WorldWideScience

Sample records for machine translation techniques

  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. 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...

  4. 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…

  5. 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.

  6. 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.

  7. 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…

  8. 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...

  9. 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...

  10. 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, ...

  11. 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…

  12. 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.

  13. 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.

  14. 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 ...

  15. 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,

  16. 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.

  17. 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

  18. 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....

  19. 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...

  20. 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...

  1. 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.

  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. 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...

  4. 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

  5. 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…

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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...

  17. 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.

  18. 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.

  19. 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…

  20. 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…

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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...

  6. 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...

  7. 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.

  8. 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...

  9. 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.

  10. 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.

  11. 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.

  12. 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...

  13. 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.

  14. 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.

  15. 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

  16. 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...

  17. Machine safety: proper safeguarding techniques.

    Science.gov (United States)

    Martin, K J

    1992-06-01

    1. OSHA mandates certain safeguarding of machinery to prevent accidents and protect machine operators. OSHA specifies moving parts that must be guarded and sets criteria for the guards. 2. A 1989 OSHA standard for lockout/tagout requires locking the energy source during maintenance, periodically inspecting for power transmission, and training maintenance workers. 3. In an amputation emergency, first aid for cardiopulmonary resuscitation, shock, and bleeding are the first considerations. The amputated part should be wrapped in moist gauze, placed in a sealed plastic bag, and placed in a container of 50% water and 50% ice for transport. 4. The role of the occupational health nurse in machine safety is to conduct worksite analyses to identify proper safeguarding and to communicate deficiencies to appropriate personnel; to train workers in safe work practices and observe compliance in the use of machine guards; to provide care to workers injured by machines; and to reinforce safe work practices among machine operators.

  18. 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.

  19. 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...

  20. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

    Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

    2016-01-01

    Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter...

  1. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

    Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

    2015-01-01

    Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework...

  2. 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

  3. 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.

  4. Machine Learning Techniques in Clinical Vision Sciences.

    Science.gov (United States)

    Caixinha, Miguel; Nunes, Sandrina

    2017-01-01

    This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

  5. 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.

  6. 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.

  7. 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…

  8. MACHINE LEARNING TECHNIQUES USED IN BIG DATA

    Directory of Open Access Journals (Sweden)

    STEFANIA LOREDANA NITA

    2016-07-01

    Full Text Available The classical tools used in data analysis are not enough in order to benefit of all advantages of big data. The amount of information is too large for a complete investigation, and the possible connections and relations between data could be missed, because it is difficult or even impossible to verify all assumption over the information. Machine learning is a great solution in order to find concealed correlations or relationships between data, because it runs at scale machine and works very well with large data sets. The more data we have, the more the machine learning algorithm is useful, because it “learns” from the existing data and applies the found rules on new entries. In this paper, we present some machine learning algorithms and techniques used in big data.

  9. 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

  10. 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....

  11. Machine learning techniques for optical communication system optimization

    DEFF Research Database (Denmark)

    Zibar, Darko; Wass, Jesper; Thrane, Jakob

    In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction.......In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction....

  12. 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

  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. 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)

  15. 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.

  16. 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,…

  17. CRDM motion analysis using machine learning technique

    International Nuclear Information System (INIS)

    Nishimura, Takuya; Nakayama, Hiroyuki; Saitoh, Mayumi; Yaguchi, Seiji

    2017-01-01

    Magnetic jack type Control Rod Drive Mechanism (CRDM) for pressurized water reactor (PWR) plant operates control rods in response to electrical signals from a reactor control system. CRDM operability is evaluated by quantifying armature's response of closed/opened time which means interval time between coil energizing/de-energizing points and armature closed/opened points. MHI has already developed an automatic CRDM motion analysis and applied it to actual plants so far. However, CRDM operational data has wide variation depending on their characteristics such as plant condition, plant, etc. In the existing motion analysis, there is an issue of analysis accuracy for applying a single analysis technique to all plant conditions, plants, etc. In this study, MHI investigated motion analysis using machine learning (Random Forests) which is flexibly accommodated to CRDM operational data with wide variation, and is improved analysis accuracy. (author)

  18. 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.

  19. Application of Machine Learning Techniques in Aquaculture

    OpenAIRE

    Rahman, Akhlaqur; Tasnim, Sumaira

    2014-01-01

    In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications.

  20. 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…

  1. 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

  2. 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

  3. 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…

  4. 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…

  5. 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

  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. 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.

  10. 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.

  11. Event Streams Clustering Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Hanen Bouali

    2015-10-01

    Full Text Available Data streams are usually of unbounded lengths which push users to consider only recent observations by focusing on a time window, and ignore past data. However, in many real world applications, past data must be taken in consideration to guarantee the efficiency, the performance of decision making and to handle data streams evolution over time. In order to build a selectively history to track the underlying event streams changes, we opt for the continuously data of the sliding window which increases the time window based on changes over historical data. In this paper, to have the ability to access to historical data without requiring any significant storage or multiple passes over the data. In this paper, we propose a new algorithm for clustering multiple data streams using incremental support vector machine and data representative points’ technique. The algorithm uses a sliding window model for the most recent clustering results and data representative points to model the old data clustering results. Our experimental results on electromyography signal show a better clustering than other present in the literature

  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. 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...

  14. 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…

  15. 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

  16. 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

  17. 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

  18. Data Mining Practical Machine Learning Tools and Techniques

    CERN Document Server

    Witten, Ian H; Hall, Mark A

    2011-01-01

    Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place

  19. 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 ...

  20. BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

    OpenAIRE

    Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

    2015-01-01

    Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data...

  1. 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.

  2. Prostate Cancer Probability Prediction By Machine Learning Technique.

    Science.gov (United States)

    Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

    2017-11-26

    The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

  3. 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...

  4. 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

  5. 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.

  6. Machine learning techniques for persuasion dectection in conversation

    OpenAIRE

    Ortiz, Pedro.

    2010-01-01

    Approved for public release; distribution is unlimited We determined that it is possible to automatically detect persuasion in conversations using three traditional machine learning techniques, naive bayes, maximum entropy, and support vector machine. These results are the first of their kind and serve as a baseline for all future work in this field. The three techniques consistently outperformed the baseline F-score, but not at a level that would be useful for real world applications. The...

  7. A Comparative Analysis of Machine Learning Techniques for Credit Scoring

    OpenAIRE

    Nwulu, Nnamdi; Oroja, Shola; İlkan, Mustafa

    2012-01-01

    Abstract Credit Scoring has become an oft researched topic in light of the increasing volatility of the global economy and the recent world financial crisis. Amidst the many methods used for credit scoring, machine learning techniques are becoming increasingly popular due to their efficient and accurate nature and relative simplicity. Furthermore machine learning techniques minimize the risk of human bias and error and maximize speed as they are able to perform computation...

  8. 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....

  9. 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.

  10. Machine learning techniques to examine large patient databases.

    Science.gov (United States)

    Meyfroidt, Geert; Güiza, Fabian; Ramon, Jan; Bruynooghe, Maurice

    2009-03-01

    Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

  11. IoT Security Techniques Based on Machine Learning

    OpenAIRE

    Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di

    2018-01-01

    Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine le...

  12. Application of Social Network Analysis Techniques to Machine Translated Documents

    Science.gov (United States)

    2010-04-01

    Mandela, por la nueva Asamblea nacional sudafricana. La Comisión electoral independiente , una gigantesca maquinaria administrativa con unos 300.000...seat perpetration 2 surpassed 1 ultramoderno 1 recount 3 party 1 sellar 1 posting 1 suspended 1 urns 1 regions 1 president 2 south_african 1

  13. Machine monitoring via current signature analysis techniques

    International Nuclear Information System (INIS)

    Smith, S.F.; Castleberry, K.N.; Nowlin, C.H.

    1992-01-01

    A significant need in the effort to provide increased production quality is to provide improved plant equipment monitoring capabilities. Unfortunately, in today's tight economy, even such monitoring instrumentation must be implemented in a recognizably cost effective manner. By analyzing the electric current drawn by motors, actuator, and other line-powered industrial equipment, significant insights into the operations of the movers, driven equipment, and even the power source can be obtained. The generic term 'current signature analysis' (CSA) has been coined to describe several techniques for extracting useful equipment or process monitoring information from the electrical power feed system. A patented method developed at Oak Ridge National Laboratory is described which recognizes the presence of line-current modulation produced by motors and actuators driving varying loads. The in-situ application of applicable linear demodulation techniques to the analysis of numerous motor-driven systems is also discussed. The use of high-quality amplitude and angle-demodulation circuitry has permitted remote status monitoring of several types of medium and high-power gas compressors in (US DOE facilities) driven by 3-phase induction motors rated from 100 to 3,500 hp, both with and without intervening speed increasers. Flow characteristics of the compressors, including various forms of abnormal behavior such as surging and rotating stall, produce at the output of the specialized detectors specific time and frequency signatures which can be easily identified for monitoring, control, and fault-prevention purposes. The resultant data are similar in form to information obtained via standard vibration-sensing techniques and can be analyzed using essentially identical methods. In addition, other machinery such as refrigeration compressors, brine pumps, vacuum pumps, fans, and electric motors have been characterized

  14. 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...

  15. 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

  16. Analysing CMS transfers using Machine Learning techniques

    CERN Document Server

    Diotalevi, Tommaso

    2016-01-01

    LHC experiments transfer more than 10 PB/week between all grid sites using the FTS transfer service. In particular, CMS manages almost 5 PB/week of FTS transfers with PhEDEx (Physics Experiment Data Export). FTS sends metrics about each transfer (e.g. transfer rate, duration, size) to a central HDFS storage at CERN. The work done during these three months, here as a Summer Student, involved the usage of ML techniques, using a CMS framework called DCAFPilot, to process this new data and generate predictions of transfer latencies on all links between Grid sites. This analysis will provide, as a future service, the necessary information in order to proactively identify and maybe fix latency issued transfer over the WLCG.

  17. Technique for Increasing Accuracy of Positioning System of Machine Tools

    Directory of Open Access Journals (Sweden)

    Sh. Ji

    2014-01-01

    Full Text Available The aim of research is to improve the accuracy of positioning and processing system using a technique for optimization of pressure diagrams of guides in machine tools. The machining quality is directly related to its accuracy, which characterizes an impact degree of various errors of machines. The accuracy of the positioning system is one of the most significant machining characteristics, which allow accuracy evaluation of processed parts.The literature describes that the working area of the machine layout is rather informative to characterize the effect of the positioning system on the macro-geometry of the part surfaces to be processed. To enhance the static accuracy of the studied machine, in principle, two groups of measures are possible. One of them points toward a decrease of the cutting force component, which overturns the slider moments. Another group of measures is related to the changing sizes of the guide facets, which may lead to their profile change.The study was based on mathematical modeling and optimization of the cutting zone coordinates. And we find the formula to determine the surface pressure of the guides. The selected parameters of optimization are vectors of the cutting force and values of slides and guides. Obtained results show that a technique for optimization of coordinates in the cutting zone was necessary to increase a processing accuracy.The research has established that to define the optimal coordinates of the cutting zone we have to change the sizes of slides, value and coordinates of applied forces, reaching the pressure equalization and improving the accuracy of positioning system of machine tools. In different points of the workspace a vector of forces is applied, pressure diagrams are found, which take into account the changes in the parameters of positioning system, and the pressure diagram equalization to provide the most accuracy of machine tools is achieved.

  18. Machine Learning Techniques in Optimal Design

    Science.gov (United States)

    Cerbone, Giuseppe

    1992-01-01

    Many important applications can be formalized as constrained optimization tasks. For example, we are studying the engineering domain of two-dimensional (2-D) structural design. In this task, the goal is to design a structure of minimum weight that bears a set of loads. A solution to a design problem in which there is a single load (L) and two stationary support points (S1 and S2) consists of four members, E1, E2, E3, and E4 that connect the load to the support points is discussed. In principle, optimal solutions to problems of this kind can be found by numerical optimization techniques. However, in practice [Vanderplaats, 1984] these methods are slow and they can produce different local solutions whose quality (ratio to the global optimum) varies with the choice of starting points. Hence, their applicability to real-world problems is severely restricted. To overcome these limitations, we propose to augment numerical optimization by first performing a symbolic compilation stage to produce: (a) objective functions that are faster to evaluate and that depend less on the choice of the starting point and (b) selection rules that associate problem instances to a set of recommended solutions. These goals are accomplished by successive specializations of the problem class and of the associated objective functions. In the end, this process reduces the problem to a collection of independent functions that are fast to evaluate, that can be differentiated symbolically, and that represent smaller regions of the overall search space. However, the specialization process can produce a large number of sub-problems. This is overcome by deriving inductively selection rules which associate problems to small sets of specialized independent sub-problems. Each set of candidate solutions is chosen to minimize a cost function which expresses the tradeoff between the quality of the solution that can be obtained from the sub-problem and the time it takes to produce it. The overall solution

  19. Adapting Virtual Machine Techniques for Seamless Aspect Support

    NARCIS (Netherlands)

    Bockisch, Christoph; Arnold, Matthew; Dinkelaker, Tom; Mezini, Mira

    2006-01-01

    Current approaches to compiling aspect-oriented programs are inefficient. This inefficiency has negative effects on the productivity of the development process and is especially prohibitive for dynamic aspect deployment. In this work, we present how well-known virtual machine techniques can be used

  20. Memory Based Machine Intelligence Techniques in VLSI hardware

    OpenAIRE

    James, Alex Pappachen

    2012-01-01

    We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high ...

  1. Contemporary machine learning: techniques for practitioners in the physical sciences

    Science.gov (United States)

    Spears, Brian

    2017-10-01

    Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

  2. 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,…

  3. 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.

  4. Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

    Directory of Open Access Journals (Sweden)

    Shuibo Hu

    2018-03-01

    Full Text Available The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs, in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD high performance liquid chromatography (HPLC database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS, artificial neural networks (ANN, support vector machine (SVM and random forests (RF, and feature selection techniques, including genetic algorithm (GA, successive projection algorithm (SPA and recursive feature elimination based on support vector machine (SVM-RFE, for inferring PSCs from remote sensing data. Results showed that: (1 SVM-RFE worked better in selecting sensitive features; (2 RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3 machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4 sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5 the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing.

  5. 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.

  6. AN ANALYSIS OF TRANSLATION TECHNIQUES IN THE ENGLISH VERSION OF ARRAHMAN SURAH

    Directory of Open Access Journals (Sweden)

    Farida Repelita Wati Kembaren

    2018-02-01

    Full Text Available The Holy Qur’an has been translated into many languages including English. However, different translator of the Holy Qur’an produces different English version of Al-Qur’an because every translator uses different translation techniques to translate Al-Qur’an. This study aims to compare the three English versions of Arrahman surah, and describe the most dominant translation techniques used by the three translators to translate the Holy Qur’an. Translation techniques proposed by Molina and Albir (2002, p.509-511 are used to analyze the data. The data for this study are words, phrases, and clauses in the 78 verses of Arrahman surah in Arabic and its three English versions translated by Maulawi Sher ‘Ali, Dr. Muhammad Taqi­ud­Din Al­Hilali and Dr. Muhammad Muhsin Khan, and Talal Itani. The result shows that there are four most dominant techniques used by the three translators in translating the Holy Qur’an; Adaptation, Amplification, Established Equivalent, and Literal Translation technique. The first and the second translator prefer to use Amplification to introduce details in brackets or footnotes, but the third translator prefers to use Established Equivalent to find a term or expression recognized (by dictionaries or language in use as an equivalent.

  7. 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.

  8. 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%.

  9. Data mining practical machine learning tools and techniques

    CERN Document Server

    Witten, Ian H

    2005-01-01

    As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same

  10. Using Machine Learning Techniques in the Analysis of Oceanographic Data

    Science.gov (United States)

    Falcinelli, K. E.; Abuomar, S.

    2017-12-01

    Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

  11. Machine Learning Techniques for Stellar Light Curve Classification

    Science.gov (United States)

    Hinners, Trisha A.; Tat, Kevin; Thorp, Rachel

    2018-07-01

    We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB of Kepler light curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light-curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a long short-term memory recurrent neural network produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (∼2%–4%) and good accuracy (∼75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger data sets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

  12. Classifying Structures in the ISM with Machine Learning Techniques

    Science.gov (United States)

    Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

    2011-01-01

    The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

  13. 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.

  14. Development of an evaluation technique for human-machine interface

    International Nuclear Information System (INIS)

    Min, Dae Hwan; Koo, Sang Hui; Ahn, Won Yeong; Ryu, Yeong Shin

    1997-07-01

    The purpose of this study is two-fold : firstly to establish an evaluation technique for HMI(Human Machine Interface) in NPPs(Nuclear Power Plants) and secondly to develop an architecture of a support system which can be used for the evaluation of HMI. In order to establish an evaluation technique, this study conducted literature review on basic theories of cognitive science studies and summarized the cognitive characteristics of humans. This study also surveyed evaluation techniques of HMI in general, and reviewed studies on the evaluation of HMI in NPPs. On the basis of this survey, the study established a procedure for the evaluation of HMI in NPPs in Korea and laid a foundation for empirical verification

  15. Development of an evaluation technique for human-machine interface

    Energy Technology Data Exchange (ETDEWEB)

    Min, Dae Hwan; Koo, Sang Hui; Ahn, Won Yeong; Ryu, Yeong Shin [Korea Univ., Seoul (Korea, Republic of)

    1997-07-15

    The purpose of this study is two-fold : firstly to establish an evaluation technique for HMI(Human Machine Interface) in NPPs(Nuclear Power Plants) and secondly to develop an architecture of a support system which can be used for the evaluation of HMI. In order to establish an evaluation technique, this study conducted literature review on basic theories of cognitive science studies and summarized the cognitive characteristics of humans. This study also surveyed evaluation techniques of HMI in general, and reviewed studies on the evaluation of HMI in NPPs. On the basis of this survey, the study established a procedure for the evaluation of HMI in NPPs in Korea and laid a foundation for empirical verification.

  16. Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

    OpenAIRE

    Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

    2015-01-01

    Machine learning techniques can be used to analyse data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful in terms of software bug prediction. In this paper, a comparative performance analysis of different machine learning techniques is explored f or software bug prediction on public available data sets. Results showed most of the mac ...

  17. Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

    Science.gov (United States)

    Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

    2009-01-01

    In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

  18. Classification of Phishing Email Using Random Forest Machine Learning Technique

    OpenAIRE

    Akinyelu, Andronicus A.; Adewumi, Aderemi O.

    2013-01-01

    Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learnin...

  19. 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...

  20. Machine-learning techniques applied to antibacterial drug discovery.

    Science.gov (United States)

    Durrant, Jacob D; Amaro, Rommie E

    2015-01-01

    The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. © 2015 John Wiley & Sons A/S.

  1. Toward accelerating landslide mapping with interactive machine learning techniques

    Science.gov (United States)

    Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

    2013-04-01

    Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also

  2. Classification of Phishing Email Using Random Forest Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    Andronicus A. Akinyelu

    2014-01-01

    Full Text Available Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN and false positive (FP rates.

  3. Estimation of Alpine Skier Posture Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Bojan Nemec

    2014-10-01

    Full Text Available High precision Global Navigation Satellite System (GNSS measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier’s neck. A key issue is how to estimate other more relevant parameters of the skier’s body, like the center of mass (COM and ski trajectories. Previously, these parameters were estimated by modeling the skier’s body with an inverted-pendulum model that oversimplified the skier’s body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier’s body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing.

  4. Modern machine learning techniques and their applications in cartoon animation research

    CERN Document Server

    Yu, Jun

    2013-01-01

    The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations

  5. Using support vector machines in the multivariate state estimation technique

    International Nuclear Information System (INIS)

    Zavaljevski, N.; Gross, K.C.

    1999-01-01

    One approach to validate nuclear power plant (NPP) signals makes use of pattern recognition techniques. This approach often assumes that there is a set of signal prototypes that are continuously compared with the actual sensor signals. These signal prototypes are often computed based on empirical models with little or no knowledge about physical processes. A common problem of all data-based models is their limited ability to make predictions on the basis of available training data. Another problem is related to suboptimal training algorithms. Both of these potential shortcomings with conventional approaches to signal validation and sensor operability validation are successfully resolved by adopting a recently proposed learning paradigm called the support vector machine (SVM). The work presented here is a novel application of SVM for data-based modeling of system state variables in an NPP, integrated with a nonlinear, nonparametric technique called the multivariate state estimation technique (MSET), an algorithm developed at Argonne National Laboratory for a wide range of nuclear plant applications

  6. Techniques Of Translating English Figurative Expressions In ‘Colours’ Magazine By Garuda Indonesia Into Indonesian

    Directory of Open Access Journals (Sweden)

    Ni Luh Putu Unix Sumartini

    2016-04-01

    Full Text Available The article is aimed to find out the Techniques of Translating English Figurative Expression in ‘Colours’ Magazine by Garuda Indonesia into Indonesian. There are three problems are discussed in this study, namely (1 types of figurative expressions found in the English version of ‘Colours’ magazine and their translation’s equivalence into Indonesian and (2 kinds of translation techniques applied in translating the English figurative expression into Indonesian, This research belongs to qualitative research and the data used in this study were taken from the ‘Colours’ magazine by Garuda Indonesia. The main theory which is applied in this study is taken from the theory of Molina & Albir (2002 in their book entitled Translation Techniques Revisited; A Dynamic and Functionalist Approach. Another theory applied is taken from the theory of McArthur (1992 in his book entitled The Oxford Companion on The English Language. Other supporting theories which are used to support this study are the theory of Larson (1998 in her book entitled Meaning-Based Translation and some other books considered relevant to the topic. The result showed that there are eleven kinds of English figurative expressions found in the data. They are antithesis, euphemism, hyperbole, idioms, irony, metaphor, metonimy, personification, pleonasm, simile and synecdoche. In translating techniques, the translator applied ten translating techniques, they are; adaptation, borrowing, compensation, description, established equivalent, linguistic comprehension, literal technique, modulation, reduction and transposition. In translating a figurative expression from SL into TL, some of the results showed that an English figurative expression is translated into Indonesian figurative translation but some of them can not maintain its figurativeness in Indonesian, therefore the English figurative expression is translated into Indonesian non-figuratively.

  7. 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

  8. Machine Learning Techniques for Arterial Pressure Waveform Analysis

    Directory of Open Access Journals (Sweden)

    João Cardoso

    2013-05-01

    Full Text Available The Arterial Pressure Waveform (APW can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1 a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2 the acquired position and amplitude of onset, Systolic Peak (SP, Point of Inflection (Pi and Dicrotic Wave (DW were used for the computation of some morphological attributes; (3 pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4 classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic, J48 (decision tree and RIPPER (rule-based induction; and (5 we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx. Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95% and high area under the curve (AUC of a Receiver Operating Characteristic (ROC curve (0.961. Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.

  9. Application of Fuzzy TOPSIS for evaluating machining techniques using sustainability metrics

    Science.gov (United States)

    Digalwar, Abhijeet K.

    2018-04-01

    Sustainable processes and techniques are getting increased attention over the last few decades due to rising concerns over the environment, improved focus on productivity and stringency in environmental as well as occupational health and safety norms. The present work analyzes the research on sustainable machining techniques and identifies techniques and parameters on which sustainability of a process is evaluated. Based on the analysis these parameters are then adopted as criteria’s to evaluate different sustainable machining techniques such as Cryogenic Machining, Dry Machining, Minimum Quantity Lubrication (MQL) and High Pressure Jet Assisted Machining (HPJAM) using a fuzzy TOPSIS framework. In order to facilitate easy arithmetic, the linguistic variables represented by fuzzy numbers are transformed into crisp numbers based on graded mean representation. Cryogenic machining was found to be the best alternative sustainable technique as per the fuzzy TOPSIS framework adopted. The paper provides a method to deal with multi criteria decision making problems in a complex and linguistic environment.

  10. Using machine learning techniques to differentiate acute coronary syndrome

    Directory of Open Access Journals (Sweden)

    Sougand Setareh

    2015-02-01

    Full Text Available Backgroud: Acute coronary syndrome (ACS is an unstable and dynamic process that includes unstable angina, ST elevation myocardial infarction, and non-ST elevation myocardial infarction. Despite recent technological advances in early diognosis of ACS, differentiating between different types of coronary diseases in the early hours of admission is controversial. The present study was aimed to accurately differentiate between various coronary events, using machine learning techniques. Such methods, as a subset of artificial intelligence, include algorithms that allow computers to learn and play a major role in treatment decisions. Methods: 1902 patients diagnosed with ACS and admitted to hospital were selected according to Euro Heart Survey on ACS. Patients were classified based on decision tree J48. Bagging aggregation algorithms was implemented to increase the efficiency of algorithm. Results: The performance of classifiers was estimated and compared based on their accuracy computed from confusion matrix. The accuracy rates of decision tree and bagging algorithm were calculated to be 91.74% and 92.53%, respectively. Conclusion: The proposed methods used in this study proved to have the ability to identify various ACS. In addition, using matrix of confusion, an acceptable number of subjects with acute coronary syndrome were identified in each class.

  11. DIAGNOSIS OF DIABETIC RETINOPATHY USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    R. Priya

    2013-07-01

    Full Text Available Diabetic retinopathy (DR is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely non-proliferative diabetic retinopathy (NPDR and proliferative diabetic retinopathy (PDR. In this paper, to diagnose diabetic retinopathy, three models like Probabilistic Neural network (PNN, Bayesian Classification and Support vector machine (SVM are described and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. The features like blood vessels, haemmoraghes of NPDR image and exudates of PDR image are extracted from the raw images using the image processing techniques and fed to the classifier for classification. A total of 350 fundus images were used, out of which 100 were used for training and 250 images were used for testing. Experimental results show that PNN has an accuracy of 89.6 % Bayes Classifier has an accuracy of 94.4% and SVM has an accuracy of 97.6%. This infers that the SVM model outperforms all other models. Also our system is also run on 130 images available from “DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy” and the results show that PNN has an accuracy of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has an accuracy of 95.38%.

  12. A critical survey of live virtual machine migration techniques

    Directory of Open Access Journals (Sweden)

    Anita Choudhary

    2017-11-01

    Full Text Available Abstract Virtualization techniques effectively handle the growing demand for computing, storage, and communication resources in large-scale Cloud Data Centers (CDC. It helps to achieve different resource management objectives like load balancing, online system maintenance, proactive fault tolerance, power management, and resource sharing through Virtual Machine (VM migration. VM migration is a resource-intensive procedure as VM’s continuously demand appropriate CPU cycles, cache memory, memory capacity, and communication bandwidth. Therefore, this process degrades the performance of running applications and adversely affects efficiency of the data centers, particularly when Service Level Agreements (SLA and critical business objectives are to be met. Live VM migration is frequently used because it allows the availability of application service, while migration is performed. In this paper, we make an exhaustive survey of the literature on live VM migration and analyze the various proposed mechanisms. We first classify the types of Live VM migration (single, multiple and hybrid. Next, we categorize VM migration techniques based on duplication mechanisms (replication, de-duplication, redundancy, and compression and awareness of context (dependency, soft page, dirty page, and page fault and evaluate the various Live VM migration techniques. We discuss various performance metrics like application service downtime, total migration time and amount of data transferred. CPU, memory and storage data is transferred during the process of VM migration and we identify the category of data that needs to be transferred in each case. We present a brief discussion on security threats in live VM migration and categories them in three different classes (control plane, data plane, and migration module. We also explain the security requirements and existing solutions to mitigate possible attacks. Specific gaps are identified and the research challenges in improving

  13. Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

    NARCIS (Netherlands)

    Misra, Ankita; Vojinovic, Zoran; Ramakrishnan, Balaji; Luijendijk, Arjen; Ranasinghe, Roshanka

    2018-01-01

    Satellite imagery along with image processing techniques prove to be efficient tools for bathymetry retrieval as they provide time and cost-effective alternatives to traditional methods of water depth estimation. In this article, a nonlinear machine learning technique of Support Vector Machine (SVM)

  14. Wire electric-discharge machining and other fabrication techniques

    Science.gov (United States)

    Morgan, W. H.

    1983-01-01

    Wire electric discharge machining and extrude honing were used to fabricate a two dimensional wing for cryogenic wind tunnel testing. Electric-discharge cutting is done with a moving wire electrode. The cut track is controlled by means of a punched-tape program and the cutting feed is regulated according to the progress of the work. Electric-discharge machining involves no contact with the work piece, and no mechanical force is exerted. Extrude hone is a process for honing finish-machined surfaces by the extrusion of an abrasive material (silly putty), which is forced through a restrictive fixture. The fabrication steps are described and production times are given.

  15. Biomedical techniques in translational studies: The journey so far ...

    African Journals Online (AJOL)

    Biomedical techniques have wide clinical application in many fields of medicine such as oncology, rheumatology, immunology, genomics, cardiology and diagnostics; among others. This has been made possible with the use of genetic engineering and a number of techniques like Immunohistochemistry (IHC), Fluorescent ...

  16. SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

  17. Machine throughput improvement achieved using innovative control technique

    International Nuclear Information System (INIS)

    Sharma, V.; Acharya, S.; Mittal, K.C.

    2012-01-01

    In any type of fully or semi automatic machine the control systems plays an important role. The control system on the one hand has to consider the human psychology, intelligence requirement for an operator, and attention needed from him. On the other hand the complexity of the control has also to be understood well before designing a control system that can be handled comfortably and safely by the operator. As far as the user experience/comfort is concerned the design of control system GUI is vital. Considering these two aspects related to the user of the machine it is evident that the control system design is very important because it is has to accommodate the human behaviour and skill sets required/available as well as the capability of the machine under the control of the control system. An intelligently designed control system can enhance the productivity of the machine. (author)

  18. Machine learning techniques applied to system characterization and equalization

    DEFF Research Database (Denmark)

    Zibar, Darko; Thrane, Jakob; Wass, Jesper

    2016-01-01

    Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.......Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals....

  19. Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

    Science.gov (United States)

    Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

    2018-05-24

    The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

  20. Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques

    CSIR Research Space (South Africa)

    Ngxande, Mkhuseli

    2017-11-01

    Full Text Available This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features...

  1. MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques

    Directory of Open Access Journals (Sweden)

    Cerqueira Fabio R

    2012-10-01

    Full Text Available Abstract Background The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. Results Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. Conclusion Our approach not only enhances the computational performance, and

  2. Machine learning in Python essential techniques for predictive analysis

    CERN Document Server

    Bowles, Michael

    2015-01-01

    Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, d

  3. Statistical translation with scarce resources: a South African case study

    CSIR Research Space (South Africa)

    Ronald, K

    2006-11-01

    Full Text Available Statistical machine translation techniques offer great promise for the development of automatic translation systems. However, the realization of this potential requires the availability of significant amounts of parallel bilingual texts. This paper...

  4. 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)

  5. Automatic Generation of English-Japanese Translation Pattern Utilizing Genetic Programming Technique

    Science.gov (United States)

    Matsumura, Koki; Tamekuni, Yuji; Kimura, Shuhei

    There are a lot of constructional differences in an English-Japanese phrase template, and that often makes the act of translation difficult. Moreover, there exist various and tremendous phrase templates and sentence to be refered to. It is not easy to prepare the corpus that covers the all. Therefore, it is very significant to generate the translation pattern of the sentence pattern automatically from a viewpoint of the translation success rate and the capacity of the pattern dictionary. Then, for the purpose of realizing the automatic generation of the translation pattern, this paper proposed the new method for the generation of the translation pattern by using the genetic programming technique (GP). The technique tries to generate the translation pattern of various sentences which are not registered in the phrase template dictionary automatically by giving the genetic operation to the parsing tree of a basic pattern. The tree consists of the pair of the English-Japanese sentence generated as the first stage population. The analysis tree data base with 50,100,150,200 pairs was prepared as the first stage population. And this system was applied and executed for an English input of 1,555 sentences. As a result, the analysis tree increases from 200 to 517, and the accuracy rate of the translation pattern has improved from 42.57% to 70.10%. And, 86.71% of the generated translations was successfully done, whose meanings are enough acceptable and understandable. It seemed that this proposal technique became a clue to raise the translation success rate, and to find the possibility of the reduction of the analysis tree data base.

  6. A Translator Verification Technique for FPGA Software Development in Nuclear Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jae Yeob; Kim, Eui Sub; Yoo, Jun Beom [Konkuk University, Seoul (Korea, Republic of)

    2014-10-15

    Although the FPGAs give a high performance than PLC (Programmable Logic Controller), the platform change from PLC to FPGA impose all PLC software engineers give up their experience, knowledge and practices accumulated over decades, and start a new FPGA-based hardware development from scratch. We have researched to fine the solution to this problem reducing the risk and preserving the experience and knowledge. One solution is to use the FBDtoVerilog translator, which translates the FBD programs into behavior-preserving Verilog programs. In general, the PLCs are usually designed with an FBD, while the FPGAs are described with a HDL (Hardware Description Language) such as Verilog or VHDL. Once PLC designer designed the FBD programs, the FBDtoVerilog translates the FBD into Verilog, mechanically. The designers, therefore, need not consider the rest of FPGA development process (e.g., Synthesis and Place and Routing) and can preserve the accumulated experience and knowledge. Even if we assure that the translation from FBD to Verilog is correct, it must be verified rigorously and thoroughly since it is used in nuclear power plants, which is one of the most safety critical systems. While the designer develops the FPGA software with the FBD program translated by the translator, there are other translation tools such as synthesis tool and place and routing tool. This paper also focuses to verify them rigorously and thoroughly. There are several verification techniques for correctness of translator, but they are hard to apply because of the outrageous cost and performance time. Instead, this paper tries to use an indirect verification technique for demonstrating the correctness of translator using the co-simulation technique. We intend to prove only against specific inputs which are under development for a target I and C system, not against all possible input cases.

  7. A Translator Verification Technique for FPGA Software Development in Nuclear Power Plants

    International Nuclear Information System (INIS)

    Kim, Jae Yeob; Kim, Eui Sub; Yoo, Jun Beom

    2014-01-01

    Although the FPGAs give a high performance than PLC (Programmable Logic Controller), the platform change from PLC to FPGA impose all PLC software engineers give up their experience, knowledge and practices accumulated over decades, and start a new FPGA-based hardware development from scratch. We have researched to fine the solution to this problem reducing the risk and preserving the experience and knowledge. One solution is to use the FBDtoVerilog translator, which translates the FBD programs into behavior-preserving Verilog programs. In general, the PLCs are usually designed with an FBD, while the FPGAs are described with a HDL (Hardware Description Language) such as Verilog or VHDL. Once PLC designer designed the FBD programs, the FBDtoVerilog translates the FBD into Verilog, mechanically. The designers, therefore, need not consider the rest of FPGA development process (e.g., Synthesis and Place and Routing) and can preserve the accumulated experience and knowledge. Even if we assure that the translation from FBD to Verilog is correct, it must be verified rigorously and thoroughly since it is used in nuclear power plants, which is one of the most safety critical systems. While the designer develops the FPGA software with the FBD program translated by the translator, there are other translation tools such as synthesis tool and place and routing tool. This paper also focuses to verify them rigorously and thoroughly. There are several verification techniques for correctness of translator, but they are hard to apply because of the outrageous cost and performance time. Instead, this paper tries to use an indirect verification technique for demonstrating the correctness of translator using the co-simulation technique. We intend to prove only against specific inputs which are under development for a target I and C system, not against all possible input cases

  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. The applicability of Lean and Six Sigma techniques to clinical and translational research.

    Science.gov (United States)

    Schweikhart, Sharon A; Dembe, Allard E

    2009-10-01

    Lean and Six Sigma are business management strategies commonly used in production industries to improve process efficiency and quality. During the past decade, these process improvement techniques increasingly have been applied outside the manufacturing sector, for example, in health care and in software development. This article concerns the potential use of Lean and Six Sigma in improving the processes involved in clinical and translational research. Improving quality, avoiding delays and errors, and speeding up the time to implementation of biomedical discoveries are prime objectives of the National Institutes of Health (NIH) Roadmap for Medical Research and the NIH's Clinical and Translational Science Award program. This article presents a description of the main principles, practices, and methods used in Lean and Six Sigma. Available literature involving applications of Lean and Six Sigma to health care, laboratory science, and clinical and translational research is reviewed. Specific issues concerning the use of these techniques in different phases of translational research are identified. Examples of Lean and Six Sigma applications that are being planned at a current Clinical and Translational Science Award site are provided, which could potentially be replicated elsewhere. We describe how different process improvement approaches are best adapted for particular translational research phases. Lean and Six Sigma process improvement methods are well suited to help achieve NIH's goal of making clinical and translational research more efficient and cost-effective, enhancing the quality of the research, and facilitating the successful adoption of biomedical research findings into practice.

  10. Phishtest: Measuring the Impact of Email Headers on the Predictive Accuracy of Machine Learning Techniques

    Science.gov (United States)

    Tout, Hicham

    2013-01-01

    The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning…

  11. 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.

  12. Relevance vector machine technique for the inverse scattering problem

    International Nuclear Information System (INIS)

    Wang Fang-Fang; Zhang Ye-Rong

    2012-01-01

    A novel method based on the relevance vector machine (RVM) for the inverse scattering problem is presented in this paper. The nonlinearity and the ill-posedness inherent in this problem are simultaneously considered. The nonlinearity is embodied in the relation between the scattered field and the target property, which can be obtained through the RVM training process. Besides, rather than utilizing regularization, the ill-posed nature of the inversion is naturally accounted for because the RVM can produce a probabilistic output. Simulation results reveal that the proposed RVM-based approach can provide comparative performances in terms of accuracy, convergence, robustness, generalization, and improved performance in terms of sparse property in comparison with the support vector machine (SVM) based approach. (general)

  13. Predicting breast screening attendance using machine learning techniques.

    Science.gov (United States)

    Baskaran, Vikraman; Guergachi, Aziz; Bali, Rajeev K; Naguib, Raouf N G

    2011-03-01

    Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale.

  14. Machine learning and evolutionary techniques in interplanetary trajectory design

    OpenAIRE

    Izzo, Dario; Sprague, Christopher; Tailor, Dharmesh

    2018-01-01

    After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth-Mars orbital transfer, extend the findings made previously for landing ...

  15. A comparison of machine learning techniques for predicting downstream acid mine drainage

    CSIR Research Space (South Africa)

    van Zyl, TL

    2014-07-01

    Full Text Available windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear...

  16. The application of machine learning techniques in the clinical drug therapy.

    Science.gov (United States)

    Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

    2018-05-25

    The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  17. Machine Learning Techniques for Prediction of Early Childhood Obesity.

    Science.gov (United States)

    Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

    2015-01-01

    This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

  18. Application of machine learning techniques to lepton energy reconstruction in water Cherenkov detectors

    Science.gov (United States)

    Drakopoulou, E.; Cowan, G. A.; Needham, M. D.; Playfer, S.; Taani, M.

    2018-04-01

    The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found that applying these techniques leads to an improvement of more than 50% in the energy resolution for all lepton energies compared to an approach based upon lookup tables. Machine learning techniques can be easily applied to different detector configurations and the results are comparable to likelihood-function based techniques that are currently used.

  19. The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

    Science.gov (United States)

    Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

    2017-09-01

    Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. A Comprehensive Review and meta-analysis on Applications of Machine Learning Techniques in Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Manojit Chattopadhyay

    2018-05-01

    Full Text Available Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for changing usage pattern by comparing different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.

  1. 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…

  2. Automatic pellet density checking machine using vision technique

    International Nuclear Information System (INIS)

    Kumar, Suman; Raju, Y.S.; Raj Kumar, J.V.; Sairam, S.; Sheela; Hemantha Rao, G.V.S.

    2012-01-01

    Uranium di-oxide powder prepared through chemical process is converted to green pellets through the powder metallurgy route of precompaction and final compaction operations. These green pellets are kept in a molybdenum boat, which consists of a molybdenum base and a shroud. The boats are passed through the high temperature sintering furnaces to achieve required density of pellets. At present MIL standard 105 E is followed for measuring density of sintered pellets in the boat. As per AQL 2.5 of MIL standard, five pellets are collected from each boat, which contains approximately 800 nos of pellets. The densities of these collected pellets are measured. If anyone pellet density is less than the required value, the entire boat of pellets are rejected and sent back for dissolution for further processing. An Automatic Pellet Density Checking Machine (APDCM) was developed to salvage the acceptable density pellets from the rejected boat of pellets

  3. Machine learning techniques for gait biometric recognition using the ground reaction force

    CERN Document Server

    Mason, James Eric; Woungang, Isaac

    2016-01-01

    This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of ...

  4. 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.

  5. Functional discrimination of membrane proteins using machine learning techniques

    Directory of Open Access Journals (Sweden)

    Yabuki Yukimitsu

    2008-03-01

    Full Text Available Abstract Background Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters. Results We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network method discriminated the channels/pores, electrochemical potential-driven transporters and active transporters with the 5-fold cross validation accuracy of 64% in a data set of 1718 membrane proteins. The application of amino acid occurrence improved the overall accuracy to 68%. In addition, we have discriminated transporters from other α-helical and β-barrel membrane proteins with the accuracy of 85% using k-nearest neighbor method. The classification of transporters and all other proteins (globular and membrane showed the accuracy of 82%. Conclusion The performance of discrimination with amino acid occurrence is better than that with amino acid composition. We suggest that this method could be effectively used to discriminate transporters from all other globular and membrane proteins, and classify them into channels/pores, electrochemical and active transporters.

  6. 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.

  7. Application of Artificial Intelligence Techniques for the Control of the Asynchronous Machine

    Directory of Open Access Journals (Sweden)

    F. Khammar

    2016-01-01

    Full Text Available The induction machine is experiencing a growing success for two decades by gradually replacing the DC machines and synchronous in many industrial applications. This paper is devoted to the study of advanced methods applied to the command of the asynchronous machine in order to obtain a system of control of high performance. While the criteria for response time, overtaking, and static error can be assured by the techniques of conventional control, the criterion of robustness remains a challenge for researchers. This criterion can be satisfied only by applying advanced techniques of command. After mathematical modeling of the asynchronous machine, it defines the control strategies based on the orientation of the rotor flux. The results of the different simulation tests highlight the properties of robustness of algorithms proposed and suggested to compare the different control strategies.

  8. Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

    Science.gov (United States)

    Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

    2017-12-01

    In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

  9. A framework for detection of malicious software in Android handheld systems using machine learning techniques

    OpenAIRE

    Torregrosa García, Blas

    2015-01-01

    The present study aims at designing and developing new approaches to detect malicious applications in Android-based devices. More precisely, MaLDroide (Machine Learning-based Detector for Android malware), a framework for detection of Android malware based on machine learning techniques, is introduced here. It is devised to identify malicious applications. Este trabajo tiene como objetivo el diseño y el desarrollo de nuevas formas de detección de aplicaciones maliciosas en los dispositivos...

  10. 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.

  11. Vacuum system and cleaning techniques in the FTU machines

    International Nuclear Information System (INIS)

    Alessandrini, C.; Apicella, M.L.; Ferro, C.

    1988-01-01

    FTU (Frascati Tokamak Upgrade) is a high magnetic field (8T) tokamak under construction at the Frascati Energy Research Center (ENEA). Its vacuum systems has been already manifactured and is presently being assembled. It consist of an all metallic fully welded vessel, pumped by six turbomolecular pumps. The vacuum system has been dimensioned to allow a base pressure lower than 2.6 x 10 -6 Pa. The paper reports the design philosophy of the vacuum system. The results of the cleaning techniques performed on a 1:1 scale toroidal sector of FTU are also presented and discussed

  12. A technique to identify some typical radio frequency interference using support vector machine

    Science.gov (United States)

    Wang, Yuanchao; Li, Mingtao; Li, Dawei; Zheng, Jianhua

    2017-07-01

    In this paper, we present a technique to automatically identify some typical radio frequency interference from pulsar surveys using support vector machine. The technique has been tested by candidates. In these experiments, to get features of SVM, we use principal component analysis for mosaic plots and its classification accuracy is 96.9%; while we use mathematical morphology operation for smog plots and horizontal stripes plots and its classification accuracy is 86%. The technique is simple, high accurate and useful.

  13. Sentiment Analysis in Geo Social Streams by using Machine Learning Techniques

    OpenAIRE

    Twanabasu, Bikesh

    2018-01-01

    Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2017-2018 Massive amounts of sentiment rich data are generated on social media in the form of Tweets, status updates, blog post, reviews, etc. Different people and organizations are using these user generated content for decision making. Symbolic techniques or Knowledge base approaches and Machine learning techniques are two main techniques used for analysis sentiment...

  14. Complex technique for studying the machine part wear

    International Nuclear Information System (INIS)

    Grishko, V.A.; Zhushma, V.F.

    1981-01-01

    A technique to determine the wear of steel details rolling with sliding with circulatory lubrication is suggested. The functional diagram of the experimental device and structural diagrams of equipment to register the wear of tested samples and forming the lubricating layer between them, are considered. Results of testing three conples of disc samples and the data characterizing the dependence of sample wear on the value of contact stress are presented. The peculiarity of the device used is synchronous registering of the lubricating layer formation in the place of contact and detail mass loss in time which is realized correspondingly over discharge voltage on the lubricating layer and the intensity of radiation from detail wear products activated by neutrons. On the basis, of the investigation the conclusion is made that MEhF-1 oil has a greater antiwear effectiveness than the universal TAD-17 1 oil used presently [ru

  15. 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...

  16. Performance Evaluation of Eleven-Phase Induction Machine with Different PWM Techniques

    Directory of Open Access Journals (Sweden)

    M.I. Masoud

    2015-06-01

    Full Text Available Multiphase induction machines are used extensively in low and medium voltage (MV drives. In MV drives, power switches have a limitation associated with switching frequency. This paper is a comparative study of the eleven-phase induction machine’s performance when used as a prototype and fed sinusoidal pulse-width-modulation (SPWM with a low switching frequency, selective harmonic elimination (SHE, and single pulse modulation (SPM techniques. The comparison depends on voltage/frequency controls for the same phase of voltage applied on the machine terminals for all previous techniques. The comparative study covers torque ripple, stator and harmonic currents, and motor efficiency.

  17. Prediction of drug synergy in cancer using ensemble-based machine learning techniques

    Science.gov (United States)

    Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

    2018-04-01

    Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

  18. Locomotion training of legged robots using hybrid machine learning techniques

    Science.gov (United States)

    Simon, William E.; Doerschuk, Peggy I.; Zhang, Wen-Ran; Li, Andrew L.

    1995-01-01

    In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible

  19. 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.

  20. Applying machine learning techniques for forecasting flexibility of virtual power plants

    DEFF Research Database (Denmark)

    MacDougall, Pamela; Kosek, Anna Magdalena; Bindner, Henrik W.

    2016-01-01

    network as well as the multi-variant linear regression. It is found that it is possible to estimate the longevity of flexibility with machine learning. The linear regression algorithm is, on average, able to estimate the longevity with a 15% error. However, there was a significant improvement with the ANN...... approach to investigating the longevity of aggregated response of a virtual power plant using historic bidding and aggregated behaviour with machine learning techniques. The two supervised machine learning techniques investigated and compared in this paper are, multivariate linear regression and single...... algorithm achieving, on average, a 5.3% error. This is lowered 2.4% when learning for the same virtual power plant. With this information it would be possible to accurately offer residential VPP flexibility for market operations to safely avoid causing further imbalances and financial penalties....

  1. 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

  2. Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

    Science.gov (United States)

    Chee, Brant Wah Kwong

    2011-01-01

    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

  3. Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

    Science.gov (United States)

    Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

    2015-08-01

    Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Techniques and applications for binaural sound manipulation in human-machine interfaces

    Science.gov (United States)

    Begault, Durand R.; Wenzel, Elizabeth M.

    1992-01-01

    The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

  5. ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    VIMALA C.

    2015-05-01

    Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

  6. 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...

  7. Process acceptance and adjustment techniques for Swiss automatic screw machine parts. Final report

    International Nuclear Information System (INIS)

    Robb, J.M.

    1976-01-01

    Product tolerance requirements for small, cylindrical, piece parts produced on swiss automatic screw machines have progressed to the reliability limits of inspection equipment. The miniature size, configuration, and tolerance requirements (plus or minus 0.0001 in.) (0.00254 mm) of these parts preclude the use of screening techniques to accept product or adjust processes during setup and production runs; therefore, existing means of product acceptance and process adjustment must be refined or new techniques must be developed. The purpose of this endeavor has been to determine benefits gained through the implementation of a process acceptance technique (PAT) to swiss automatic screw machine processes. PAT is a statistical approach developed for the purpose of accepting product and centering processes for parts produced by selected, controlled processes. Through this endeavor a determination has been made of the conditions under which PAT can benefit a controlled process and some specific types of screw machine processes upon which PAT could be applied. However, it was also determined that PAT, if used indiscriminately, may become a record keeping burden when applied to more than one dimension at a given machining operation

  8. An Effective Performance Analysis of Machine Learning Techniques for Cardiovascular Disease

    Directory of Open Access Journals (Sweden)

    Vinitha DOMINIC

    2015-03-01

    Full Text Available Machine learning techniques will help in deriving hidden knowledge from clinical data which can be of great benefit for society, such as reduce the number of clinical trials required for precise diagnosis of a disease of a person etc. Various areas of study are available in healthcare domain like cancer, diabetes, drugs etc. This paper focuses on heart disease dataset and how machine learning techniques can help in understanding the level of risk associated with heart diseases. Initially, data is preprocessed then analysis is done in two stages, in first stage feature selection techniques are applied on 13 commonly used attributes and in second stage feature selection techniques are applied on 75 attributes which are related to anatomic structure of the heart like blood vessels of the heart, arteries etc. Finally, validation of the reduced set of features using an exhaustive list of classifiers is done.In parallel study of the anatomy of the heart is done using the identified features and the characteristics of each class is understood. It is observed that these reduced set of features are anatomically relevant. Thus, it can be concluded that, applying machine learning techniques on clinical data is beneficial and necessary.

  9. A Computer Program for Simplifying Incompletely Specified Sequential Machines Using the Paull and Unger Technique

    Science.gov (United States)

    Ebersole, M. M.; Lecoq, P. E.

    1968-01-01

    This report presents a description of a computer program mechanized to perform the Paull and Unger process of simplifying incompletely specified sequential machines. An understanding of the process, as given in Ref. 3, is a prerequisite to the use of the techniques presented in this report. This process has specific application in the design of asynchronous digital machines and was used in the design of operational support equipment for the Mariner 1966 central computer and sequencer. A typical sequential machine design problem is presented to show where the Paull and Unger process has application. A description of the Paull and Unger process together with a description of the computer algorithms used to develop the program mechanization are presented. Several examples are used to clarify the Paull and Unger process and the computer algorithms. Program flow diagrams, program listings, and a program user operating procedures are included as appendixes.

  10. Towards large-scale FAME-based bacterial species identification using machine learning techniques.

    Science.gov (United States)

    Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

    2009-05-01

    In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species

  11. Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

    Science.gov (United States)

    Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

    2017-07-01

    Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

  12. Electric-Discharge Machining Techniques for Evaluating Tritium Effects on Materials

    International Nuclear Information System (INIS)

    Morgan, M.J.

    2003-01-01

    In this investigation, new ways to evaluate the long-term effects of tritium on the structural properties of components were developed. Electric-discharge machining (EDM) techniques for cutting tensile and fracture toughness samples from tritium exposed regions of returned reservoirs were demonstrated. An existing electric discharge machine was used to cut sub-size tensile and fracture toughness samples from the inside surfaces of reservoir mockups. Tensile properties from the EDM tensile samples were similar to those measured using full-size samples cut from similar stock. Although the existing equipment could not be used for machining tritium-exposed hardware, off-the shelf EDM units are available that could. With the right equipment and the required radiological controls in place, similar machining and testing techniques could be used to directly measure the effects of tritium on the properties of material cut from reservoir returns. Stress-strain properties from tritium-exposed reservoirs would improve finite element modeling of reservoir performance because the data would be representative of the true state of the reservoir material in the field. Tensile data from samples cut directly from reservoirs would also complement existing shelf storage and burst test data of the Life Storage Program and help answer questions about a specific reservoir's processing history and properties

  13. 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.

  14. A Sociolinguistic Analysis of Polish and American Prison Slang within the Context of Selected Translation Techniques in Films with Subtitles

    Directory of Open Access Journals (Sweden)

    Katarzyna Sandra Nosek

    2016-11-01

    Full Text Available Cultural differences around the world may pose problems for translators who face issues connected with finding equivalents in source and target language occurred in the films. One of the most difficult styles is constantly changing, the hermetic and colloquial variety known as slang. Depending on the environment, it may vary, even in one language, of which an example is prison slang used by convicts to communicate with one another. Although very pejorative and full of negative connotations, it is a very curious subject matter to analyze, as well as, to investigate how it is translated, because more and more films about criminal environments are being produced. This study examines which translation techniques were used in the cases of the movies: Lockdown (2000, American Me (1992 and Animal Factory (2000. The research focuses on the issues connected with the most often used translation techniques, the reasons of using them, the other possible solutions, the untranslatable phrases and with translating taboo words.

  15. An experimental result of estimating an application volume by machine learning techniques.

    Science.gov (United States)

    Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

    2015-01-01

    In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

  16. Statistical and Machine-Learning Data Mining Techniques for Better Predictive Modeling and Analysis of Big Data

    CERN Document Server

    Ratner, Bruce

    2011-01-01

    The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has

  17. ROBUSTNESS OF A FACE-RECOGNITION TECHNIQUE BASED ON SUPPORT VECTOR MACHINES

    OpenAIRE

    Prashanth Harshangi; Koshy George

    2010-01-01

    The ever-increasing requirements of security concerns have placed a greater demand for face recognition surveillance systems. However, most current face recognition techniques are not quite robust with respect to factors such as variable illumination, facial expression and detail, and noise in images. In this paper, we demonstrate that face recognition using support vector machines are sufficiently robust to different kinds of noise, does not require image pre-processing, and can be used with...

  18. Approximate multi-state reliability expressions using a new machine learning technique

    International Nuclear Information System (INIS)

    Rocco S, Claudio M.; Muselli, Marco

    2005-01-01

    The machine-learning-based methodology, previously proposed by the authors for approximating binary reliability expressions, is now extended to develop a new algorithm, based on the procedure of Hamming Clustering, which is capable to deal with multi-state systems and any success criterion. The proposed technique is presented in details and verified on literature cases: experiment results show that the new algorithm yields excellent predictions

  19. Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques

    Science.gov (United States)

    2007-08-01

    individual players to take: Productions Meaning of predicates DIRECTIVE → (do PLAYER ACTION) PLAYER should take ACTION. DIRECTIVE → ( dont PLAYER...Computational Lin- guistics (COLING-ACL-2006), Poster Sessions, pp. 263–270. Sydney, Australia. 170 Daniel Gildea and Daniel Jurafsky (2002

  20. Machine Learning Techniques for Modelling Short Term Land-Use Change

    Directory of Open Access Journals (Sweden)

    Mileva Samardžić-Petrović

    2017-11-01

    Full Text Available The representation of land use change (LUC is often achieved by using data-driven methods that include machine learning (ML techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT, Neural Networks (NN, and Support Vector Machines (SVM for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.

  1. Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

    Directory of Open Access Journals (Sweden)

    Laura Cornejo-Bueno

    2017-11-01

    Full Text Available Wind Power Ramp Events (WPREs are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains. Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

  2. Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

    Directory of Open Access Journals (Sweden)

    Wei-Chien Wang

    2018-05-01

    Full Text Available Deep learning has become the most popular research subject in the fields of artificial intelligence (AI and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA, support vector machines (SVM, k-nearest neighbors algorithm (K-NN, and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW, with machine-learning classifiers, and histogram of oriented gradients (HOG/pyramid histogram of oriented gradients (PHOG with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10−4, and this accuracy is far higher than those achieved by BOW with SVM (80.50% and PHOG with SVM (53.0%.

  3. Optimization of Coolant Technique Conditions for Machining A319 Aluminium Alloy Using Response Surface Method (RSM)

    Science.gov (United States)

    Zainal Ariffin, S.; Razlan, A.; Ali, M. Mohd; Efendee, A. M.; Rahman, M. M.

    2018-03-01

    Background/Objectives: The paper discusses about the optimum cutting parameters with coolant techniques condition (1.0 mm nozzle orifice, wet and dry) to optimize surface roughness, temperature and tool wear in the machining process based on the selected setting parameters. The selected cutting parameters for this study were the cutting speed, feed rate, depth of cut and coolant techniques condition. Methods/Statistical Analysis Experiments were conducted and investigated based on Design of Experiment (DOE) with Response Surface Method. The research of the aggressive machining process on aluminum alloy (A319) for automotive applications is an effort to understand the machining concept, which widely used in a variety of manufacturing industries especially in the automotive industry. Findings: The results show that the dominant failure mode is the surface roughness, temperature and tool wear when using 1.0 mm nozzle orifice, increases during machining and also can be alternative minimize built up edge of the A319. The exploration for surface roughness, productivity and the optimization of cutting speed in the technical and commercial aspects of the manufacturing processes of A319 are discussed in automotive components industries for further work Applications/Improvements: The research result also beneficial in minimizing the costs incurred and improving productivity of manufacturing firms. According to the mathematical model and equations, generated by CCD based RSM, experiments were performed and cutting coolant condition technique using size nozzle can reduces tool wear, surface roughness and temperature was obtained. Results have been analyzed and optimization has been carried out for selecting cutting parameters, shows that the effectiveness and efficiency of the system can be identified and helps to solve potential problems.

  4. 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

  5. Binary translation using peephole translation rules

    Science.gov (United States)

    Bansal, Sorav; Aiken, Alex

    2010-05-04

    An efficient binary translator uses peephole translation rules to directly translate executable code from one instruction set to another. In a preferred embodiment, the translation rules are generated using superoptimization techniques that enable the translator to automatically learn translation rules for translating code from the source to target instruction set architecture.

  6. Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

    2016-01-01

    Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

  7. GAPscreener: An automatic tool for screening human genetic association literature in PubMed using the support vector machine technique

    Directory of Open Access Journals (Sweden)

    Khoury Muin J

    2008-04-01

    Full Text Available Abstract Background Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM, a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies. Results The data source for this research was the HuGE Navigator, formerly known as the HuGE Pub Lit database. Weighted SVM feature selection based on a keyword list obtained by the two-way z score method demonstrated the best screening performance, achieving 97.5% recall, 98.3% specificity and 31.9% precision in performance testing. Compared with the traditional screening process based on a complex PubMed query, the SVM tool reduced by about 90% the number of abstracts requiring individual review by the database curator. The tool also ascertained 47 articles that were missed by the traditional literature screening process during the 4-week test period. We examined the literature on genetic associations with preterm birth as an example. Compared with the traditional, manual process, the GAPscreener both reduced effort and improved accuracy. Conclusion GAPscreener is the first free SVM-based application available for screening the human genetic association literature in PubMed with high recall and specificity. The user-friendly graphical user interface makes this a practical, stand-alone application. The software can be downloaded at no charge.

  8. Classification of Cytochrome P450 1A2 Inhibitors and Non-Inhibitors by Machine Learning Techniques

    DEFF Research Database (Denmark)

    Vasanthanathan, Poongavanam; Taboureau, Olivier; Oostenbrink, Chris

    2009-01-01

    of CYP1A2 inhibitors and non-inhibitors. Training and test sets consisted of about 400 and 7000 compounds, respectively. Various machine learning techniques, like binary QSAR, support vector machine (SVM), random forest, kappa nearest neighbors (kNN), and decision tree methods were used to develop...

  9. LARA. Localization of an automatized refueling machine by acoustical sounding in breeder reactors - implementation of artificial intelligence techniques

    International Nuclear Information System (INIS)

    Lhuillier, C.; Malvache, P.

    1987-01-01

    The automatic control of the machine which handles the nuclear subassemblies in fast neutron reactors requires autonomous perception and decision tools. An acoustical device allows the machine to position in the work area. Artificial intelligence techniques are implemented to interpret the data: pattern recognition, scene analysis. The localization process is managed by an expert system. 6 refs.; 8 figs

  10. Machine Learning or Information Retrieval Techniques for Bug Triaging: Which is better?

    Directory of Open Access Journals (Sweden)

    Anjali Goyal

    2017-07-01

    Full Text Available Bugs are the inevitable part of a software system. Nowadays, large software development projects even release beta versions of their products to gather bug reports from users. The collected bug reports are then worked upon by various developers in order to resolve the defects and make the final software product more reliable. The high frequency of incoming bugs makes the bug handling a difficult and time consuming task. Bug assignment is an integral part of bug triaging that aims at the process of assigning a suitable developer for the reported bug who corrects the source code in order to resolve the bug. There are various semi and fully automated techniques to ease the task of bug assignment. This paper presents the current state of the art of various techniques used for bug report assignment. Through exhaustive research, the authors have observed that machine learning and information retrieval based bug assignment approaches are most popular in literature. A deeper investigation has shown that the trend of techniques is taking a shift from machine learning based approaches towards information retrieval based approaches. Therefore, the focus of this work is to find the reason behind the observed drift and thus a comparative analysis is conducted on the bug reports of the Mozilla, Eclipse, Gnome and Open Office projects in the Bugzilla repository. The results of the study show that the information retrieval based technique yields better efficiency in recommending the developers for bug reports.

  11. Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

    Directory of Open Access Journals (Sweden)

    Manoja Kumar Behera

    2018-06-01

    Full Text Available Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC maximum power point tracking (MPPT technique that is based on proportional integral (PI controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN, ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO techniques and their performance are compared with existing models like back propagation (BP forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network

  12. Teachers and Learners’ Perceptions of Applying Translation as a Method, Strategy, or Technique in an Iranian EFL Setting

    Directory of Open Access Journals (Sweden)

    Fatemeh Mollaei

    2017-04-01

    Full Text Available It has been found that translation is an efficient means to teach/learn grammar, syntax, and lexis of a foreign language. Meanwhile, translation is good for beginners who do not still enjoy the critical level of proficiency in their target language for expression.  This study was conducted to examine the teachers and learners’ perceptions of employing translation in the foreign language classroom; i.e., the effects, merits, demerits, limitations, as well as its use as a method, strategy or technique. Both quantitative and qualitative methods were used to collect and analyze the data from graduate and undergraduate learners (n=56 and teachers (n=44, male and female, who responded to two questionnaires. Additionally, only the teachers were interviewed to gain richer insight into their perceptions and attitudes. According to the results of independent samples t-test, there was no significant difference between teachers and learners’ attitude to applying translation as a method, strategy, or technique in learning a foreign language.  Based on the interview results, some teachers believed that employing translation in the foreign language context was helpful but not constantly. They claimed that translation was only effective in teaching vocabulary and grammar apart from leaners’ proficiency level as it can clarify meaning. But some other teachers noted that mother tongue would interfere with learning foreign language; they considered translation as a time-consuming activity through which students cannot capture the exact meaning.

  13. Digital Mayhem 3D machine techniques where inspiration, techniques and digital art meet

    CERN Document Server

    Evans, Duncan

    2014-01-01

    From Icy Tundras to Desert savannahs, master the art of landscape and environment design for 2D and 3D digital content. Make it rain, shower your digital scene with a snow storm or develop a believable urban scene with a critical eye for modeling, lighting and composition. Move beyond the limitations of gallery style coffee table books with Digital Mayhem: 3D Landscapes-offering leading professional techniques, groundbreaking inspiration, and artistic mastery from some of the greatest digital artists. More than just a gallery book - each artist has written a breakdown overview, with supporting

  14. Prediction of lung cancer patient survival via supervised machine learning classification techniques.

    Science.gov (United States)

    Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

    2017-12-01

    Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time

  15. Engagement techniques and playing level impact the biomechanical demands on rugby forwards during machine-based scrummaging

    OpenAIRE

    Preatoni, Ezio; Stokes, Keith A.; England, Michael E.; Trewartha, Grant

    2014-01-01

    Objectives This cross-sectional study investigated the factors that may influence the physical loading on rugby forwards performing a scrum by studying the biomechanics of machine-based scrummaging under different engagement techniques and playing levels.Methods 34 forward packs from six playing levels performed repetitions of five different types of engagement techniques against an instrumented scrum machine under realistic training conditions. Applied forces and body movements were recorded...

  16. The influence of cooling techniques on cutting forces and surface roughness during cryogenic machining of titanium alloys

    Directory of Open Access Journals (Sweden)

    Wstawska Iwona

    2016-12-01

    Full Text Available Titanium alloys are one of the materials extensively used in the aerospace industry due to its excellent properties of high specific strength and corrosion resistance. On the other hand, they also present problems wherein titanium alloys are extremely difficult materials to machine. In addition, the cost associated with titanium machining is also high due to lower cutting velocities and shorter tool life. The main objective of this work is a comparison of different cooling techniques during cryogenic machining of titanium alloys. The analysis revealed that applied cooling technique has a significant influence on cutting force and surface roughness (Ra parameter values. Furthermore, in all cases observed a positive influence of cryogenic machining on selected aspects after turning and milling of titanium alloys. This work can be also the starting point to the further research, related to the analysis of cutting forces and surface roughness during cryogenic machining of titanium alloys.

  17. A relevance vector machine technique for the automatic detection of clustered microcalcifications (Honorable Mention Poster Award)

    Science.gov (United States)

    Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.

    2005-04-01

    Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

  18. Fuzzy classification for strawberry diseases-infection using machine vision and soft-computing techniques

    Science.gov (United States)

    Altıparmak, Hamit; Al Shahadat, Mohamad; Kiani, Ehsan; Dimililer, Kamil

    2018-04-01

    Robotic agriculture requires smart and doable techniques to substitute the human intelligence with machine intelligence. Strawberry is one of the important Mediterranean product and its productivity enhancement requires modern and machine-based methods. Whereas a human identifies the disease infected leaves by his eye, the machine should also be capable of vision-based disease identification. The objective of this paper is to practically verify the applicability of a new computer-vision method for discrimination between the healthy and disease infected strawberry leaves which does not require neural network or time consuming trainings. The proposed method was tested under outdoor lighting condition using a regular DLSR camera without any particular lens. Since the type and infection degree of disease is approximated a human brain a fuzzy decision maker classifies the leaves over the images captured on-site having the same properties of human vision. Optimizing the fuzzy parameters for a typical strawberry production area at a summer mid-day in Cyprus produced 96% accuracy for segmented iron deficiency and 93% accuracy for segmented using a typical human instant classification approximation as the benchmark holding higher accuracy than a human eye identifier. The fuzzy-base classifier provides approximate result for decision making on the leaf status as if it is healthy or not.

  19. Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning

    Science.gov (United States)

    Prabakaran, S.; Mitra, Shilpa

    2018-04-01

    Data mining is the field containing procedures for finding designs or patterns in a huge dataset, it includes strategies at the convergence of machine learning and database framework. It can be applied to various fields like future healthcare, market basket analysis, education, manufacturing engineering, crime investigation etc. Among these, crime investigation is an interesting application to process crime characteristics to help the society for a better living. This paper survey various data mining techniques used in this domain. This study may be helpful in designing new strategies for crime prediction and analysis.

  20. Particle identification at LHCb: new calibration techniques and machine learning classification algorithms

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic uncertainties. This talk highlights recent developments in PID that use innovative machine learning techniques, as well as novel data-driven approaches which ensure that PID performance is well reproduced in simulation.

  1. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    KAUST Repository

    Harrou, Fouzi; Ramahaleomiarantsoa, Jacques F.; Nounou, Mohamed N.; Nounou, Hazem N.

    2016-01-01

    Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM

  2. Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

    Directory of Open Access Journals (Sweden)

    Palika Chopra

    2018-01-01

    Full Text Available A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT model, random forest (RF model, and neural network (NN model have been used and compared with the help of coefficient of determination (R2 and root-mean-square error (RMSE, and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

  3. Efficiency improvement of the maximum power point tracking for PV systems using support vector machine technique

    International Nuclear Information System (INIS)

    Kareim, Ameer A; Mansor, Muhamad Bin

    2013-01-01

    The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P and O), and incremental conductance (IC) algorithms were used to compare with proposed SVM algorithm. MATLAB models for PV module, theoretical, SVM, P and O, and IC algorithms are implemented. The improved MPPT uses the SVM method to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The SVM technique used two inputs which are solar radiation and ambient temperature of the modeled PV module. The results show that the proposed SVM technique has less Root Mean Square Error (RMSE) and higher efficiency than P and O and IC methods.

  4. Closing the gap: accelerating the translational process in nanomedicine by proposing standardized characterization techniques.

    Science.gov (United States)

    Khorasani, Ali A; Weaver, James L; Salvador-Morales, Carolina

    2014-01-01

    On the cusp of widespread permeation of nanomedicine, academia, industry, and government have invested substantial financial resources in developing new ways to better treat diseases. Materials have unique physical and chemical properties at the nanoscale compared with their bulk or small-molecule analogs. These unique properties have been greatly advantageous in providing innovative solutions for medical treatments at the bench level. However, nanomedicine research has not yet fully permeated the clinical setting because of several limitations. Among these limitations are the lack of universal standards for characterizing nanomaterials and the limited knowledge that we possess regarding the interactions between nanomaterials and biological entities such as proteins. In this review, we report on recent developments in the characterization of nanomaterials as well as the newest information about the interactions between nanomaterials and proteins in the human body. We propose a standard set of techniques for universal characterization of nanomaterials. We also address relevant regulatory issues involved in the translational process for the development of drug molecules and drug delivery systems. Adherence and refinement of a universal standard in nanomaterial characterization as well as the acquisition of a deeper understanding of nanomaterials and proteins will likely accelerate the use of nanomedicine in common practice to a great extent.

  5. Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

    KAUST Repository

    AlQuerm, Ismail A.

    2018-02-21

    There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for

  6. Classification of breast tumour using electrical impedance and machine learning techniques

    International Nuclear Information System (INIS)

    Amin, Abdullah Al; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

    2014-01-01

    When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy. (paper)

  7. A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

    Energy Technology Data Exchange (ETDEWEB)

    Xu Xiaoying; Ho, Shirley; Trac, Hy; Schneider, Jeff; Ntampaka, Michelle [McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States); Poczos, Barnabas [School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States)

    2013-08-01

    We investigate machine learning (ML) techniques for predicting the number of galaxies (N{sub gal}) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N{sub gal}. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N{sub gal} by training our algorithms on the following six halo properties: number of particles, M{sub 200}, {sigma}{sub v}, v{sub max}, half-mass radius, and spin. For Millennium, our predicted N{sub gal} values have a mean-squared error (MSE) of {approx}0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to {approx}5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N{sub gal}. Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M{sub star}, low M{sub star}). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs.

  8. A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

    International Nuclear Information System (INIS)

    Xu Xiaoying; Ho, Shirley; Trac, Hy; Schneider, Jeff; Ntampaka, Michelle; Poczos, Barnabas

    2013-01-01

    We investigate machine learning (ML) techniques for predicting the number of galaxies (N gal ) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N gal . In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N gal by training our algorithms on the following six halo properties: number of particles, M 200 , σ v , v max , half-mass radius, and spin. For Millennium, our predicted N gal values have a mean-squared error (MSE) of ∼0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to ∼5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N gal . Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M star , low M star ). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs

  9. Classification of breast tumour using electrical impedance and machine learning techniques.

    Science.gov (United States)

    Al Amin, Abdullah; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

    2014-06-01

    When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy.

  10. Issues and Techniques in Translating Scientific Terms from English to Khmer for a University-Level Text in Cambodia

    Science.gov (United States)

    Quigley, Cassie; Oliviera, Alandeom W.; Curry, Alastair; Buck, Gayle

    2011-01-01

    Teachers and students spend much time interacting with written resources such as textbooks, tests, or worksheets during classroom instruction. What if no text is available, however, in the language of the learners? This case study describes the processes and techniques adopted by two university lecturers in Cambodia, as they translated an L1…

  11. Hubble Tarantula Treasury Project - VI. Identification of Pre-Main-Sequence Stars using Machine Learning techniques

    Science.gov (United States)

    Ksoll, Victor F.; Gouliermis, Dimitrios A.; Klessen, Ralf S.; Grebel, Eva K.; Sabbi, Elena; Anderson, Jay; Lennon, Daniel J.; Cignoni, Michele; de Marchi, Guido; Smith, Linda J.; Tosi, Monica; van der Marel, Roeland P.

    2018-05-01

    The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire star-burst region of 30 Doradus down to the half Solar mass limit. We use the deep stellar catalogue of HTTP to identify all the pre-main-sequence (PMS) stars of the region, i.e., stars that have not started their lives on the main-sequence yet. The photometric distinction of these stars from the more evolved populations is not a trivial task due to several factors that alter their colour-magnitude diagram positions. The identification of PMS stars requires, thus, sophisticated statistical methods. We employ Machine Learning Classification techniques on the HTTP survey of more than 800,000 sources to identify the PMS stellar content of the observed field. Our methodology consists of 1) carefully selecting the most probable low-mass PMS stellar population of the star-forming cluster NGC2070, 2) using this sample to train classification algorithms to build a predictive model for PMS stars, and 3) applying this model in order to identify the most probable PMS content across the entire Tarantula Nebula. We employ Decision Tree, Random Forest and Support Vector Machine classifiers to categorise the stars as PMS and Non-PMS. The Random Forest and Support Vector Machine provided the most accurate models, predicting about 20,000 sources with a candidateship probability higher than 50 percent, and almost 10,000 PMS candidates with a probability higher than 95 percent. This is the richest and most accurate photometric catalogue of extragalactic PMS candidates across the extent of a whole star-forming complex.

  12. Translation of questionnaires into Arabic in cross-cultural research: techniques and equivalence issues.

    Science.gov (United States)

    Khalaila, Rabia

    2013-10-01

    To describe the translation process of nursing instruments into Arabic and discuss the equivalence issues arising from this process. Review of the literature. The Arabic language is essentially three different languages: Classical Arabic; Modern Standard Arabic (fuS-Ha or MSA); and colloquial Arabic (Lahja A'mmeya), which is itself divided into five different regional Arabic dialects. The Arabic fuS-Ha language is the dialect most widely used in the translation of instruments into Arabic. The literature reveals that only a few studies focused on the linguistic issues in the translation of instruments into Arabic. Brislin's back-translation emerged as the most common method widely used by researchers in studies with Arabic-speaking subjects, but not the perfect one. Linguistic issues in nursing research have not been sufficiently described and discussed in the context of Arabic language and culture. Although there is no standard guideline for instrument translation, the combined translation model is the most recommended procedure to use in cross-cultural research. Linguistic differences between the source culture and the target Arabic culture should be taken into account. Finally, we recommend the use of the fuS-Ha dialect and trilingual translators in the translation of nursing instruments into Arabic.

  13. Automatic Quality Inspection of Percussion Cap Mass Production by Means of 3D Machine Vision and Machine Learning Techniques

    Science.gov (United States)

    Tellaeche, A.; Arana, R.; Ibarguren, A.; Martínez-Otzeta, J. M.

    The exhaustive quality control is becoming very important in the world's globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement. Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, machine learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.

  14. A hybrid stock trading framework integrating technical analysis with machine learning techniques

    Directory of Open Access Journals (Sweden)

    Rajashree Dash

    2016-03-01

    Full Text Available In this paper, a novel decision support system using a computational efficient functional link artificial neural network (CEFLANN and a set of rules is proposed to generate the trading decisions more effectively. Here the problem of stock trading decision prediction is articulated as a classification problem with three class values representing the buy, hold and sell signals. The CEFLANN network used in the decision support system produces a set of continuous trading signals within the range 0–1 by analyzing the nonlinear relationship exists between few popular technical indicators. Further the output trading signals are used to track the trend and to produce the trading decision based on that trend using some trading rules. The novelty of the approach is to engender the profitable stock trading decision points through integration of the learning ability of CEFLANN neural network with the technical analysis rules. For assessing the potential use of the proposed method, the model performance is also compared with some other machine learning techniques such as Support Vector Machine (SVM, Naive Bayesian model, K nearest neighbor model (KNN and Decision Tree (DT model.

  15. Enhanced Quality Control in Pharmaceutical Applications by Combining Raman Spectroscopy and Machine Learning Techniques

    Science.gov (United States)

    Martinez, J. C.; Guzmán-Sepúlveda, J. R.; Bolañoz Evia, G. R.; Córdova, T.; Guzmán-Cabrera, R.

    2018-06-01

    In this work, we applied machine learning techniques to Raman spectra for the characterization and classification of manufactured pharmaceutical products. Our measurements were taken with commercial equipment, for accurate assessment of variations with respect to one calibrated control sample. Unlike the typical use of Raman spectroscopy in pharmaceutical applications, in our approach the principal components of the Raman spectrum are used concurrently as attributes in machine learning algorithms. This permits an efficient comparison and classification of the spectra measured from the samples under study. This also allows for accurate quality control as all relevant spectral components are considered simultaneously. We demonstrate our approach with respect to the specific case of acetaminophen, which is one of the most widely used analgesics in the market. In the experiments, commercial samples from thirteen different laboratories were analyzed and compared against a control sample. The raw data were analyzed based on an arithmetic difference between the nominal active substance and the measured values in each commercial sample. The principal component analysis was applied to the data for quantitative verification (i.e., without considering the actual concentration of the active substance) of the difference in the calibrated sample. Our results show that by following this approach adulterations in pharmaceutical compositions can be clearly identified and accurately quantified.

  16. Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique

    Directory of Open Access Journals (Sweden)

    Rakan Kh. Antar

    2017-12-01

    Full Text Available In this paper, Artificial Neural Network (ANN technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm and mechanical torque (±7 N.m in steady state and dynamic conditions. The simulation results illustratethe effectiveness of the proposed controller without speed or torque sensors.

  17. An analysis of a digital variant of the Trail Making Test using machine learning techniques.

    Science.gov (United States)

    Dahmen, Jessamyn; Cook, Diane; Fellows, Robert; Schmitter-Edgecombe, Maureen

    2017-01-01

    The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility. This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities. Using digital Trail Making Test (dTMT) data collected from (N = 54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features. Predicted TMT scores correlate well with clinical digital test scores (r = 0.98) and paper time to completion scores (r = 0.65). Predicted TICS exhibited a small correlation with clinically derived TICS scores (r = 0.12 Part A, r = 0.10 Part B). Predicted FAB scores exhibited a small correlation with clinically derived FAB scores (r = 0.13 Part A, r = 0.29 for Part B). Digitally derived features were also used to predict diagnosis (AUC of 0.65). Our findings indicate that the dTMT is capable of measuring the same aspects of cognition as the paper-based TMT. Furthermore, the dTMT's additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone.

  18. Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Evanthia E. Tripoliti

    Full Text Available Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years. About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented. Keywords: Heart failure, Diagnosis, Prediction, Severity estimation, Classification, Data mining

  19. A comparison of machine learning techniques for survival prediction in breast cancer.

    Science.gov (United States)

    Vanneschi, Leonardo; Farinaccio, Antonella; Mauri, Giancarlo; Antoniotti, Mauro; Provero, Paolo; Giacobini, Mario

    2011-05-11

    The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

  20. A comparison of machine learning techniques for survival prediction in breast cancer

    Directory of Open Access Journals (Sweden)

    Vanneschi Leonardo

    2011-05-01

    Full Text Available Abstract Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

  1. Controlling the Adhesion of Superhydrophobic Surfaces Using Electrolyte Jet Machining Techniques

    Science.gov (United States)

    Yang, Xiaolong; Liu, Xin; Lu, Yao; Zhou, Shining; Gao, Mingqian; Song, Jinlong; Xu, Wenji

    2016-01-01

    Patterns with controllable adhesion on superhydrophobic areas have various biomedical and chemical applications. Electrolyte jet machining technique (EJM), an electrochemical machining method, was firstly exploited in constructing dimples with various profiles on the superhydrophobic Al alloy surface using different processing parameters. Sliding angles of water droplets on those dimples firstly increased and then stabilized at a certain value with the increase of the processing time or the applied voltages of the EJM, indicating that surfaces with different adhesion force could be obtained by regulating the processing parameters. The contact angle hysteresis and the adhesion force that restricts the droplet from sliding off were investigated through experiments. The results show that the adhesion force could be well described using the classical Furmidge equation. On account of this controllable adhesion force, water droplets could either be firmly pinned to the surface, forming various patterns or slide off at designed tilting angles at specified positions on a superhydrophobic surface. Such dimples on superhydrophopbic surfaces can be applied in water harvesting, biochemical analysis and lab-on-chip devices. PMID:27046771

  2. Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Carla Iglesias

    2017-01-01

    Full Text Available The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470:2008, fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines were tested. Classification and regression trees (CART was the most accurate model for the prediction of pulp ISO brightness (R = 0.85. The other parameters could be predicted with fair results (R = 0.64–0.75 by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.

  3. Modeling, Control and Analyze of Multi-Machine Drive Systems using Bond Graph Technique

    Directory of Open Access Journals (Sweden)

    J. Belhadj

    2006-03-01

    Full Text Available In this paper, a system viewpoint method has been investigated to study and analyze complex systems using Bond Graph technique. These systems are multimachine multi-inverter based on Induction Machine (IM, well used in industries like rolling mills, textile, and railway traction. These systems are multi-domains, multi-scales time and present very strong internal and external couplings, with non-linearity characterized by a high model order. The classical study with analytic model is difficult to manipulate and it is limited to some performances. In this study, a “systemic approach” is presented to design these kinds of systems, using an energetic representation based on Bond Graph formalism. Three types of multimachine are studied with their control strategies. The modeling is carried out by Bond Graph and results are discussed to show the performances of this methodology

  4. Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

    Science.gov (United States)

    Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

  5. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

    Science.gov (United States)

    del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

    2015-06-17

    Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

  6. HTGR Metallic Reactor Internals Core Shell Cutting & Machining Antideformation Technique Study

    International Nuclear Information System (INIS)

    Xing Huiping; Xue Song

    2014-01-01

    The reactor shell assembly of HTGR nuclear power station demonstration project metallic reactor internals is key components of reactor, remains with high-precision large component with large-sized thin-walled straight cylinder-shaped structure, and is the first manufacture in China. As compared with other reactor shell, it has a larger ID (Φ5360mm), a longer length (19000mm), a smaller wall thickness (40mm) and a higher precision requirement. During the process of manufacture, the deformation due to cutting & machining will directly affect the final result of manufacture, the control of structural deformation and cutting deformation shall be throughout total manufacture process of such assembly. To realize the control of entire core shell assembly geometry, the key is to innovate and make breakthroughs on anti-deformation technique and then provide reliable technological foundations for the manufacture of HTGR metallic reactor internals. (author)

  7. Markerless gating for lung cancer radiotherapy based on machine learning techniques

    International Nuclear Information System (INIS)

    Lin Tong; Li Ruijiang; Tang Xiaoli; Jiang, Steve B; Dy, Jennifer G

    2009-01-01

    In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks-ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.

  8. Development of Experimental Setup of Metal Rapid Prototyping Machine using Selective Laser Sintering Technique

    Science.gov (United States)

    Patil, S. N.; Mulay, A. V.; Ahuja, B. B.

    2018-04-01

    Unlike in the traditional manufacturing processes, additive manufacturing as rapid prototyping, allows designers to produce parts that were previously considered too complex to make economically. The shift is taking place from plastic prototype to fully functional metallic parts by direct deposition of metallic powders as produced parts can be directly used for desired purpose. This work is directed towards the development of experimental setup of metal rapid prototyping machine using selective laser sintering and studies the various parameters, which plays important role in the metal rapid prototyping using SLS technique. The machine structure in mainly divided into three main categories namely, (1) Z-movement of bed and table, (2) X-Y movement arrangement for LASER movements and (3) feeder mechanism. Z-movement of bed is controlled by using lead screw, bevel gear pair and stepper motor, which will maintain the accuracy of layer thickness. X-Y movements are controlled using timing belt and stepper motors for precise movements of LASER source. Feeder mechanism is then developed to control uniformity of layer thickness metal powder. Simultaneously, the study is carried out for selection of material. Various types of metal powders can be used for metal RP as Single metal powder, mixture of two metals powder, and combination of metal and polymer powder. Conclusion leads to use of mixture of two metals powder to minimize the problems such as, balling effect and porosity. Developed System can be validated by conducting various experiments on manufactured part to check mechanical and metallurgical properties. After studying the results of these experiments, various process parameters as LASER properties (as power, speed etc.), and material properties (as grain size and structure etc.) will be optimized. This work is mainly focused on the design and development of cost effective experimental setup of metal rapid prototyping using SLS technique which will gives the feel of

  9. Performance optimization in electro- discharge machining using a suitable multiresponse optimization technique

    Directory of Open Access Journals (Sweden)

    I. Nayak

    2017-06-01

    Full Text Available In the present research work, four different multi response optimization techniques, viz. multiple response signal-to-noise (MRSN ratio, weighted signal-to-noise (WSN ratio, Grey relational analysis (GRA and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian methods have been used to optimize the electro-discharge machining (EDM performance characteristics such as material removal rate (MRR, tool wear rate (TWR and surface roughness (SR simultaneously. Experiments have been planned on a D2 steel specimen based on L9 orthogonal array. Experimental results are analyzed using the standard procedure. The optimum level combinations of input process parameters such as voltage, current, pulse-on-time and pulse-off-time, and percentage contributions of each process parameter using ANOVA technique have been determined. Different correlations have been developed between the various input process parameters and output performance characteristics. Finally, the optimum performances of these four methods are compared and the results show that WSN ratio method is the best multiresponse optimization technique for this process. From the analysis, it is also found that the current has the maximum effect on the overall performance of EDM operation as compared to other process parameters.

  10. Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

    Science.gov (United States)

    Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

    2017-08-01

    The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

  11. Machine Learning Techniques for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Thrane, Jakob; Wass, Jesper; Piels, Molly

    2017-01-01

    Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal...... detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical...

  12. 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)

  13. MACHINE LEARNING TECHNIQUES APPLIED TO LIGNOCELLULOSIC ETHANOL IN SIMULTANEOUS HYDROLYSIS AND FERMENTATION

    Directory of Open Access Journals (Sweden)

    J. Fischer

    Full Text Available Abstract This paper investigates the use of machine learning (ML techniques to study the effect of different process conditions on ethanol production from lignocellulosic sugarcane bagasse biomass using S. cerevisiae in a simultaneous hydrolysis and fermentation (SHF process. The effects of temperature, enzyme concentration, biomass load, inoculum size and time were investigated using artificial neural networks, a C5.0 classification tree and random forest algorithms. The optimization of ethanol production was also evaluated. The results clearly depict that ML techniques can be used to evaluate the SHF (R2 between actual and model predictions higher than 0.90, absolute average deviation lower than 8.1% and RMSE lower than 0.80 and predict optimized conditions which are in close agreement with those found experimentally. Optimal conditions were found to be a temperature of 35 ºC, an SHF time of 36 h, enzymatic load of 99.8%, inoculum size of 29.5 g/L and bagasse concentration of 24.9%. The ethanol concentration and volumetric productivity for these conditions were 12.1 g/L and 0.336 g/L.h, respectively.

  14. Machine learning techniques in disease forecasting: a case study on rice blast prediction

    Directory of Open Access Journals (Sweden)

    Kapoor Amar S

    2006-11-01

    Full Text Available Abstract Background Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG approach achieved an average correlation coefficient (r of 0.50, which increased to 0.60 and percent mean absolute error (%MAE decreased from 65.42 to 52.24 when back-propagation neural network (BPNN was used. With generalized regression neural network (GRNN, the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also

  15. A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

    Science.gov (United States)

    Tsipouras, Markos G; Giannakeas, Nikolaos; Tzallas, Alexandros T; Tsianou, Zoe E; Manousou, Pinelopi; Hall, Andrew; Tsoulos, Ioannis; Tsianos, Epameinondas

    2017-03-01

    Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

    Directory of Open Access Journals (Sweden)

    Ivana Šemanjski

    2015-12-01

    Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

  17. Concept Map Technique as a New Method for Whole Text Translation

    Science.gov (United States)

    Krishan, Tamara Mohd Altabieri

    2017-01-01

    This study discusses the use of concept map tool as a new method for teaching translation (from English language to Arabic language). This study comprised 80 students divided into two groups. The first group was taught the new vocabulary by using the concept tool method, whereas the second group was taught the new vocabulary by the traditional…

  18. Machine Learning and Radiology

    Science.gov (United States)

    Wang, Shijun; Summers, Ronald M.

    2012-01-01

    In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

  19. Machine learning and radiology.

    Science.gov (United States)

    Wang, Shijun; Summers, Ronald M

    2012-07-01

    In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

  20. Preoperative Planning and Intraoperative Technique for Accurate Translation of a Distal First Metatarsal Osteotomy.

    Science.gov (United States)

    Wynes, Jacob; Lamm, Bradley M; Andrade, Bijan J; Malay, D Scot

    2016-01-01

    We used preoperative radiographic and intraoperative anatomic measurements to predict and achieve, respectively, the precise amount of capital fragment lateral translation required to restore anatomic balance to the first metatarsophalangeal joint. Correlation was used to relate the amount of capital fragment translation and operative reduction of the first intermetatarsal angle (IMA), hallux abductus angle (HAA), tibial sesamoid position (TSP), metatarsus adductus angle, and first metatarsal length. The mean capital fragment lateral translation was 5.54 ± 1.64 mm, and the mean radiographic reductions included a first IMA of 5.04° ± 2.85°, an HAA of 9.39° ± 8.38°, and a TSP of 1.38 ± 0.9. These changes were statistically (p < .001) and clinically (≥32.55%) significant. The mean reduction of the metatarsus adductus angle was 0.66° ± 4.44° and that for the first metatarsal length was 0.33 ± 7.27 mm, and neither of these were statistically (p = .5876 and 0.1247, respectively) or clinically (≤3.5%) significant. Pairwise correlations between the amount of lateral translation of the capital fragment and the first IMA, HAA, and TSP values were moderately positive and statistically significant (r = 0.4412, p = .0166; r = 0.5391, p = .0025; and r = 0.3729, p = .0463; respectively). In contrast, the correlation with metatarsus adductus and the first metatarsal shortening were weak and not statistically significant (r = 0.2296, p = .2308 and r = -0.2394, p = .2109, respectively). The results of our study indicate that predicted preoperative and executed intraoperative lateral translation of the capital fragment correlates with statistically and clinically significant reductions in the first IMA, HAA, and TSP. Copyright © 2016 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

  1. Machine learning and statistical techniques : an application to the prediction of insolvency in Spanish non-life insurance companies

    OpenAIRE

    Díaz, Zuleyka; Segovia, María Jesús; Fernández, José

    2005-01-01

    Prediction of insurance companies insolvency has arisen as an important problem in the field of financial research. Most methods applied in the past to tackle this issue are traditional statistical techniques which use financial ratios as explicative variables. However, these variables often do not satisfy statistical assumptions, which complicates the application of the mentioned methods. In this paper, a comparative study of the performance of two non-parametric machine learning techniques ...

  2. A comparison of machine learning techniques for detection of drug target articles.

    Science.gov (United States)

    Danger, Roxana; Segura-Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

    2010-12-01

    Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure. Copyright © 2010 Elsevier Inc. All rights reserved.

  3. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  4. Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

    KAUST Repository

    Fernandes, José Antonio

    2015-01-01

    The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.

  5. Combining machine learning and matching techniques to improve causal inference in program evaluation.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Program evaluations often utilize various matching approaches to emulate the randomization process for group assignment in experimental studies. Typically, the matching strategy is implemented, and then covariate balance is assessed before estimating treatment effects. This paper introduces a novel analytic framework utilizing a machine learning algorithm called optimal discriminant analysis (ODA) for assessing covariate balance and estimating treatment effects, once the matching strategy has been implemented. This framework holds several key advantages over the conventional approach: application to any variable metric and number of groups; insensitivity to skewed data or outliers; and use of accuracy measures applicable to all prognostic analyses. Moreover, ODA accepts analytic weights, thereby extending the methodology to any study design where weights are used for covariate adjustment or more precise (differential) outcome measurement. One-to-one matching on the propensity score was used as the matching strategy. Covariate balance was assessed using standardized difference in means (conventional approach) and measures of classification accuracy (ODA). Treatment effects were estimated using ordinary least squares regression and ODA. Using empirical data, ODA produced results highly consistent with those obtained via the conventional methodology for assessing covariate balance and estimating treatment effects. When ODA is combined with matching techniques within a treatment effects framework, the results are consistent with conventional approaches. However, given that it provides additional dimensions and robustness to the analysis versus what can currently be achieved using conventional approaches, ODA offers an appealing alternative. © 2016 John Wiley & Sons, Ltd.

  6. Online laboratory evaluation of seeding-machine application by an acoustic technique

    Energy Technology Data Exchange (ETDEWEB)

    Karimi, H.; Navid, H.; Mahmoudi, A.

    2015-07-01

    Researchers and planter manufacturers have been working closely to develop an automated system for evaluating performance of seeding. In the present study, an innovative use of acoustic signal for laboratory evaluation of seeding-machine application is described. Seed detection technique of the proposed system was based on a rising voltage value that a microphone sensed in each impaction of seeds to a steel plate. Online determining of seed spacing was done with a script which was written in MATLAB software. To evaluate the acoustic system with desired seed spacing, a testing rig was designed. Seeds of wheat, corn and pelleted tomato were used as experimental material. Typical seed patterns were positioned manually on a belt stand with different spacing patterns. When the belt was running, the falling seeds from the end point of the belt impacted to the steel plate, and their acoustic signal was sensed by the microphone. In each impact, data was processed and spacing between the seeds was automatically obtained. Coefficient of determination of gathered data from the belt system and the corresponding seeds spacing measured with the acoustic system in all runs was about 0.98. This strong correlation indicates that the acoustic system worked well in determining the seeds spacing. (Author)

  7. A data-driven predictive approach for drug delivery using machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Li

    Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

  8. Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques.

    Science.gov (United States)

    Cui, De-Mi; Yan, Weizhong; Wang, Xiao-Quan; Lu, Lie-Min

    2017-10-25

    Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT's turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts' interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology's effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.

  9. Advanced Digitization Techniques in Retrieval of Mechanism and Machine Science Resources

    Science.gov (United States)

    Lovasz, E.-Ch.; Gruescu, C. M.; Ciupe, V.; Carabas, I.; Margineanu, D.; Maniu, I.; Dehelean, N.

    The European project thinkMOTION works on the purpose of retrieving all-times content regarding mechanisms and machine science by means of creating a digital library, accessible to a broad public through the portal Europeana. DMG-Lib is intended to display the development in the field, from its very beginning up to now days. There is a large range of significant objects available, physically very heterogeneous and needing all to be digitized. The paper presents the workflow, the equipments and specific techniques used in digitization of documents featuring very different characteristics (size, texture, color, degree of preservation, resolution and so on). Once the workflow established on very detailed steps, the development of the workstation is treated. Special equipments designed and assembled at Universitatea "Politehnica" Timisoara are presented. A large series of software applications, including original programs, work for digitization itself, processing of images, management of files, automatic optoelectronic control of capture, storage of information in different stages of processing. An illustrating example is explained, showing the steps followed in order to obtain a clear, high-resolution image from an old original document (very valuable as a historical proof but very poor in quality regarding clarity, contrast and resolution).

  10. A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

    OpenAIRE

    Ademujimi , Toyosi ,; Brundage , Michael ,; Prabhu , Vittaldas ,

    2017-01-01

    Part 6: Intelligent Diagnostics and Maintenance Solutions; International audience; Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learnin...

  11. Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

    Science.gov (United States)

    Crossley, Scott A.

    2013-01-01

    This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

  12. Current breathomics-a review on data pre-processing techniques and machine learning in metabolomics breath analysis

    DEFF Research Database (Denmark)

    Smolinska, A.; Hauschild, A. C.; Fijten, R. R. R.

    2014-01-01

    been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start...... different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus...

  13. Modelling risk of tick exposure in southern Scandinavia using machine learning techniques, satellite imagery, and human population density maps

    DEFF Research Database (Denmark)

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

    30 sites (forests and meadows) in each of Denmark, southern Norway and south-eastern Sweden. At each site we measured presence/absence of ticks, and used the data obtained along with environmental satellite images to run Boosted Regression Tree machine learning algorithms to predict overall spatial...... and Sweden), areas with high population densities tend to overlap with these zones.Machine learning techniques allow us to predict for larger areas without having to perform extensive sampling all over the region in question, and we were able to produce models and maps with high predictive value. The results...

  14. EXPLICITATION AND ADDITION TECHNIQUES IN AUDIOVISUAL TRANSLATION: A MULTIMODAL APPROACH OF ENGLISHINDONESIAN SUBTITLES

    Directory of Open Access Journals (Sweden)

    Ichwan Suyudi

    2017-12-01

    Full Text Available In audiovisual translation, the multimodality of the audiovisual text is both a challenge and a resource for subtitlers. This paper illustrates how multi-modes provide information that helps subtitlers to gain a better understanding of meaning-making practices that will influence them to make a decision-making in translating a certain verbal text. Subtitlers may explicit, add, and condense the texts based on the multi-modes as seen on the visual frames. Subtitlers have to consider the distribution and integration of the meanings of multi-modes in order to create comprehensive equivalence between the source and target texts. Excerpts of visual frames in this paper are taken from English films Forrest Gump (drama, 1996, and James Bond (thriller, 2010.

  15. 3D CT cerebral angiography technique using a 320-detector machine with a time–density curve and low contrast medium volume: Comparison with fixed time delay technique

    International Nuclear Information System (INIS)

    Das, K.; Biswas, S.; Roughley, S.; Bhojak, M.; Niven, S.

    2014-01-01

    Aim: To describe a cerebral computed tomography angiography (CTA) technique using a 320-detector CT machine and a small contrast medium volume (35 ml, 15 ml for test bolus). Also, to compare the quality of these images with that of the images acquired using a larger contrast medium volume (90 or 120 ml) and a fixed time delay (FTD) of 18 s using a 16-detector CT machine. Materials and methods: Cerebral CTA images were acquired using a 320-detector machine by synchronizing the scanning time with the time of peak enhancement as determined from the time–density curve (TDC) using a test bolus dose. The quality of CTA images acquired using this technique was compared with that obtained using a FTD of 18 s (by 16-detector CT), retrospectively. Average densities in four different intracranial arteries, overall opacification of arteries, and the degree of venous contamination were graded and compared. Results: Thirty-eight patients were scanned using the TDC technique and 40 patients using the FTD technique. The arterial densities achieved by the TDC technique were higher (significant for supraclinoid and basilar arteries, p < 0.05). The proportion of images deemed as having “good” arterial opacification was 95% for TDC and 90% for FTD. The degree of venous contamination was significantly higher in images produced by the FTD technique (p < 0.001%). Conclusion: Good diagnostic quality CTA images with significant reduction of venous contamination can be achieved with a low contrast medium dose using a 320-detector machine by coupling the time of data acquisition with the time of peak enhancement

  16. Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

    Science.gov (United States)

    Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

    2017-08-04

    Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the

  17. Clinical translation of autologous cell-based tissue engineering techniques as Class III therapeutics in China: Taking cartilage tissue engineering as an example

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2014-04-01

    Full Text Available Autologous cell-based tissue engineering (TE techniques have been clinically approved for approximately 4 years in China, since the first cartilage TE technique was approved for clinical use by the Zhejiang Health Bureau. TE techniques offer a promising alternative to traditional transplantation surgery, and are different from those for transplanted tissues (biologics or pharmaceutical, the clinical translational procedures are unique and multitasked, and the requirements may differ from those of the target tissues. Thus, the translational procedure is still unfamiliar to most researchers and needs further improvement. This perspectives paper describes the key guidelines and regulations involved in the current translational process, and shares our translational experiences in cartilage TE to provide an example of autologous cell-based TE translation in China. Finally, we discuss the scientific and social challenges and provide some suggestions for future improvements.

  18. 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.

  19. Modification-specific proteomics: strategies for characterization of post-translational modifications using enrichment techniques

    DEFF Research Database (Denmark)

    Zhao, Yingming; Jensen, Ole N

    2009-01-01

    More than 300 different types of protein post-translational modifications (PTMs) have been described, many of which are known to have pivotal roles in cellular physiology and disease. Nevertheless, only a handful of PTMs have been extensively investigated at the proteome level. Knowledge of protein...... substrates and their PTM sites is key to dissection of PTM-mediated cellular processes. The past several years have seen a tremendous progress in developing MS-based proteomics technologies for global PTM analysis, including numerous studies of yeast and other microbes. Modification-specific enrichment...

  20. Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

    DEFF Research Database (Denmark)

    Zibar, Darko; de Carvalho, Luis Henrique Hecker; Piels, Molly

    2015-01-01

    In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms...

  1. Exploration of machine learning techniques in predicting multiple sclerosis disease course

    OpenAIRE

    Zhao, Yijun; Healy, Brian C.; Rotstein, Dalia; Guttmann, Charles R. G.; Bakshi, Rohit; Weiner, Howard L.; Brodley, Carla E.; Chitnis, Tanuja

    2017-01-01

    Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 1693 CLIMB study patients were classified as increased EDSS?1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up. Results Baseline data...

  2. Applying machine-learning techniques to Twitter data for automatic hazard-event classification.

    Science.gov (United States)

    Filgueira, R.; Bee, E. J.; Diaz-Doce, D.; Poole, J., Sr.; Singh, A.

    2017-12-01

    The constant flow of information offered by tweets provides valuable information about all sorts of events at a high temporal and spatial resolution. Over the past year we have been analyzing in real-time geological hazards/phenomenon, such as earthquakes, volcanic eruptions, landslides, floods or the aurora, as part of the GeoSocial project, by geo-locating tweets filtered by keywords in a web-map. However, not all the filtered tweets are related with hazard/phenomenon events. This work explores two classification techniques for automatic hazard-event categorization based on tweets about the "Aurora". First, tweets were filtered using aurora-related keywords, removing stop words and selecting the ones written in English. For classifying the remaining between "aurora-event" or "no-aurora-event" categories, we compared two state-of-art techniques: Support Vector Machine (SVM) and Deep Convolutional Neural Networks (CNN) algorithms. Both approaches belong to the family of supervised learning algorithms, which make predictions based on labelled training dataset. Therefore, we created a training dataset by tagging 1200 tweets between both categories. The general form of SVM is used to separate two classes by a function (kernel). We compared the performance of four different kernels (Linear Regression, Logistic Regression, Multinomial Naïve Bayesian and Stochastic Gradient Descent) provided by Scikit-Learn library using our training dataset to build the SVM classifier. The results shown that the Logistic Regression (LR) gets the best accuracy (87%). So, we selected the SVM-LR classifier to categorise a large collection of tweets using the "dispel4py" framework.Later, we developed a CNN classifier, where the first layer embeds words into low-dimensional vectors. The next layer performs convolutions over the embedded word vectors. Results from the convolutional layer are max-pooled into a long feature vector, which is classified using a softmax layer. The CNN's accuracy

  3. In vitro biological characterization of macroporous 3D Bonelike structures prepared through a 3D machining technique

    International Nuclear Information System (INIS)

    Laranjeira, M.S.; Dias, A.G.; Santos, J.D.; Fernandes, M.H.

    2009-01-01

    3D bioactive macroporous structures were prepared using a 3D machining technique. A virtual 3D structure model was created and a computer numerically controlled (CNC) milling device machined Bonelike samples. The resulting structures showed a reproducible macroporosity and interconnective structure. Macropores size after sintering was approximately 2000 μm. In vitro testing using human bone marrow stroma showed that cells were able to adhere and proliferate on 3D structures surface and migrate into all macropore channels. In addition, these cells were able to differentiate, since mineralized globular structures associated with cell layer were identified. Results obtained showed that 3D structures of Bonelike successfully allow cell migration into all macropores, and allow human bone marrow stromal cells to proliferate and differentiate. This innovative technique may be considered as a step-forward preparation for 3D interconnective macroporous structures that allow bone ingrowth while maintaining mechanical integrity.

  4. The smart aerial release machine, a universal system for applying the sterile insect technique: Manuscript Draft

    International Nuclear Information System (INIS)

    Mubarqui, Leal Ruben; Perez, Rene Cano; Klad, Roberto Angulo; Lopez, Jose L. Zavale; Parker, Andrew; Seck, Momar Talla; Sall, Baba; Bouyer, Jeremy

    2014-01-01

    Beyond insecticides, alternative methods to control insect pests for agriculture and vectors of diseases are needed. Management strategies involving the mass-release of living control agents have been developed, including genetic control with sterile insects and biological control with parasitoids, for which aerial release of insects is often required. Aerial release in genetic control programmes often involves the use of chilled sterile insects, which can improve dispersal, survival and competitiveness of sterile males. Currently available means of aerially releasing chilled fruit flies are however insufficiently precise to ensure homogeneous distribution at low release rates and no device is available for tsetse. Here we present the smart aerial release machine, a new design by the Mubarqui Company, based on the use of vibrating conveyors. The machine is controlled through Bluetooth by a tablet with Android Operating System including a completely automatic guidance and navigation system (MaxNav software). The tablet is also connected to an online relational database facilitating the preparation of flight schedules and automatic storage of flight reports. The new machine was compared with a conveyor release machine in Mexico using two fruit flies species (Anastrepha ludens and Ceratitis capitata) and we obtained better dispersal homogeneity (% of positive traps, p < 0.001) for both species and better recapture rates for Anastrepha ludens (p < 0.001), especially at low release densities (<1500 per ha). We also demonstrated that the machine can replace paper boxes for aerial release of tsetse in Senegal.This technology limits damages to insects and allows a large range of release rates from 10 flies/km"2 for tsetse flies up to 600 000 flies/km"2 for fruit flies. The potential of this machine to release other species like mosquitoes is discussed. Plans and operating of the machine are provided to allow its use worldwide.

  5. The smart aerial release machine, a universal system for applying the sterile insect technique.

    Directory of Open Access Journals (Sweden)

    Ruben Leal Mubarqui

    Full Text Available Beyond insecticides, alternative methods to control insect pests for agriculture and vectors of diseases are needed. Management strategies involving the mass-release of living control agents have been developed, including genetic control with sterile insects and biological control with parasitoids, for which aerial release of insects is often required. Aerial release in genetic control programmes often involves the use of chilled sterile insects, which can improve dispersal, survival and competitiveness of sterile males. Currently available means of aerially releasing chilled fruit flies are however insufficiently precise to ensure homogeneous distribution at low release rates and no device is available for tsetse.Here we present the smart aerial release machine, a new design by the Mubarqui Company, based on the use of vibrating conveyors. The machine is controlled through Bluetooth by a tablet with Android Operating System including a completely automatic guidance and navigation system (MaxNav software. The tablet is also connected to an online relational database facilitating the preparation of flight schedules and automatic storage of flight reports. The new machine was compared with a conveyor release machine in Mexico using two fruit flies species (Anastrepha ludens and Ceratitis capitata and we obtained better dispersal homogeneity (% of positive traps, p<0.001 for both species and better recapture rates for Anastrepha ludens (p<0.001, especially at low release densities (<1500 per ha. We also demonstrated that the machine can replace paper boxes for aerial release of tsetse in Senegal.This technology limits damages to insects and allows a large range of release rates from 10 flies/km2 for tsetse flies up to 600,000 flies/km2 for fruit flies. The potential of this machine to release other species like mosquitoes is discussed. Plans and operating of the machine are provided to allow its use worldwide.

  6. Machine-learning techniques for family demography: an application of random forests to the analysis of divorce determinants in Germany

    OpenAIRE

    Arpino, Bruno; Le Moglie, Marco; Mencarini, Letizia

    2018-01-01

    Demographers often analyze the determinants of life-course events with parametric regression-type approaches. Here, we present a class of nonparametric approaches, broadly defined as machine learning (ML) techniques, and discuss advantages and disadvantages of a popular type known as random forest. We argue that random forests can be useful either as a substitute, or a complement, to more standard parametric regression modeling. Our discussion of random forests is intuitive and...

  7. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques

    OpenAIRE

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-01-01

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content...

  8. Conceptual dissonance: evaluating the efficacy of natural language processing techniques for validating translational knowledge constructs.

    Science.gov (United States)

    Payne, Philip R O; Kwok, Alan; Dhaval, Rakesh; Borlawsky, Tara B

    2009-03-01

    The conduct of large-scale translational studies presents significant challenges related to the storage, management and analysis of integrative data sets. Ideally, the application of methodologies such as conceptual knowledge discovery in databases (CKDD) provides a means for moving beyond intuitive hypothesis discovery and testing in such data sets, and towards the high-throughput generation and evaluation of knowledge-anchored relationships between complex bio-molecular and phenotypic variables. However, the induction of such high-throughput hypotheses is non-trivial, and requires correspondingly high-throughput validation methodologies. In this manuscript, we describe an evaluation of the efficacy of a natural language processing-based approach to validating such hypotheses. As part of this evaluation, we will examine a phenomenon that we have labeled as "Conceptual Dissonance" in which conceptual knowledge derived from two or more sources of comparable scope and granularity cannot be readily integrated or compared using conventional methods and automated tools.

  9. Training in Techniques and Translation: Novel Nuclear Medicine Imaging Agents for Oncology and Neurology

    Energy Technology Data Exchange (ETDEWEB)

    Tu, Zhude [Washington Univ., St. Louis, MO (United States)

    2012-08-01

    The goal of this grant was to provide critical interdisciplinary research training for the next generation of radiochemists and nuclear medicine physicians through a collaboration between basic science and clinical faculty who are actively involved in the development, application, and translation of radiopharmaceuticals. Following the four year funding support period, the 10 postdocs, graduate students, as well as clinical physicians who received training have become faculty members, or senior radiochemists at different academic institutes or industry. With respect to scientific accomplishments, 26 peer-reviewed articles have been published to date as well as numerous poster and oral presentations. The goals of all four scientific projects were completed and several promising radiotracers identified for transfer into clinical investigation for human use. Some preliminary data generated from this training grant led several successful NIH grant proposals for the principal investigators.

  10. GeckoFTL: Scalable Flash Translation Techniques For Very Large Flash Devices

    DEFF Research Database (Denmark)

    Dayan, Niv; Bonnet, Philippe; Idreos, Stratos

    2016-01-01

    The volume of metadata needed by a flash translation layer (FTL) is proportional to the storage capacity of a flash device. Ideally, this metadata should reside in the device's integrated RAM to enable fast access. However, as flash devices scale to terabytes, the necessary volume of metadata...... thereby harming performance and device lifetime. In this paper, we identify a key component of the metadata called the Page Validity Bitmap (PVB) as the bottleneck. PVB is used by the garbage-collectors of state-of-the-art FTLs to keep track of which physical pages in the device are invalid. PVB...... constitutes 95% of the FTL's RAM-resident metadata, and recovering PVB after power fails takes a significant proportion of the overall recovery time. To solve this problem, we propose a page-associative FTL called GeckoFTL, whose central innovation is replacing PVB with a new data structure called Logarithmic...

  11. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

    Science.gov (United States)

    Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

    2017-06-14

    Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

  12. Possibilities of radiation technique application in machine-building industry of Bulgaria

    International Nuclear Information System (INIS)

    Petrov, A.; Avramov, D.; Kostov, St.

    1979-01-01

    In last ten years, in development of machine-building industry, tendency has been outlined for creation of machines and constructions having minimum weight and elevated reliability from one side due to improvement of design and technology of production and from the other side due to application of materials with improved parameters. Solution of these problems is closely connected with application of the radiation methods. State-of-art of the radiation technology application in the machine-building industry is analyzed and mainly for investigation of wear resistance of friction machineparts. Use of spatial radioactive labelling in investigation of materials and application of radiation methods for optimization of technological processes in metallurgy, foundry and so on is considered. Estimation is give of perspectives of further growth of introduction of radiation methods in Bulgaria [ru

  13. Chemically intuited, large-scale screening of MOFs by machine learning techniques

    Science.gov (United States)

    Borboudakis, Giorgos; Stergiannakos, Taxiarchis; Frysali, Maria; Klontzas, Emmanuel; Tsamardinos, Ioannis; Froudakis, George E.

    2017-10-01

    A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

  14. Accuracy comparison among different machine learning techniques for detecting malicious codes

    Science.gov (United States)

    Narang, Komal

    2016-03-01

    In this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Naïve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.

  15. Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

    Directory of Open Access Journals (Sweden)

    Steren Chabert

    2017-01-01

    Full Text Available Cerebral aneurysm is a cerebrovascular disorder characterized by a bulging in a weak area in the wall of an artery that supplies blood to the brain. It is relevant to understand the mechanisms leading to the apparition of aneurysms, their growth and, more important, leading to their rupture. The purpose of this study is to study the impact on aneurysm rupture of the combination of different parameters, instead of focusing on only one factor at a time as is frequently found in the literature, using machine learning and feature extraction techniques. This discussion takes relevance in the context of the complex decision that the physicians have to take to decide which therapy to apply, as each intervention bares its own risks, and implies to use a complex ensemble of resources (human resources, OR, etc. in hospitals always under very high work load. This project has been raised in our actual working team, composed of interventional neuroradiologist, radiologic technologist, informatics engineers and biomedical engineers, from Valparaiso public Hospital, Hospital Carlos van Buren, and from Universidad de Valparaíso – Facultad de Ingeniería and Facultad de Medicina. This team has been working together in the last few years, and is now participating in the implementation of an “interdisciplinary platform for innovation in health”, as part of a bigger project leaded by Universidad de Valparaiso (PMI UVA1402. It is relevant to emphasize that this project is made feasible by the existence of this network between physicians and engineers, and by the existence of data already registered in an orderly manner, structured and recorded in digital format. The present proposal arises from the description in nowadays literature that the actual indicators, whether based on morphological description of the aneurysm, or based on characterization of biomechanical factor or others, these indicators were shown not to provide sufficient information in order

  16. Elevating Virtual Machine Introspection for Fine-Grained Process Monitoring: Techniques and Applications

    Science.gov (United States)

    Srinivasan, Deepa

    2013-01-01

    Recent rapid malware growth has exposed the limitations of traditional in-host malware-defense systems and motivated the development of secure virtualization-based solutions. By running vulnerable systems as virtual machines (VMs) and moving security software from inside VMs to the outside, the out-of-VM solutions securely isolate the anti-malware…

  17. Engagement techniques and playing level impact the biomechanical demands on rugby forwards during machine-based scrummaging.

    Science.gov (United States)

    Preatoni, Ezio; Stokes, Keith A; England, Michael E; Trewartha, Grant

    2015-04-01

    This cross-sectional study investigated the factors that may influence the physical loading on rugby forwards performing a scrum by studying the biomechanics of machine-based scrummaging under different engagement techniques and playing levels. 34 forward packs from six playing levels performed repetitions of five different types of engagement techniques against an instrumented scrum machine under realistic training conditions. Applied forces and body movements were recorded in three orthogonal directions. The modification of the engagement technique altered the load acting on players. These changes were in a similar direction and of similar magnitude irrespective of the playing level. Reducing the dynamics of the initial engagement through a fold-in procedure decreased the peak compression force, the peak downward force and the engagement speed in excess of 30%. For example, peak compression (horizontal) forces in the professional teams changed from 16.5 (baseline technique) to 8.6 kN (fold-in procedure). The fold-in technique also reduced the occurrence of combined high forces and head-trunk misalignment during the absorption of the impact, which was used as a measure of potential hazard, by more than 30%. Reducing the initial impact did not decrease the ability of the teams to produce sustained compression forces. De-emphasising the initial impact against the scrum machine decreased the mechanical stresses acting on forward players and may benefit players' welfare by reducing the hazard factors that may induce chronic degeneration of the spine. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  18. 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....

  19. Scoping Study of Machine Learning Techniques for Visualization and Analysis of Multi-source Data in Nuclear Safeguards

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Yonggang

    2018-05-07

    In implementation of nuclear safeguards, many different techniques are being used to monitor operation of nuclear facilities and safeguard nuclear materials, ranging from radiation detectors, flow monitors, video surveillance, satellite imagers, digital seals to open source search and reports of onsite inspections/verifications. Each technique measures one or more unique properties related to nuclear materials or operation processes. Because these data sets have no or loose correlations, it could be beneficial to analyze the data sets together to improve the effectiveness and efficiency of safeguards processes. Advanced visualization techniques and machine-learning based multi-modality analysis could be effective tools in such integrated analysis. In this project, we will conduct a survey of existing visualization and analysis techniques for multi-source data and assess their potential values in nuclear safeguards.

  20. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

    Science.gov (United States)

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-08-15

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

  1. Ability of the Tightrope® and percutaneous lateral fabellar suture techniques to control cranial tibial translation.

    Science.gov (United States)

    Biskup, Jeffery J; Griffon, Dominique J; Socie, Mike; Schaeffer, David J; Kurath, Peter

    2014-11-01

    To compare the ability of the Tightrope® (TR) cranial cruciate ligament (CCL) technique, percutaneous lateral fabella suture (pLFS) technique, and normal CCL to control cranial tibial translation (CTT). In vitro biomechanical study. Cadaveric canine pelvic limbs (n = 18 pairs). Six small animal surgical residents (1 pair each) and a Diplomate of the American College of Veterinary Surgeons (10 pairs) performed TR and pLFS techniques on paired limbs. Two intact limb pairs served as controls. Limbs were assessed by palpation, radiographs, and dissection before mechanical testing of resistance to CTT. Forces resisted during displacement were compared between groups with a mixed ANOVA and post hoc tests. With 5 mm of displacement, the pLFS resisted 72 ± 45 N and the TR resisted 66 ± 48 N of load. The intact CCL resisted 400 ± 35 N. The intact CCL resisted displacement significantly more than either surgical technique. TR and pLFS had similar ability to resist CTT but neither restored the biomechanical properties of an intact CCL. © Copyright 2014 by The American College of Veterinary Surgeons.

  2. Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

    Directory of Open Access Journals (Sweden)

    H Kimura

    2009-04-01

    Full Text Available In climate research, pressure patterns are often very important. When a climatologists need to know the days of a specific pressure pattern, for example "low pressure in Western areas of Japan and high pressure in Eastern areas of Japan (Japanese winter-type weather," they have to visually check a huge number of surface weather charts. To overcome this problem, we propose an automatic classification system using a support vector machine (SVM, which is a machine-learning method. We attempted to classify pressure patterns into two classes: "winter type" and "non-winter type". For both training datasets and test datasets, we used the JRA-25 dataset from 1981 to 2000. An experimental evaluation showed that our method obtained a greater than 0.8 F-measure. We noted that variations in results were based on differences in training datasets.

  3. Free focus radiography with miniaturized dental x-ray machines: a comparison of ''midline'' and ''lateral'' techniques

    International Nuclear Information System (INIS)

    Jensen, T.W.

    1983-01-01

    The use of free focus radiography (FFR) employing miniaturized dental x-ray machines with radiation probes has never been generally accepted in dentistry despite its recognized radiographic potential. The present investigation studied ways to improve imaging and lower radiation burdens in dental free focus radiography. Relatively high air exposures ranging from 42,050 mR per film for high-resolution images to 3,214 mR per film for lower-resolution images using a current midline radiographic technique for panoramic FFR were found. In a proposed lateral FFR panoramic technique, reduced exposures ranged from 420 mR per film for high-resolution images to 14 mR per film for lower-resolution images. In each technique the lower exposure was obtained with a rare earth imaging system. A proposed modification of the current midline FFR technique using a rare earth imaging system and heavy added copper filtration was found to produce exposures in the range normally used in dentistry (207 mr), and the resultant image was high in contrast with relatively low detail. A comparison of essential characteristics of midline and lateral FFR techniques failed to identify specific advantages for the midline technique in current use. Lateral exposure modes in dental FFR should receive increased attention in the interest of good imaging and radiation control. It was noted that existing miniaturized dental x-ray machines may have been designed specifically for use of the midline FFR exposure technique, and modification of this equipment to support reliable lateral exposure modes was recommended

  4. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    KAUST Repository

    Harrou, Fouzi

    2016-05-09

    Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM). The proposed fault detection approach is based on the use of principal components analysis (PCA). However, conventional PCA-based detection indices, such as the T2T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA) control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes.

  5. Quran Vibrations in Braille Codes Using the Finite State Machine Technique

    OpenAIRE

    Abualkishik, Abdallah M; Omar, Khairuddin

    2010-01-01

    In this study, the Quran Braille System was developed. It provides blind Muslims an easy way to read and understand the Holy Quran as well as the chance for other blind people to learn about Islam. The experiments have produced a full translation prototype for the Quran verses including associated vibrations. The result of the experiment will be printed out using a Braille printer to introduce the usefulness of this study particularly to researcher and society at large. This study will adhere...

  6. THE IMPACT OF TRANSLATION TECHNIQUES AND SUBTITLING RULES TO THE EQUIVALENCE OF MEANING OF CULINARY TERMS IN THE FRESH WITH ANNA OLSON SEASON 1 SUBTITLE

    Directory of Open Access Journals (Sweden)

    Hetty Hartati Novita

    2017-12-01

    Full Text Available The emergence of cable TV in Indonesia has made this author interested in focusing on the topic of culinary terms translation in the subtitle of Fresh with Anna Olson Season 1 program broadcasted on Asian Food Channel station. In this paper, this author analyzes the translation techniques suggested by Molina and Albir used by the translators to create the more accurate, natural, and communicative subtitle even though there are subtitling rules to follow. The result is, most of the translated terms have equivalence in meaning between the ST and the TT, while the ones which are not equivalent are due to the application of two particular techniques, Discursive Creation and Adaptation.

  7. Translating Public Policy: Enhancing the Applicability of Social Impact Techniques for Grassroots Community Groups

    Directory of Open Access Journals (Sweden)

    Melissa Edwards

    2013-08-01

    Full Text Available This paper reports on an exploratory action research study designed to understand how grassroots community organisations engage in the measurement and reporting of social impact and how they demonstrate their social impact to local government funders. Our findings suggest that the relationships between small non-profit organisations, the communities they serve or represent and their funders are increasingly driven from the top down formalised practices. Volunteer-run grassroots organisations can be marginalized in this process. Members may lack awareness of funders’ strategic approaches or the formalized auditing and control requirements of funders mean grassroots organisations lose capacity to define their programs and projects. We conclude that, to help counter this trend, tools and techniques which open up possibilities for dialogue between those holding power and those seeking support are essential.

  8. The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine

    Science.gov (United States)

    Taha, Z.; Razman, M. A. M.; Adnan, F. A.; Ghani, A. S. Abdul; Majeed, A. P. P. Abdul; Musa, R. M.; Sallehudin, M. F.; Mukai, Y.

    2018-03-01

    Fish Hunger behaviour is one of the important element in determining the fish feeding routine, especially for farmed fishes. Inaccurate feeding routines (under-feeding or over-feeding) lead the fishes to die and thus, reduces the total production of fishes. The excessive food which is not eaten by fish will be dissolved in the water and thus, reduce the water quality (oxygen quantity in the water will be reduced). The reduction of oxygen (water quality) leads the fish to die and in some cases, may lead to fish diseases. This study correlates Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique. The behaviour is clustered with respect to the position of the centre of gravity of the school of fish prior feeding, during feeding and after feeding. The clustered fish behaviour is then classified by means of a machine learning technique namely Support vector machine (SVM). It has been shown from the study that the Fine Gaussian variation of SVM is able to provide a reasonably accurate classification of fish feeding behaviour with a classification accuracy of 79.7%. The proposed integration technique may increase the usefulness of the captured data and thus better differentiates the various behaviour of farmed fishes.

  9. High Classification Rates for Continuous Cow Activity Recognition using Low-cost GPS Positioning Sensors and Standard Machine Learning Techniques

    DEFF Research Database (Denmark)

    Godsk, Torben; Kjærgaard, Mikkel Baun

    2011-01-01

    activities. By preprocessing the raw cow position data, we obtain high classification rates using standard machine learning techniques to recognize cow activities. Our objectives were to (i) determine to what degree it is possible to robustly recognize cow activities from GPS positioning data, using low...... and their activities manually logged to serve as ground truth. For our dataset we managed to obtain an average classification success rate of 86.2% of the four activities: eating/seeking (90.0%), walking (100%), lying (76.5%), and standing (75.8%) by optimizing both the preprocessing of the raw GPS data...

  10. 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

  11. Technique to reduce the shaft torque stress at an induction machine

    Directory of Open Access Journals (Sweden)

    Adrian Tulbure

    2005-10-01

    Full Text Available For the active attenuation at load stress in the drive shaft, the control system should receive as input signal the instantaneous shaft torque value. In this context an intelligent observer for shaft tongue of mains operatea induction machine, which is able to responding by variation of LIF (Load Input Function[1] must be developed. Extensive computer simulation prove the effectiveness of the proposed solution. In order to obtain a practical validation, the stimulated regulator has been designed and tested in the Institute of Electrical Engineering in Clausthal/Germany [2]. This paper contains following parts: Developing the mathematical model, Practical realisation, Simulations and measurements, Evaluating the control solutions and Conclusions.

  12. Optimization of fuel exchange machine operation for boiling water reactors using an artificial intelligence technique

    International Nuclear Information System (INIS)

    Sekimizu, K.; Araki, T.; Tatemichi, S.I.

    1987-01-01

    Optimization of fuel assembly exchange machine movements during periodic refueling outage is discussed. The fuel assembly movements during a fuel shuffling were examined, and it was found that the fuel assembly movements consist of two different movement sequences;one is the ''PATH,'' which begins at a discharged fuel assembly and terminates at a fresh fuel assembly, and the other is the ''LOOP,'' where fuel assemblies circulate in the core. It is also shown that fuel-loading patterns during the fuel shuffling can be expressed by the state of each PATH, which is the number of elements already accomplished in the PATH actions. Based on this fact, a scheme to determine a fuel assembly movement sequence within the constraint was formulated using the artificial intelligence language PROLOG. An additional merit to the scheme is that it can simultaneously evaluate fuel assembly movement, due to the control rods and local power range monitor exchange, in addition to normal fuel shuffling. Fuel assembly movements, for fuel shuffling in a 540-MW(electric) boiling water reactor power plant, were calculated by this scheme. It is also shown that the true optimization to minimize the fuel exchange machine movements would be costly to obtain due to the number of alternatives that would need to be evaluated. However, a method to obtain a quasi-optimum solution is suggested

  13. Exploration of machine learning techniques in predicting multiple sclerosis disease course.

    Directory of Open Access Journals (Sweden)

    Yijun Zhao

    Full Text Available To explore the value of machine learning methods for predicting multiple sclerosis disease course.1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening or not (non-worsening at up to five years after baseline visit. Support vector machines (SVM were used to build the classifier, and compared to logistic regression (LR using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%. Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

  14. Machine Learning Techniques for Characterizing IEEE 802.11b Encrypted Data Streams

    National Research Council Canada - National Science Library

    Henson, Michael

    2004-01-01

    .... Even though there have been major advancements in encryption technology, security protocols and packet header obfuscation techniques, other distinguishing characteristics do exist in wireless network traffic...

  15. Estimating Global Seafloor Total Organic Carbon Using a Machine Learning Technique and Its Relevance to Methane Hydrates

    Science.gov (United States)

    Lee, T. R.; Wood, W. T.; Dale, J.

    2017-12-01

    Empirical and theoretical models of sub-seafloor organic matter transformation, degradation and methanogenesis require estimates of initial seafloor total organic carbon (TOC). This subsurface methane, under the appropriate geophysical and geochemical conditions may manifest as methane hydrate deposits. Despite the importance of seafloor TOC, actual observations of TOC in the world's oceans are sparse and large regions of the seafloor yet remain unmeasured. To provide an estimate in areas where observations are limited or non-existent, we have implemented interpolation techniques that rely on existing data sets. Recent geospatial analyses have provided accurate accounts of global geophysical and geochemical properties (e.g. crustal heat flow, seafloor biomass, porosity) through machine learning interpolation techniques. These techniques find correlations between the desired quantity (in this case TOC) and other quantities (predictors, e.g. bathymetry, distance from coast, etc.) that are more widely known. Predictions (with uncertainties) of seafloor TOC in regions lacking direct observations are made based on the correlations. Global distribution of seafloor TOC at 1 x 1 arc-degree resolution was estimated from a dataset of seafloor TOC compiled by Seiter et al. [2004] and a non-parametric (i.e. data-driven) machine learning algorithm, specifically k-nearest neighbors (KNN). Built-in predictor selection and a ten-fold validation technique generated statistically optimal estimates of seafloor TOC and uncertainties. In addition, inexperience was estimated. Inexperience is effectively the distance in parameter space to the single nearest neighbor, and it indicates geographic locations where future data collection would most benefit prediction accuracy. These improved geospatial estimates of TOC in data deficient areas will provide new constraints on methane production and subsequent methane hydrate accumulation.

  16. Prediction of Five Softwood Paper Properties from its Density using Support Vector Machine Regression Techniques

    Directory of Open Access Journals (Sweden)

    Esperanza García-Gonzalo

    2016-01-01

    Full Text Available Predicting paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kernel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris and cypress species (Cupressus lusitanica, C. sempervirens, and C. arizonica beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination with two variables: the numerical variable density and the categorical variable species.

  17. Classification of fMRI resting-state maps using machine learning techniques: A comparative study

    Science.gov (United States)

    Gallos, Ioannis; Siettos, Constantinos

    2017-11-01

    We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

  18. Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

    Science.gov (United States)

    Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

    The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

  19. Analysis and design of machine learning techniques evolutionary solutions for regression, prediction, and control problems

    CERN Document Server

    Stalph, Patrick

    2014-01-01

    Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. Nevertheless, motor skills are not easy to learn for humans and this is also an active research topic in robotics. However, most solutions are optimized for industrial applications and, thus, few are plausible explanations for human learning. The fundamental challenge, that motivates Patrick Stalph, originates from the cognitive science: How do humans learn their motor skills? The author makes a connection between robotics and cognitive sciences by analyzing motor skill learning using implementations that could be found in the human brain – at least to some extent. Therefore three suitable machine learning algorithms are selected – algorithms that are plausible from a cognitive viewpoint and feasible for the roboticist. The power and scalability of those algorithms is evaluated in theoretical simulations and more realistic scenarios with the iCub humanoid robot. Convincing results confirm the...

  20. 3D Cloud Field Prediction using A-Train Data and Machine Learning Techniques

    Science.gov (United States)

    Johnson, C. L.

    2017-12-01

    Validation of cloud process parameterizations used in global climate models (GCMs) would greatly benefit from observed 3D cloud fields at the size comparable to that of a GCM grid cell. For the highest resolution simulations, surface grid cells are on the order of 100 km by 100 km. CloudSat/CALIPSO data provides 1 km width of detailed vertical cloud fraction profile (CFP) and liquid and ice water content (LWC/IWC). This work utilizes four machine learning algorithms to create nonlinear regressions of CFP, LWC, and IWC data using radiances, surface type and location of measurement as predictors and applies the regression equations to off-track locations generating 3D cloud fields for 100 km by 100 km domains. The CERES-CloudSat-CALIPSO-MODIS (C3M) merged data set for February 2007 is used. Support Vector Machines, Artificial Neural Networks, Gaussian Processes and Decision Trees are trained on 1000 km of continuous C3M data. Accuracy is computed using existing vertical profiles that are excluded from the training data and occur within 100 km of the training data. Accuracy of the four algorithms is compared. Average accuracy for one day of predicted data is 86% for the most successful algorithm. The methodology for training the algorithms, determining valid prediction regions and applying the equations off-track is discussed. Predicted 3D cloud fields are provided as inputs to the Ed4 NASA LaRC Fu-Liou radiative transfer code and resulting TOA radiances compared to observed CERES/MODIS radiances. Differences in computed radiances using predicted profiles and observed radiances are compared.

  1. Reverse engineering smart card malware using side channel analysis with machine learning techniques

    CSIR Research Space (South Africa)

    Djonon Tsague, Hippolyte

    2016-12-01

    Full Text Available as much variance of the original data as possible. Among feature extraction techniques, PCA and LDA are very common dimensionality reduction algorithms that have successfully been applied in many classification problems like face recognition, character...

  2. 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...

  3. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

    Science.gov (United States)

    Yassin, Nisreen I R; Omran, Shaimaa; El Houby, Enas M F; Allam, Hemat

    2018-03-01

    The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

    Directory of Open Access Journals (Sweden)

    Jonas eKaplan

    2015-03-01

    Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

  5. Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.

    Science.gov (United States)

    Alejo, Luz; Atkinson, John; Guzmán-Fierro, Víctor; Roeckel, Marlene

    2018-05-16

    Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.

  6. Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

    Science.gov (United States)

    Bassier, M.; Vergauwen, M.; Van Genechten, B.

    2017-08-01

    Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

  7. submitter Studies of CMS data access patterns with machine learning techniques

    CERN Document Server

    De Luca, Silvia

    This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy ove...

  8. Identifying tropical dry forests extent and succession via the use of machine learning techniques

    Science.gov (United States)

    Li, Wei; Cao, Sen; Campos-Vargas, Carlos; Sanchez-Azofeifa, Arturo

    2017-12-01

    Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages.

  9. Prediction of Driver's Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques.

    Science.gov (United States)

    Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

    2017-06-10

    Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver's intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver's intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver's intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver's intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

  10. Dynamic augmentation restores anterior tibial translation in ACL suture repair: a biomechanical comparison of non-, static and dynamic augmentation techniques.

    Science.gov (United States)

    Hoogeslag, Roy A G; Brouwer, Reinoud W; Huis In 't Veld, Rianne; Stephen, Joanna M; Amis, Andrew A

    2018-02-03

    There is a lack of objective evidence investigating how previous non-augmented ACL suture repair techniques and contemporary augmentation techniques in ACL suture repair restrain anterior tibial translation (ATT) across the arc of flexion, and after cyclic loading of the knee. The purpose of this work was to test the null hypotheses that there would be no statistically significant difference in ATT after non-, static- and dynamic-augmented ACL suture repair, and they will not restore ATT to normal values across the arc of flexion of the knee after cyclic loading. Eleven human cadaveric knees were mounted in a test rig, and knee kinematics from 0° to 90° of flexion were recorded by use of an optical tracking system. Measurements were recorded without load and with 89-N tibial anterior force. The knees were tested in the following states: ACL-intact, ACL-deficient, non-augmented suture repair, static tape augmentation and dynamic augmentation after 10 and 300 loading cycles. Only static tape augmentation and dynamic augmentation restored ATT to values similar to the ACL-intact state directly postoperation, and maintained this after cyclic loading. However, contrary to dynamic augmentation, the ATT after static tape augmentation failed to remain statistically less than for the ACL-deficient state after cyclic loading. Moreover, after cyclic loading, ATT was significantly less with dynamic augmentation when compared to static tape augmentation. In contrast to non-augmented ACL suture repair and static tape augmentation, only dynamic augmentation resulted in restoration of ATT values similar to the ACL-intact knee and decreased ATT values when compared to the ACL-deficient knee immediately post-operation and also after cyclic loading, across the arc of flexion, thus allowing the null hypotheses to be rejected. This may assist healing of the ruptured ACL. Therefore, this study would support further clinical evaluation of dynamic augmentation of ACL repair.

  11. Evolving techniques of diagnosis. Toward establishment of new paradigm for human machine cooperation

    International Nuclear Information System (INIS)

    Kitamura, Masaharu; Takahashi, Makoto; Kanamoto, Shigeru; Saeki, Akira; Washio, Takashi; Ohga, Yukiharu; Furuta, Kazuo; Yoshikawa, Shinji

    1998-01-01

    By monitoring equipments of a plant and state of a process, the diagnostic technique to detect a sign of abnormality properly to identify its reason has often been advanced on a lot of researches in various industrial fields containing atomic force. Some fundamental studies expected for such diagnostic technique to play an important role to keep and improve operational safety of a nuclear plant have been conducted since early period of the nuclear reaction development, but their contents are evolved and changed rapidly, in recent. The technique on the diagnosis was related closely to a statistical analysis method on signal fluctuation component, so-called reactor noise analysis method in early 1980s, but technical innovation step of their recent advancement were remarkable by introduction of new techniques such as chaos theory, wavelet analysis, model base application of expert system, artificial intelligence, and so on at middle of 1980s. And, when diagnosing in the field of atomic force, owing to be required for much high ability, studies on a multi method integration system considered complementary application of a plurality of technical methods and a cooperative method between human and mechanical intelligences, are also forwarded actively faster than those in other industrial areas. In this paper, in each important item, its technical nature and present state of its application to diagnosis are described with their future technical view. (G.K.)

  12. Application of a support vector machine algorithm to the safety precaution technique of medium-low pressure gas regulators

    Science.gov (United States)

    Hao, Xuejun; An, Xaioran; Wu, Bo; He, Shaoping

    2018-02-01

    In the gas pipeline system, safe operation of a gas regulator determines the stability of the fuel gas supply, and the medium-low pressure gas regulator of the safety precaution system is not perfect at the present stage in the Beijing Gas Group; therefore, safety precaution technique optimization has important social and economic significance. In this paper, according to the running status of the medium-low pressure gas regulator in the SCADA system, a new method for gas regulator safety precaution based on the support vector machine (SVM) is presented. This method takes the gas regulator outlet pressure data as input variables of the SVM model, the fault categories and degree as output variables, which will effectively enhance the precaution accuracy as well as save significant manpower and material resources.

  13. A New Profile Learning Model for Recommendation System based on Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    Shereen H. Ali

    2016-03-01

    Full Text Available Recommender systems (RSs have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent Adaptive Vertical Recommendation (IAVR system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy.

  14. Machine learning techniques for medical diagnosis of diabetes using iris images.

    Science.gov (United States)

    Samant, Piyush; Agarwal, Ravinder

    2018-04-01

    Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for disease diagnosis is reliable and fastest growing field in biomedical. Proposed model is an attempt to evaluate diagnostic validity of an old complementary and alternative medicine technique, iridology for diagnosis of type-2 diabetes using soft computing methods. Investigation was performed over a close group of total 338 subjects (180 diabetic and 158 non-diabetic). Infra-red images of both the eyes were captured simultaneously. The region of interest from the iris image was cropped as zone corresponds to the position of pancreas organ according to the iridology chart. Statistical, texture and discrete wavelength transformation features were extracted from the region of interest. The results show best classification accuracy of 89.63% calculated from RF classifier. Maximum specificity and sensitivity were absorbed as 0.9687 and 0.988, respectively. Results have revealed the effectiveness and diagnostic significance of proposed model for non-invasive and automatic diabetes diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. 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....

  16. Mechanical properties of micro-sized copper bending beams machined by the focused ion beam technique

    International Nuclear Information System (INIS)

    Motz, C.; Schoeberl, T.; Pippan, R.

    2005-01-01

    Micro-sized bending beams with thicknesses, t, from 7.5 down to 1.0 μm were fabricated with the focused ion beam technique from a copper single crystal with an {1 1 1} orientation. The beams were loaded with a nano-indenter and the force vs. displacement curves were recorded. A strong size effect was found where the flow stress reaches almost 1 GPa for the thinnest beams. A common strain gradient plasticity approach was used to explain the size effect. However, the strong t -1.14 dependence of the flow stress could not be explained by this model. Additionally, the combination of two other dislocation mechanisms is discussed: the limitation of available dislocation sources and a dislocation pile-up at the beam centre. The contribution of the pile-up stress to the flow stress gives a t -1 dependence, which is in good agreement with the experimental results

  17. Tracer techniques for the investigation of wear mechanisms in coated or surface-treated machine parts

    International Nuclear Information System (INIS)

    Goedecke, T.; Grosch, J.

    1990-01-01

    Tracer techniques allow wear measurement down to rates of only some μg/h, and these measurements can be done continuously within an inspection test run, not requiring dismantling of the parts to be examined. The measurements revealed the materials pair of a chilled cast iron camshaft and a hard metal coated rocker arm to be superior in terms of wear behaviour over the materials pair of a malleable cast iron camshaft with induction hardening and a rocker arm with hard chromium plating. The total wear of a chilled cast iron camshaft was measured to be approx. 90% less than that of the malleable cast iron camshaft, under equal loading conditions. With the rocker arms, this ratio is approx. 1:3. Another disadvantage of the latter pair is the overall wear ratio of 19:1. The best wear resistance was measured with a TiN-coated rocker arm combined with a chilled cast iron camshaft. (orig./MM) [de

  18. Hair analysis by means of laser induced breakdown spectroscopy technique and support vector machine model for diagnosing addiction

    Directory of Open Access Journals (Sweden)

    M Vahid Dastjerdi

    2018-02-01

    Full Text Available Along with the development of laboratory methods for diagnosing addiction, concealment ways, either physically or chemically, for creating false results have been in progress. In this research based on the Laser Induced Breakdown Spectroscopy technique (LIBS and analyzing hair of addicted and normal people, we are proposing a new method to overcome problems in conventional methods and reduce possibility of cheating in the process of diagnosing addiction. For this purpose, at first we have sampled hair of 17 normal and addicted people and recorded 5 spectrums for each sample, overall 170 spectrums. After analyzing the recorded LIBS spectra and detecting the atomic and ionic lines as well as molecular bands, relative intensities of emission lines for Aluminum to Calcium (Al/Ca and Aluminum to Sodium (Al/Na were selected as the input variables for the Support Vector Machine model (SVM.The Radial Basis, Polynomial Kernel functions and a linear function were chosen for classifying the data in SVM model. The results of this research showed that by the combination of LIBS technique and SVM one can distinguish addicted person with precision of 100%. Because of several advantages of LIBS such as high speed analysis and being portable, this method can be used individually or together with available methods as an automatic method for diagnosing addiction through hair analysis.

  19. Techniques for optimizing human-machine information transfer related to real-time interactive display systems

    Science.gov (United States)

    Granaas, Michael M.; Rhea, Donald C.

    1989-01-01

    In recent years the needs of ground-based researcher-analysts to access real-time engineering data in the form of processed information has expanded rapidly. Fortunately, the capacity to deliver that information has also expanded. The development of advanced display systems is essential to the success of a research test activity. Those developed at the National Aeronautics and Space Administration (NASA), Western Aeronautical Test Range (WATR), range from simple alphanumerics to interactive mapping and graphics. These unique display systems are designed not only to meet basic information display requirements of the user, but also to take advantage of techniques for optimizing information display. Future ground-based display systems will rely heavily not only on new technologies, but also on interaction with the human user and the associated productivity with that interaction. The psychological abilities and limitations of the user will become even more important in defining the difference between a usable and a useful display system. This paper reviews the requirements for development of real-time displays; the psychological aspects of design such as the layout, color selection, real-time response rate, and interactivity of displays; and an analysis of some existing WATR displays.

  20. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Science.gov (United States)

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  1. Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.

    Science.gov (United States)

    Sakr, Sherif; Elshawi, Radwa; Ahmed, Amjad M; Qureshi, Waqas T; Brawner, Clinton A; Keteyian, Steven J; Blaha, Michael J; Al-Mallah, Mouaz H

    2017-12-19

    Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness

  2. 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....

  3. CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

    Energy Technology Data Exchange (ETDEWEB)

    Saz Parkinson, P. M. [Department of Physics, The University of Hong Kong, Pokfulam Road, Hong Kong (China); Xu, H.; Yu, P. L. H. [Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong (China); Salvetti, D.; Marelli, M. [INAF—Istituto di Astrofisica Spaziale e Fisica Cosmica Milano, via E. Bassini 15, I-20133, Milano (Italy); Falcone, A. D. [Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA 16802 (United States)

    2016-03-20

    We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.

  4. CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

    International Nuclear Information System (INIS)

    Saz Parkinson, P. M.; Xu, H.; Yu, P. L. H.; Salvetti, D.; Marelli, M.; Falcone, A. D.

    2016-01-01

    We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies

  5. Quality assurance of a helical tomotherapy machine

    International Nuclear Information System (INIS)

    Fenwick, J D; Tome, W A; Jaradat, H A; Hui, S K; James, J A; Balog, J P; DeSouza, C N; Lucas, D B; Olivera, G H; Mackie, T R; Paliwal, B R

    2004-01-01

    Helical tomotherapy has been developed at the University of Wisconsin, and 'Hi-Art II' clinical machines are now commercially manufactured. At the core of each machine lies a ring-gantry-mounted short linear accelerator which generates x-rays that are collimated into a fan beam of intensity-modulated radiation by a binary multileaf, the modulation being variable with gantry angle. Patients are treated lying on a couch which is translated continuously through the bore of the machine as the gantry rotates. Highly conformal dose-distributions can be delivered using this technique, which is the therapy equivalent of spiral computed tomography. The approach requires synchrony of gantry rotation, couch translation, accelerator pulsing and the opening and closing of the leaves of the binary multileaf collimator used to modulate the radiation beam. In the course of clinically implementing helical tomotherapy, we have developed a quality assurance (QA) system for our machine. The system is analogous to that recommended for conventional clinical linear accelerator QA by AAPM Task Group 40 but contains some novel components, reflecting differences between the Hi-Art devices and conventional clinical accelerators. Here the design and dosimetric characteristics of Hi-Art machines are summarized and the QA system is set out along with experimental details of its implementation. Connections between this machine-based QA work, pre-treatment patient-specific delivery QA and fraction-by-fraction dose verification are discussed

  6. 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…

  7. 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...

  8. 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.

  9. Machine Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  10. Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant.

    Science.gov (United States)

    Cilla, Myriam; Borgiani, Edoardo; Martínez, Javier; Duda, Georg N; Checa, Sara

    2017-01-01

    Today, different implant designs exist in the market; however, there is not a clear understanding of which are the best implant design parameters to achieve mechanical optimal conditions. Therefore, the aim of this project was to investigate if the geometry of a commercial short stem hip prosthesis can be further optimized to reduce stress shielding effects and achieve better short-stemmed implant performance. To reach this aim, the potential of machine learning techniques combined with parametric Finite Element analysis was used. The selected implant geometrical parameters were: total stem length (L), thickness in the lateral (R1) and medial (R2) and the distance between the implant neck and the central stem surface (D). The results show that the total stem length was not the only parameter playing a role in stress shielding. An optimized implant should aim for a decreased stem length and a reduced length of the surface in contact with the bone. The two radiuses that characterize the stem width at the distal cross-section in contact with the bone were less influential in the reduction of stress shielding compared with the other two parameters; but they also play a role where thinner stems present better results.

  11. Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

    Directory of Open Access Journals (Sweden)

    Ralph Olusola Aluko

    2016-12-01

    Full Text Available In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

  12. Rainfall Prediction of Indian Peninsula: Comparison of Time Series Based Approach and Predictor Based Approach using Machine Learning Techniques

    Science.gov (United States)

    Dash, Y.; Mishra, S. K.; Panigrahi, B. K.

    2017-12-01

    Prediction of northeast/post monsoon rainfall which occur during October, November and December (OND) over Indian peninsula is a challenging task due to the dynamic nature of uncertain chaotic climate. It is imperative to elucidate this issue by examining performance of different machine leaning (ML) approaches. The prime objective of this research is to compare between a) statistical prediction using historical rainfall observations and global atmosphere-ocean predictors like Sea Surface Temperature (SST) and Sea Level Pressure (SLP) and b) empirical prediction based on a time series analysis of past rainfall data without using any other predictors. Initially, ML techniques have been applied on SST and SLP data (1948-2014) obtained from NCEP/NCAR reanalysis monthly mean provided by the NOAA ESRL PSD. Later, this study investigated the applicability of ML methods using OND rainfall time series for 1948-2014 and forecasted up to 2018. The predicted values of aforementioned methods were verified using observed time series data collected from Indian Institute of Tropical Meteorology and the result revealed good performance of ML algorithms with minimal error scores. Thus, it is found that both statistical and empirical methods are useful for long range climatic projections.

  13. 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...

  14. Estimating photometric redshifts for X-ray sources in the X-ATLAS field using machine-learning techniques

    Science.gov (United States)

    Mountrichas, G.; Corral, A.; Masoura, V. A.; Georgantopoulos, I.; Ruiz, A.; Georgakakis, A.; Carrera, F. J.; Fotopoulou, S.

    2017-12-01

    We present photometric redshifts for 1031 X-ray sources in the X-ATLAS field using the machine-learning technique TPZ. X-ATLAS covers 7.1 deg2 observed with XMM-Newton within the Science Demonstration Phase of the H-ATLAS field, making it one of the largest contiguous areas of the sky with both XMM-Newton and Herschel coverage. All of the sources have available SDSS photometry, while 810 additionally have mid-IR and/or near-IR photometry. A spectroscopic sample of 5157 sources primarily in the XMM/XXL field, but also from several X-ray surveys and the SDSS DR13 redshift catalogue, was used to train the algorithm. Our analysis reveals that the algorithm performs best when the sources are split, based on their optical morphology, into point-like and extended sources. Optical photometry alone is not enough to estimate accurate photometric redshifts, but the results greatly improve when at least mid-IR photometry is added in the training process. In particular, our measurements show that the estimated photometric redshifts for the X-ray sources of the training sample have a normalized absolute median deviation, nmad ≈ 0.06, and a percentage of outliers, η = 10-14%, depending upon whether the sources are extended or point like. Our final catalogue contains photometric redshifts for 933 out of the 1031 X-ray sources with a median redshift of 0.9. The table of the photometric redshifts is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/608/A39

  15. An investigation of penetrant techniques for detection of machining-induced surface-breaking cracks on monolithic ceramics

    Energy Technology Data Exchange (ETDEWEB)

    Forster, G.A.; Ellingson, W.A.

    1996-02-01

    The purpose of this effort was to evaluate penetrant methods for their ability to detect surface-breaking cracks in monolithic ceramic materials with an emphasis on detection of cracks generated by machining. There are two basic penetrant types, visible and fluorescent. The visible penetrant method is usually augmented by powder developers and cracks detected can be seen in visible light. Cracks detected by fluorescent penetrant are visible only under ultraviolet light used with or without a developer. The developer is basically a powder that wicks up penetrant from a crack to make it more observable. Although fluorescent penetrants were recommended in the literature survey conducted early in this effort, visible penetrants and two non-standard techniques, a capillary gaseous diffusion method under development at the institute of Chemical Physics in Moscow, and the {open_quotes}statiflux{close_quotes} method which involves use of electrically charged particles, were also investigated. SiAlON ring specimens (1 in. diameter, 3/4 in. wide) which had been subjected to different thermal-shock cycles were used for these tests. The capillary gaseous diffusion method is based on ammonia; the detector is a specially impregnated paper much like litmus paper. As expected, visible dye penetrants offered no detection sensitivity for tight, surface-breaking cracks in ceramics. Although the non-standard statiflux method showed promise on high-crack-density specimens, it was ineffective on limited-crack-density specimens. The fluorescent penetrant method was superior for surface-breaking crack detection, but successful application of this procedure depends greatly on the skill of the user. Two presently available high-sensitivity fluorescent penetrants were then evaluated for detection of microcracks on Si{sub 3}N{sub 4} and SiC from different suppliers. Although 50X optical magnification may be sufficient for many applications, 200X magnification provides excellent delectability.

  16. Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

    Science.gov (United States)

    Foulquier, Nathan; Redou, Pascal; Le Gal, Christophe; Rouvière, Bénédicte; Pers, Jacques-Olivier; Saraux, Alain

    2018-05-17

    Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

  17. Historical and Epistemological Reflections on the Culture of Machines around the Renaissance: How Science and Technique Work?

    Directory of Open Access Journals (Sweden)

    Raffaele Pisano

    2014-10-01

    Full Text Available This paper is divided into two parts, this being the first one. The second is entitled ‘Historical and Epistemological Reflections on the Culture of Machines around Renaissance: Machines, Machineries and Perpetual Motion’ and will be published in Acta Baltica Historiae et Philosophiae Scientiarum in 2015. Based on our recent studies, we provide here a historical and epistemological feature on the role played by machines and machineries. Ours is an epistemological thesis based on a series of historical examples to show that the relations between theoretical science and the construction of machines cannot be taken for granted, a priori. Our analysis is mainly based on the culture of machines around 15th and 17th centuries, namely the epoch of Late Renaissance and Early Modern Age. For this is the period of scientific revolution and this age offers abundant interesting material for researches into the relations of theoretical science/construction of machines as well. However, to prove our epistemological thesis, we will also exploit examples of machines built in other historical periods. Particularly, a discussion concerning the relationship between science theory and the development of science art crafts produced by non-recognized scientists in a certain historical time is presented. The main questions are: when and why did the tension between science (physics, mathematics and geometry give rise to a new scientific approach to applied discipline such as studies on machines and machineries? What kind of science was used (if at all for projecting machines and machineries? Was science at the time a necessary precondition to build a machine? In the first part we will focus on the difference between Aristotelian-Euclidean and Archimedean approaches and we will outline the heritage of these two different approaches in late medieval and Renaissance science. In the second part, we will apply our reconstructions to some historical and epistemological

  18. Application of machine learning techniques for solving real world business problems : the case study - target marketing of insurance policies

    OpenAIRE

    Juozenaite, Ineta

    2018-01-01

    The concept of machine learning has been around for decades, but now it is becoming more and more popular not only in the business, but everywhere else as well. It is because of increased amount of data, cheaper data storage, more powerful and affordable computational processing. The complexity of business environment leads companies to use data-driven decision making to work more efficiently. The most common machine learning methods, like Logistic Regression, Decision Tree, Artificial Neural...

  19. 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.

  20. 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....

  1. 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...

  2. A study on ultra-precision machining technique for Al6061-T6 to fabricate space infrared optics

    Science.gov (United States)

    Ryu, Geun-man; Lee, Gil-jae; Hyun, Sang-won; Sung, Ha-yeong; Chung, Euisik; Kim, Geon-hee

    2014-08-01

    In this paper, analysis of variance on designed experiments with full factorial design was applied to determine the optimized machining parameters for ultra-precision fabrication of the secondary aspheric mirror, which is one of the key elements of the space cryogenic infrared optics. A single point diamond turning machine (SPDTM, Nanotech 4μpL Moore) was adopted to fabricate the material, AL6061-T6, and the three machining parameters of cutting speed, feed rate and depth of cut were selected. With several randomly assigned experimental conditions, surface roughness of each condition was measured by a non-contact optical profiler (NT2000; Vecco). As a result of analysis using Minitab, the optimum cutting condition was determined as following; cutting speed: 122 m/min, feed rate: 3 mm/min and depth of cut: 1 μm. Finally, a 120 mm diameter aspheric secondary mirror was attached to a particularly designed jig by using mixture of paraffin and wax and successfully fabricated under the optimum machining parameters. The profile of machined surface was measured by a high-accuracy 3-D profilometer(UA3P; Panasonic) and we obtained the geometrical errors of 30.6 nm(RMS) and 262.4 nm(PV), which satisfy the requirements of the space cryogenic infrared optics.

  3. Multidsciplinary Approaches to Coastal Adaptation - Aplying Machine Learning Techniques to assess coastal risk in Latin America and The Caribbean

    Science.gov (United States)

    Calil, J.

    2016-12-01

    The global population, currently at 7.3 billion, is increasing by nearly 230,000 people every day. As the world's population grows to an estimated 11.2 billion by 2100, the number of people living in low elevation areas, exposed to coastal hazards, is continuing to increase. In 2013, 22 million people were displaced by extreme weather events, with 37 events displacing at least 100,000 people each. Losses from natural disasters and disaster risk are determined by a complex interaction between physical hazards and the vulnerability of a society or social-ecological system, and its exposure to such hazards. Impacts from coastal hazards depend on the number of people, value of assets, and presence of critical resources in harm's way. Moreover, coastal risks are amplified by challenging socioeconomic dynamics, including ill-advised urban development, income inequality, and poverty level. Our results demonstrate that in Latin America and the Caribbean (LAC), more than half a million people live in areas where coastal hazards, exposure (of people, assets and ecosystems), and poverty converge, creating the ideal conditions for a perfect storm. In order to identify the population at greatest risk to coastal hazards in LAC, and in response to a growing demand for multidisciplinary coastal adaptation approaches, this study employs a combination of machine learning clustering techniques (K-Means and Self Organizing Maps), and a spatial index, to assess coastal risks on a comparative scale. Data for more than 13,000 coastal locations in LAC were collected and allocated into three categories: (1) Coastal Hazards (including storm surge, wave energy and El Niño); (2) Geographic Exposure (including population, agriculture, and ecosystems); and (3) Vulnerability (including income inequality, infant mortality rate and malnutrition). This study identified hotspots of coastal vulnerability, the key drivers of coastal risk at each geographic location. Our results provide important

  4. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

    Science.gov (United States)

    de Ávila, Maurício Boff; Xavier, Mariana Morrone; Pintro, Val Oliveira; de Azevedo, Walter Filgueira

    2017-12-09

    Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC 50 ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. LINGUISTIC ANALYSIS FOR THE BELARUSIAN CORPUS WITH THE APPLICATION OF NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Yu. S. Hetsevich

    2017-01-01

    Full Text Available The article focuses on the problems existing in text-to-speech synthesis. Different morphological, lexical and syntactical elements were localized with the help of the Belarusian unit of NooJ program. Those types of errors, which occur in Belarusian texts, were analyzed and corrected. Language model and part of speech tagging model were built. The natural language processing of Belarusian corpus with the help of developed algorithm using machine learning was carried out. The precision of developed models of machine learning has been 80–90 %. The dictionary was enriched with new words for the further using it in the systems of Belarusian speech synthesis.

  6. Predicting Post-Editor Profiles from the Translation Process

    DEFF Research Database (Denmark)

    Singla, Karan; Orrego-Carmona, David; Gonzales, Ashleigh Rhea

    2014-01-01

    The purpose of the current investigation is to predict post-editor profiles based on user behaviour and demographics using machine learning techniques to gain a better understanding of post-editor styles. Our study extracts process unit features from the CasMaCat LS14 database from the CRITT...... of translation process features. The classification and clustering of participants resulting from our study suggest this type of exploration could be used as a tool to develop new translation tool features or customization possibilities....

  7. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

    Science.gov (United States)

    Hussain, Lal

    2018-06-01

    Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

  8. Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

    Directory of Open Access Journals (Sweden)

    Ole Marius Hoel Rindal

    2017-12-01

    Full Text Available The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.

  9. The Changing Face of the of Former Soviet Cities: Elucidated by Remote Sensing and Machine Learning Techniques

    Science.gov (United States)

    Poghosyan, Armen

    2017-04-01

    Despite remote sensing of urbanization emerged as a powerful tool to acquire critical knowledge about urban growth and its effects on global environmental change, human-environment interface as well as environmentally sustainable urban development, there is lack of studies utilizing remote sensing techniques to investigate urbanization trends in the Post-Soviet states. The unique challenges accompanying the urbanization in the Post-Soviet republics combined with the expected robust urban growth in developing countries over the next several decades highlight the critical need for a quantitative assessment of the urban dynamics in the former Soviet states as they navigate towards a free market democracy. This study uses total of 32 Level-1 precision terrain corrected (L1T) Landsat scenes with 30-m resolution as well as further auxiliary population and economic data for ten cities distributed in nine former Soviet republics to quantify the urbanization patterns in the Post-Soviet region. Land cover in each urban center of this study was classified by using Support Vector Machine (SVM) learning algorithm with overall accuracies ranging from 87 % to 97 % for 29 classification maps over three time steps during the past twenty-five years in order to estimate quantities, trends and drivers of urban growth in the study area. The results demonstrated several spatial and temporal urbanization patterns observed across the Post-Soviet states and based on urban expansion rates the cities can be divided into two groups, fast growing and slow growing urban centers. The relatively fast-growing urban centers have an average urban expansion rate of about 2.8 % per year, whereas the slow growing cities have an average urban expansion rate of about 1.0 % per year. The total area of new land converted to urban environment ranged from as low as 26 km2 to as high as 780 km2 for the ten cities over the 1990 - 2015 period, while the overall urban land increase ranged from 11.3 % to 96

  10. Translational Creativity

    DEFF Research Database (Denmark)

    Nielsen, Sandro

    2010-01-01

    A long-established approach to legal translation focuses on terminological equivalence making translators strictly follow the words of source texts. Recent research suggests that there is room for some creativity allowing translators to deviate from the source texts. However, little attention...... is given to genre conventions in source texts and the ways in which they can best be translated. I propose that translators of statutes with an informative function in expert-to-expert communication may be allowed limited translational creativity when translating specific types of genre convention....... This creativity is a result of translators adopting either a source-language or a target-language oriented strategy and is limited by the pragmatic principle of co-operation. Examples of translation options are provided illustrating the different results in target texts. The use of a target-language oriented...

  11. Translation Method and Computer Programme for Assisting the Same

    DEFF Research Database (Denmark)

    2013-01-01

    The present invention relates to a translation method comprising the steps of: a translator speaking a translation of a written source text in a target language, an automatic speech recognition system converting the spoken translation into a set of phone and word hypotheses in the target language......, a machine translation system translating the written source text into a set of translations hypotheses in the target language, and an integration module combining the set of spoken word hypotheses and the set of machine translation hypotheses obtaining a text in the target language. Thereby obtaining...

  12. An evaluation of machine processing techniques of ERTS-1 data for user applications. [urban land use and soil association mapping in Indiana

    Science.gov (United States)

    Landgrebe, D.

    1974-01-01

    A broad study is described to evaluate a set of machine analysis and processing techniques applied to ERTS-1 data. Based on the analysis results in urban land use analysis and soil association mapping together with previously reported results in general earth surface feature identification and crop species classification, a profile of general applicability of this procedure is beginning to emerge. Put in the hands of a user who knows well the information needed from the data and also is familiar with the region to be analyzed it appears that significantly useful information can be generated by these methods. When supported by preprocessing techniques such as the geometric correction and temporal registration capabilities, final products readily useable by user agencies appear possible. In parallel with application, through further research, there is much potential for further development of these techniques both with regard to providing higher performance and in new situations not yet studied.

  13. Quantum machine learning.

    Science.gov (United States)

    Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

    2017-09-13

    Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

  14. Comments on Frequency Swept Rotating Input Perturbation Techniques and Identification of the Fluid Force Models in Rotor/bearing/seal Systems and Fluid Handling Machines

    Science.gov (United States)

    Muszynska, Agnes; Bently, Donald E.

    1991-01-01

    Perturbation techniques used for identification of rotating system dynamic characteristics are described. A comparison between two periodic frequency-swept perturbation methods applied in identification of fluid forces of rotating machines is presented. The description of the fluid force model identified by inputting circular periodic frequency-swept force is given. This model is based on the existence and strength of the circumferential flow, most often generated by the shaft rotation. The application of the fluid force model in rotor dynamic analysis is presented. It is shown that the rotor stability is an entire rotating system property. Some areas for further research are discussed.

  15. Machinability of Al 6061 Deposited with Cold Spray Additive Manufacturing

    Science.gov (United States)

    Aldwell, Barry; Kelly, Elaine; Wall, Ronan; Amaldi, Andrea; O'Donnell, Garret E.; Lupoi, Rocco

    2017-10-01

    Additive manufacturing techniques such as cold spray are translating from research laboratories into more mainstream high-end production systems. Similar to many additive processes, finishing still depends on removal processes. This research presents the results from investigations into aspects of the machinability of aluminum 6061 tubes manufactured with cold spray. Through the analysis of cutting forces and observations on chip formation and surface morphology, the effect of cutting speed, feed rate, and heat treatment was quantified, for both cold-sprayed and bulk aluminum 6061. High-speed video of chip formation shows changes in chip form for varying material and heat treatment, which is supported by the force data and quantitative imaging of the machined surface. The results shown in this paper demonstrate that parameters involved in cold spray directly impact on machinability and therefore have implications for machining parameters and strategy.

  16. Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor

    Science.gov (United States)

    Effati, Meysam; Thill, Jean-Claude; Shabani, Shahin

    2015-04-01

    The contention of this paper is that many social science research problems are too "wicked" to be suitably studied using conventional statistical and regression-based methods of data analysis. This paper argues that an integrated geospatial approach based on methods of machine learning is well suited to this purpose. Recognizing the intrinsic wickedness of traffic safety issues, such approach is used to unravel the complexity of traffic crash severity on highway corridors as an example of such problems. The support vector machine (SVM) and coactive neuro-fuzzy inference system (CANFIS) algorithms are tested as inferential engines to predict crash severity and uncover spatial and non-spatial factors that systematically relate to crash severity, while a sensitivity analysis is conducted to determine the relative influence of crash severity factors. Different specifications of the two methods are implemented, trained, and evaluated against crash events recorded over a 4-year period on a regional highway corridor in Northern Iran. Overall, the SVM model outperforms CANFIS by a notable margin. The combined use of spatial analysis and artificial intelligence is effective at identifying leading factors of crash severity, while explicitly accounting for spatial dependence and spatial heterogeneity effects. Thanks to the demonstrated effectiveness of a sensitivity analysis, this approach produces comprehensive results that are consistent with existing traffic safety theories and supports the prioritization of effective safety measures that are geographically targeted and behaviorally sound on regional highway corridors.

  17. Evaluation of bent-crystal x-ray backlighting and microscopy techniques for the Sandia Z machine.

    Science.gov (United States)

    Sinars, Daniel B; Bennett, Guy R; Wenger, David F; Cuneo, Michael E; Porter, John L

    2003-07-01

    X-ray backlighting and microscopy systems for the 1-10-keV range based on spherically or toroidally bent crystals are discussed. These systems are ideal for use on the Sandia Z machine, a megajoule-class x-ray facility. Near-normal-incidence crystal microscopy systems have been shown to be more efficient than pinhole cameras with the same spatial resolution and magnification [Appl. Opt. 37, 1784 (1998)]. We show that high-resolution (< or = 10 microm) x-ray backlighting systems using bent crystals can be more efficient than analogous point-projection imaging systems. Examples of bent-crystal-backlighting results that demonstrate 10-microm resolution over a 20-mm field of view are presented.

  18. Evaluation of bent-crystal x-ray backlighting and microscopy techniques for the Sandia Z machine

    International Nuclear Information System (INIS)

    Sinars, Daniel B.; Wenger, David F.; Cuneo, Michael E.; Porter, John L.; Bennett, Guy R.

    2003-01-01

    X-ray backlighting and microscopy systems for the 1-10-keV range based on spherically or toroidally bent crystals are discussed. These systems are ideal for use on the Sandia Z machine, a megajoule-class x-ray facility. Near-normal-incidence crystal microscopy systems have been shown to be more efficient than pinhole cameras with the same spatial resolution and magnification [Appl. Opt. 37, 1784 (1998)]. We show that high-resolution (≤10 μm) x-ray backlighting systems using bent crystals can be more efficient than analogous point-projection imaging systems. Examples of bent-crystal-backlighting results that demonstrate 10-μm resolution over a 20-mm field of view are presented

  19. 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...

  20. 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...

  1. Translating India

    CERN Document Server

    Kothari, Rita

    2014-01-01

    The cultural universe of urban, English-speaking middle class in India shows signs of growing inclusiveness as far as English is concerned. This phenomenon manifests itself in increasing forms of bilingualism (combination of English and one Indian language) in everyday forms of speech - advertisement jingles, bilingual movies, signboards, and of course conversations. It is also evident in the startling prominence of Indian Writing in English and somewhat less visibly, but steadily rising, activity of English translation from Indian languages. Since the eighties this has led to a frenetic activity around English translation in India's academic and literary circles. Kothari makes this very current phenomenon her chief concern in Translating India.   The study covers aspects such as the production, reception and marketability of English translation. Through an unusually multi-disciplinary approach, this study situates English translation in India amidst local and global debates on translation, representation an...

  2. Translating Inclusion

    DEFF Research Database (Denmark)

    Fallov, Mia Arp; Birk, Rasmus

    2018-01-01

    The purpose of this paper is to explore how practices of translation shape particular paths of inclusion for people living in marginalized residential areas in Denmark. Inclusion, we argue, is not an end-state, but rather something which must be constantly performed. Active citizenship, today......, is not merely a question of participation, but of learning to become active in all spheres of life. The paper draws on empirical examples from a multi-sited field work in 6 different sites of local community work in Denmark, to demonstrate how different dimensions of translation are involved in shaping active...... citizenship. We propose the following different dimensions of translation: translating authority, translating language, translating social problems. The paper takes its theoretical point of departure from assemblage urbanism, arguing that cities are heterogeneous assemblages of socio-material interactions...

  3. Machine Shop Grinding Machines.

    Science.gov (United States)

    Dunn, James

    This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…

  4. Machine-assisted verification of latent fingerprints: first results for nondestructive contact-less optical acquisition techniques with a CWL sensor

    Science.gov (United States)

    Hildebrandt, Mario; Kiltz, Stefan; Krapyvskyy, Dmytro; Dittmann, Jana; Vielhauer, Claus; Leich, Marcus

    2011-11-01

    A machine-assisted analysis of traces from crime scenes might be possible with the advent of new high-resolution non-destructive contact-less acquisition techniques for latent fingerprints. This requires reliable techniques for the automatic extraction of fingerprint features from latent and exemplar fingerprints for matching purposes using pattern recognition approaches. Therefore, we evaluate the NIST Biometric Image Software for the feature extraction and verification of contact-lessly acquired latent fingerprints to determine potential error rates. Our exemplary test setup includes 30 latent fingerprints from 5 people in two test sets that are acquired from different surfaces using a chromatic white light sensor. The first test set includes 20 fingerprints on two different surfaces. It is used to determine the feature extraction performance. The second test set includes one latent fingerprint on 10 different surfaces and an exemplar fingerprint to determine the verification performance. This utilized sensing technique does not require a physical or chemical visibility enhancement of the fingerprint residue, thus the original trace remains unaltered for further investigations. No particular feature extraction and verification techniques have been applied to such data, yet. Hence, we see the need for appropriate algorithms that are suitable to support forensic investigations.

  5. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    Directory of Open Access Journals (Sweden)

    Wei Luo

    Full Text Available For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD outcomes (four NCDs and two major clinical risk factors, based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88 and those excluded from the development for use as a completely separated validation sample (median correlation 0.85, demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

  6. A noninvasive technique for real-time detection of bruises in apple surface based on machine vision

    Science.gov (United States)

    Zhao, Juan; Peng, Yankun; Dhakal, Sagar; Zhang, Leilei; Sasao, Akira

    2013-05-01

    Apple is one of the highly consumed fruit item in daily life. However, due to its high damage potential and massive influence on taste and export, the quality of apple has to be detected before it reaches the consumer's hand. This study was aimed to develop a hardware and software unit for real-time detection of apple bruises based on machine vision technology. The hardware unit consisted of a light shield installed two monochrome cameras at different angles, LED light source to illuminate the sample, and sensors at the entrance of box to signal the positioning of sample. Graphical Users Interface (GUI) was developed in VS2010 platform to control the overall hardware and display the image processing result. The hardware-software system was developed to acquire the images of 3 samples from each camera and display the image processing result in real time basis. An image processing algorithm was developed in Opencv and C++ platform. The software is able to control the hardware system to classify the apple into two grades based on presence/absence of surface bruises with the size of 5mm. The experimental result is promising and the system with further modification can be applicable for industrial production in near future.

  7. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    Science.gov (United States)

    Luo, Wei; Nguyen, Thin; Nichols, Melanie; Tran, Truyen; Rana, Santu; Gupta, Sunil; Phung, Dinh; Venkatesh, Svetha; Allender, Steve

    2015-01-01

    For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

  8. Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

    Science.gov (United States)

    Roberts, J. Brent; Robertson, Franklin R.; Funk, Chris

    2014-01-01

    Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.

  9. A self-centering active probing technique for kinematic parameter identification and verification of articulated arm coordinate measuring machines

    International Nuclear Information System (INIS)

    Santolaria, J; Brau, A; Velázquez, J; Aguilar, J J

    2010-01-01

    A crucial task in the procedure of identifying the parameters of a kinematic model of an articulated arm coordinate measuring machine (AACMM) or robot arm is the process of capturing data. In this paper a capturing data method is analyzed using a self-centering active probe, which drastically reduces the capture time and the required number of positions of the gauge as compared to the usual standard and manufacturer methods. The mathematical models of the self-centering active probe and AACMM are explained, as well as the mathematical model that links the AACMM global reference system to the probe reference system. We present a self-calibration method that will allow us to determine a homogeneous transformation matrix that relates the probe's reference system to the AACMM last reference system from the probing of a single sphere. In addition, a comparison between a self-centering passive probe and self-centering active probe is carried out to show the advantages of the latter in the procedures of kinematic parameter identification and verification of the AACMM

  10. Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

    Science.gov (United States)

    Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

    2017-01-01

    Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. PMID:28604582

  11. Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Il-Hwan Kim

    2017-06-01

    Full Text Available Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN models, and the augmented information is fed to a support vector machine (SVM to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

  12. Machining of uranium and uranium alloys

    International Nuclear Information System (INIS)

    Morris, T.O.

    1981-01-01

    Uranium and uranium alloys can be readily machined by conventional methods in the standard machine shop when proper safety and operating techniques are used. Material properties that affect machining processes and recommended machining parameters are discussed. Safety procedures and precautions necessary in machining uranium and uranium alloys are also covered. 30 figures

  13. Translating state-of-the-art spinal cord MRI techniques to clinical use: A systematic review of clinical studies utilizing DTI, MT, MWF, MRS, and fMRI.

    Science.gov (United States)

    Martin, Allan R; Aleksanderek, Izabela; Cohen-Adad, Julien; Tarmohamed, Zenovia; Tetreault, Lindsay; Smith, Nathaniel; Cadotte, David W; Crawley, Adrian; Ginsberg, Howard; Mikulis, David J; Fehlings, Michael G

    2016-01-01

    A recent meeting of international imaging experts sponsored by the International Spinal Research Trust (ISRT) and the Wings for Life Foundation identified 5 state-of-the-art MRI techniques with potential to transform the field of spinal cord imaging by elucidating elements of the microstructure and function: diffusion tensor imaging (DTI), magnetization transfer (MT), myelin water fraction (MWF), MR spectroscopy (MRS), and functional MRI (fMRI). However, the progress toward clinical translation of these techniques has not been established. A systematic review of the English literature was conducted using MEDLINE, MEDLINE-in-Progress, Embase, and Cochrane databases to identify all human studies that investigated utility, in terms of diagnosis, correlation with disability, and prediction of outcomes, of these promising techniques in pathologies affecting the spinal cord. Data regarding study design, subject characteristics, MRI methods, clinical measures of impairment, and analysis techniques were extracted and tabulated to identify trends and commonalities. The studies were assessed for risk of bias, and the overall quality of evidence was assessed for each specific finding using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. A total of 6597 unique citations were identified in the database search, and after full-text review of 274 articles, a total of 104 relevant studies were identified for final inclusion (97% from the initial database search). Among these, 69 studies utilized DTI and 25 used MT, with both techniques showing an increased number of publications in recent years. The review also identified 1 MWF study, 11 MRS studies, and 8 fMRI studies. Most of the studies were exploratory in nature, lacking a priori hypotheses and showing a high (72%) or moderately high (20%) risk of bias, due to issues with study design, acquisition techniques, and analysis methods. The acquisitions for each technique varied widely across

  14. Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

    Science.gov (United States)

    Feng, Jingwen; Feng, Tong; Yang, Chengwen; Wang, Wei; Sa, Yu; Feng, Yuanming

    2018-06-01

    This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.

  15. Toward Determining the Comprehensibility of Machine Translations

    Science.gov (United States)

    2012-01-01

    responses to a stimulus (Macmillan and Creelman , 1991). It has been applied in areas such as lie detection (truth/lie), inspection (ac- ceptable...1-1/(2N) (Macmil- lan and Creelman , 1991). Negative values, which usually indicate response confusion, were eliminated. The results of...Macmillan, Neil and C. Douglas Creelman . (1991). Detection theory: A User’s guide. Cambridge Univer- sity Press, pp. 10 &125. Marchant

  16. Semantics and artificial intelligence in machine translation

    Energy Technology Data Exchange (ETDEWEB)

    King, M

    1981-01-01

    The author exemplifies three types of ambiguity that the introduction of semantics or of AI methods might be expected to solve: word sense, structural, and referential ambiguity. From this point of view she examines the works of Schank, Riesbeck, Minsky, Charniak, and Wilks, and she comes to the conclusion that the systems described will not be of much help for the development of operational MT-systems, except within a well-defined, constrained world. The latter aspect is illustrated by the author by means of a description of the Edinburgh Mecho-project. But, as the vast majority of texts destined for MT does not come from a constrained world, such systems will hardly be used as MT production systems. Still, MT-systems like Eurotra give the chance of making intelligent use of AI ideas. 16 references.

  17. Dependency Structures for Statistical Machine Translation

    Science.gov (United States)

    Bach, Nguyen

    2012-01-01

    Dependency structures represent a sentence as a set of dependency relations. Normally the dependency structures from a tree connect all the words in a sentence. One of the most defining characters of dependency structures is the ability to bring long distance dependency between words to local dependency structures. Another the main attraction of…

  18. Meter-scale Urban Land Cover Mapping for EPA EnviroAtlas Using Machine Learning and OBIA Remote Sensing Techniques

    Science.gov (United States)

    Pilant, A. N.; Baynes, J.; Dannenberg, M.; Riegel, J.; Rudder, C.; Endres, K.

    2013-12-01

    US EPA EnviroAtlas is an online collection of tools and resources that provides geospatial data, maps, research, and analysis on the relationships between nature, people, health, and the economy (http://www.epa.gov/research/enviroatlas/index.htm). Using EnviroAtlas, you can see and explore information related to the benefits (e.g., ecosystem services) that humans receive from nature, including clean air, clean and plentiful water, natural hazard mitigation, biodiversity conservation, food, fuel, and materials, recreational opportunities, and cultural and aesthetic value. EPA developed several urban land cover maps at very high spatial resolution (one-meter pixel size) for a portion of EnviroAtlas devoted to urban studies. This urban mapping effort supported analysis of relations among land cover, human health and demographics at the US Census Block Group level. Supervised classification of 2010 USDA NAIP (National Agricultural Imagery Program) digital aerial photos produced eight-class land cover maps for several cities, including Durham, NC, Portland, ME, Tampa, FL, New Bedford, MA, Pittsburgh, PA, Portland, OR, and Milwaukee, WI. Semi-automated feature extraction methods were used to classify the NAIP imagery: genetic algorithms/machine learning, random forest, and object-based image analysis (OBIA). In this presentation we describe the image processing and fuzzy accuracy assessment methods used, and report on some sustainability and ecosystem service metrics computed using this land cover as input (e.g., carbon sequestration from USFS iTREE model; health and demographics in relation to road buffer forest width). We also discuss the land cover classification schema (a modified Anderson Level 1 after the National Land Cover Data (NLCD)), and offer some observations on lessons learned. Meter-scale urban land cover in Portland, OR overlaid on NAIP aerial photo. Streets, buildings and individual trees are identifiable.

  19. Mapping Urban Areas with Integration of DMSP/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Wenlong Jing

    2015-09-01

    Full Text Available Mapping urban areas at global and regional scales is an urgent and crucial task for detecting urbanization and human activities throughout the world and is useful for discerning the influence of urban expansion upon the ecosystem and the surrounding environment. DMSP-OLS stable nighttime lights have provided an effective way to monitor human activities on a global scale. Threshold-based algorithms have been widely used for extracting urban areas and estimating urban expansion, but the accuracy can decrease because of the empirical and subjective selection of threshold values. This paper proposes an approach for extracting urban areas with the integration of DMSP-OLS stable nighttime lights and MODIS data utilizing training sample datasets selected from DMSP-OLS and MODIS NDVI based on several simple strategies. Four classification algorithms were implemented for comparison: the classification and regression tree (CART, k-nearest-neighbors (k-NN, support vector machine (SVM, and random forests (RF. A case study was carried out on the eastern part of China, covering 99 cities and 1,027,700 km2. The classification results were validated using an independent land cover dataset, and then compared with an existing contextual classification method. The results showed that the new method can achieve results with comparable accuracies, and is easier to implement and less sensitive to the initial thresholds than the contextual method. Among the four classifiers implemented, RF achieved the most stable results and the highest average Kappa. Meanwhile CART produced highly overestimated results compared to the other three classifiers. Although k-NN and SVM tended to produce similar accuracy, less-bright areas around the urban cores seemed to be ignored when using SVM, which led to the underestimation of urban areas. Furthermore, quantity assessment showed that the results produced by k-NN, SVM, and RFs exhibited better agreement in larger cities and low

  20. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jeffrey De Fauw

    2017-06-01

    Full Text Available There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet” age-related macular degeneration (wet AMD and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves. Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  1. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jeffrey De Fauw

    2016-07-01

    Full Text Available There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases.   Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet” age-related macular degeneration (wet AMD and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves. Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges.   This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients.   Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, Google DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  2. Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field

    Science.gov (United States)

    Pérez-Ortiz, M. F.; García-Varela, A.; Quiroz, A. J.; Sabogal, B. E.; Hernández, J.

    2017-09-01

    Context. Optical and infrared variability surveys produce a large number of high quality light curves. Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to train an automatic classification system. Quantities related to the magnitude density of the light curves and their Fourier coefficients have been chosen as features in previous studies. However, some of these features are not robust to the presence of outliers and the calculation of Fourier coefficients is computationally expensive for large data sets. Aims: We propose and evaluate the performance of a new robust set of features using supervised classifiers in order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic pole field. Methods: We calculated the proposed set of features on six types of variable stars and also on a set of Be star candidates reported in the literature. We evaluated the performance of these features using classification trees and random forests along with the K-nearest neighbours, support vector machines, and gradient boosted trees methods. We tuned the classifiers with a 10-fold cross-validation and grid search. We then validated the performance of the best classifier on a set of OGLE-IV light curves and applied this to find new Be star candidates. Results: The random forest classifier outperformed the others. By using the random forest classifier and colours criteria we found 50 Be star candidates in the direction of the Gaia south ecliptic pole field, four of which have infrared colours that are consistent with Herbig Ae/Be stars. Conclusions: Supervised methods are very useful in order to obtain preliminary samples of variable stars extracted from large databases. As usual, the stars classified as Be stars candidates must be checked for the colours and spectroscopic characteristics

  3. A Benchmark for Banks’ Strategy in Online Presence – An Innovative Approach Based on Elements of Search Engine Optimization (SEO and Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Camelia Elena CIOLAC

    2011-06-01

    Full Text Available This paper aims to offer a new decision tool to assist banks in evaluating their efficiency of Internet presence and in planning the IT investments towards gaining better Internet popularity. The methodology used in this paper goes beyond the simple website interface analysis and uses web crawling as a source for collecting website performance data and employed web technologies and servers. The paper complements this technical perspective with a proposed scorecard used to assess the efforts of banks in Internet presence that reflects the banks’ commitment to Internet as a distribution channel. An innovative approach based on Machine Learning Techniques, the K-Nearest Neighbor Algorithm, is proposed by the author to estimate the Internet Popularity that a bank is likely to achieve based on its size and efforts in Internet presence.

  4. Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

    Science.gov (United States)

    Rienow, A.; Menz, G.

    2015-12-01

    Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future

  5. Design of rotating electrical machines

    CERN Document Server

    Pyrhonen , Juha; Hrabovcova , Valeria

    2013-01-01

    In one complete volume, this essential reference presents an in-depth overview of the theoretical principles and techniques of electrical machine design. This timely new edition offers up-to-date theory and guidelines for the design of electrical machines, taking into account recent advances in permanent magnet machines as well as synchronous reluctance machines. New coverage includes: Brand new material on the ecological impact of the motors, covering the eco-design principles of rotating electrical machinesAn expanded section on the design of permanent magnet synchronous machines, now repo

  6. Precision translator

    Science.gov (United States)

    Reedy, Robert P.; Crawford, Daniel W.

    1984-01-01

    A precision translator for focusing a beam of light on the end of a glass fiber which includes two turning fork-like members rigidly connected to each other. These members have two prongs each with its separation adjusted by a screw, thereby adjusting the orthogonal positioning of a glass fiber attached to one of the members. This translator is made of simple parts with capability to keep adjustment even in condition of rough handling.

  7. Understanding and Writing G & M Code for CNC Machines

    Science.gov (United States)

    Loveland, Thomas

    2012-01-01

    In modern CAD and CAM manufacturing companies, engineers design parts for machines and consumable goods. Many of these parts are cut on CNC machines. Whether using a CNC lathe, milling machine, or router, the ideas and designs of engineers must be translated into a machine-readable form called G & M Code that can be used to cut parts to precise…

  8. Detecting Neolithic Burial Mounds from LiDAR-Derived Elevation Data Using a Multi-Scale Approach and Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Alexandre Guyot

    2018-02-01

    Full Text Available Airborne LiDAR technology is widely used in archaeology and over the past decade has emerged as an accurate tool to describe anthropomorphic landforms. Archaeological features are traditionally emphasised on a LiDAR-derived Digital Terrain Model (DTM using multiple Visualisation Techniques (VTs, and occasionally aided by automated feature detection or classification techniques. Such an approach offers limited results when applied to heterogeneous structures (different sizes, morphologies, which is often the case for archaeological remains that have been altered throughout the ages. This study proposes to overcome these limitations by developing a multi-scale analysis of topographic position combined with supervised machine learning algorithms (Random Forest. Rather than highlighting individual topographic anomalies, the multi-scalar approach allows archaeological features to be examined not only as individual objects, but within their broader spatial context. This innovative and straightforward method provides two levels of results: a composite image of topographic surface structure and a probability map of the presence of archaeological structures. The method was developed to detect and characterise megalithic funeral structures in the region of Carnac, the Bay of Quiberon, and the Gulf of Morbihan (France, which is currently considered for inclusion on the UNESCO World Heritage List. As a result, known archaeological sites have successfully been geo-referenced with a greater accuracy than before (even when located under dense vegetation and a ground-check confirmed the identification of a previously unknown Neolithic burial mound in the commune of Carnac.

  9. Selection of Levels of Dressing Process Parameters by Using TOPSIS Technique for Surface Roughness of En-31 Work piece in CNC Cylindrical Grinding Machine

    Science.gov (United States)

    Patil, Sanjay S.; Bhalerao, Yogesh J.

    2017-02-01

    Grinding is metal cutting process used for mainly finishing the automobile components. The grinding wheel performance becomes dull by using it most of times. So it should be reshaping for consistent performance. It is necessary to remove dull grains of grinding wheel which is known as dressing process. The surface finish produced on the work piece is dependent on the dressing parameters in sub-sequent grinding operation. Multi-point diamond dresser has four important parameters such as the dressing cross feed rate, dressing depth of cut, width of the diamond dresser and drag angle of the dresser. The range of cross feed rate level is from 80-100 mm/min, depth of cut varies from 10 - 30 micron, width of diamond dresser is from 0.8 - 1.10mm and drag angle is from 40o - 500, The relative closeness to ideal levels of dressing parameters are found for surface finish produced on the En-31 work piece during sub-sequent grinding operation by using Technique of Order Preference by Similarity to Ideal Solution (TOPSIS).In the present work, closeness to ideal solution i.e. levels of dressing parameters are found for Computer Numerical Control (CNC) cylindrical angular grinding machine. After the TOPSIS technique, it is found that the value of Level I is 0.9738 which gives better surface finish on the En-31 work piece in sub-sequent grinding operation which helps the user to select the correct levels (combinations) of dressing parameters.

  10. A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

    Science.gov (United States)

    Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

    2017-02-15

    The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

    Directory of Open Access Journals (Sweden)

    Kamran Siddique

    2017-09-01

    Full Text Available Anomaly detection systems, also known as intrusion detection systems (IDSs, continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii employs logistic regression and extreme gradient boosting techniques for classification; (iii introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

  12. Evolution of Replication Machines

    Science.gov (United States)

    Yao, Nina Y.; O'Donnell, Mike E.

    2016-01-01

    The machines that decode and regulate genetic information require the translation, transcription and replication pathways essential to all living cells. Thus, it might be expected that all cells share the same basic machinery for these pathways that were inherited from the primordial ancestor cell from which they evolved. A clear example of this is found in the translation machinery that converts RNA sequence to protein. The translation process requires numerous structural and catalytic RNAs and proteins, the central factors of which are homologous in all three domains of life, bacteria, archaea and eukarya. Likewise, the central actor in transcription, RNA polymerase, shows homology among the catalytic subunits in bacteria, archaea and eukarya. In contrast, while some “gears” of the genome replication machinery are homologous in all domains of life, most components of the replication machine appear to be unrelated between bacteria and those of archaea and eukarya. This review will compare and contrast the central proteins of the “replisome” machines that duplicate DNA in bacteria, archaea and eukarya, with an eye to understanding the issues surrounding the evolution of the DNA replication apparatus. PMID:27160337

  13. Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.

    Science.gov (United States)

    Nilsson, M; Herlin, A H; Ardö, H; Guzhva, O; Åström, K; Bergsten, C

    2015-11-01

    In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640 × 480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness.

  14. Patient-related quality assurance with different combinations of treatment planning systems, techniques, and machines. A multi-institutional survey

    Energy Technology Data Exchange (ETDEWEB)

    Steiniger, Beatrice; Schwedas, Michael; Weibert, Kirsten; Wiezorek, Tilo [University Hospital Jena, Department of Radiation Oncology, Jena (Germany); Berger, Rene [SRH Hospital Gera, Department of Radiation Oncology, Gera (Germany); Eilzer, Sabine [Martin-Luther-Hospital, Radiation Therapy, Berlin (Germany); Kornhuber, Christine [University Hospital Halle, Department of Radiation Oncology, Halle (Saale) (Germany); Lorenz, Kathleen [Hospital of Chemnitz, Department for Radiation Oncology, Chemnitz (Germany); Peil, Torsten [MVZ Center for Radiation Oncology Halle GmbH, Halle (Saale) (Germany); Reiffenstuhl, Carsten [University Hospital Carl Gustav Carus, Department of Radiation Oncology, Dresden (Germany); Schilz, Johannes [Helios Hospital Erfurt, Department of Radiation Oncology, Erfurt (Germany); Schroeder, Dirk [SRH Central Hospital Suhl, Department of Radiation Oncology, Suhl (Germany); Pensold, Stephanie [Community Hospital Dresden-Friedrichstadt, Department of Radiation Oncology, Dresden (Germany); Walke, Mathias [Otto-von-Guericke University Magdeburg, Department of Radiation Oncology, Magdeburg (Germany); Wolf, Ulrich [University Hospital Leipzig, Department of Radiation Oncology, Leipzig (Germany)

    2017-01-15

    This project compares the different patient-related quality assurance systems for intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) techniques currently used in the central Germany area with an independent measuring system. The participating institutions generated 21 treatment plans with different combinations of treatment planning systems (TPS) and linear accelerators (LINAC) for the QUASIMODO (Quality ASsurance of Intensity MODulated radiation Oncology) patient model. The plans were exposed to the ArcCHECK measuring system (Sun Nuclear Corporation, Melbourne, FL, USA). The dose distributions were analyzed using the corresponding software and a point dose measured at the isocenter with an ionization chamber. According to the generally used criteria of a 10 % threshold, 3 % difference, and 3 mm distance, the majority of plans investigated showed a gamma index exceeding 95 %. Only one plan did not fulfill the criteria and three of the plans did not comply with the commonly accepted tolerance level of ±3 % in point dose measurement. Using only one of the two examined methods for patient-related quality assurance is not sufficiently significant in all cases. (orig.) [German] Im Rahmen des Projekts sollten die verschiedenen derzeit im mitteldeutschen Raum eingesetzten patientenbezogenen Qualitaetssicherungssysteme zur intensitaetsmodulierten Radiotherapie (IMRT) und volumenmodulierten Arc-Radiotherapie (VMAT) mit einem unabhaengigen Messsystem verglichen werden. Die teilnehmenden Einrichtungen berechneten insgesamt 21 Bestrahlungsplaene mit verschiedenen Planungssystemen (TPS) und Linearbeschleunigern (LINAC) fuer das Patientenmodell QUASIMODO (Quality ASsurance of Intensity MODulated radiation Oncology), die dann auf das ArcCHECK-Phantom (Sun Nuclear Corporation, Melbourne, FL, USA) uebertragen und abgestrahlt wurden. Zur Auswertung wurde sowohl eine Punktmessung im Isozentrum als auch die Dosisverteilung in der Diodenebene des

  15. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

    Science.gov (United States)

    Kassahun, Yohannes; Yu, Bingbin; Tibebu, Abraham Temesgen; Stoyanov, Danail; Giannarou, Stamatia; Metzen, Jan Hendrik; Vander Poorten, Emmanuel

    2016-04-01

    Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical

  16. Sustainable machining

    CERN Document Server

    2017-01-01

    This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

  17. Machine learning in healthcare informatics

    CERN Document Server

    Acharya, U; Dua, Prerna

    2014-01-01

    The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.

  18. Massively collaborative machine learning

    NARCIS (Netherlands)

    Rijn, van J.N.

    2016-01-01

    Many scientists are focussed on building models. We nearly process all information we perceive to a model. There are many techniques that enable computers to build models as well. The field of research that develops such techniques is called Machine Learning. Many research is devoted to develop

  19. National machine guarding program: Part 1. Machine safeguarding practices in small metal fabrication businesses

    OpenAIRE

    Parker, David L.; Yamin, Samuel C.; Brosseau, Lisa M.; Xi, Min; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

    2015-01-01

    Background Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. Methods The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardize...

  20. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

    Science.gov (United States)

    Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

    2017-11-01

    The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

  1. A Novel Flavour Tagging Algorithm using Machine Learning Techniques and a Precision Measurement of the $B^0 - \\overline{B^0}$ Oscillation Frequency at the LHCb Experiment

    CERN Document Server

    Kreplin, Katharina

    This thesis presents a novel flavour tagging algorithm using machine learning techniques and a precision measurement of the $B^0 -\\overline{B^0}$ oscillation frequency $\\Delta m_d$ using semileptonic $B^0$ decays. The LHC Run I data set is used which corresponds to $3 \\textrm{fb}^{-1}$ of data taken by the LHCb experiment at a center-of-mass energy of 7 TeV and 8 TeV. The performance of flavour tagging algorithms, exploiting the $b\\bar{b}$ pair production and the $b$ quark hadronization, is relatively low at the LHC due to the large amount of soft QCD background in inelastic proton-proton collisions. The standard approach is a cut-based selection of particles, whose charges are correlated to the production flavour of the $B$ meson. The novel tagging algorithm classifies the particles using an artificial neural network (ANN). It assigns higher weights to particles, which are likely to be correlated to the $b$ flavour. A second ANN combines the particles with the highest weights to derive the tagging decision. ...

  2. Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Shirin Enshaeifar

    Full Text Available The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM. TIHM is a technology assisted monitoring system that uses Internet of Things (IoT enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

  3. Creativity in Machine Learning

    OpenAIRE

    Thoma, Martin

    2016-01-01

    Recent machine learning techniques can be modified to produce creative results. Those results did not exist before; it is not a trivial combination of the data which was fed into the machine learning system. The obtained results come in multiple forms: As images, as text and as audio. This paper gives a high level overview of how they are created and gives some examples. It is meant to be a summary of the current work and give people who are new to machine learning some starting points.

  4. Translation Competence

    DEFF Research Database (Denmark)

    Vandepitte, Sonia; Mousten, Birthe; Maylath, Bruce

    2014-01-01

    After Kiraly (2000) introduced the collaborative form of translation in classrooms, Pavlovic (2007), Kenny (2008), and Huertas Barros (2011) provided empirical evidence that testifies to the impact of collaborative learning. This chapter sets out to describe the collaborative forms of learning at...

  5. Translating Harbourscapes

    DEFF Research Database (Denmark)

    Diedrich, Lisa Babette

    -specific design are proposed for all actors involved in harbour transformation. The study ends with an invitation to further investigate translation as a powerful metaphor for the way existing qualities of a site can be transformed, rather than erased or rewritten, and to explore how this metaphor can foster new...

  6. Word translation entropy in translation

    DEFF Research Database (Denmark)

    Schaeffer, Moritz; Dragsted, Barbara; Hvelplund, Kristian Tangsgaard

    2016-01-01

    This study reports on an investigation into the relationship between the number of translation alternatives for a single word and eye movements on the source text. In addition, the effect of word order differences between source and target text on eye movements on the source text is studied....... In particular, the current study investigates the effect of these variables on early and late eye movement measures. Early eye movement measures are indicative of processes that are more automatic while late measures are more indicative of conscious processing. Most studies that found evidence of target...... language activation during source text reading in translation, i.e. co-activation of the two linguistic systems, employed late eye movement measures or reaction times. The current study therefore aims to investigate if and to what extent earlier eye movement measures in reading for translation show...

  7. Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample.

    Science.gov (United States)

    Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

    2016-01-01

    Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study. A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009-2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary. Five clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the five key clusters. This

  8. Interactive Translation Prediction versus Conventional Post-editing in Practice

    DEFF Research Database (Denmark)

    Sanchis-Trilles, German; Alabau, Vicent; Buck, Christian

    2014-01-01

    We conducted a field trial in computer-assisted professional translation to compare Interactive Translation Prediction (ITP) against conventional post- editing (PE) of machine translation (MT) output. In contrast to the conventional PE set-up, where an MT system first produces a static translatio...

  9. The development of damage identification methods for buildings with image recognition and machine learning techniques utilizing aerial photographs of the 2016 Kumamoto earthquake

    Science.gov (United States)

    Shohei, N.; Nakamura, H.; Fujiwara, H.; Naoichi, M.; Hiromitsu, T.

    2017-12-01

    It is important to get schematic information of the damage situation immediately after the earthquake utilizing photographs shot from an airplane in terms of the investigation and the decision-making for authorities. In case of the 2016 Kumamoto earthquake, we have acquired more than 1,800 orthographic projection photographs adjacent to damaged areas. These photos have taken between April 16th and 19th by airplanes, then we have distinguished damages of all buildings with 4 levels, and organized as approximately 296,000 GIS data corresponding to the fundamental Geospatial data published by Geospatial Information Authority of Japan. These data have organized by effort of hundreds of engineers. However, it is not considered practical for more extensive disasters like the Nankai Trough earthquake by only human powers. So, we have been developing the automatic damage identification method utilizing image recognition and machine learning techniques. First, we have extracted training data of more than 10,000 buildings which have equally damage levels divided in 4 grades. With these training data, we have been raster scanning in each scanning ranges of entire images, then clipping patch images which represents damage levels each. By utilizing these patch images, we have been developing discriminant models by two ways. One is a model using the Support Vector Machine (SVM). First, extract a feature quantity of each patch images. Then, with these vector values, calculate the histogram density as a method of Bag of Visual Words (BoVW), then classify borders with each damage grades by SVM. The other one is a model using the multi-layered Neural Network. First, design a multi-layered Neural Network. Second, input patch images and damage levels based on a visual judgement, and then, optimize learning parameters with error backpropagation method. By use of both discriminant models, we are going to discriminate damage levels in each patches, then create the image that shows

  10. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

    2014-08-15

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

  11. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    International Nuclear Information System (INIS)

    Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

    2014-01-01

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

  12. Translating Signs, Producing Subjects

    Directory of Open Access Journals (Sweden)

    Brett Neilson

    2009-08-01

    Full Text Available This paper moves between two streets: Liverpool Road in the Sydney suburb of Ashfield and Via Sarpi in the Italian city of Milan. What connects these streets is that both have become important sites for businesses in the Chinese diaspora. Moreover, both are streets on which locals have expressed desires for Chinese signs to be translated into the national lingua franca. The paper argues that the cultural politics inherent in this demand for translation cannot be fully understood in the context of national debates about diversity and integration. It is also necessary to consider the emergence of the official Chinese Putonghua as global language, which competes with English but also colonizes dialects and minority languages. In the case of these dual language signs, the space between languages can neither be reduced to a contact zone of minority and majority cultures nor celebrated as a ‘third space’ where the power relations implied by such differences are subverted. At stake is rather a space characterised by what Naoki Sakai calls the schema of co-figuration, which allows the representation of translation as the passage between two equivalents that resemble each other and thus makes possible their determination as conceptually different and comparable. Drawing on arguments about translation and citizenship, the paper critically interrogates the ethos of interchangeability implied by this regime of translation. A closing argument is made for a vision of the common that implies neither civilisational harmony nor the translation of all values into a general equivalent. Primary sources include government reports, internet texts and media stories. These are analyzed using techniques of discourse analysis and interpreted with the help of secondary literature concerning globalisation, language and migration. The disciplinary matrix cuts and mixes between cultural studies, translation studies, citizenship studies, globalization studies and

  13. PC-assisted translation of photogrammetric papers

    Science.gov (United States)

    Güthner, Karlheinz; Peipe, Jürgen

    A PC-based system for machine translation of photogrammetric papers from the English into the German language and vice versa is described. The computer-assisted translating process is not intended to create a perfect interpretation of a text but to produce a rough rendering of the content of a paper. Starting with the original text, a continuous data flow is effected into the translated version by means of hardware (scanner, personal computer, printer) and software (OCR, translation, word processing, DTP). An essential component of the system is a photogrammetric microdictionary which is being established at present. It is based on several sources, including e.g. the ISPRS Multilingual Dictionary.

  14. Translational genomics

    Directory of Open Access Journals (Sweden)

    Martin Kussmann

    2014-09-01

    Full Text Available The term “Translational Genomics” reflects both title and mission of this new journal. “Translational” has traditionally been understood as “applied research” or “development”, different from or even opposed to “basic research”. Recent scientific and societal developments have triggered a re-assessment of the connotation that “translational” and “basic” are either/or activities: translational research nowadays aims at feeding the best science into applications and solutions for human society. We therefore argue here basic science to be challenged and leveraged for its relevance to human health and societal benefits. This more recent approach and attitude are catalyzed by four trends or developments: evidence-based solutions; large-scale, high dimensional data; consumer/patient empowerment; and systems-level understanding.

  15. Beyond Translation

    DEFF Research Database (Denmark)

    Olwig, Mette Fog

    2013-01-01

    This article contributes to the growing scholarship on local development practitioners by re-examining conceptualizations of practitioners as ‘brokers’ strategically translating between ‘travelling’ (development institution) rationalities and ‘placed’ (recipient area) rationalities in relation...... and practice spurred by new challenges deriving from climate change anxiety, the study shows how local practitioners often make local activities fit into travelling development rationalities as a matter of habit, rather than as a conscious strategy. They may therefore cease to ‘translate’ between different...... rationalities. This is shown to have important implications for theory, research and practice concerning disaster risk reduction and climate change adaptation in which such translation is often expected....

  16. Revising Translations

    DEFF Research Database (Denmark)

    Rasmussen, Kirsten Wølch; Schjoldager, Anne

    2011-01-01

    The paper explains the theoretical background and findings of an empirical study of revision policies, using Denmark as a case in point. After an overview of important definitions, types and parameters, the paper explains the methods and data gathered from a questionnaire survey and an interview...... survey. Results clearly show that most translation companies regard both unilingual and comparative revisions as essential components of professional quality assurance. Data indicate that revision is rarely fully comparative, as the preferred procedure seems to be a unilingual revision followed by a more...... or less comparative rereading. Though questionnaire data seem to indicate that translation companies use linguistic correctness and presentation as the only revision parameters, interview data reveal that textual and communicative aspects are also considered. Generally speaking, revision is not carried...

  17. Simple machines

    CERN Document Server

    Graybill, George

    2007-01-01

    Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s

  18. Machine Learning for Security

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Applied statistics, aka ‘Machine Learning’, offers a wealth of techniques for answering security questions. It’s a much hyped topic in the big data world, with many companies now providing machine learning as a service. This talk will demystify these techniques, explain the math, and demonstrate their application to security problems. The presentation will include how-to’s on classifying malware, looking into encrypted tunnels, and finding botnets in DNS data. About the speaker Josiah is a security researcher with HP TippingPoint DVLabs Research Group. He has over 15 years of professional software development experience. Josiah used to do AI, with work focused on graph theory, search, and deductive inference on large knowledge bases. As rules only get you so far, he moved from AI to using machine learning techniques identifying failure modes in email traffic. There followed digressions into clustered data storage and later integrated control systems. Current ...

  19. 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.

  20. Predicting transmission of structure-borne sound power from machines by including terminal cross-coupling

    DEFF Research Database (Denmark)

    Ohlrich, Mogens

    2011-01-01

    of translational terminals in a global plane. This paired or bi-coupled power transmission represents the simplest case of cross-coupling. The procedure and quality of the predicted transmission using this improved technique is demonstrated experimentally for an electrical motor unit with an integrated radial fan......Structure-borne sound generated by audible vibration of machines in vehicles, equipment and house-hold appliances is often a major cause of noise. Such vibration of complex machines is mostly determined and quantified by measurements. It has been found that characterization of the vibratory source...

  1. Machine musicianship

    Science.gov (United States)

    Rowe, Robert

    2002-05-01

    The training of musicians begins by teaching basic musical concepts, a collection of knowledge commonly known as musicianship. Computer programs designed to implement musical skills (e.g., to make sense of what they hear, perform music expressively, or compose convincing pieces) can similarly benefit from access to a fundamental level of musicianship. Recent research in music cognition, artificial intelligence, and music theory has produced a repertoire of techniques that can make the behavior of computer programs more musical. Many of these were presented in a recently published book/CD-ROM entitled Machine Musicianship. For use in interactive music systems, we are interested in those which are fast enough to run in real time and that need only make reference to the material as it appears in sequence. This talk will review several applications that are able to identify the tonal center of musical material during performance. Beyond this specific task, the design of real-time algorithmic listening through the concurrent operation of several connected analyzers is examined. The presentation includes discussion of a library of C++ objects that can be combined to perform interactive listening and a demonstration of their capability.

  2. Status On Multi-microsecond Prepulse Technique On Sphinx Machine Going From Nested To Single Wire Array For 800 ns Implosion Time Z-pinch

    Science.gov (United States)

    Maury, P.; Calamy, H.; Grunenwald, J.; Lassalle, F.; Zucchini, F.; Loyen, A.; Georges, A.; Morell, A.; Bedoch, J. P.

    2009-01-01

    The Sphinx machine[1] is a 6 MA, 1 μS driver based on the LTD technology, used for Z-pinch experiments. Important improvements of Sphinx radiation output were recently obtained using a multi-microsecond current prepulse[2]. Total power per unit of length is multiplied by a factor of 6 and FWHM divided by a factor of 2.5. Early breakdown of the wires during the prepulse phase dramatically changes the ablation phase leading to an improvement of axial homogeneity of both the implosion and the final radiating column. As a consequence, the cathode bubble observed on classical shots is definitively removed. The implosion is then centered and zippering effect is reduced, leading to simultaneous x-ray emission of the whole length. A great reproducibility is obtained. Nested arrays were used before to mitigate the Rayleigh-Taylor instabilities during the implosion phase. Further experiments with pre-pulse technique are described here were inner array was removed. The goal of these experiments was to see if long prepulse could give stable enough implosion with single array and at the same time increase the η parameter by reducing the mass of the load. Experimental results of single wire array loads of typical dimension 5 cm in height with implosion time between 700 and 900 ns and diameter varying between 80 and 140 mm are given. Parameters of the loads were varying in term of radius and number of wires. Comparisons with nested wire array loads are done and trends are proposed. Characteristics of both the implosion and the final radiating column are shown. 2D MHD numerical simulations of single wire array become easier as there is no interaction between outer and inner array anymore. A systematic study was done using injection mass model to benchmark simulation with experiments.

  3. Status On Multi-microsecond Prepulse Technique On Sphinx Machine Going From Nested To Single Wire Array For 800 ns Implosion Time Z-pinch

    International Nuclear Information System (INIS)

    Maury, P.; Calamy, H.; Grunenwald, J.; Lassalle, F.; Zucchini, F.; Loyen, A.; Georges, A.; Morell, A.; Bedoch, J. P.

    2009-01-01

    The Sphinx machine [1] is a 6 MA, 1 μS driver based on the LTD technology, used for Z-pinch experiments. Important improvements of Sphinx radiation output were recently obtained using a multi-microsecond current prepulse [2] . Total power per unit of length is multiplied by a factor of 6 and FWHM divided by a factor of 2.5. Early breakdown of the wires during the prepulse phase dramatically changes the ablation phase leading to an improvement of axial homogeneity of both the implosion and the final radiating column. As a consequence, the cathode bubble observed on classical shots is definitively removed. The implosion is then centered and zippering effect is reduced, leading to simultaneous x-ray emission of the whole length. A great reproducibility is obtained. Nested arrays were used before to mitigate the Rayleigh-Taylor instabilities during the implosion phase. Further experiments with pre-pulse technique are described here were inner array was removed. The goal of these experiments was to see if long prepulse could give stable enough implosion with single array and at the same time increase the η parameter by reducing the mass of the load. Experimental results of single wire array loads of typical dimension 5 cm in height with implosion time between 700 and 900 ns and diameter varying between 80 and 140 mm are given. Parameters of the loads were varying in term of radius and number of wires. Comparisons with nested wire array loads are done and trends are proposed. Characteristics of both the implosion and the final radiating column are shown. 2D MHD numerical simulations of single wire array become easier as there is no interaction between outer and inner array anymore. A systematic study was done using injection mass model to benchmark simulation with experiments.

  4. Face machines

    Energy Technology Data Exchange (ETDEWEB)

    Hindle, D.

    1999-06-01

    The article surveys latest equipment available from the world`s manufacturers of a range of machines for tunnelling. These are grouped under headings: excavators; impact hammers; road headers; and shields and tunnel boring machines. Products of thirty manufacturers are referred to. Addresses and fax numbers of companies are supplied. 5 tabs., 13 photos.

  5. Electric machine

    Science.gov (United States)

    El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI

    2012-07-17

    An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.

  6. Machine Learning.

    Science.gov (United States)

    Kirrane, Diane E.

    1990-01-01

    As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

  7. Nonplanar machines

    International Nuclear Information System (INIS)

    Ritson, D.

    1989-05-01

    This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs

  8. Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample

    Science.gov (United States)

    Dipnall, Joanna F.

    2016-01-01

    Background Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study. Methods A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009–2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary. Results Five clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the

  9. Machine learning with R cookbook

    CERN Document Server

    Chiu, Yu-Wei

    2015-01-01

    If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.

  10. An improved excitation control technique of three-phase induction machine operating as dual winding generator for micro-wind domestic application

    International Nuclear Information System (INIS)

    Chatterjee, Arunava; Chatterjee, Debashis

    2015-01-01

    Highlights: • A three-phase induction machine working as single phase generator is studied. • The generator is assisted by an inverter and photovoltaic panel for excitation. • Proposed control involves operating the machine as balanced two-phase generator. • Torque pulsations associated with unbalanced phase currents are minimized. • The generator can be used for grid-isolated micro-wind power generation. - Abstract: Single-phase generation schemes are widely utilized for harnessing wind power in remote and grid secluded applications. This paper presents a novel control methodology for a three-phase induction machine working as a single-phase dual winding induction generator. Three-phase induction machines providing single-phase output with proper control strategy can be beneficial in grid secluded micro-wind energy conversion systems compared to single-phase induction generators. Three-phase induction machines operating in single-phase mode are mostly excited asymmetrically to provide single-phase power leading to unbalanced current flow in the stator windings causing heating and insulation breakdown. The asymmetrical excitation also initiates torque pulsations which results in additional stress and vibration at the machine shaft and bearings degrading the machine performance. The proposed control is chiefly aimed to minimize this unbalance. The variable excitation required for the proposed generator is provided through a single-phase inverter with photovoltaic panels. The suitability for such a generator along with its control is tested with appropriate simulations and experimental results. The induction generator with the proposed control strategy is expected to be useful in remote and grid isolated households as a standalone source of single-phase electrical power

  11. The Machine within the Machine

    CERN Multimedia

    Katarina Anthony

    2014-01-01

    Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

  12. Translational research in medicine

    Directory of Open Access Journals (Sweden)

    Bakir Mehić

    2011-05-01

    Full Text Available Translational medicine is a medical practice based on interventional epidemiology. It is regarded by its proponents as a natural progression from Evidence-Based Medicine. It integrates research from the basic sciences, social sciences and political sciences with the aim of optimizing patient care and preventive measures which may extend beyond healthcare services. In short, it is the process of turning appropriate biological discoveries into drugs and medical devices that can be used in the treatment of patients.[1]Scientific research and the development of modern powerful techniques are crucial for improving patient care in a society that is increasingly demanding the highest quality health services.[2] Indeed, effective patient care requires the continuous improvement of knowledge on the pathophysiology of the diseases, diagnostic procedures and therapeutic tools available. To this end, development of both clinical and basic research in health sciences is required. However, what is most effective in improving medical knowledge, and hence patient care, is the cross-fertilization between basic and clinical science. This has been specifically highlighted in recent years with the coining of the term “translational research”.[3] Translational research is of great importance in all medical specialties.Translational Research is the basis for Translational Medicine. It is the process which leads from evidence based medicine to sustainable solutions for public health problems.[4] It aims to improve the health and longevity of the world’s populations and depends on developing broad-based teams of scientists and scholars who are able to focus their efforts to link basic scientific discoveries with the arena of clinical investigation, and translating the results of clinical trials into changes in clinical practice, informed by evidence from the social and political sciences. Clinical science and ecological support from effective policies can

  13. A novel flavour tagging algorithm using machine learning techniques and a precision measurement of the B0- anti B0 oscillation frequency at the LHCb experiment

    International Nuclear Information System (INIS)

    Kreplin, Katharina

    2015-01-01

    This thesis presents a novel flavour tagging algorithm using machine learning techniques and a precision measurement of the B 0 - anti B 0 oscillation frequency Δm d using semileptonic B 0 decays. The LHC Run I data set is used which corresponds to 3 fb -1 of data taken by the LHCb experiment at a center-of-mass energy of 7 TeV and 8 TeV. The performance of flavour tagging algorithms, exploiting the b anti b pair production and the b quark hadronization, is relatively low at the LHC due to the large amount of soft QCD background in inelastic proton-proton collisions. The standard approach is a cut-based selection of particles, whose charges are correlated to the production flavour of the B meson. The novel tagging algorithm classifies the particles using an artificial neural network (ANN). It assigns higher weights to particles, which are likely to be correlated to the b flavour. A second ANN combines the particles with the highest weights to derive the tagging decision. An increase of the opposite side kaon tagging performance of 50% and 30% is achieved on B + → J/ψK + data. The second number corresponds to a readjustment of the algorithm to the B 0 s production topology. This algorithm is employed in the precision measurement of Δm d . A data set of 3.2 x 10 6 semileptonic B 0 decays is analysed, where the B 0 decays into a D - (K + π - π - ) or D *- (π - anti D 0 (K + π - )) and a μ + ν μ pair. The ν μ is not reconstructed, therefore, the B 0 momentum needs to be statistically corrected for the missing momentum of the neutrino to compute the correct B 0 decay time. A result of Δm d =0.503±0.002(stat.)±0.001(syst.) ps -1 is obtained. This is the world's best measurement of this quantity.

  14. Translating democracy

    DEFF Research Database (Denmark)

    Doerr, Nicole

    2012-01-01

    Linguistic barriers may pose problems for politicians trying to communicate delicate decisions to a European-wide public, as well as for citizens wishing to protest at the European level. In this article I present a counter-intuitive position on the language question, one that explores how...... Forum (ESF). I compare deliberative practices in the multilingual ESF preparatory meetings with those in monolingual national Social Forum meetings in three Western European countries. My comparison shows that multilingualism does not reduce the inclusivity of democratic deliberation as compared...... in institutionalized habits and norms of deliberation. Addressing democratic theorists, my findings suggest that translation could be a way to think about difference not as a hindrance but as a resource for democracy in linguistically heterogeneous societies and public spaces, without presupposing a shared language...

  15. Translator's preface.

    Science.gov (United States)

    Lamiell, James T

    2013-08-01

    Presents a preface from James T. Lamiell, who translates Wilhelm Wundt's Psychology's Struggle for Existence (Die Psychologie im Kampf ums Dasein), in which Wundt advised against the impending divorce of psychology from philosophy, into English. Lamiell comments that more than a decade into the 21st century, it appears that very few psychologists have any interest at all in work at the interface of psychology and philosophy. He notes that one clear indication of this is that the Society for Theoretical and Philosophical Psychology, which is Division 24 of the American Psychological Association (APA), remains one of the smallest of the APA's nearly 60 divisions. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

  16. Mapping Translation Technology Research in Translation Studies

    DEFF Research Database (Denmark)

    Schjoldager, Anne; Christensen, Tina Paulsen; Flanagan, Marian

    2017-01-01

    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......Due to the growing uptake of translation technology in the language industry and its documented impact on the translation profession, translation students and scholars need in-depth and empirically founded knowledge of the nature and influences of translation technology (e.g. Christensen....../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...

  17. English-to-Japanese Translation vs. Dictation vs. Post-editing

    DEFF Research Database (Denmark)

    Carl, Michael; Aizawa, Akiko; Yamada, Masaru

    2016-01-01

    of text production. This paper introduces and evaluates a corpus of more than 55 hours of English-to-Japanese user activity data that were collected within the ENJA15 project, in which translators were observed while writing and speaking translations (translation dictation) and during machine translation...

  18. Optimizing Distributed Machine Learning for Large Scale EEG Data Set

    Directory of Open Access Journals (Sweden)

    M Bilal Shaikh

    2017-06-01

    Full Text Available Distributed Machine Learning (DML has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks   and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.

  19. Searching to Translate and Translating to Search: When Information Retrieval Meets Machine Translation

    Science.gov (United States)

    Ture, Ferhan

    2013-01-01

    With the adoption of web services in daily life, people have access to tremendous amounts of information, beyond any human's reading and comprehension capabilities. As a result, search technologies have become a fundamental tool for accessing information. Furthermore, the web contains information in multiple languages, introducing another barrier…

  20. Machine learning techniques for the verification of refueling activities in CANDU-type nuclear power plants (NPPs) with direct applications in nuclear safeguards

    International Nuclear Information System (INIS)

    Budzinski, J.

    2006-06-01

    This dissertation deals with the problem of automated classification of the signals obtained from certain radiation monitoring systems, specifically from the Core Discharge Monitor (CDM) systems, that are successfully operated by the International Atomic Energy Agency (IAEA) at various CANDU-type nuclear power plants around the world. In order to significantly reduce the costly and error-prone manual evaluation of the large amounts of the collected CDM signals, a reliable and efficient algorithm for the automated data evaluation is necessary, which might ensure real-time performance with maximum of 0.01 % misclassification ratio. This thesis describes the research behind finding a successful prototype implementation of such automated analysis software. The finally adopted methodology assumes a nonstationary data-generating process that has a finite number of states or basic fueling activities, each of which can emit observable data patterns having particular stationary characteristics. To find out the underlying state sequences, a unified probabilistic approach known as the hidden Markov model (HMM) is used. Each possible fueling sequence is modeled by a distinct HMM having a left-right profile topology with explicit insert and delete states. Given an unknown fueling sequence, a dynamic programming algorithm akin to the Viterbi search is used to find the maximum likelihood state path through each model and eventually the overall best-scoring path is picked up as the recognition hypothesis. Machine learning techniques are applied to estimate the observation densities of the states, because the densities are not simply parameterizable. Unlike most present applications of continuous monitoring systems that rely on heuristic approaches to the recognition of possibly risky events, this research focuses on finding techniques that make optimal use of prior knowledge and computer simulation in the recognition task. Thus, a suitably modified, approximate n-best variant of

  1. Computer vision and machine learning for archaeology

    NARCIS (Netherlands)

    van der Maaten, L.J.P.; Boon, P.; Lange, G.; Paijmans, J.J.; Postma, E.

    2006-01-01

    Until now, computer vision and machine learning techniques barely contributed to the archaeological domain. The use of these techniques can support archaeologists in their assessment and classification of archaeological finds. The paper illustrates the use of computer vision techniques for

  2. The complexity of translationally invariant low-dimensional spin lattices in 3D

    Science.gov (United States)

    Bausch, Johannes; Piddock, Stephen

    2017-11-01

    In this theoretical paper, we consider spin systems in three spatial dimensions and consider the computational complexity of estimating the ground state energy, known as the local Hamiltonian problem, for translationally invariant Hamiltonians. We prove that the local Hamiltonian problem for 3D lattices with face-centered cubic unit cells and 4-local translationally invariant interactions between spin-3/2 particles and open boundary conditions is QMAEXP-complete, where QMAEXP is the class of problems which can be verified in exponential time on a quantum computer. We go beyond a mere embedding of past hard 1D history state constructions, for which the local spin dimension is enormous: even state-of-the-art constructions have local dimension 42. We avoid such a large local dimension by combining some different techniques in a novel way. For the verifier circuit which we embed into the ground space of the local Hamiltonian, we utilize a recently developed computational model, called a quantum ring machine, which is especially well suited for translationally invariant history state constructions. This is encoded with a new and particularly simple universal gate set, which consists of a single 2-qubit gate applied only to nearest-neighbour qubits. The Hamiltonian construction involves a classical Wang tiling problem as a binary counter which translates one cube side length into a binary description for the encoded verifier input and a carefully engineered history state construction that implements the ring machine on the cubic lattice faces. These novel techniques allow us to significantly lower the local spin dimension, surpassing the best translationally invariant result to date by two orders of magnitude (in the number of degrees of freedom per coupling). This brings our models on par with the best non-translationally invariant construction.

  3. Translating state-of-the-art spinal cord MRI techniques to clinical use: A systematic review of clinical studies utilizing DTI, MT, MWF, MRS, and fMRI

    Directory of Open Access Journals (Sweden)

    Allan R. Martin

    2016-01-01

    Conclusions: State-of-the-art spinal cord MRI techniques are emerging with great potential to improve the diagnosis and management of various spinal pathologies, but the current body of evidence has only showed limited clinical utility to date. Among these imaging tools DTI is the most mature, but further work is necessary to standardize and validate its use before it will be adopted in the clinical realm. Large, well-designed studies with a priori hypotheses, standardized acquisition methods, detailed clinical data collection, and robust automated analysis techniques are needed to fully demonstrate the potential of these rapidly evolving techniques.

  4. Electrical machines diagnosis

    CERN Document Server

    Trigeassou, Jean-Claude

    2013-01-01

    Monitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives.This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit.Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is perf

  5. Machine learning in virtual screening.

    Science.gov (United States)

    Melville, James L; Burke, Edmund K; Hirst, Jonathan D

    2009-05-01

    In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.

  6. NICeSim: an open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making.

    Science.gov (United States)

    Cerqueira, Fabio Ribeiro; Ferreira, Tiago Geraldo; de Paiva Oliveira, Alcione; Augusto, Douglas Adriano; Krempser, Eduardo; Corrêa Barbosa, Helio José; do Carmo Castro Franceschini, Sylvia; de Freitas, Brunnella Alcantara Chagas; Gomes, Andreia Patricia; Siqueira-Batista, Rodrigo

    2014-11-01

    This paper describes NICeSim, an open-source simulator that uses machine learning (ML) techniques to aid health professionals to better understand the treatment and prognosis of premature newborns. The application was developed and tested using data collected in a Brazilian hospital. The available data were used to feed an ML pipeline that was designed to create a simulator capable of predicting the outcome (death probability) for newborns admitted to neonatal intensive care units. However, unlike previous scoring systems, our computational tool is not intended to be used at the patients bedside, although it is possible. Our primary goal is to deliver a computational system to aid medical research in understanding the correlation of key variables with the studied outcome so that new standards can be established for future clinical decisions. In the implemented simulation environment, the values of key attributes can be changed using a user-friendly interface, where the impact of each change on the outcome is immediately reported, allowing a quantitative analysis, in addition to a qualitative investigation, and delivering a totally interactive computational tool that facilitates hypothesis construction and testing. Our statistical experiments showed that the resulting model for death prediction could achieve an accuracy of 86.7% and an area under the receiver operating characteristic curve of 0.84 for the positive class. Using this model, three physicians and a neonatal nutritionist performed simulations with key variables correlated with chance of death. The results indicated important tendencies for the effect of each variable and the combination of variables on prognosis. We could also observe values of gestational age and birth weight for which a low Apgar score and the occurrence of respiratory distress syndrome (RDS) could be less or more severe. For instance, we have noticed that for a newborn with 2000 g or more the occurrence of RDS is far less problematic

  7. Machine Protection

    International Nuclear Information System (INIS)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012

  8. 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.

  9. Machine Protection

    CERN Document Server

    Zerlauth, Markus; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  10. Machine Protection

    Energy Technology Data Exchange (ETDEWEB)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg [European Organization for Nuclear Research, Geneva (Switzerland)

    2012-07-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  11. Machines and Metaphors

    Directory of Open Access Journals (Sweden)

    Ángel Martínez García-Posada

    2016-10-01

    Full Text Available The edition La ley del reloj. Arquitectura, máquinas y cultura moderna (Cátedra, Madrid, 2016 registers the useful paradox of the analogy between architecture and technique. Its author, the architect Eduardo Prieto, also a philosopher, professor and writer, acknowledges the obvious distance from machines to buildings, so great that it can only be solved using strange comparisons, since architecture does not move nor are the machines habitable, however throughout the book, from the origin of the metaphor of the machine, with clarity in his essay and enlightening erudition, he points out with certainty some concomitances of high interest, drawing throughout history a beautiful cartography of the fruitful encounter between organics and mechanics.

  12. Teletherapy machine

    International Nuclear Information System (INIS)

    Panyam, Vinatha S.; Rakshit, Sougata; Kulkarni, M.S.; Pradeepkumar, K.S.

    2017-01-01

    Radiation Standards Section (RSS), RSSD, BARC is the national metrology institute for ionizing radiation. RSS develops and maintains radiation standards for X-ray, beta, gamma and neutron radiations. In radiation dosimetry, traceability, accuracy and consistency of radiation measurements is very important especially in radiotherapy where the success of patient treatment is dependent on the accuracy of the dose delivered to the tumour. Cobalt teletherapy machines have been used in the treatment of cancer since the early 1950s and India had its first cobalt teletherapy machine installed at the Cancer Institute, Chennai in 1956

  13. 1- to 10-keV x-ray backlighting of annular wire arrays on the Sandia Z-machine using bent-crystal imaging techniques

    International Nuclear Information System (INIS)

    Rambo, Patrick K.; Wenger, David Franklin; Bennett, Guy R.; Sinars, Daniel Brian; Smith, Ian Craig; Porter, John Larry Jr.; Cuneo, Michael Edward; Rovang, Dean Curtis; Anderson, Jessica E.

    2003-01-01

    Annular wire array implosions on the Sandia Z-machine can produce >200 TW and 1-2 MJ of soft x rays in the 0.1-10 keV range. The x-ray flux and debris in this environment present significant challenges for radiographic diagnostics. X-ray backlighting diagnostics at 1865 and 6181 eV using spherically-bent crystals have been fielded on the Z-machine, each with a ∼0.6 eVspectral bandpass, 10 (micro)m spatial resolution, and a 4 mm by 20mm field of view. The Z-Beamlet laser, a 2-TW, 2-kJ Nd:glass laser(λ = 527 nm), is used to produce 0.1-1 J x-ray sources for radiography. The design, calibration, and performance of these diagnostics is presented.

  14. Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis.

    Science.gov (United States)

    Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

    2014-01-01

    Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

  15. Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

    Directory of Open Access Journals (Sweden)

    Cíntia Matsuda Toledo

    Full Text Available Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario.OBJECTIVE: The aims were to describe how to: (i develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii automatically identify the features that best distinguish the groups.METHODS: The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age. In this study, the descriptions by 144 of the subjects studied in Toledo18 were used, which included 200 healthy Brazilians of both genders.RESULTS AND CONCLUSION:A Support Vector Machine (SVM with a radial basis function (RBF kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS is a strong candidate to replace manual feature selection methods.

  16. Strategies for Translating Vocative Texts

    Directory of Open Access Journals (Sweden)

    Olga COJOCARU

    2014-12-01

    Full Text Available The paper deals with the linguistic and cultural elements of vocative texts and the techniques used in translating them by giving some examples of texts that are typically vocative (i.e. advertisements and instructions for use. Semantic and communicative strategies are popular in translation studies and each of them has its own advantages and disadvantages in translating vocative texts. The advantage of semantic translation is that it takes more account of the aesthetic value of the SL text, while communicative translation attempts to render the exact contextual meaning of the original text in such a way that both content and language are readily acceptable and comprehensible to the readership. Focus is laid on the strategies used in translating vocative texts, strategies that highlight and introduce a cultural context to the target audience, in order to achieve their overall purpose, that is to sell or persuade the reader to behave in a certain way. Thus, in order to do that, a number of advertisements from the field of cosmetics industry and electronic gadgets were selected for analysis. The aim is to gather insights into vocative text translation and to create new perspectives on this field of research, now considered a process of innovation and diversion, especially in areas as important as economy and marketing.

  17. Applying Sight Translation as a Means to Enhance Reading Ability of Iranian EFL Students

    Science.gov (United States)

    Fatollahi, Moslem

    2016-01-01

    Sight translation is the oral translation of a written text and is a mixture of translation and interpreting. Sight translation is a widely-used activity in translation training programs. Yet, this mode of translation has rarely been applied as a reading instruction technique in Iranian EFL instruction context in spite of the growing interest in…

  18. 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)

  19. Machine testning

    DEFF Research Database (Denmark)

    De Chiffre, Leonardo

    This document is used in connection with a laboratory exercise of 3 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercise includes a series of tests carried out by the student on a conventional and a numerically controled lathe, respectively. This document...

  20. Machine rates for selected forest harvesting machines

    Science.gov (United States)

    R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

    2002-01-01

    Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...