5軸制御マシニングセンターの運動精度の向上に向けた新たなキャリブレーション手法の開発に取り組んだ．また、直進および旋回軸の各種の運動誤差要因を診断する手法を構築した．さらにサーボ系の位置フィードバックで，サーボ系に起因する運動誤差の診断法も提案した.旋回軸にDDモータ方式を採用することで機械全体のサーボ特性を大きく改善できることも解明した．最後に，複雑形状の実加工により開発機で高い加工精度を確認することができた． / A new calibration method to improve the motion accuracy of a 5-axis control machining center has been developed. A method to diagnose various causes of motion errors on linear and rotary axes has been also established. Furthermore, a diagnosis method of motion errors caused by servo motors using the position feedback function is suggested in this paper. This study also figured out employing the direct drive motor for the rotary axes improved the characteristics of all the servo motors on the machine. Finally, the development test machine proved its high machining accuracy through actual machining of a complex-shaped workpiece. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

In today’s digital era, establishing an online presence and maintaining a well-structured website is vitalfor companies to remain competitive in their respective markets. A crucial aspect of online success liesin strategically selecting the right words to optimize customer engagement and search engine visibility.However, this process is often time-consuming, involving extensive analysis of a company’s website aswell as its competitors’. This thesis focuses on developing an efficient binary classification approachto identify key words and phrases extracted from multiple company websites. The data set used forthis solution consists of approximately 92,000 scraped samples, primarily comprising non-key samples.Various features were extracted, and a word embedding model was employed to assess each sample’srelevance to its specific industry and topic. The logistic regression, decision tree and random forestalgorithms were all explored and implemented as different solutions to the classification problem. Theresults indicated that the logistic regression model excelled in retaining keywords but was less effectivein eliminating non-keywords. Conversely, the tree-based methods demonstrated superior classificationof keywords, albeit at the cost of misclassifying a few keywords. Overall, the random forest approachoutperformed the others, achieving a result of 76 percent in recall and 20 percent in precision whenpredicting key samples. In summary, this thesis presents a solution for classifying words and phrasesfrom company websites into key and non-key categories, and the developed methodology could offervaluable insights for companies seeking to enhance their website optimization strategies.

No description available.

It is becoming more common for researchers to use existing recordings as a source for data rather than to generate new media for research. Prior to the examination of recordings, data must be extracted from the recordings and the recordings must be described with metadata to allow users to search for the recordings and to search information within the recordings. The purpose of this small-scale study was to develop a web based search tool that will permit a comprehensive search of spoken information within a collection of existing digital recordings archived in an open-access digital repository. The study is significant to the field of instructional design and technology (IDT) as the digital recordings used in this study are interviews, which contain personal histories and insight from leaders and scholars who have influenced and advanced the field of IDT. This study explored and used design and development research methods for the development of a search tool for use with digital video interviews. The study applied speech recognition technology, tool prototypes, usability testing, expert review, and the skills of a program developer. Results from the study determined that the produced tool provided a more comprehensive and flexible search for users to locate content from within AECT Legends and Legacies Project video interviews. / Doctor of Philosophy / It is becoming more common for researchers to use existing recordings in studies. Prior to examination, the information about the recordings and within the recordings must be determined to allow users the ability to search information. The purpose of this small-scale study was to develop an online search tool that allows users to locate spoken words within a video interview. The study is important to the field of instructional design and technology (IDT) as the video interviews used in this study contain experience and insight from people who have advanced the field of IDT. Using current and free technology, this study developed a practical search tool to search information from AECT Legends and Legacies Project video interviews.

Närvaron på Internet har aldrig varit större och användningen av sökmotorer motsvarar en stor del av hur människor upptäcker tillgängligt innehåll på Internet. Innehållet som utgörs av hemsidor kategoriseras av sökmotorers automatiserade tjänster. Ett arbete för att förbättra hemsidors möjlighet att kategoriseras görs för att få ökad synlighet genom sökmotorer. Förbättrad synlighet kan också erhållas genom sökmotormarknadsföring, vilket är en strategi för att annonsera via sökmotorer. Genom sökmotorer finns verktyg som assisterar detta ändamål. Företaget som rapporten berör erbjuder tjänster både för sökmotoroptimering och marknadsföring. Problemet med företagets befintliga hantering är att det inte erbjuder tillräckliga möjligheter till analys av statistik för annonseringar på sökmotorerna. Begränsningarna kommer av att statistiken för företagets kunder är separerade på flera olika platser. Konsulter på företaget som ansvarar för vissa kunder får inte dela med sig av statistik för administrerade annonseringar till andra konsulter på företaget. Projektet syftar finna en metod för företagets anställda att göra bättre analyser av sökord och annonseringar. Metoden realiseras i en applikationsplattform, där de anställda erhåller en webbapplikation som kan användas för att finna relevant statistik. Med en kvalitativ metod samlas underlag om företaget och deras förväntningar på systemet in, den insamlade informationen används i den fas som mynnar ut i systemets framställning. Systemet utvecklas iterativt och implementeras sedan i företagets befintliga driftmiljö. Genom arbetet som gjorts i projektet har ett användbart system kunnat realiseras för relevanta användare på företaget. Applikationen erbjuder djupare insikt i vilka faktorer som bidrar till annonseringars framgång. Dock har framgången kring systemets införande inte kunnat mätas i tillräckligt stor utsträckning. En sammanfattning av studien bidrar till rekommendationer till framtida arbete. / Internet usage has never been greater. Search engines provide a gateway to discover content online. The content comes in the form of web pages, which are categorized by the automated services of a search engine. Certain steps can be taken to optimize the way web sites are categorized, all of which are done to improve the visibility on search engines. Another way to increase visibility of a web site can be achieved with search engine marketing. Search engine marketing describes the process of advertising content on search engines. Search engine providers have different tools that may assist the process of advertising. The company that this report concerns provides services in the areas of optimizing and advertising content on search engines. The problem manifests in the company’s current system, which doesn’t provide an acceptable way of analyzing statistics of their search engine marketing efforts. Employees involved with their respective customers may not share information about their strategies and statistics to co-workers. The purpose of the project is to define a method for the company and its employees to achieve a higher level analysis of keyword and advertisement data. The work is realized in a platform that provides employees a way to extract relevant statistics from a web application. A qualitative methodology is defined to collect descriptive information about the company’s processes and their expectations on the system to be developed. The system is developed in a series of iterations and is deployed on a server provided by the company. The efforts have resulted in a useful system that may provide employees with deeper insights as to which factors that might be key for the success of certain advertisement strategies. However, the effects of the system have not been measured to confirm if the method actually improved the company’s search engine marketing efforts. To conclude the study, a set of recommendations has been given for future work.

The soil-borne, rhizosphere-competent, filamentous fungus Trichoderma virens is a well-known biocontrol agent able to control pathogenic fungi through the production of antibiotics, the induction of systemic resistance in host plants, or by directly parasitizing the competing fungus. Competition for iron is another means by which Trichoderma can hinder competing microorganisms, and siderophores are a means by which microorganisms obtain iron. In silico analysis of the T. virens genome suggested that two genes putatively encoding extracellular siderophore-producing nonribosomal peptide synthetases (NRPSs) were present. In this study, a disruption was created in one of the genes, TvNPS6, to create a mutant unable to produce the NRPS TvNps6 (DeltaTvnps6). Previously, a mutant (DeltaTvsidD) had been generated with a disruption in the second gene (TvSIDD) encoding an NRPS thought to be involved in siderophore biosynthesis. A double mutant (DeltaDeltaTvsidDTvnps6) was generated by transformation of a DeltaTvsidD strain with a vector targeting disruption of TvNPS6. This resulted in transformants disrupted within both the putative siderophore-producing NRPSs. Thus, three mutants were available for analysis of the role of these genes in the ecology of T. virens. Transformants were confirmed by PCR and Southern blotting analysis. Phenotypic characterization of the mutants included both HPLC analysis of siderophore production, growth on agar and in liquid media, conidiation, germination in the presence of hydrogen peroxide, biocontrol against Pythium ultimum, in vitro confrontation against Rhizoctonia solani and growth with iron chelators to determine the contribution of reductive iron assimilation (RIA) compared to that of siderophores. The HPLC analysis demonstrated that T. virens Gv 29-8 (wild-type) produced a single siderophore peak when grown in an iron-depleted medium. This peak was not present in the DeltaTvnps6 and DeltaDeltaTvsidDTvnps6 mutants but was apparent with the DeltaTvsidD mutants. From the HPLC analysis, T. virens evidently produces a coprogen-type siderophore. Few differences were observed in the other phenotypic tests, though hydrogen peroxide showed some small inhibitory effects towards the DeltaTvnps6 mutants. The addition of chelators, which inhibit RIA, exerted some negative effects on all strains growing under iron-limited media, particularly the DeltaTvnps6 and DeltaDeltaTvsidDTvnps6 strains. This study demonstrated that although T. virens has two genes putatively encoding siderophore producing NRPSs, only the TvNPS6 gene was required for extracellular siderophore production. The greater sensitivity of the mutants towards the iron chelators suggests that unlike other other fungi studied, Trichoderma virens utilizes RIA, rather than siderophore production, as the primary means by which the fungus obtains iron in an iron-limited environment.

Darbe nagrinėjamas temų identifikavimo uždavinys, kuris siejamas su teksto klasifikavimu į tam tikras kategorijas, t.y. įvairių tekstinių duomenų grupavimas pagal atitinkamas temas. Žinutės naujienų agentūrose yra skirstomos į atskiras grupes ir pogrupius pagal temas. Šis darbas atliekamas rankomis, t.y. perskaitomas tekstas ir priskiriamas kokiai nors temai. Vis dėlto, vystantis žiniasklaidai ir kuriantis įvairiems naujienų portalams, aktualu naujienas skirstyti ne rankiniu, o automatiniu būdu, todėl galimybė automatizuoti šį procesą galėtų būti naudinga įvairiems naujienų portalams, padedant skirstyti pranešimus ir taupant laiko bei energijos sąnaudas. Darbo objektą apima 2007 metų BNS spaudos centro žinutės. Darbo tikslas – išsiaiškinti, kaip atskiri žodžiai padeda nustatyti teksto temą. Temos nustatymui taikomi trys metodai: dažnų žodžių, dvižodžių junginių (bigramų) ir prasminių žodžių. Darbas susideda iš trijų dalių. Pirmoje dalyje buvo aptarti teoriniai pagrindai (temos nustatymas, tekstų klasifikavimas, žinių kalba). Apžvelgus žinučių ypatumus pastebėta, kad šis informacinis žanras iš kitų išsiskiria tekstų glaustumu, faktų konstatavimu. Taip pat daroma prielaida, kad temos nustatymo tikslumui yra svarbu žinutės apimtis ir aktualumas. Antroje dalyje aprašyti dažnų žodžių ir dvižodžių junginių sąrašų sudarymo bei prasminių žodžių ištraukimo būdai. Apžvelgus naujienų skirstymą pagal temas, buvo sudarytas temų sąrašas ir juo remiantis, buvo anotuoti dažnų žodžių ir... [toliau žr. visą tekstą] / The thesis is based on topic detection in BNS news reports. The reports are divided into different groups and sub-grouped according to topics. This topic analysis is manual; namely, reading texts and assigning to any topic. However, media and various news portals are developing very quickly, so the possibility to distribute reports automatically is quite relevant problem. The automated topic detection process would be useful for various news portals, automated distribution would save time and energy costs. Therefore, the task of the paper is topic detection issue, which is associated with the classification of text into certain categories, in other words, various text data is classified by subject. The object of the thesis is reports from BNS news agency received in 2007. The aim of the paper is to analyze how separate words help identify the topic. Three methods are applied to detect the topic: high frequency words, bigrams (two-word compounds) and the keywords. The paper consists of three parts. The first part is theoretical; it presents the bases of topic detection, text classification and report language. The report was chosen because this information genre is concise and clearly stating facts. What is more, it is hypothesized that the accuracy of topic detection depends on the size and relevance of the report. The second part describes the formation of frequent words’ and bigram lists and keyword extraction techniques. Those frequent word and bigram lists were... [to full text]

Les résultats de la Stimulation Magnétique Transcrânienne répétée (rTMS) dans le traitement des troubles dépressifs résistants, bien que positifs, sont modestes. Ces résultats modérés pourraient s’expliquer par une mauvaise définition de la cible thérapeutique. En effet, la cible thérapeutique dans cette indication est le cortex dorsolatéral préfrontal, c’est-à-dire une large région corticale constituée de plusieurs sub-régions cyto-architecturalement différentes, dont les aires 9 et 46 de Brodmann (BA 9 et BA 46). A partir de l’hypothèse que seule l’une de ces 2 sub-régions pourrait représenter une cible thérapeutique efficace en rTMS, une étude est réalisée afin de comparer la réponse thérapeutique observée par stimulations appliquées sur l’aire 9 et sur l’aire 46 de Brodmann.Quinze patients souffrant de troubles dépressifs (âge moyen : 55 ans) sont randomisés dans une étude en cross-over. Les patients reçoivent 10 séances de rTMS sur chacune des 2 aires (wash-out de 4 semaines entre les 2 séries de stimulation). La rTMS est administrée à 1 Hz sur le cortex droit (120 % du seuil moteur, 360 stimuli par séance). Un neuronavigateur est utilisé pour cibler la BA 9 et BA 46. Les effets thérapeutiques sont mesurés en aveugle avec des échelles standardisées (échelles de dépression de Hamilton et de Montgomery).Les résultats montrent que la rTMS peut être efficace aussi bien sur l’aire 9 que sur l’aire 46 de Brodmann. Toutefois, parmi les répondants, seulement deux d’entre eux ont présenté une réponse thérapeutique sur les 2 aires cérébrales. La plupart des participants n’ont répondu qu’à une seule des deux cibles corticales.Ce résultat suggère que l’identification systématique de la meilleure cible corticale pourrait augmenter les résultats thérapeutiques de la rTMS dans le traitement des troubles dépressifs. Par ailleurs, d’autres paramètres (anatomiques, génétiques, endocriniens) pourraient être déterminants dans l’efficacité des stimulations cérébrales / No abstract

The scale of the scholarly community complicates searches within scholarly databases, necessitating keywords to index the topics of any given work. As a result, an author’s choice in keywords affects the visibility of each publication; making the sum of these choices a key representation of the author’s academic profile. As such the underlying network of investigators are often viewed through the lens of their keyword networks. Current keyword networks connect publications only if they use the exact same keyword, meaning uncontrolled keyword choice prevents connections despite semantic similarity. Computational understanding of semantic similarity has already been achieved through the process of word embedding, which transforms words to numerical vectors with context-correlated values. The resulting vectors preserve semantic relations and can be analyzed mathematically. Here we develop a model that uses embedded keywords to construct a network which circumvents the limitations caused by uncontrolled vocabulary. The model pipeline begins with a set of faculty, the publications and keywords of which are retrieved by SCOPUS API. These keywords are processed and then embedded. This work develops a novel method of network construction that leverages the interdisciplinarity of each publication, resulting in a unique network construction for any given set of publications. Postconstruction the network is visualized and analyzed with topological data analysis (TDA). TDA is used to calculate the connectivity and the holes within the network, referred to as the zero and first homology. These homologies inform how each author connects and where publication data is sparse. This platform has successfully modelled collaborations within the biomedical department at Purdue University and provides insight into potential future collaborations.

[pt] O objetivo desta dissertação é melhorar a busca por palavra-chave em formato RDF. Propomos uma abordagem escalável, baseada numa representação tensorial, que permite o armazenamento distribuído e, como consequência, o uso de técnicas de paralelismo para agilizar a busca sobre grandes bases de RDF, em particular, as publicadas como Linked Data. Um volume sem precedentes de informação está sendo disponibilizado seguindo os princípios de Linked Data, formando o que chamamos de Web of Data. Esta informação, tipicamente codificada como triplas RDF, costuma ser representada como um grafo, onde sujeitos e objetos são vértices, e predicados são arestas ligando os vértices. Em consequência da ampla adoção de mecanismos de busca na World Wide Web, usuários estão familiarizados com a busca por palavra-chave. No caso de grafos RDF, no entanto, a extração de uma partição coerente de grafos para enriquecer os resultados da busca é uma tarefa cara, demorada, e cuja expectativa do usuário é de que seja executada em tempo real. Este trabalho tem como objetivo o tratamento deste problema. Parte de uma solução proposta recentemente prega a indexação do grafo RDF como uma matriz esparsa, que contém um conjunto de informações pré-computadas para agilizar a extração de seções do grafo, e o uso de consultas baseadas em tensores sobre a matriz esparsa. Esta abordagem baseada em tensores permite que se tome vantagem de técnicas modernas de programação distribuída, e.g., a utilização de bases de dados não-relacionais fracionadas e o modelo de MapReduce. Nesta dissertação, propomos o desenho e exploramos a viabilidade da abordagem baseada em tensores, com o objetivo de construir um depósito de dados distribuído e agilizar a busca por palavras-chave com uma abordagem paralela. / [en] The goal of this dissertation is to improve RDF keyword search. We propose a scalable approach, based on a tensor representation that allows for distributed storage, and thus the use of parallel techniques to speed up the search over large linked data sets, in particular those published as Linked Data. An unprecedented amount of information is becoming available following the principles of Linked Data, forming what is called the Web of Data. This information, typically codified as RDF subject-predicate-object triples, is commonly abstracted as a graph which subjects and objects are nodes, and predicates are edges connecting them. As a consequence of the widespread adoption of search engines on the World Wide Web, users are familiar with keyword search. For RDF graphs, however, extracting a coherent subset of data graphs to enrich search results is a time consuming and expensive task, and it is expected to be executed on-the-fly at user prompt. The dissertation s goal is to handle this problem. A recent proposal has been made to index RDF graphs as a sparse matrix with the pre-computed information necessary for faster retrieval of sub-graphs, and the use of tensor-based queries over the sparse matrix. The tensor approach can leverage modern distributed computing techniques, e.g., nonrelational database sharding and the MapReduce model. In this dissertation, we propose a design and explore the viability of the tensor-based approach to build a distributed datastore and speed up keyword search with a parallel approach.