Automatiserad dokumentklassificering är en process eller metod som syftar till att bearbeta ochhantera dokument i digitala former. Många företag strävar efter en textklassificeringsmetodiksom kan lösa olika problem. Ett av dessa problem är att klassificera och organisera ett stort antaldokument baserat på en uppsättning av fördefinierade kategorier.Detta examensarbete syftar till att hjälpa Medius, vilket är ett företag som arbetar med fakturaarbetsflöde, att klassificera dokumenten som behandlas i deras fakturaarbetsflöde till fakturoroch icke-fakturor. Detta har åstadkommits genom att implementera och utvärdera olika klassificeringsmetoder för maskininlärning med avseende på deras noggrannhet och effektivitet för attklassificera finansiella dokument, där endast fakturor är av intresse.I denna avhandling har två dokumentrepresentationsmetoder "Term Frequency Inverse DocumentFrequency (TF-IDF) och Doc2Vec" använts för att representera dokumenten som vektorer. Representationen syftar till att minska komplexiteten i dokumenten och göra de lättare att hantera.Dessutom har tre klassificeringsmetoder använts för att automatisera dokumentklassificeringsprocessen för fakturor. Dessa metoder var Logistic Regression, Multinomial Naïve Bayes och SupportVector Machine.Resultaten från denna avhandling visade att alla klassificeringsmetoder som använde TF-IDF, föratt representera dokumenten som vektorer, gav goda resultat i from av prestanda och noggranhet.Noggrannheten för alla tre klassificeringsmetoderna var över 90%, vilket var kravet för att dennastudie skulle anses vara lyckad. Dessutom verkade Logistic Regression att ha det lättare att klassificera dokumenten jämfört med andra metoder. Ett test på riktiga data "dokument" som flödarin i Medius fakturaarbetsflöde visade att Logistic Regression lyckades att korrekt klassificeranästan 96% av dokumenten.Avslutningsvis, fastställdes Logistic Regression tillsammans med TF-IDF som de övergripandeoch mest lämpliga metoderna att klara av problmet om dokumentklassficering. Dessvärre, kundeDoc2Vec inte ge ett bra resultat p.g.a. datamängden inte var anpassad och tillräcklig för attmetoden skulle fungera bra. / Automated document classification is an essential technique that aims to process and managedocuments in digital forms. Many companies strive for a text classification methodology thatcan solve a plethora of problems. One of these problems is classifying and organizing a massiveamount of documents based on a set of predefined categories.This thesis aims to help Medius, a company that works with invoice workflow, to classify theirdocuments into invoices and non-invoices. This has been accomplished by implementing andevaluating various machine learning classification methods in terms of their accuracy and efficiencyfor the task of financial document classification, where only invoices are of interest. Furthermore,the necessary pre-processing steps for achieving good performance are considered when evaluatingthe mentioned classification methods.In this study, two document representation methods "Term Frequency Inverse Document Frequency (TF-IDF) and Doc2Vec" were used to represent the documents as fixed-length vectors.The representation aims to reduce the complexity of the documents and make them easier tohandle. In addition, three classification methods have been used to automate the document classification process for invoices. These methods were Logistic Regression, Multinomial Naïve Bayesand Support Vector Machine.The results from this thesis indicate that all classification methods used TF-IDF, to represent thedocuments as vectors, give high performance and accuracy. The accuracy of all three classificationmethods is over 90%, which is the prerequisite for the success of this study. Moreover, LogisticRegression appears to cope with this task very easily, since it classifies the documents moreefficiently compared to the other methods. A test of real data flowing into Medius’ invoiceworkflow shows that Logistic Regression is able to correctly classify up to 96% of the data.In conclusion, the Logistic Regression together with TF-IDF is determined to be the overall mostappropriate method out of the other tested methods. In addition, Doc2Vec suffers to providea good result because the data set is not customized and sufficient for the method to workwell.

Efter att EU införde ett ramverk år 2000 rörande regionens vattenanvändning, vattendirektivet, beslöt Sveriges regering att från och med sommaren 2020 ompröva rikets vattendammar. I de fall rådande vattenanvändning inte uppfyller de krav som anges i ramverket kan dammutrivning bli aktuellt. Syftet med studien är undersöka och utveckla en metod att utvärdera förändringar av strömhabitat uppströms ett vattendrag efter en dammutrivning. Studieområdet utgörs och begränsas av datamängden i form av flygfoton insamlade med UAV vid två tillfällen över samma område. Även batymetriska data över vattendragets botten från en bottenskanning har använts således även Lantmäteriets nationella höjdmodell. Två fotogrammetriprogram användes i arbetet, dels för att skapa en ortomosaik från flygfoton men även för att utföra en bildnormalisering. GIS programvaran ArcGIS Pro tillhandahåller flera algoritmer för klassificering av raster. Algoritmerna SVM och RT, viktades mot varandra och SVM användes vidare i metoden. Med olika generaliserings-verktyg kunde strömhabitat identifieras och förstärkas. Även olika terrängmodeller skapades från flygfoton och Lantmäteriets nationella höjdmodell. Dessa granskades mot varandra utifrån olika aspekter som variationer i bland annat detaljrikedom, generaliseringsgrad och återspeglandet av vattenytan.  Slutsatsen av studien är att klassificering av strömhabitat kan göras i ett GIS-program med en lägesosäkerhet på mellan 25 och 40 %, beroende på vilka strömhabitat som ska klassificeras. Efter utrivningen uppstod 17 zoner med förändrade strömhabitat vilket var två mer än vad prognoser förutsatt. Vidare påverkades vattenvolymen markant då en minskning på ca 40 % skedde från 2018 till 2020. En areal av ca 1,5 hektar berördes då gammal älvbotten blev torrlagd i samband med dammutrivningen. Ett samband syntes mellan avståndet från kraftverket och torrlagd botten då dessa ytor sågs minska i storlek i takt med att avståndet ökade. Att undersöka vart vattennivån påverkats som mest var inte möjligt i brist på data. Studien har utvecklat en metod att analysera en dammutrivnings påverkan på ett vattendrag med data från UAV och bottenskanning.

This thesis focuses on bare hand gesture recognition by proposing a new architecture to solve the problem of real-time vision-based hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bare hand in a cluttered background using face subtraction, skin detection and contour comparison. The second stage allows recognizing hand gestures using bag-of-features and multi-class Support Vector Machine (SVM) algorithms. Finally, a grammar has been developed to generate gesture commands for application control. Our hand gesture recognition system consists of two steps: offline training and online testing. In the training stage, after extracting the keypoints for every training image using the Scale Invariance Feature Transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multi-class SVM to build the classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using my algorithm. Then, the keypoints are extracted for every small image that contains the detected hand posture and fed into the cluster model to map them into a bag-of-words vector, which is fed into the multi-class SVM classifier to recognize the hand gesture. Another hand gesture recognition system was proposed using Principle Components Analysis (PCA). The most eigenvectors and weights of training images are determined. In the testing stage, the hand posture is detected for every frame using my algorithm. Then, the small image that contains the detected hand is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined among the test weights and the training weights of each training image to recognize the hand gesture. Two application of gesture-based interaction with a 3D gaming virtual environment were implemented. The exertion videogame makes use of a stationary bicycle as one of the main inputs for game playing. The user can control and direct left-right movement and shooting actions in the game by a set of hand gesture commands, while in the second game, the user can control and direct a helicopter over the city by a set of hand gesture commands.

Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program. In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007. Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid technique considerably improves the electricity price forecasting.

A level set of a function is defined as the region, where the function gets over the specified level. A level set of the probability density function can be considered an alternative to the traditional confidence region because on certain conditions the level set covers the region with minimal volume over all regions with a given confidence level. The benefits of using level sets arise in situations where, for example, the given random variables are multimodal or the given random vectors have strongly correlated components. This thesis describes estimates of the level set by means of a so called plug-in method, which first estimates density from the data set and then specifies the level set from the estimated density. In addition, explicit direct methods are also studied, such as algorithms based on support vectors or dyadic decision trees. Special attention is paid to the nonparametric probability density estimates, which form an essential tool for plug-in estimates. Namely, the second chapter describes histograms, averaged shifted histograms, kernel density estimates and its generalization. A new technique transforming kernel supports is proposed to avoid the so called boundary effect in multidimensional data domains. Ultimately, all methods are implemented in Mathematica and compared on financial data sets.

The development of technologies for preventing drowsiness at the wheel is a major challenge in the field of accident avoidance systems. Preventing drowsiness during driving requires a method for accurately detecting a decline in driver alertness and a method for alerting and refreshing the driver. As a detection method, the authors have developed a system that uses image processing technology to analyse images of the road lane with a video camera integrated with steering wheel angle data collection from a car simulation system. The main contribution of this study is a novel algorithm for drowsiness detection and tracking, which is based on the incorporation of information from a road vision system and vehicle performance parameters. Refinement of the algorithm is more precisely detected the level of drowsiness by the implementation of a support vector machine classification for robust and accurate drowsiness warning system. The Support Vector Machine (SVM) classification technique diminished drowsiness level by using non intrusive systems, using standard equipment sensors, aim to reduce these road accidents caused by drowsiness drivers. This detection system provides a non-contact technique for judging various levels of driver alertness and facilitates early detection of a decline in alertness during driving. The presented results are based on a selection of drowsiness database, which covers almost 60 hours of driving data collection measurements. All the parameters extracted from vehicle parameter data are collected in a driving simulator. With all the features from a real vehicle, a SVM drowsiness detection model is constructed. After several improvements, the classification results showed a very good indication of drowsiness by using those systems.

There was an increase in a number of applications for a master degree program with the growth in time. It takes huge time to process all the application documents of each and every applicant manually and requires a high volume of the workforce. This can be reduced if automation is used for this process. In any case, before that, an analysis of the complete strides required in preparing was precisely the automation must be utilized to diminish the time and workforces must be finished. The application process for the applicant is actually participating in several steps. First, the applicant sends the complete scanned documents to the uni-assist; from there the applications are received by the student assistant team at the particular university to which the applicant had applied, and then they are sent to the individual departments. At the individual sections, the individual applications will be handled by leading an intensive study to know whether the applicant by their past capabilities scopes to satisfy the prerequisites of further study system to which they have applied. What's more, by considering the required points of interest of the applicant without investigating every single report, and to pack the information and diminish the preparing time for the specific division, by this postulation extend a solitary web apparatus is being produced that can procedure the application which is much dependable in the basic leadership procedure of application.

Este trabalho apresenta a pesquisa sobre a extração de parâmetros característicos de sinais acústicos para fins de detecção automática de vazamento de água em tubulações enterradas. Os sinais acústicos foram adquiridos com o auxílio de um geofone eletrônico e também catalogados por técnicos especialistas em detecção acústica. De todos os sinais foram extraídos os modelos de predição linear perceptual de várias ordens, determinando-se como melhor a ordem 2. A partir de um conjunto de modelos de referência de sinais de vazamento, a distância média de Itakura dos outros modelos em relação a estas referências foram calculadas. Em conjunto com estas distâncias, quatro características espectrais são também extraídas do sinal a fim de compor o vetor de parâmetros característicos do sinal. Parte destes vetores de parâmetros característicos são utilizados para treinar o classificador de máquina de vetores de suporte. O restante dos dados são, então, submetidos a este classificador que obteve a taxa de acerto de classificação em torno de 93%. Experimentos anteriores, utilizando modelos de predição linear, de ordem 10, obtiveram uma taxa de acerto em torno de 82%. Isso demonstra que estes novos parâmetros característicos propostos alcançam os objetivos deste trabalho, que são algoritmos com melhor taxa de acerto na detecção de vazamentos. / This work presents a research about feature extraction of acoustic signals for detection of water leak in buried pipes. Acoustic signals were acquired by means of an electronic geophone and also labeled by technicians specialized in acoustic water leak detection. For every signals, its linear predictive model was estimated for a range of prediction orders, concluding for the best order 2. Out of this group of models, some leaky ones are used as reference for calculating the Itakura mean distance with respect to the other models. Completing this measure, four spectral features are extracted to compose the signal feature vector. Some of these vectors were used to train a support vector machine to be used as a classifier. The remaining ones were used to evaluate the classification. The resulting accuracy rate achieved is around 93%. Earlier experiments, which use linear prediction of order 10 had an accuracy rate around 82%. This shows that this novel proposal of feature vector achieves the main goal of this research, which is the increase in the leak detection accuracy rate.

Made available in DSpace on 2015-03-05T13:58:25Z (GMT). No. of bitstreams: 0 Previous issue date: 8 / Nenhuma / A estrutura tridimensional de uma proteína está diretamente ligada a sua função. Diversos projetos de seqüenciamento genéticos acumulam um grande número de seqüências de proteínas cujas estruturas primárias e secundárias são conhecidas. Entretanto, as informações sobre suas estruturas tridimensionais estão disponíveis somente para uma pequena fração destas proteínas. Este fato evidencia a necessidade da criação de métodos automáticos para a predição de estruturas terciárias de proteínas a partir de suas estruturas primárias. Conseqüentemente, ferramentas computacionais são utilizadas para o tratamento, seleção e análise destes dados. Atualmente, um novo método de aprendizado de máquina denominado Máquina de Suporte Vetorial (MSV) tem superado métodos tradicionais como as Redes Neurais Artificiais (RNA) no tratamento de problemas de classicação. Nesta dissertação utilizamos as MSV para a classicação automática de proteínas. A principal contribuição deste trabalho foi a metodologia proposta para o tratamen / The three-dimensional structure of a protein is directly related to its function. Many projects of genetic sequence analysis accumulate a great number of protein sequences whose primary and secondary structures are known. However, the information on its three-dimensional structures are available only for a small fraction of these proteins. This fact evidences the necessity of creation of automatic methods for the prediction of tertiary protein structures from its primary structures. Consequently, computational tools are used for the treatment, election and analysis of these data. Currently, a new method of machine learning called Support Vector Machine (SVM) has surpassed traditional methods as Artificial Neural Networks (ANN) in the treatment of classication problems. In this master thesis we use the SVM for the automatic protein classication. The main contribution of this work was the methodology proposal for the treatment of the problem. This methodology consists in composing the support vectors with the v

Electrified vehicles (EVs) with a large electric energy storage system (ESS), including Plug-in Hybrid Electric Vehicles (PHEVs) and Pure Electric Vehicles (PEVs), provide a promising solution to utilize clean grid energy that can be generated from renewable sources and to address the increasing environmental concerns. Effectively extending the operation life of the large and costly ESS, thus lowering the lifecycle cost of EVs presents a major technical challenge at present. A hybrid energy storage system (HESS) that combines batteries and ultracapacitors (UCs) presents unique energy storage capability over traditional ESS made of pure batteries or UCs. With optimal energy management system (EMS) techniques, the HESS can considerably reduce the frequent charges and discharges on the batteries, extending their life, and fully utilizing their high energy density advantage. In this work, an intelligent energy allocation (IEA) algorithm that is based on Q-learning has been introduced. The new IEA method dynamically generate sub-optimal energy allocation strategy for the HESS based on each recognized trip of the EV. In each repeated trip, the self-learning IEA algorithm generates the optimal control schemes to distribute required current between the batteries and UCs according to the learned Q values. A RBF neural networks is trained and updated to approximate the Q values during the trip. This new method provides continuously improved energy sharing solutions better suited to each trip made by the EV, outperforming the present passive HESS and fixed-cutoff-frequency method. To efficiently recognize the repeated trips, an extended Support Vector Machine (e-SVM) method has been developed to extract significant features for classification. Comparing with the standard 2-norm SVM and linear 1-norm SVM, the new e-SVM provides a better balance between quality of classification and feature numbers, and measures feature observability. The e-SVM method is thus able to replace features with bad observability with other more observable features. Moreover, a novel pattern classification algorithm, Inertial Matching Pursuit Classification (IMPC), has been introduced for recognizing vehicle driving patterns within a shorter period of time, allowing timely update of energy management strategies, leading to improved Driver Performance Record (DPR) system resolution and accuracy. Simulation results proved that the new IMPC method is able to correctly recognize driving patterns with incomplete and inaccurate vehicle signal sample data. The combination of intelligent energy allocation (IEA) with improved e-SVM feature extraction and IMPC pattern classification techniques allowed the best characteristics of batteries and UCs in the integrated HESS to be fully utilized, while overcoming their inherent drawbacks, leading to optimal EMS for EVs with improved energy efficiency, performance, battery life, and lifecycle cost. / Graduate