critica e multimodale degli script’ (MuReTS, e la sua costruzione e applicazione sono frutto di un approccio multidisciplinare all’analisi e traduzione dei testi audiovisivi, al fine di esaminare le

  18. Developing a dengue forecast model using machine learning: A case study in China.

    Science.gov (United States)

    Guo, Pi; Liu, Tao; Zhang, Qin; Wang, Li; Xiao, Jianpeng; Zhang, Qingying; Luo, Ganfeng; Li, Zhihao; He, Jianfeng; Zhang, Yonghui; Ma, Wenjun

    2017-10-01

    In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011-2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics.

  19. Are there intelligent Turing machines?

    OpenAIRE

    Bátfai, Norbert

    2015-01-01

    This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines. Like universal Turing machines, orchestrated machines are also designed to simulate Turing machines but they can also modify the original operation of the included Turing machines to create a new layer of some kind of collective behavior. Using this new model we can define some interested notions related to cooperation ability of Turing machines such as the intelligence quo...

  20. MULTIFUNCTION OF INTERNET IN TRANSLATION

    Directory of Open Access Journals (Sweden)

    Bayu Budiharjo

    2017-04-01

    Full Text Available Technology affects almost all areas, including translation. Many products of technology have made translational works easier, one of which is internet. Despite the wide use of internet, the potentials it has are sometimes unnoticed. While web-based dictionaries or thesaurus often serve as translators’ assistants and online Machine Translation issues become topics of many researches, other uses of internet related to translation may not be known by many. Internet can help disseminate newborn ideas, theories and findings worldwide to enhance translation theories. Besides, the contact between internet and translation generates new areas to examine. Internet also provides helping hand in the area of translation research. Researcher or anyone conducting research in the field of translation can find a range of research gaps as well as reference. Those who need group discussions to collect required data from informants, or researchers of the same interest coming from all over the world can meet and conduct Focus Group Discussion (FGD on virtual world. Furthermore, internet offers various forms of assistance for translation practitioners. The commonly used internet assistance consists of dictionaries, thesaurus and Machine Translations available on the internet. Other forms of aid provided by internet take form of parallel texts, images, and videos, which can be very helpful. Internet provides many things which can be utilized for the purpose of translation. Internet keeps on providing more as it develops from time to time in line with the development of technology. Internet awaits utilization of theorists, researchers, practitioners and those having concern on translation.

  1. Multifrequency spiral vector model for the brushless doubly-fed induction machine

    DEFF Research Database (Denmark)

    Han, Peng; Cheng, Ming; Zhu, Xinkai

    2017-01-01

    This paper presents a multifrequency spiral vector model for both steady-state and dynamic performance analysis of the brushless doubly-fed induction machine (BDFIM) with a nested-loop rotor. Winding function theory is first employed to give a full picture of the inductance characteristics...... analytically, revealing the underlying relationship between harmonic components of stator-rotor mutual inductances and the airgap magnetic field distribution. Different from existing vector models, which only model the fundamental components of mutual inductances, the proposed vector model takes...... into consideration the low-order space harmonic coupling by incorporating nonsinusoidal inductances into modeling process. A new model order reduction approach is then proposed to transform the nested-loop rotor into an equivalent single-loop one. The effectiveness of the proposed modelling method is verified by 2D...

  2. Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

    Science.gov (United States)

    Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

    2017-02-01

    Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

  3. A general electromagnetic excitation model for electrical machines considering the magnetic saturation and rub impact

    Science.gov (United States)

    Xu, Xueping; Han, Qinkai; Chu, Fulei

    2018-03-01

    The electromagnetic vibration of electrical machines with an eccentric rotor has been extensively investigated. However, magnetic saturation was often neglected. Moreover, the rub impact between the rotor and stator is inevitable when the amplitude of the rotor vibration exceeds the air-gap. This paper aims to propose a general electromagnetic excitation model for electrical machines. First, a general model which takes the magnetic saturation and rub impact into consideration is proposed and validated by the finite element method and reference. The dynamic equations of a Jeffcott rotor system with electromagnetic excitation and mass imbalance are presented. Then, the effects of pole-pair number and rubbing parameters on vibration amplitude are studied and approaches restraining the amplitude are put forward. Finally, the influences of mass eccentricity, resultant magnetomotive force (MMF), stiffness coefficient, damping coefficient, contact stiffness and friction coefficient on the stability of the rotor system are investigated through the Floquet theory, respectively. The amplitude jumping phenomenon is observed in a synchronous generator for different pole-pair numbers. The changes of design parameters can alter the stability states of the rotor system and the range of parameter values forms the zone of stability, which lays helpful suggestions for the design and application of the electrical machines.

  4. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Science.gov (United States)

    Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris

    2015-04-01

    The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  5. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Directory of Open Access Journals (Sweden)

    Ickwon Choi

    2015-04-01

    Full Text Available The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release. We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  6. Understanding Translation

    DEFF Research Database (Denmark)

    Schjoldager, Anne Gram; Gottlieb, Henrik; Klitgård, Ida

    Understanding Translation is designed as a textbook for courses on the theory and practice of translation in general and of particular types of translation - such as interpreting, screen translation and literary translation. The aim of the book is to help you gain an in-depth understanding...... of the phenomenon of translation and to provide you with a conceptual framework for the analysis of various aspects of professional translation. Intended readers are students of translation and languages, but the book will also be relevant for others who are interested in the theory and practice of translation...... - translators, language teachers, translation users and literary, TV and film critics, for instance. Discussions focus on translation between Danish and English....

  7. Computer-aided translation tools

    DEFF Research Database (Denmark)

    Christensen, Tina Paulsen; Schjoldager, Anne

    2016-01-01

    in Denmark is rather high in general, but limited in the case of machine translation (MT) tools: While most TSPs use translation-memory (TM) software, often in combination with a terminology management system (TMS), only very few have implemented MT, which is criticised for its low quality output, especially......The paper reports on a questionnaire survey from 2013 of the uptake and use of computer-aided translation (CAT) tools by Danish translation service providers (TSPs) and discusses how these tools appear to have impacted on the Danish translation industry. According to our results, the uptake...

  8. Modeling and simulation of control system for electron beam machine (EBM) using programmable automation controller (PAC)

    International Nuclear Information System (INIS)

    Leo Kwee Wah; Lojius Lombigit; Abu Bakar Mhd Ghazali; Muhamad Zahidee Taat; Ayub Mohamed; Chong Foh Yoong

    2006-01-01

    An EBM electronic model is designed to simulate the control system of the Nissin EBM, which is located at Block 43, MINT complex of Jalan Dengkil with maximum output of 3 MeV, 30 mA using a Programmable Automation Controllers (PAC). This model operates likes a real EBM system where all the start-up, interlocking and stopping procedures are fully followed. It also involves formulating the mathematical models to relate certain output with the input parameters using data from actual operation on EB machine. The simulation involves a set of PAC system consisting of the digital and analogue input/output modules. The program code is written using Labview software (real-time version) on a PC and then downloaded into the PAC stand-alone memory. All the 23 interlocking signals required by the EB machine are manually controlled by mechanical switches and represented by LEDs. The EB parameters are manually controlled by potentiometers and displayed on analogue and digital meters. All these signals are then interfaced to the PC via a wifi wireless communication built-in at the PAC controller. The program is developed in accordance to the specifications and requirement of the original real EB system and displays them on the panel of the model and also on the PC monitor. All possible chances from human errors, hardware and software malfunctions, including the worst-case conditions will be tested, evaluated and modified. We hope that the performance of our model complies the requirements of operating the EB machine. It also hopes that this electronic model can replace the original PC interfacing being utilized in the Nissin EBM in the near future. The system can also be used to study the fault tolerance analysis and automatic re-configuration for advanced control of the EB system. (Author)

  9. Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models

    Directory of Open Access Journals (Sweden)

    Lucky eMehra

    2016-03-01

    Full Text Available Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB, caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum. The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early

  10. Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

    Science.gov (United States)

    Mehra, Lucky K; Cowger, Christina; Gross, Kevin; Ojiambo, Peter S

    2016-01-01

    Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF), in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of

  11. FEM BASED PARAMETRIC DESIGN STUDY OF TIRE PROFILE USING DEDICATED CAD MODEL AND TRANSLATION CODE

    Directory of Open Access Journals (Sweden)

    Nikola Korunović

    2014-12-01

    Full Text Available In this paper a finite element method (FEM based parametric design study of the tire profile shape and belt width is presented. One of the main obstacles that similar studies have faced is how to change the finite element mesh after a modification of the tire geometry is performed. In order to overcome this problem, a new approach is proposed. It implies automatic update of the finite elements mesh, which follows the change of geometric design parameters on a dedicated CAD model. The mesh update is facilitated by an originally developed mapping and translation code. In this way, the performance of a large number of geometrically different tire design variations may be analyzed in a very short time. Although a pilot one, the presented study has also led to the improvement of the existing tire design.

  12. Translation Techniques

    OpenAIRE

    Marcia Pinheiro

    2015-01-01

    In this paper, we discuss three translation techniques: literal, cultural, and artistic. Literal translation is a well-known technique, which means that it is quite easy to find sources on the topic. Cultural and artistic translation may be new terms. Whilst cultural translation focuses on matching contexts, artistic translation focuses on matching reactions. Because literal translation matches only words, it is not hard to find situations in which we should not use this technique.  Because a...

  13. Maintaining the clinical relevance of animal models in translational studies of post-traumatic stress disorder.

    Science.gov (United States)

    Cohen, Hagit; Matar, Michael A; Zohar, Joseph

    2014-01-01

    The diagnosis of Post-Traumatic Stress Disorder (PTSD) is conditional on directly experiencing or witnessing a significantly threatening event and the presence of a certain minimal number of symptoms from each of four symptom clusters (re-experiencing, avoidance, negative cognition and mood, and hyperarousal) at least one month after the event (DSM 5) (American Psychiatric Association 2013). Only a proportion of the population exposed develops symptoms fulfilling the criteria. The individual heterogeneity in responses of stress-exposed animals suggested that adapting clearly defined and reliably reproducible "diagnostic", i.e. behavioral, criteria for animal responses would augment the clinical validity of the analysis of study data. We designed cut-off (inclusion/exclusion) behavioral criteria (CBC) which classify study subjects as being severely, minimally or partially affected by the stress paradigm, to be applied retrospectively in the analysis of behavioral data. Behavioral response classification enables the researcher to correlate (retrospectively) specific anatomic, bio-molecular and physiological parameters with the degree and pattern of the individual behavioral response, and also introduces "prevalence rates" as a valid study-parameter. The cumulative results of our studies indicate that, by classifying the data from individual subjects according to their response patterns, the animal study can more readily be translated into clinical "follow-up" studies and back again. This article will discuss the concept of the model and its background, and present a selection of studies employing and examining the model, alongside the underlying translational rationale of each. © The Author 2014. Published by Oxford University Press on behalf of the Institute for Laboratory Animal Research. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  14. A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping

    Directory of Open Access Journals (Sweden)

    Wang Yan

    2014-01-01

    Full Text Available The main challenges that marine heterogeneous data integration faces are the problem of accurate schema mapping between heterogeneous data sources. In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method basing on multianalyzer machine learning model. The multianalyzer analysis the learning results comprehensively, and a fuzzy comprehensive evaluation system is introduced for output results’ evaluation and multi factor quantitative judging. Finally, the data mapping comparison experiment on the East China Sea observing data confirms the effectiveness of the model and shows multianalyzer’s obvious improvement of mapping error rate.

  15. A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping

    Science.gov (United States)

    Yan, Wang; Jiajin, Le; Yun, Zhang

    2014-01-01

    The main challenges that marine heterogeneous data integration faces are the problem of accurate schema mapping between heterogeneous data sources. In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method basing on multianalyzer machine learning model. The multianalyzer analysis the learning results comprehensively, and a fuzzy comprehensive evaluation system is introduced for output results' evaluation and multi factor quantitative judging. Finally, the data mapping comparison experiment on the East China Sea observing data confirms the effectiveness of the model and shows multianalyzer's obvious improvement of mapping error rate. PMID:25250372

  16. MODELS OF FATIGUE LIFE CURVES IN FATIGUE LIFE CALCULATIONS OF MACHINE ELEMENTS – EXAMPLES OF RESEARCH

    Directory of Open Access Journals (Sweden)

    Grzegorz SZALA

    2014-03-01

    Full Text Available In the paper there was attempted to analyse models of fatigue life curves possible to apply in calculations of fatigue life of machine elements. The analysis was limited to fatigue life curves in stress approach enabling cyclic stresses from the range of low cycle fatigue (LCF, high cycle fatigue (HCF, fatigue limit (FL and giga cycle fatigue (GCF appearing in the loading spectrum at the same time. Chosen models of the analysed fatigue live curves will be illustrated with test results of steel and aluminium alloys.

  17. Research on Modeling and Control of Regenerative Braking for Brushless DC Machines Driven Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Jian-ping Wen

    2015-01-01

    Full Text Available In order to improve energy utilization rate of battery-powered electric vehicle (EV using brushless DC machine (BLDCM, the model of braking current generated by regenerative braking and control method are discussed. On the basis of the equivalent circuit of BLDCM during the generative braking period, the mathematic model of braking current is established. By using an extended state observer (ESO to observe actual braking current and the unknown disturbances of regenerative braking system, the autodisturbances rejection controller (ADRC for controlling the braking current is developed. Experimental results show that the proposed method gives better recovery efficiency and is robust to disturbances.

  18. Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

    Science.gov (United States)

    Sung, Yao-Ting; Chen, Ju-Ling; Cha, Ji-Her; Tseng, Hou-Chiang; Chang, Tao-Hsing; Chang, Kuo-En

    2015-06-01

    Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.

  19. Estimating the complexity of 3D structural models using machine learning methods

    Science.gov (United States)

    Mejía-Herrera, Pablo; Kakurina, Maria; Royer, Jean-Jacques

    2016-04-01

    Quantifying the complexity of 3D geological structural models can play a major role in natural resources exploration surveys, for predicting environmental hazards or for forecasting fossil resources. This paper proposes a structural complexity index which can be used to help in defining the degree of effort necessary to build a 3D model for a given degree of confidence, and also to identify locations where addition efforts are required to meet a given acceptable risk of uncertainty. In this work, it is considered that the structural complexity index can be estimated using machine learning methods on raw geo-data. More precisely, the metrics for measuring the complexity can be approximated as the difficulty degree associated to the prediction of the geological objects distribution calculated based on partial information on the actual structural distribution of materials. The proposed methodology is tested on a set of 3D synthetic structural models for which the degree of effort during their building is assessed using various parameters (such as number of faults, number of part in a surface object, number of borders, ...), the rank of geological elements contained in each model, and, finally, their level of deformation (folding and faulting). The results show how the estimated complexity in a 3D model can be approximated by the quantity of partial data necessaries to simulated at a given precision the actual 3D model without error using machine learning algorithms.

  20. A hybrid prognostic model for multistep ahead prediction of machine condition

    Science.gov (United States)

    Roulias, D.; Loutas, T. H.; Kostopoulos, V.

    2012-05-01

    Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.