根據過去研究指出，社群網站上的貼文訊息會對群眾情緒造成影響，進而影響股市波動，故對於投資者而言，如果能快速分析大量社群網站的財經文本來推測投資情緒進而預測股市走勢，將可提升投資獲利。 過去文本情感分析的研究中已證實監督式學習方法可以透過簡單量化的方式達到良好的分類效果，但監督式學習方法所使用的訓練資料集須有事先定義好的已知類別，故其有無法預期未知類別的限制，所以本研究透過深度學習方法，從巨量資料及裡抓出有關於股市之文章，並透過財經文本的混合監督式學習與非監督式學習之情感分析方法，透過非監督式學習對微博財經貼文進行文本主題判別、情緒指數計算與情緒傾向標注，並且透過監督式學習的方式，建立分類模型以預測上海指數走勢，最後配合視覺化工具作趨勢線圖分析，找出具有領先指標特性之主題。 在實驗結果中，深度學習方面，本研究透過word2vec抓取有效之股市主題文章，有效篩選了需要分析之文本，主題模型方面，我們最後使用LDA作為本研究標註主題之方法，因為其文本數量大於議題詞數量造成TFIDF矩陣過於稀疏，造成Kmeans分群效果不佳，故後續採用LDA主題模型進行主題標注。情緒傾向標注方面，透過擴充後的情感詞集比起NTUSD有更好的詞性分數判斷效果，計算出的情緒指數之趨勢線能有效預測上海指數之趨勢。此外，並非所有主題模型之情緒指數皆具有領先特性，僅公司表現與上海指數之主題模型的情緒指數能提前反應上海指數趨勢，故本研究用此二主題之文本的情緒指數來建立分類模型。 本研究透過比較情緒指數與單純指數指標分類模型的準確度，前者較後者高出7%的準確率。故證實了情感分析確實能有效提升上海指數趨勢預測準確度，幫助投資者增加股市報酬率。

Using best-practices and supports that apply the least dangerous assumption (LDA) is a powerful tool for increasing overall student quality of life and keeping alive a vision of high achievement for all students. This presentation will focus on research and evidence based strategies to promote the LDA for students with significant disabilities regarding accessing personally relevant academic instruction with meaningful student centered outcomes. OBJECTIVES: By the end of this session, participants will be able to: a) identify four criteria to promote Least Dangerous Assumption b) discuss ways to successfully implement the four criteria in their classroom to meet the needs of a wide range of diverse students with significant disabilities c) identify resources that incorporate these four criteria and are applicable to students from diverse backgrounds

Das Ziel der Dissertation ist die Charakterisierung und Optimierung der Prozessbedingungen beim Hochgeschwindigkeitsdrahtflammspritzen. Dazu werden diagnostische Methoden wie das LDA-Verfahren, die Bewertung von Schichten und numerische Betrachtungen herangezogen. Verschiedene Spritzzusatzwerkstoffe wie Massiv- und Fülldrähte sowie hoch- und niedrigschmelzende Materialien werden verarbeitet. Zur Bewertung der Gebrauchseigenschaften erfolgen Korrosions- und Verschleißtests. Aus den Untersuchungsergebnissen resultieren Hinweise und Konzepte zur Verbesserung der Brennertechnik. Die Entwicklung eines neuen HVCW-Systems wird vorgestellt, welches Spritzpartikelgeschwindigkeiten im Überschallbereich ermöglicht, die deutlich über denen klassischer Systeme liegen. In einem gesonderten Abschnitt der Dissertation wird ein neuartiges Verfahren zur Herstellung hydrolysefähigen Materials vorgestellt. Der hergestellte Al-Sn-Werkstoff zersetzt sich in Kontakt mit Wasser unter Abgabe großer Mengen Wasserstoff in kürzester Zeit vollständig.

Face recognition is a popular research area where there are different approaches studied in the literature. In this thesis, a holistic Principal Component Analysis (PCA) based method, namely Eigenface method is studied in detail and three of the methods based on the Eigenface method are compared. These are the Bayesian PCA where Bayesian classifier is applied after dimension reduction with PCA, the Subspace Linear Discriminant Analysis (LDA) where LDA is applied after PCA and Eigenface where Nearest Mean Classifier applied after PCA. All the three methods are implemented on the Olivetti Research Laboratory (ORL) face database, the Face Recognition Technology (FERET) database and the CNN-TURK Speakers face database. The results are compared with respect to the effects of changes in illumination, pose and aging. Simulation results show that Subspace LDA and Bayesian PCA perform slightly well with respect to PCA under changes in pose / however, even Subspace LDA and Bayesian PCA do not perform well under changes in illumination and aging although they perform better than PCA.

Monoatomic chain formation for Ag, Au, Pd and Pt has been investigated using a model for the tip structure. First-principles calculations, mostly spin polarized, were performed within the framework of the Density Functional theory. Results are presented and discussed on the basis of the electronic structure. Tendencies for chain formation were noted for Ag, Au and Pt.

<p>The fluid flow in the mold during up-hill teeming is of great importance for the quality of the cast ingot and therefore the quality of the final steel products. At the early stage of the filling of an up-hill teeming mold, liquid steel enters, with high velocity, from the runner into the mold and the turbulence on the meniscus could lead to entrainment of mold flux. The entrained mold flux might subsequently end up as defects in the final product. It is therefore very important to get a mild and stable inlet flow in the entrance region of the mold. It has been acknowledged recently that swirling motion induced using a helix shaped swirl blade, in the submerged entry nozzle is remarkably effective to control the fluid flow pattern in both the slab and billet type continuous casting molds. This result in increased productivity and quality of the produced steel. Due to the result with continuous casting there is reason to investigate the swirling effect for up-hill teeming, a casting method with similar problem with turbulence.</p><p>With this thesis we will study the effect of swirling flow generated through a swirl blade inserted into the entry nozzle, as a new method of reducing the deformation of the rising surface and the unevenness of the flow during filling of the up-hill teeming mold. The swirling blade has two features: (1) to generate a swirling flow in the entrance nozzle and (2) to suppress the uneven flow, generated/developed after flowing through the elbow. The effect of the use of a helix shaped swirl blade was studied using both numerical calculations and physical modelling. Water modelling was used to assert the effect of the swirling blade on rectifying of tangential and axial velocities in the filling tube for the up-hill teeming and also to verify the results from the numerical calculations. The effect of swirl in combination with diverged nozzle was also investigated in a similar way, i. e. with water model trials and numerical calculations.</p>

The main task in this thesis is to design a robot vision system with face detection and tracking capability. Hence there are two main works in the thesis: Firstly, the detection of the face on an image that is taken from the camera on the robot must be achieved. Hence this is a serious real time image processing task and time constraints are very important because of this reason. A processing rate of 1 frame/second is tried to be achieved and hence a fast face detection algorithm had to be used. The Eigenface method and the Subspace LDA (Linear Discriminant Analysis) method are implemented, tested and compared for face detection and Eigenface method proposed by Turk and Pentland is decided to be used. The images are first passed through a number of preprocessing algorithms to obtain better performance, like skin detection, histogram equalization etc. After this filtering process the face candidate regions are put through the face detection algorithm to understand whether there is a face or not in the image. Some modifications are applied to the eigenface algorithm to detect the faces better and faster. Secondly, the robot must move towards the face in the image. This task includes robot motion. The robot to be used for this purpose is a Pioneer 2-DX8 Plus, which is a product of ActivMedia Robotics Inc. and only the interfaces to move the robot have been implemented in the thesis software. The robot is to detect the faces at different distances and arrange its position according to the distance of the human to the robot. Hence a scaling mechanism must be used either in the training images, or in the input image taken from the camera. Because of timing constraint and low camera resolution, a limited number of scaling is applied in the face detection process. With this reason faces of people who are very far or very close to the robot will not be detected. A background independent face detection system is tried to be designed. However the resultant algorithm is slightly dependent to the background. There is no any other constraints in the system.

Different approaches to the face recognition are studied in this thesis. These approaches are PCA (Eigenface), Kernel Eigenface and Fisher LDA. Principal component analysis extracts the most important information contained in the face to construct a computational model that best describes the face. In Eigenface approach, variation between the face images are described by using a set of characteristic face images in order to find out the eigenvectors (Eigenfaces) of the covariance matrix of the distribution, spanned by a training set of face images. Then, every face image is represented by a linear combination of these eigenvectors. Recognition is implemented by projecting a new image into the face subspace spanned by the Eigenfaces and then classifying the face by comparing its position in face space with the positions of known individuals. In Kernel Eigenface method, non-linear mapping of input space is implemented before PCA in order to handle non-linearly embedded properties of images (i.e. background differences, illumination changes, and facial expressions etc.). In Fisher LDA, LDA is applied after PCA to increase the discrimination between classes. These methods are implemented on three databases that are: Yale face database, AT&amp / T (formerly Olivetti Research Laboratory) face database, and METU Vision Lab face database. Experiment results are compared with respect to the effects of changes in illumination, pose and expression. Kernel Eigenface and Fisher LDA show slightly better performance with respect to Eigenfaces method under changes in illumination. Expression differences did not affect the performance of Eigenfaces method. From test results, it can be observed that Eigenfaces approach is an adequate method that can be used in face recognition systems due to its simplicity, speed and learning capability. By this way, it can easily be used in real time systems.

Flow separation is a source of aerodynamic in-efficiency; however by using vortex generators the issue of flow separation can be controlled. This is of particular benefit to flows around bluff bodies which are susceptible to large scale separated flows, such as bodies in ground effect. Previous studies concerning heat transfer applications focused on the ability of low aspect ratio spherical wells (dimples) to produce vortices for flow mixing. Dimpled surfacing on an inverted Tyrrell026 airfoil in ground effect (indicative of high performance automotive aerodynamic applications e.g. Formula One) has been investigated for similar vortex enhanced wake reductions. Experimental measurements using Laser Doppler Anemometry (LDA) and Particle Image Velocimetry (PIV) were taken inside a dimple to provide validation and verification of numerical analyses of dimple flows. The k-ω SST turbulence model showed good agreement to the experimental measurements. Additionally experiments were conducted using LDA and PIV with various configurations of dimple arrays placed from a fixed separation point of a 16˚ rearward facing ramp to determine how the array configuration influenced the large scale separation. The airfoil wake with numerous dimple configurations and placements were measured using LDA. Results showed that an array of dimples with close dimple to dimple spacing there was flow recovery in the airfoil wake from the velocity deficit with no dimples of u/Uo,min=-0.1, to u/Uo,min=0.4 with a dimple array, (at α=10˚, ground clearance h/c=0.313). At α=10˚ reductions in the wake size of 30%, 33%, 58% and 68% were found for the ground clearances of h/c=0.112, 0.134, 0.224 and 0.313 respectively. For numerous dimple array configurations, closely spaced dimple arrays were more effective in reducing the wake size, turbulence intensity and Reynolds stresses than those where dimple spacing was further apart. The chord wise location of the array on the wing affected the angle of incidence of the wing for which the wake was able to be reduced. Arrays placed towards the trailing edge improved wake losses at lower angles of incidence. Dimples placed further forward yielded the most improvement at higher angles of incidence, in part due to the increased venturi effect under the wing.

Text visualization is a field dedicated to the visual representation of textual data by using computer technology. A large number of visualization techniques are available, and now it is becoming harder for researchers and practitioners to choose an optimal technique for a particular task among the existing techniques. To overcome this problem, the ISOVIS Group developed an interactive survey browser for text visualization techniques. ISOVIS researchers gathered papers which describe text visualization techniques or tools and categorized them according to a taxonomy. Several categories were manually assigned to each visualization technique. In this thesis, we aim to analyze the dataset of this browser. We carried out several analyses to find temporal trends and correlations of the categories present in the browser dataset. In addition, a comparison of these categories with a computational approach has been made. Our results show that some categories became more popular than before whereas others have declined in popularity. The cases of positive and negative correlation between various categories have been found and analyzed. Comparison between manually labeled datasets and results of computational text analyses were presented to the experts with an opportunity to refine the dataset. Data which is analyzed in this thesis project is specific to text visualization field, however, methods that are used in the analyses can be generalized for applications to other datasets of scientific literature surveys or, more generally, other manually curated collections of textual documents.